Danielle Lívia

José Heliane

Flausino

Ana Laíse

Sofia Cristina

Ana Graci Brito

Rodrigo Carol

Núbia

Gabriel Thaís

Laurence Fauvel

Danielle Guilloux

Nathalie Le Marre

Sandrine Parayre

Florian Blanchet

Lucie Rault

Martine Deplanche

Ludovica Marinelli

Agnès Burel

Aurélien

Marie-Noëlle Madec

Loir. For their advice, collaboration and kindness, I also thank Yves Le

Julien Jardin

Valérie Briard-Bion

Nadia Berkova

Valérie I Gagnaire

Françoise Prigent

Carlos Pineda

Sameh Obeid

Manon Hiolle

Fanny Canon

Julien Bauland

François Martin

Stefano Nebbia

Ilario Mennella

Human Protein Reference Database IFNγ Interferon gamma IL-10 Interleukin 10 IL-12 Interleukin 12 IL-17a Interleukin 17a IL-1α Interleukin 1α IL-1β Interleukin 1β IL-2 Interleukin 2 IL-4 Interleukin 4 IL-6 Interleukin 6 xii IL-8 Interleukin 8 IM Inner membrane ISEV International Society for Extracellular Vesicles

Keywords: extracellular vesicles, probiotics, propionobacteria, proteomics, immunomodulation, anti-inflammatory, NF-κB. vii vésicules extracellulaires, probiotiques, propionobactéries, protéomique, immunomodulation, anti-inflammatoire, NF-κB. viii vesículas extracelulares, probióticos, propionibactérias, proteômica, imunomodulação, anti-inflamatório, NF-κB

The second geographic border that I crossed got me to Belo Horizonte, the capital of Minas Gerais, to pursue my dream to study Bioinformatics. I thank Dr. Vasco Azevedo, my Brazilian supervisor, for accepting me in his lab, for

vi

Résumé

Les vésicules extracellulaires (VEs) sont des médiateurs ubiquitaires de la communication intercellulaire et consistent en un système d'exportation impliqué dans des processus essentiels de la vie, tels que la nutrition, l'adaptation, la défense et l'interaction hôte-microbe. Les VEs sont des nanoparticules sphériques non réplicatives composées d'une bicouche lipidique et d'une lumière aqueuse interne qui abrite un ensemble diversifié de molécules, notamment des protéines, des acides nucléiques, des lipides, des glycanes et d'autres métabolites. La recherche sur les VEs dérivés de bactéries s'est concentrée initialement sur les bactéries Gram-négatives et pathogènes, mais le scénario a changé au cours des dernières années, intégrant les VEs dérivés d'autres groupes, comme les bactéries Gram-positives et non pathogènes. Une bactérie Gram-positive et probiotique pertinente est Propionibacterium freudenreichii, qui est utilisée depuis longtemps et en toute sécurité dans la production de fromage de type suisse, d'acides organiques et de vitamine B12. P. freudenreichii a émergé dans le panorama des probiotiques en raison de propriétés notables, telles que les activités antipathogènes et anticancéreuses, la modulation de la composition du microbiote et l'immunomodulation. Certaines de ces propriétés ont été attribuées à des facteurs exposés à la surface ou sécrétés. Par conséquent, nous avons cherché à déterminer si P. freudenreichii produisait des VEs qui pourraient médier ses propriétés bénéfiques, en particulier l'immunomodulation. En conséquence, la bactérie a été cultivée en milieu ultrafiltrat de lait (UF) ou extrait de levure-lactate (YEL) jusqu'au début de la phase stationnaire. Des protocoles de purification de VEs basés sur la chromatographie d'exclusion de taille (SEC) ou l'ultracentrifugation (UC) à gradient de densité ont été appliqués. Les échantillons résultants présentaient des particules de taille nanométrique avec une morphologie typique, confirmant la production de VEs par P. freudenreichii. Le contenu en protéines de VEs a été étudiée avec des approches de protéomiques shotgun, dévoilant un ensemble diversifié comprenant des protéines immunomodulatrices et cytoplasmiques. L'analyse du contenu protéique et d'interactomique ont indiqué des rôles immunomodulateurs potentiels pour les VEs dérivés de P. freudenreichii. Cela a été confirmé par la réduction médiée par les VEs de la libération d'IL-8 et de l'activité de NF-kB dans les cellules épithéliales intestinales humaines traitées avec du LPS bactérien, indiquant un rôle anti-inflammatoire. De plus, les propriétés des VEs dépendaient du milieu de culture utilisé pour cultiver les bactéries, car elles variaient pour les VEs dérivés de l'UF ou du YEL, y compris la taille, l'abondance, le contenu en protéines et l'activité anti-inflammatoire des VEs. En outre, la méthode utilisée pour la purification des VEs a également eu un impact sur les propriétés des VEs, en particulier le contenu en protéines. Bien qu'il y ait eu des protéines exclusives pour certaines conditions, des centaines de protéines ont été identifiées dans toutes les conditions étudiées, indiquant l'existence d'un protéome

List of illustrations

International collaboration

This thesis is the result of an international collaboration between Brazilian and French institutions, as part of an International Joint Laboratory that formalized an ongoing collaboration since 2000. Accordingly, this thesis was carried out under the joint supervision of: ▪ Dr. Vasco Ariston de Carvalho Azevedo, from the Laboratory of Cellular and Molecular Genetics (Laboratório de Genética Celular e Molecular, LGCM), in Belo Horizonte, Brazil.

▪ Dr. Eric Guédon, from Science and Technology of Milk and Eggs (Science et Technologie de Lait et de l'OEuf, STLO), in Rennes, France.

LGCM is associated to the Institute of Biological Sciences (Instituto de Ciências Biológicas, ICB) and the Interunit Postgraduation Program in Bioinformatics (Programa Interunidades de Pós-graduação em Bioinformática, PGBioinfo), both from the Federal University of Minas Gerais (Universidade Federal de Minas Gerais, UFMG).

STLO is associated to the French National Research Institute for Agriculture, Food and Environment (Institut National de Recherche pour l'Agriculture, l'alimentation et l'Environnement, INRAE) and the French School of Agriculture, Food, Horticultural and EVs are spherical nanostructured particles, composed of a marginal lipid bilayer and an internal aqueous lumen that harbors a potentially diverse biological cargo (Figure 1) [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF]. This cargo may include DNA, RNA, proteins, lipids, carbohydrates and small molecules (Kim et al., 2015b;[START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF][START_REF] Skotland | Lipids in exosomes: Current knowledge and the way forward[END_REF][START_REF] Jeppesen | Reassessment of Exosome Composition[END_REF]Williams et al., 2019a). EVs were reported to additionally contain proteins, lipoproteins, glycoproteins, polysaccharides, lipoteichoic acid (LTA) and other elements on their surface or imbedded in their lipid bilayers [START_REF] Salverda | Surface display of a borrelial lipoprotein on meningococcal outer membrane vesicles[END_REF][START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF][START_REF] Charoenviriyakul | Role of Extracellular Vesicle Surface Proteins in the Pharmacokinetics of Extracellular Vesicles[END_REF][START_REF] Shiraishi | Lipoteichoic acids are embedded in cell walls during logarithmic phase, but exposed on membrane vesicles in Lactobacillus gasseri JCM 1131[END_REF][START_REF] Valguarnera | Surface Exposure and Packing of Lipoproteins into Outer Membrane Vesicles Are Coupled Processes in Bacteroides[END_REF]Williams et al., 2019b;[START_REF] Cerezo-Magaña | The pleiotropic role of proteoglycans in extracellular vesicle mediated communication in the tumor microenvironment[END_REF]. Depending on specific features, such as size and origin, EVs have also been referred to as exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, outer membrane vesicles, membrane vesicles and others [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF]. Mounting evidence confirmed EV release by cells in all kingdoms of life, including bacteria, archaea, fungi, plants and human cells [START_REF] Deatheragea | Membrane vesicle release in bacteria, eukaryotes, and archaea: A conserved yet underappreciated aspect of microbial life[END_REF]Brown et al., 2015;Woith et al., 2019). Initially, there was the assumption that their major role was the disposal of unnecessary material [START_REF] Johnstone | Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes)[END_REF]. However, by the end of the 20th century, a paradigm shift took place with the postulation that EVs could perform alternative roles, such as antigen presenting and immunomodulation [START_REF] Raposo | B lymphocytes secrete antigen-presenting vesicles[END_REF]. Thereafter, a multitude of biological roles have been associated to EVs, both in normal physiology and in pathology [START_REF] Van Niel | Shedding light on the cell biology of extracellular vesicles[END_REF]Woith et al., 2019). For example, in a human perspective, EVs were associated to implications as diverse as neurological pathologies [START_REF] Park | Metagenome Analysis of Bodily Microbiota in a Mouse Model of Alzheimer Disease Using Bacteria-derived Membrane Vesicles in Blood[END_REF][START_REF] Choi | Extracellular Vesicles Derived from Lactobacillus plantarum Increase BDNF Expression in Cultured Hippocampal Neurons and Produce Antidepressant-like Effects in Mice[END_REF], cancer [START_REF] Anfossi | Gut microbiota: a new player in regulating immune-and chemotherapy efficacy[END_REF], and microbiota-mediated improvement of inflammatory diseases [START_REF] Kang | Extracellular Vesicles Derived from Gut Microbiota, Especially Akkermansia muciniphila, Protect the Progression of Dextran Sulfate Sodium-Induced Colitis[END_REF][START_REF] Chu | Gene-microbiota interactions contribute to the pathogenesis of inflammatory bowel disease[END_REF][START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF].

Bacteria-derived EVs

EVs derived from bacteria have raised particular interest, due to implications in infection [START_REF] Pathirana | Bacterial membrane vesicles: Biogenesis, immune regulation and pathogenesis[END_REF][START_REF] Ñahui Palomino | Extracellular vesicles from symbiotic vaginal lactobacilli inhibit HIV-1 infection of human tissues[END_REF], antibiotic resistance [START_REF] Uddin | The Role of Bacterial Membrane Vesicles in the Dissemination of Antibiotic Resistance and as Promising Carriers for Therapeutic Agent Delivery[END_REF], microbiota-derived beneficial effects [START_REF] Kang | Extracellular Vesicles Derived from Gut Microbiota, Especially Akkermansia muciniphila, Protect the Progression of Dextran Sulfate Sodium-Induced Colitis[END_REF][START_REF] Chu | Gene-microbiota interactions contribute to the pathogenesis of inflammatory bowel disease[END_REF][START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF], among others. Bacterial EVs were first microscopically documented in Gram-negative bacteria, specifically Escherichia coli grew in lysine-limiting condition [START_REF] Work | THE CHEMISTRY AND ELECTRON MICROSCOPY OF AN EXTRACELLULAR LIPOPOLYSACCHARIDE FROM ESCHERICHIA COLI[END_REF]. Due to differences in cellular envelope, EV production was initially considered restricted to Gram-negative bacteria, which present an exposed outer membrane (Schwechheimer and Kuehn, 2015). Contrastingly, Gram-positive bacteria have a thick peptidoglycan layer enclosing the cell wall, which could function as an impenetrable barrier for EV release [START_REF] Rohde | The Gram-Positive Bacterial Cell Wall[END_REF]. However, EV production by Gram-positive bacteria was reported, after more than two decades since its identification in E. coli [START_REF] Dorward | DNA Is Packaged within Membrane-Derived Vesicles of Gram-Negative but Not Gram-Positive Bacteria[END_REF]. At that point, researchers constated that the Gram-positive bacteria Bacillus cereus and Bacillus subtilis also produced EVs [START_REF] Dorward | DNA Is Packaged within Membrane-Derived Vesicles of Gram-Negative but Not Gram-Positive Bacteria[END_REF]. However, the first comprehensive analysis of the properties and content of EVs derived from Grampositive bacteria was performed only in 2009 with Staphylococcus aureus (Lee et al., 2009). Nowadays, it is clear that Gram-negative, Gram-positive and mycolic acidcontaining bacteria produce EVs with diverse cargo (Brown et al., 2015;Kim et al., 2015b;Toyofuku et al., 2019).

Biogenesis of bacterial EVs

Several types of EVs were recognized to be produced by bacteria (Figure 2), with different routes of formation (membrane blebbing or endolysin-triggered cell lysis) and origins (outer membrane or cytoplasmic membrane) (Toyofuku et al., 2019). Membrane blebbing has been extensively demonstrated for Gram-negative bacteria and consists in the protrusion of vesicles from regions of the outer membrane with weakened peptidoglycan crosslinking [START_REF] Song | A new Vibrio cholerae sRNA modulates colonization and affects release of outer membrane vesicles[END_REF]Deatherage et al., 2009;[START_REF] Wessel | Role of Pseudomonas aeruginosa peptidoglycan-associated outer membrane proteins in vesicle formation[END_REF]. Contrastingly, endolysin-triggered cell lysis is based on the enzymatic action of endolysins that act on peptidoglycan layer, demonstrated for both Gram-negative [START_REF] Turnbull | Explosive cell lysis as a mechanism for the biogenesis of bacterial membrane vesicles and biofilms[END_REF] and Gram-positive [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF] bacteria. Regarding nomenclature, Gram-negative bacteria are associated to the production of outermembrane vesicles (OMVs), explosive outer-membrane vesicles (EOMVs), outer-inner membrane vesicles (OIMVs), and tube-shaped membranous structures (TSMSs); whereas Gram-positive bacteria are associated to cytoplasmic membrane vesicles (CMVs or MVs) and TSMSs (Toyofuku et al., 2019;[START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. However, the generic term "extracellular vesicles" or "EVs" encompass all types of vesicles [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF]. Regarding mechanisms of biogenesis, studies with different bacteria species focused on OMVs and MVs, and indicated that, although encompassing complex genetic and environmental determinants, EV formation is mainly related to factors affecting membrane homeostasis and cell wall permeability (Volgers et al., 2018;[START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF].

Gram-negative bacteria

The cellular envelope of Gram-negative bacteria is composed of an outer membrane (OM), an inner membrane (IM) and a periplasmic space in between, which contains a thin peptidoglycan (PG) layer covalently and non-covalently linked to the membranes via lipoproteins [START_REF] Ruiz | Advances in understanding bacterial outer-membrane biogenesis[END_REF][START_REF] Silhavy | The Bacterial Cell Envelope[END_REF]Schwechheimer and Kuehn, 2015). Several factors affecting envelope structure were identified as important for EV formation, including the interactions between PG layer and the membranes, the turgor pressor mediated by misfolded or fragmented proteins and OM modifications (Figure 3) (Schwechheimer and Kuehn, 2015). Turgor pressure mediated by misfolded or fragmented proteins. C) Alterations in membrane homeostasis, including LPS modifications, enrichment of specific lipids and interactions with hydrophobic compounds. Based on Brown et al. (2015), Haurat et al. (2015) and Schwechheimer and Kuehn (2015).

The modulation of the envelope cross-linking involves the depletion of OM proteins and lipoproteins, as demonstrated with mutant studies in E. coli [START_REF] Sonntag | Cell envelope and shape of Escherichia coli: multiple mutants missing the outer membrane lipoprotein and other major outer membrane proteins[END_REF][START_REF] Wensink | Outer-Membrane Vesicles Released by Normally Growing Escherichia coli Contain Very Little Lipoprotein[END_REF]Schwechheimer et al., 2015). OM proteins also impacted EV production in Salmonella enterica serovar Typhimurium (Deatherage et al., 2009), Acinetobacter baumannii [START_REF] Moon | Acinetobacter baumannii outer membrane protein a modulates the biogenesis of outer membrane vesicles[END_REF], Pseudomonas aeruginosa [START_REF] Wessel | Role of Pseudomonas aeruginosa peptidoglycan-associated outer membrane proteins in vesicle formation[END_REF], Porphyromonas gingivalis [START_REF] Iwami | Further evidence that major outer membrane proteins homologous to OmpA in Porphyromonas gingivalis stabilize bacterial cells[END_REF] and Vibrio cholerae [START_REF] Song | A new Vibrio cholerae sRNA modulates colonization and affects release of outer membrane vesicles[END_REF].

Additionally, the accumulation of proteins, PG fragments and other envelope components in the periplasmic space and the consequent increase in turgor pressure was also associated to EV production (Zhou et al., 1998;[START_REF] Schwechheimer | Modulation of bacterial outer membrane vesicle production by envelope structure and content[END_REF]. The evidences include EVs derived from V. cholerae, Helicobacter pylori, P. aeruginosa, Neisseria gonorrhoea, and P. gingivalis, which contained PG-derived fragments (Zhou et al., 1998;[START_REF] Kaparakis | Bacterial membrane vesicles deliver peptidoglycan to NOD1 in epithelial cells[END_REF][START_REF] Bielig | NOD-like receptor activation by outer membrane vesicles from Vibrio cholerae non-O1 non-O139 strains is modulated by the quorum-sensing regulator HapR[END_REF]. Furthermore, the accumulation of lipopolysaccharide (LPS), PG fragments and misfolded proteins was associated to a stress response, involving increased periplasmic pression and EV production in E. coli and S. enterica serovar Typhimurium (McBroom and Kuehn, 2007;Schwechheimer and Kuehn, 2013;[START_REF] Schwechheimer | Modulation of bacterial outer membrane vesicle production by envelope structure and content[END_REF]. In this context, it was suggested that one of the functional aspects of EV production might comprise a regulated stress response for the removal of undesirable molecules (McBroom and Kuehn, 2007).

Membrane homeostasis is also considered determinant for EV biogenesis, since it is related to curvature tendency [START_REF] Schertzer | A bilayer-couple model of bacterial outer membrane vesicle biogenesis[END_REF]. Membrane homeostasis may be affected by lipid composition, as showed in P. aeruginosa and Lysobacter sp. XL1, whose EVs were enriched in specific types of lipids, such as phosphatidylglycerol, stearic acid, and cardiolipin [START_REF] Tashiro | Characterization of phospholipids in membrane vesicles derived from Pseudomonas aeruginosa[END_REF][START_REF] Kudryakova | Studying factors involved in biogenesis of Lysobacter sp. XL1 outer membrane vesicles[END_REF]. Furthermore, mutants for genes related to lipid transport in Haemophilus influenzae, V. cholerae and N. gonorrhoeae resulted in increased EV production [START_REF] Roier | A novel mechanism for the biogenesis of outer membrane vesicles in Gram-negative bacteria[END_REF][START_REF] Baarda | Neisseria gonorrhoeae MlaA influences gonococcal virulence and membrane vesicle production[END_REF]. Alterations in LPS, another component of the OM, were also associated to EV formation in P. aeruginosa, S. enterica serovar Typhimurium, and Citrobacter rodentium [START_REF] Sabra | Alterations in the formation of lipopolysaccharide and membrane vesicles on the surface of Pseudomonas aeruginosa PAO1 under oxygen stress conditions[END_REF][START_REF] Elhenawy | LPS Remodeling Triggers Formation of Outer Membrane Vesicles in Salmonella[END_REF][START_REF] Sinha | PmrC (EptA) and CptA Negatively Affect Outer Membrane Vesicle Production in Citrobacter rodentium[END_REF].

EV biogenesis may also be mediated by the addition of hydrophobic compounds in the outer leaflet of the OM (Tashiro et al., 2010a;[START_REF] Schertzer | A bilayer-couple model of bacterial outer membrane vesicle biogenesis[END_REF]. One of such compounds is 2-heptyl-3-hydroxy-4-quinolone, the hydrophobic Pseudomonas quinolone signal (PQS), related to quorum sensing and the transcriptional regulation of virulence genes [START_REF] Mashburn | Membrane vesicles traffic signals and facilitate group activities in a prokaryote[END_REF][START_REF] Lin | The Pseudomonas Quinolone Signal (PQS): Not Just for Quorum Sensing Anymore[END_REF]. Mutant studies showed that PQS is required and sufficient for EV formation in P. aeruginosa, acting via direct interaction with OM components, including phospholipids and LPS, and inducing curvatures in the lipidic structure [START_REF] Mashburn | Membrane vesicles traffic signals and facilitate group activities in a prokaryote[END_REF][START_REF] Mashburn-Warren | Interaction of quorum signals with outer membrane lipids: Insights into prokaryotic membrane vesicle formation[END_REF][START_REF] Schertzer | A bilayer-couple model of bacterial outer membrane vesicle biogenesis[END_REF]. Interestingly, PQS induced EV production in other bacteria species, including Gram-negative E. coli K12, Burkholderia spp and Pseudomonas spp., as well as Gram-positive Bacillus subtilis 168 (Tashiro et al., 2010a). Other quorum sensing molecules have been associated to EV production in other bacteria species, such as Stenotrophomonas maltophilia and Chromobacterium violaceum [START_REF] Devos | The effect of imipenem and diffusible signaling factors on the secretion of outer membrane vesicles and associated Ax21 proteins in Stenotrophomonas maltophilia[END_REF][START_REF] Batista | Interplay between two quorum sensing-regulated pathways, violacein biosynthesis and VacJ/Yrb, dictates outer membrane vesicle biogenesis in Chromobacterium violaceum[END_REF].

Furthermore, EV production in Gram-negative bacteria increased in response to antibiotics stress, as verified for pathogenic strains of E. coli exposed to gentamicin, ciprofloxacin, meropenem, fosfomycin and polymyxin B [START_REF] Bielaszewska | Antibiotic-mediated modulations of outer membrane vesicles in enterohemorrhagic Escherichia coli O104:H4 and O157:H7[END_REF]Chan et al., 2017). In multidrug-resistant strain A. baumannii DU202, imipenem treatment increased EV production, modulated the EV protein cargo and cytotoxic activity (Yun et al., 2018). In Burkholderia cepacian, subinhibitory concentrations of ceftazidime increased EV production, with the enhancing of pro-inflammatory activity [START_REF] Kim | Outer membrane vesicles produced by Burkholderia cepacia cultured with subinhibitory concentrations of ceftazidime enhance pro-inflammatory responses[END_REF]. These increases in EV production due to antibiotic exposure are related to specific interferences in bacterial envelope homeostasis [START_REF] Bielaszewska | Antibiotic-mediated modulations of outer membrane vesicles in enterohemorrhagic Escherichia coli O104:H4 and O157:H7[END_REF]Yun et al., 2018).

Gram-positive and mycolic acid-containing bacteria

Relative to research on Gram-negative bacteria, there are fewer data on the biogenesis of EVs in Gram-positive and mycolic acid-containing bacteria [START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF]. However, there are evidences that the enzymatic remodeling of the thick peptidoglycan wall, interaction with membrane-targeting molecules and turgor pression might be important factors for EV production (Figure 4) [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF][START_REF] Schlatterer | The Mechanism behind Bacterial Lipoprotein Release: Phenol-Soluble Modulins Mediate Toll-Like Receptor 2 Activation via Extracellular Vesicle Release from Staphylococcus aureus[END_REF].

The first proteomic study in EVs derived from Gram-positive bacteria, with S. aureus, identified penicillin-binding proteins, lipoteichoic acid synthase and peptidoglycan hydrolase, which are related to cell wall remodeling and could be associated to EV biogenesis (Lee et al., 2009). Proteomics studies in other species, including Listeria monocytogenes, Bacillus anthracis and Mycobacterium tuberculosis also identified EV proteins with cell wall remodeling roles (Rivera et al., 2010;Prados-Rosales et al., 2011;Lee et al., 2013a;Resch et al., 2016). In B. subtilis, EV formation was associated to the expression of endolysin, a PG-hydrolyzing enzyme encoded by a defective prophage, which resulted in the creation of holes in the cell wall and the release of EVs into the extracellular space [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF]. Although the cell wall did not completely disintegrate, there was loss of membrane integrity and cell death [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF]. Brown et al. (2015) and Wang et al. (2018).

In S. aureus, the surfactant-like phenol-soluble modulin α (PSMα) was associated to EV production [START_REF] Schlatterer | The Mechanism behind Bacterial Lipoprotein Release: Phenol-Soluble Modulins Mediate Toll-Like Receptor 2 Activation via Extracellular Vesicle Release from Staphylococcus aureus[END_REF]Wang et al., 2018). These are proinflammatory peptides required for the mobilization of lipoproteins and the export of cytoplasmic proteins [START_REF] Ebner | Non-classical Protein Excretion Is Boosted by PSMα-Induced Cell Leakage[END_REF]. The deletion of psmα genes resulted in reduced EV yield, indicating that PSMα is important for EV production via membrane targeting (Wang et al., 2018). Moreover, the same study showed that the mutant for PG hydrolase Sle1 presented a reduction in EV yield (Wang et al., 2018). Finally, a deletion of penicillin-binding protein 4 (pbp4) gene showed reductions in PG cross-linking and increased EV production and size (Wang et al., 2018). Therefore, this study showed the multifactorial control of EV release in S. aureus, including PSMα-mediated membrane disrupting and PG remodeling (Wang et al., 2018). Furthermore, another mutant study with S. aureus showed that PSMα increased bacterial membrane fluidity and promoted EV release dependent on the existence of a turgor pressure in bacterial cells [START_REF] Schlatterer | The Mechanism behind Bacterial Lipoprotein Release: Phenol-Soluble Modulins Mediate Toll-Like Receptor 2 Activation via Extracellular Vesicle Release from Staphylococcus aureus[END_REF]. The importance of membrane fluidity in EV biogenesis was also demonstrated in a mutant strain of S. aureus which was deficient in the lipidation and maturation of lipoproteins, resulting in increased EV yield (Wang et al., 2020c). The absence of functional lipoproteins was associated to augmented cytoplasmic membrane fluidity, as measured by excimer-forming lipid technique (Wang et al., 2020c). These studies with S. aureus reinforced that a combination of factors might be determinant for EV production and release.

Antibiotics were also associated to the control of EV biogenesis in Gram-positive bacteria (Andreoni et al., 2018;Kim et al., 2020c). In S. aureus, the treatment with quinolone ciprofloxacin in a phage-bearing strain resulted in increased EV production, via endolysin-triggered cell death (Andreoni et al., 2018). Contrastingly, the treatment with β-lactam flucloxacillin or ceftaroline increased EV production in a phageindependent manner, probably via another mechanism related to cell wall weakening (Andreoni et al., 2018). Interestingly, EVs produced under antibiotic stress also served bacteria as an decoy defense mechanism, even containing antibiotic-hydrolyzing enzymes, which is coherent with increased EV production in these situations (Lee et al., 2013b;Andreoni et al., 2018;Kim et al., 2020c).

Content of bacterial EVs

Since EVs are derived from cellular membranes, their composition naturally includes lipids [START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. Additionally, several other types of biomolecules have been identified in bacterial EVs cargo, including proteins of diverse origins, DNA, RNA and metabolites (Toyofuku, 2019;[START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. EVs content has been extensively characterized by multi-omics approaches to shed light over their biogenesis, cargo sorting and functional aspects (Choi et al., 2015a). Although many aspects of bacterial EV formation and content sorting remain elusive, there is evidence that this is not a random, but rather controlled process (Schwechheimer et al., 2013;Volgers et al., 2018;Caruana and Walper, 2020).

Proteins

For the characterization of EV cargo, much attention has been allotted to protein identification, since proteins can be more directly associated to EV functions [START_REF] Lee | Front cover: Proteomic profiling of Gram-negative bacterial outer membrane vesicles: Current perspectives[END_REF]. EV protein content may be investigated by several approaches, including the determination of total protein content by conventional colorimetric analyses, such as bicinchoninic acid (BCA) and Bradford assays [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. More directed assays have also been applied to detect specific proteins in EVs, including immunoblotting and immunosorbent assays [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. However, over the last years, the highthroughput methodologies based on mass spectrometry (MS) have been crucial for boosting the knowledge about EV protein content (Kim et al., 2015b;[START_REF] Rosa-Fernandes | A Perspective on Extracellular Vesicles Proteomics[END_REF]. Particularly, bottom-up MS-based strategies have been widely applied in EV research, which involve the extraction of proteins, their digestion into peptides and separation by gel electrophoresis or liquid chromatography. Then, the resulting fractions of peptides are ionized and fragmented in the gas phase of mass spectrometers, with the measurement of their mass/charge ratio [START_REF] Rosa-Fernandes | A Perspective on Extracellular Vesicles Proteomics[END_REF]. The interpretation of mass/charge spectra intensities and their comparison to theoretical spectra allow protein identification and quantification [START_REF] Wysocki | Mass spectrometry of peptides and proteins[END_REF]. Several approaches are applied in MS-based EV proteomics, including shotgun proteomics and targeted proteomics, in which peptide ions are heuristically or selectively fragmented, respectively [START_REF] Rosa-Fernandes | A Perspective on Extracellular Vesicles Proteomics[END_REF].

In the case of bacteria, early studies with OMVs applied polyacrylamide gel electrophoresis and immunoblotting for specific protein identification, but MS-based approaches dominated since 2007, with the proteomic characterization of EVs derived from E. coli, which were enriched in outer membrane proteins [START_REF] Kesty | Incorporation of Heterologous Outer Membrane and Periplasmic Proteins into Escherichia coli Outer Membrane Vesicles[END_REF][START_REF] Lee | Global proteomic profiling of native outer membrane vesicles derived from Escherichia coli[END_REF][START_REF] Lee | Front cover: Proteomic profiling of Gram-negative bacterial outer membrane vesicles: Current perspectives[END_REF]. For Gram-positive bacteria, in 2009, the first comprehensive characterization of EV content applied liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC-ESI MS/MS) for proteomic characterization of S. aureus-derived EVs, identifying mainly cytoplasmic proteins related to metabolism, information storage and processing, and virulence (Lee et al., 2009).

Since these initial observations, the protein cargo of bacterial EVs has been consistently associated with their origin. For instance, for Gram-negative bacteria, certain lipoproteins present in the outer membrane, such as outer membrane proteins A (OmpA), C (OmpC) and F (OmpF) were also identified in OMVs [START_REF] Kesty | Incorporation of Heterologous Outer Membrane and Periplasmic Proteins into Escherichia coli Outer Membrane Vesicles[END_REF]Kim et al., 2015b). The same is true for proteins from the periplasmic space, such as alkaline phosphatases and multidrug efflux pump subunits, as well as adhesins, invasins and other proteins that function as virulence factors [START_REF] Kesty | Incorporation of Heterologous Outer Membrane and Periplasmic Proteins into Escherichia coli Outer Membrane Vesicles[END_REF]Kim et al., 2015b). However, these EVs contained not only outer membrane and periplasmic proteins, as one would expect, but inner membrane proteins and cytoplasmic proteins were also consistently identified [START_REF] Kadurugamuwa | Virulence factors are released from Pseudomonas aeruginosa in association with membrane vesicles during normal growth and exposure to gentamicin: a novel mechanism of enzyme secretion[END_REF][START_REF] Oliver | The Proteome of Biologically Active Membrane Vesicles from Piscirickettsia salmonis LF-89 Type Strain Identifies Plasmid-Encoded Putative Toxins[END_REF][START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. The occurrence of such proteins from other cellular localizations in EVs derived from Gram-negative bacteria was justified by the enzymatic degradation of the peptidoglycan layer, which could trigger an explosive cell lysis and result in membrane fragments that round up and wrap intracellular components (Toyofuku et al., 2019;[START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. Therefore, these corresponded to other types of EVs, such as EOMVs and OIMVs, resulting from cell lysis and rearrangement of fragments derived from Gram-negative bacteria membranes (Toyofuku et al., 2019).

In the case of Gram-positive bacteria, proteins related to peptidoglycan hydrolysis or cross-linking were also reported in EVs (Lee et al., 2009;Wang et al., 2018;[START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF]. Moreover, since EV formation involves budding of the cytoplasmic membrane, several cytoplasmic and membrane proteins were also identified in EVs derived from Gram-positive bacteria (Lee et al., 2009;Kim et al., 2015b). Furthermore, in pathogenic S. aureus, EVs were demonstrated to carry several virulence factors (Lee et al., 2009(Lee et al., , 2013b;;Kim et al., 2015b) and in probiotic Lactobacillus spp., EVs carried proteins related to probiotic effect (Rubio et al., 2017;[START_REF] Ñahui Palomino | Extracellular vesicles from symbiotic vaginal lactobacilli inhibit HIV-1 infection of human tissues[END_REF].

Overall, it is currently considered that, depending on origin and environmental conditions, bacteria-derived EVs transport diverse groups of proteins, including outer membrane proteins, hydrolyzing enzymes and multidrug efflux pumps related to antibiotic resistance, virulence factors, ABC transporters, motility-related proteins, proteases, chaperones, proteins implicated in adhesion, invasion, killing of competing bacteria, coagulation and host cell modulation, as well as several cytoplasmic proteins, such as ribosomal proteins, DNA-processing enzymes and metabolic enzymes (Lee et al., 2009;[START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF][START_REF] Uddin | The Role of Bacterial Membrane Vesicles in the Dissemination of Antibiotic Resistance and as Promising Carriers for Therapeutic Agent Delivery[END_REF]. Some evidences suggest that the packaging of such proteins inside EVs is not a random process, although the precise mechanisms for cargo sorting remain unknow [START_REF] Bonnington | Protein selection and export via outer membrane vesicles[END_REF][START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF][START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. These evidences include the enrichment of certain types of proteins in EVs, such as lipoproteins in EVs derived from S. aureus (Lee et al., 2009;Tartaglia et al., 2020) and cytoplasmic proteins in EVs derived from Gram-negative bacteria [START_REF] Kadurugamuwa | Virulence factors are released from Pseudomonas aeruginosa in association with membrane vesicles during normal growth and exposure to gentamicin: a novel mechanism of enzyme secretion[END_REF][START_REF] Oliver | The Proteome of Biologically Active Membrane Vesicles from Piscirickettsia salmonis LF-89 Type Strain Identifies Plasmid-Encoded Putative Toxins[END_REF][START_REF] Clarke | The "hole" story of predatory outer-membrane vesicles[END_REF]. Moreover, a multi-strain study indicated that the EV proteome of S. aureus is conserved in the analyzed strains (Tartaglia et al., 2020). In Gram-negative bacteria, cargo selection was associated to cell envelope stress responses, since the accumulation of misfolded proteins in the periplasmic space triggered OMVs formation (McBroom and Kuehn, 2007;Schwechheimer et al., 2013). Moreover, specific proteins, such as chaperones and some enzymes, are implicated in the selection of other proteins to be packaged inside EVs [START_REF] Altindis | Proteomic analysis of Vibrio cholerae outer membrane vesicles[END_REF][START_REF] Hussain | The Bam complex catalyzes efficient insertion of bacterial outer membrane proteins into membrane vesicles of variable lipid composition[END_REF]. Furthermore, there are also evidences of lipid-dependent [START_REF] Roier | A novel mechanism for the biogenesis of outer membrane vesicles in Gram-negative bacteria[END_REF][START_REF] Yokoyama | Characterization of extracellular membrane vesicles of an Antarctic bacterium, Shewanella livingstonensis Ac10, and their enhanced production by alteration of phospholipid composition[END_REF], size-dependent [START_REF] Turner | Helicobacter pylori Outer Membrane Vesicle Size Determines Their Mechanisms of Host Cell Entry and Protein Content[END_REF] and isoelectric point-dependent [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF] cargo selections.

DNA

Early reports of bacterial EVs containing DNA date from the decade of 1980, when its protection against enzymatic degradation during H. influenzae transformation was associated to membranous structures so-called "transformasomes" [START_REF] Kahn | Transformasomes: specialized membranous structures that protect DNA during Haemophilus transformation[END_REF]Gill et al., 2019). Then, subsequent studies showed that the transfer of virulence genes was also mediated by vesicles in E. coli O157:H7 [START_REF] Kolling | Export of virulence genes and Shiga toxin by membrane vesicles of Escherichia coli O157:H7[END_REF][START_REF] Yaron | Vesicle-mediated transfer of virulence genes from Escherichia coli O157:H7 to other enteric bacteria[END_REF]. Currently, several reports show that DNA of different origins is packaged inside bacterial EVs, including chromosome fragments, natural plasmids, artificial vectors and even viral DNA [START_REF] Pérez-Cruz | New Type of Outer Membrane Vesicle Produced by the Gram-Negative Bacterium Shewanella vesiculosa M7 T : Implications for DNA Content[END_REF][START_REF] Fulsundar | Gene Transfer Potential of Outer Membrane Vesicles of Acinetobacter baylyi and Effects of Stress on Vesiculation[END_REF][START_REF] Domingues | Membrane vesicles and horizontal gene transfer in prokaryotes[END_REF]. Sizes may achieve up to hundreds of kilobases, but smaller fragments are more common [START_REF] Domingues | Membrane vesicles and horizontal gene transfer in prokaryotes[END_REF]. Regarding mechanisms of content selection, little is known, although it is assumed that DNA may end up in EVs via cytoplasmic or periplasmic routes, as well as cell death or even EV lyses and rebounding [START_REF] Domingues | Membrane vesicles and horizontal gene transfer in prokaryotes[END_REF][START_REF] Uddin | The Role of Bacterial Membrane Vesicles in the Dissemination of Antibiotic Resistance and as Promising Carriers for Therapeutic Agent Delivery[END_REF]. Furthermore, it is unclear if sequences are randomly packaged inside EVs, or if there is an active transport into EVs [START_REF] Domingues | Membrane vesicles and horizontal gene transfer in prokaryotes[END_REF].

As other sorts of characterization, the investigation of DNA content and functional implications in EVs derived from Gram-negative bacteria is relatively more advanced than in other bacteria [START_REF] Uddin | The Role of Bacterial Membrane Vesicles in the Dissemination of Antibiotic Resistance and as Promising Carriers for Therapeutic Agent Delivery[END_REF]. Vesicle-mediated horizontal gene transfer (HGT) has been demonstrated for several Gram-negative donor species, such as E. coli [START_REF] Yaron | Vesicle-mediated transfer of virulence genes from Escherichia coli O157:H7 to other enteric bacteria[END_REF], N. gonorrhoeae [START_REF] Dorward | Export and intercellular transfer of DNA via membrane blebs of Neisseria gonorrhoeae[END_REF], A. baumannii [START_REF] Rumbo | Horizontal Transfer of the OXA-24 Carbapenemase Gene via Outer Membrane Vesicles: a New Mechanism of Dissemination of Carbapenem Resistance Genes in Acinetobacter baumannii[END_REF], Acinetobacter baylyi [START_REF] Fulsundar | Gene Transfer Potential of Outer Membrane Vesicles of Acinetobacter baylyi and Effects of Stress on Vesiculation[END_REF], P. gingivalis (Ho et al., 2015), Thermus thermophilus and Thermus scotoductus [START_REF] Blesa | Contribution of vesicle-protected extracellular DNA to horizontal gene transfer in Thermus spp[END_REF]. In some of these studies, DNA was tracked with techniques such as immunogold labelling [START_REF] Fulsundar | Gene Transfer Potential of Outer Membrane Vesicles of Acinetobacter baylyi and Effects of Stress on Vesiculation[END_REF] and transformation rate was higher for EV-associated DNA than naked DNA [START_REF] Rumbo | Horizontal Transfer of the OXA-24 Carbapenemase Gene via Outer Membrane Vesicles: a New Mechanism of Dissemination of Carbapenem Resistance Genes in Acinetobacter baumannii[END_REF]. Fewer data are available for Gram-positive bacteria, but a pioneer study with Ruminococcus spp. of ruminal origin showed these bacteria were capable of transferring DNA via EVs and that the transformation restored the ability of mutants to degrade crystalline cellulose [START_REF] Klieve | Naturally occurring DNA transfer system associated with membrane vesicles in cellulolytic Ruminococcus spp. of ruminal origin[END_REF]. The EV-mediated transfer of DNA was also reported in other Gram-positive bacteria, such as Streptococcus mutans (Liao et al., 2014), Clostridium perfringens [START_REF] Jiang | Membrane vesicles of Clostridium perfringens type A strains induce innate and adaptive immunity[END_REF] and S. aureus (Rodriguez and Kuehn, 2020). Evidently, the horizontal gene transfer mediated by EVs has functional implications in antibiotic resistance, virulence and bacterial evolution (Gill et al., 2019).

RNA

RNA was also detected in association with bacterial EVs, in experiments with N. gonorrhoeae in the decade of 1980 [START_REF] Dorward | Export and intercellular transfer of DNA via membrane blebs of Neisseria gonorrhoeae[END_REF]. After that, a long time passed before the emergence of reports characterizing comprehensively the RNA content of bacteria-derived EVs (Biller et al., 2014;Ghosal et al., 2015). A pioneer study with marine cyanobacterium Prochlorococcus marinus employed RNA sequencing on Illumina platform to investigate EVs cargo, resulting in the detection of ribosomal RNA (rRNA) in more than 94% of total sample and reaching a coverage of 95% of the open reading frames in the genome (Biller et al., 2014). Since them, researchers found RNA in EVs derived from several bacteria, such as Gram-negative E. coli (Ghosal et al., 2015;Blenkiron et al., 2016;Dauros Singorenko et al., 2017), P. gingivalis (Ho et al., 2015;Choi et al., 2017), Aggregatibacter actinomycetesmcomitans (Choi et al., 2017), P. aeruginosa (Koeppen et al., 2016), V. cholerae (Sjöström et al., 2015), and Treponema denticola (Choi et al., 2017); as well as Gram-positive Streptococcus pyogenes (Resch et al., 2016), Streptococcus sanguinis [START_REF] Choi | Correction to: Isolation and Characterization of a microRNA-size Secretable Small RNA in Streptococcus sanguinis[END_REF], L. monocytogenes (Frantz et al., 2019) and S. aureus (Rodriguez and Kuehn, 2020).

Cutting-edge technologies in transcriptomics allowed the description of a great diversity of RNA types associated to bacterial EVs, although the lack of standardization of protocols remains a great challenge to be addressed (Dauros-Singorenko et al., 2018). The referred diversity includes ribosomal RNA (rRNA), transfer RNA (tRNA), messenger RNA (mRNA) and several types of small RNA (sRNA) (Gill et al., 2019). The detection of enriched mRNAs in bacterial EVs suggested that they could trigger protein synthesis in recipient cells (Resch et al., 2016;Gill et al., 2019). In turn, sRNAs are known to regulate gene expression post-transcriptionally and those sRNAs detected in EVs derived from P. aeruginosa and L. monocytogenes had targets in human host cells (Koeppen et al., 2016;Frantz et al., 2019).

Whether RNA content in EVs is merely representative of the physiological state of parental cells in the absence of a selection process, or if there is an active loading into EVs consistent with the enrichment of specific transcripts, remains an open question (Lécrivain and Beckmann, 2020). Chances are this sort of enquiries will be elucidated soon, with the rising application of high-throughput and single-vesicle RNA sequencing techniques (Lécrivain and Beckmann, 2020;Wang et al., 2020b).

Other molecules

Although the majority of the studies on the content of bacteria-derived EVs focused in proteins and nucleic acids, the importance of other types of cargo is increasingly recognized, including lipids, glycans and small molecules (Kuipers et al., 2018). For example, two recent studies highlighted the importance of lipidomics

Functional aspects of bacterial EVs

The diversity of composition and biogenesis mechanisms of bacterial EVs has been linked to a series of functional consequences (Toyofuku et al., 2019). Although the majority of bacterial EV research has focused in pathogenesis (Kim et al., 2015b), the roles of EVs are also associated to interactions with the environment or microbial communities, as well as beneficial effects in the interactions with the host (Figure 5) (Caruana and Walper, 2020).

Bacteria-bacteria communication

In the context of the interactions in microbial communities, the propagation of quorum sensing molecules are of crucial importance, since they are implicated in processes such as pathogenesis and nutrition (Caruana and Walper, 2020). Some of these molecules are hydrophobic and their diffusion is facilitated by bacterial EVs, such as the quinolone signal of P. aeruginosa [START_REF] Mashburn | Membrane vesicles traffic signals and facilitate group activities in a prokaryote[END_REF], the N-acyl homoserine lactone signal of Paracoccus denitrificans [START_REF] Morinaga | Paracoccus denitrificans can utilize various long-chain N -acyl homoserine lactones and sequester them in membrane vesicles[END_REF] and the long-chain amino ketone molecule of V. harveyi [START_REF] Brameyer | Outer Membrane Vesicles Facilitate Trafficking of the Hydrophobic Signaling Molecule CAI-1 between Vibrio harveyi Cells[END_REF]. Additionally, EVs are reported as components of biofilm matrices, with functions of structural support, cooperation, nutrition and survival [START_REF] Flemming | Biofilms: an emergent form of bacterial life[END_REF]Caruana and Walper, 2020). As an example, in P. aeruginosa, the EVs implicated in biofilm formation also packaged virulence factors and bound antibiotics [START_REF] Schooling | Membrane Vesicles: an Overlooked Component of the Matrices of Biofilms[END_REF]. Furthermore, S. aureus EVs were able to increase bacterial adhesion and cell aggregation, contributing to biofilm formation (He et al., 2017). Besides interactions that may benefit other bacteria, EVs also mediate antagonistic interactions and competition [START_REF] Li | Gram-Negative Bacteria Produce Membrane Vesicles Which Are Capable of Killing Other Bacteria[END_REF]. Some of them export antimicrobial factors targeting other bacteria, that is the case of P. aeruginosa [START_REF] Tashiro | Interspecies Interaction between Pseudomonas aeruginosa and Other Microorganisms[END_REF], Myxococcus xanthus [START_REF] Berleman | The lethal cargo of Myxococcus xanthus outer membrane vesicles[END_REF], and Cystobacter velatus (Yun et al., 2018). The probiotic Lactobacillus acidophilus was reported to deliver bacteriocin peptides to opportunistic pathogens through EVs [START_REF] Dean | Lactobacillus acidophilus Membrane Vesicles as a Vehicle of Bacteriocin Delivery[END_REF] and the environmental bacterium C. violaceum produced EVs containing violacein, a hydrophobic microbicidal purple pigment, with roles in interbacterial competition [START_REF] Batista | Interplay between two quorum sensing-regulated pathways, violacein biosynthesis and VacJ/Yrb, dictates outer membrane vesicle biogenesis in Chromobacterium violaceum[END_REF].

Bacteria-environment communication

EVs functional roles also concern adaptations to a changing environment. That is the case of iron acquisition, especially in environments where this is a limiting condition. In such conditions, EVs derived from M. tuberculosis and P. aeruginosa were showed to contain iron-chelating proteins (Prados-Rosales et al., 2014;[START_REF] Lin | A Pseudomonas T6SS effector recruits PQS-containing outer membrane vesicles for iron acquisition[END_REF]. EVmediated adaptation also occurs in the human gastrointestinal tract, since Bacteroides fragilis and B. thetaiotaomicron produce EVs implicated in polysaccharide breakdown, which is also beneficial for other species in the intestine, since they could consume the resulting products, even if they could not metabolize those polysaccharides themselves [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF]. In marine ecosystems, EVs produced by the photosynthetic cyanobacterium Prochlorococcus had implications in marine carbon flux, since they supported the growth of heterotrophic marine bacteria and interacted with virus particles (Biller et al., 2014[START_REF] Biller | Membrane vesicles in sea water: heterogeneous DNA content and implications for viral abundance estimates[END_REF].

Microbial defense is also achieved by EVs, particularly regarding resistance to antibiotics. This is carried out by enzymatic degradation, such as with the EV-mediated export of beta-lactamases in S. aureus (Lee et al., 2013b), Moraxella catarrhalis [START_REF] Schaar | Outer membrane vesicles shield Moraxella catarrhalis β-lactamase from neutralization by serum IgG[END_REF], H. influenzae [START_REF] Schaar | Group a streptococci are protected from amoxicillin-mediated killing by vesicles containing β-lactamase derived from Haemophilus influenzae[END_REF] and P. aeruginosa [START_REF] Ciofu | Chromosomal β-lactamase is packaged into membrane vesicles and secreted from Pseudomonas aeruginosa[END_REF]. As an example, S. aureus-derived EVs containing beta-lactamase were able to confer a transient resistance against ampicillin to susceptible surrounding Gram-negative and Gram-positive bacteria, including different strains of E. coli, Salmonella enterica serovar Enteritidis, Staphylococcus epidermidis, and S. aureus (Lee et al., 2013b). EVs may also act as decoys in the depletion of lipophilic antimicrobials that are titrated out by them, as demonstrated in strains of E. coli [START_REF] Manning | Contribution of bacterial outer membrane vesicles to innate bacterial defense[END_REF] and in M. catarrhalis [START_REF] Roszkowiak | Interspecies Outer Membrane Vesicles (OMVs) Modulate the Sensitivity of Pathogenic Bacteria and Pathogenic Yeasts to Cationic Peptides and Serum Complement[END_REF].

Bacteria-host communication

Some bacteria communicate not only with neighboring bacteria, but also with mammalian host cells. Indeed, due to the immediate implications in human health and disease, these interactions mediated by bacterial EVs have been extensively researched, especially regarding pathogenesis [START_REF] Pathirana | Bacterial membrane vesicles: Biogenesis, immune regulation and pathogenesis[END_REF][START_REF] Cecil | Outer Membrane Vesicle-Host Cell Interactions[END_REF]. EVs derived from pathogenic bacteria were investigated in niches as diverse as human lungs, oral cavity, and gastrointestinal tract (Caruana and Walper, 2020). One of the key features of EV-mediated pathogenesis is the delivery of virulence factors and toxins, which results in cytotoxicity and has been verified in species such as E. coli [START_REF] Bielaszewska | Enterohemorrhagic Escherichia coli Hemolysin Employs Outer Membrane Vesicles to Target Mitochondria and Cause Endothelial and Epithelial Apoptosis[END_REF], Shigella dysenteriae [START_REF] Dutta | Release of Shiga toxin by membrane vesicles in Shigella dysenteriae serotype 1 strains and in vitro effects of antimicrobials on toxin production and release[END_REF], Campylobacter jejuni [START_REF] Lindmark | Outer membrane vesicle-mediated release of cytolethal distending toxin (CDT) from Campylobacter jejuni[END_REF], V. cholerae [START_REF] Chatterjee | Association of cholera toxin with Vibrio cholerae outer membrane vesicles which are internalized by human intestinal epithelial cells[END_REF] and S. aureus (Thay et al., 2013). In the human oral cavity, bacterial EVs are associated with periodontal plaque formation, particularly P. gingivalis-derived EVs, that carry cysteine proteases linked to bacterial spread throughout the periodontal tissue [START_REF] Nakao | Effect of Porphyromonas gingivalis outer membrane vesicles on gingipain-mediated detachment of cultured oral epithelial cells and immune responses[END_REF]. In the gastrointestinal tract, an example of EV-mediated pathogenesis is triggered by H. pylori, whose EVs carry the vacuolating cytotoxin VacA, resulting in the dysregulation of inflammatory processes, such as by the induction of T cell apoptosis [START_REF] Fiocca | Release of Helicobacter pylori vacuolating cytotoxin by both a specific secretion pathway and budding of outer membrane vesicles. Uptake of released toxin and vesicles by gastric epithelium[END_REF][START_REF] Winter | Helicobacter pylori Membrane Vesicles Stimulate Innate Pro-and Anti-Inflammatory Responses and Induce Apoptosis in Jurkat T Cells[END_REF]. Moreover, bacteria-derived EVs exerted immunomodulatory roles mediated by toll-like receptor 2 (TLR2) or 4 (TLR4), as demonstrated for B. fragilis [START_REF] Shen | Outer Membrane Vesicles of a Human Commensal Mediate Immune Regulation and Disease Protection[END_REF], Brucella abortus [START_REF] Pollak | Outer Membrane Vesicles from Brucella abortus Promote Bacterial Internalization by Human Monocytes and Modulate Their Innate Immune Response[END_REF] and mycobacteria species (Prados-Rosales et al., 2011).

Contrastingly, bacterial-derived EVs may also be beneficial to the host. That is the case of commensal or probiotic bacteria whose EVs produce anti-inflammatory effects, including Akkermansia muciniphila [START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF], B. fragilis [START_REF] Shen | Outer Membrane Vesicles of a Human Commensal Mediate Immune Regulation and Disease Protection[END_REF], E. coli Nissle 1917[START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF], Bifidobacterium bifidum [START_REF] López | Treginducing membrane vesicles from Bifidobacterium bifidum LMG13195 as potential adjuvants in immunotherapy[END_REF], and several Lactobacillus species [START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF][START_REF] Vargoorani | Stimulatory effects of Lactobacillus casei derived extracellular vesicles on toll-like receptor 9 gene expression and cytokine profile in human intestinal epithelial cells[END_REF]. These beneficial effects mediated by bacterial-derived EVs will be reviewed in detail in section 4.

Clinical and technological applications of bacterial EVs

Bacteria-derived EVs present a broad range of potential applications, since they promote content exchange, resist to enzymatic degradation and low pH, as well as spread to distant tissues without self-replicating [START_REF] Stentz | Fantastic voyage: The journey of intestinal microbiota-derived microvesicles through the body[END_REF]. For example, EVs have been regarded as promising vaccine candidates [START_REF] Gerritzen | Bioengineering bacterial outer membrane vesicles as vaccine platform[END_REF], since they are implicated in the interaction with mammal hosts and in the induction of a broad range of immune responses [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. Moreover, EVs are stable in different temperatures and chemical environments [START_REF] Arigita | Stability of mono-and trivalent meningococcal outer membrane vesicle vaccines[END_REF]. Another remarkable feature is self-adjuvanting properties of EVs, which simplify vaccine design, eliminating the need for additional adjuvants, and may result in more efficient products [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF][START_REF] Gerritzen | Bioengineering bacterial outer membrane vesicles as vaccine platform[END_REF]. Several bacterial EVbased vaccines showed positive results in vivo, including those derived from Bordetella pertussis [START_REF] Raeven | Bordetella pertussis outer membrane vesicle vaccine confers equal efficacy in mice with milder inflammatory responses compared to a whole-cell vaccine[END_REF]), A. baumannii (McConnell et al., 2011) and S. aureus (Choi et al., 2015b).

EVs also find applications in drug delivery, similarly to their synthetic counterparts, liposomes, widely used for drug encapsulation due to favorable pharmacokinetic properties (Nguyen et al., 2016;[START_REF] Jain | Bacterial membrane vesicles as novel nanosystems for drug delivery[END_REF][START_REF] Wang | Outer membrane vesicles for vaccination and targeted drug delivery[END_REF]. Furthermore, bacteria-derived EVs show potential advantages over conventional drug delivery systems, such as protection against enzymatic degradation and naturally occurring surface proteins for targeting [START_REF] Wang | Outer membrane vesicles for vaccination and targeted drug delivery[END_REF]. As an example, OMVs derived from B. thetaiotaomicron were employed for the delivery of proteins to the gastrointestinal and respiratory tract in mice (Carvalho et al., 2019a) and E. coli-derived EVs were bioengineered to reduce toxic outer membrane components and deliver chemotherapeutic drugs to tumor cells and tissues [START_REF] Kim | Bacterial protoplastderived nanovesicles for tumor targeted delivery of chemotherapeutics[END_REF].

EVs derived from non-pathogenic bacteria also find promising applications in biotechnological and food industries [START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]. For example, EVs naturally harboring nucleic acids could be employed as systems for the delivery of DNA or RNA to recipient cells, conferring them resistance to stress or enhancing productivity [START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]. Moreover, EVs derived from probiotic bacteria show potential applications as anti-inflammatory and anti-pathogenic tools [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. For example, EVs derived from several probiotic bacteria of the Lactobacillus genus showed anti-inflammatory properties in vitro and in vivo (Kim et al., 2018b;Molina-Tijeras et al., 2019;Choi et al., 2020b;[START_REF] Vargoorani | Stimulatory effects of Lactobacillus casei derived extracellular vesicles on toll-like receptor 9 gene expression and cytokine profile in human intestinal epithelial cells[END_REF]. Moreover, the anti-pathogenic activity associated to some bacterial EVs suggest that they could be used in infection therapy, especially those carrying antimicrobial compounds and bacteriolytic enzymes [START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]Yun et al., 2018;[START_REF] Ñahui Palomino | Extracellular vesicles from symbiotic vaginal lactobacilli inhibit HIV-1 infection of human tissues[END_REF][START_REF] Dean | Lactobacillus acidophilus Membrane Vesicles as a Vehicle of Bacteriocin Delivery[END_REF]. These applications are already diverse and promising, but there is potential for further improvement with the modulation of EV properties.

Modulation of bacterial EVs properties

Since bacterial EVs biogenesis is triggered by genetic and environmental factors, and EVs content is related to their functions, there has been some efforts to understand and modulate EVs properties, in order to improve their applicability (Orench- Rivera and Kuehn, 2016). As an example, some studies identified genetic factors related to increased production of EVs, such as a genetic mutation in probiotic E. coli Nissle 1917[START_REF] Pérez-Cruz | Membrane Vesicles Released by a hypervesiculating Escherichia coli Nissle 1917 tolR Mutant Are Highly Heterogeneous and Show Reduced Capacity for Epithelial Cell Interaction and Entry[END_REF] and DNA inversions at distantly localized promoters in B. fragilis [START_REF] Nakayama-Imaohji | DNA inversion regulates outer membrane vesicle production in Bacteroides fragilis[END_REF]. Besides genetic elements, culture medium supplementation with glycine also resulted in increased production of EVs and reduced endotoxin concentration in E. coli Nissle 1917 (Hirayama and[START_REF] Hirayama | Glycine significantly enhances bacterial membrane vesicle production: a powerful approach for isolation of LPS-reduced membrane vesicles of probiotic Escherichia coli[END_REF]. Therefore, these genetic and environmental triggers could be manipulated to increase EV production in technological contexts.

Besides EV concentration, some studies showed that it is also possible to modulate their content and functions. Environmental conditions, such as antibiotic treatment (Chan et al., 2017;Yun et al., 2018), salt stress (Lee et al., 2018;Jun et al., 2019), pH (Lynch et al., 2019), bacterial growth phase (Tashiro et al., 2010b;Zavan et al., 2019) and culture media composition (Keenan and Allardyce, 2000;Bager et al., 2013;Adriani et al., 2018;Hong et al., 2019;Taboada et al., 2019) were capable of modulating bacterial EV proteomic content and hold potential for designing content optimization.

Lastly, nanotechnology and biotechnology have provided additional tools for modulating EV properties. For example, L. plantarum-derived EVs were coupled with microparticles to allow controlled administration [START_REF] Kuhn | Probiomimetics-Novel Lactobacillus-Mimicking Microparticles Show Anti-Inflammatory and Barrier-Protecting Effects in Gastrointestinal Models[END_REF]. Additionally, EVs derived from pathogenic E. coli were coated with gold nanoparticles in order to enhance stability and elicit an immunological response with promising applications in antibacterial vaccines development [START_REF] Gao | Modulating Antibacterial Immunity via Bacterial Membrane-Coated Nanoparticles[END_REF]. In a biotechnological perspective, EVs derived from E. coli Nissle 1917 were engineered to carry recombinant subunit vaccines [START_REF] Rosenthal | Mechanistic insight into the TH1-biased immune response to recombinant subunit vaccines delivered by probiotic bacteria-derived outer membrane vesicles[END_REF] and other OMVs may be used for enzyme encapsulation, with potential applications in bioremediation, enzyme catalysis and vaccine development [START_REF] Turner | Bacterial bioreactors: Outer membrane vesicles for enzyme encapsulation[END_REF].

Experimental approaches in bacterial EVs research

Preparation of bacterial EVs samples

In laboratory settings, EVs are generally released by bacteria grew in complex culture media and coexist with bacterial cells, spent medium components, and secreted proteins, lipids and metabolites. In addition, EV concentration varies largely, depending on growth conditions and bacterial strains [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]Orench-Rivera and Kuehn, 2016). Therefore, the obtention of high quality EV samples require an initial step to separate the supernatants from bacterial cells, a concentration step to reach valuable yields and a final separation step to recover EVs and deplete medium contaminants (Figure 6) (Brown et al., 2015;[START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF].

Initial step

In the first step of EV obtention, the supernatants of fresh bacterial cultures are isolated from bacterial cells, which is normally performed with low-speed centrifugation and filtration [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]. Centrifugation at low speeds is generally enough to precipitate bacterial cells, but further steps of differential centrifugation might be included to eliminate cell debris and large aggregates (Brown et al., 2015;[START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]. To ensure the elimination of residual bacterial cells, the resultant supernatants are then filtered through pores of 0.22 µm, although EVs may be lost at some extent in this step, particularly large aggregates [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF].

Concentration step

After the obtention of clear cell-free supernatants, their volumes may be reduced in order to concentrate EVs. One of the common approaches is ultrafiltration through membranes of molecular weight cut-off varying from 10 to 100 kDa, which results in EV concentration in the retentate and depletion of contaminant molecules (Brown et al., 2015;[START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF][START_REF] Vergauwen | Confounding factors of ultrafiltration and protein analysis in extracellular vesicle research[END_REF]. Among the available settings for ultrafiltration, there are stirred cells, tangential flow devices and centrifugal units. In stirred cells, a gentle pressure direct large volumes of supernatant through the membrane while stirring avoids excessive membrane clogging. Tangential flow devices direct the flow of the solution parallelly to the membrane, also protecting it from clogging. Centrifugal units make use of the centrifugal force to direct the solution through membranes, although they are generally available in tubes of smaller volumes [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]. Another concentration approach is precipitation, which is based on the addition of salts, such as ammonium sulphate, to the solution containing EVs. The salt changes the way EVs interact with the solvent, i.e., the patterns of hydrogen bonds and surface charges, thus reducing their solubility and facilitating precipitation [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF].

Separation step

Concentrated samples containing EVs are then submitted to a final separation step, with the aim of further depleting contaminants, such as proteins, lipids and other residual molecules from culture media. One of the oldest and most used approaches for EV separation is ultracentrifugation (UC), which makes use of high-speed centrifugation to differentially sediment EVs and contaminants [START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF]. The protocols vary regarding number of UC steps, centrifugal g-forces, inclusion of density gradients and washing steps. An UC round of 100,000×g for 1-2 h is usually employed to obtain a pellet of vesicles, while contaminant macromolecules are discarded with the supernatants. The EV pellet can be resuspended and washed with an extra UC round to improve purity. Additionally, an UC round with a density gradient or cushion may also be included to improve EV purification based on density. In this case, iodixanol, sucrose or commercial solutions with varying density in the vertical axis are employed for the recovery of EVs in specific density fractions [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]Monguió-Tortajada et al., 2019a). In spite of its popularity, straightforward implementation and reliability, UC-based methods present some drawbacks, such as being time consuming and operator-dependent, EV aggregation after pelleting and EV damaging due to shearing forces [START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a).

Alternatively, the separation step may also be performed by size-based methods, such as size exclusion chromatography (SEC). In this case, the chromatographic column contains a buffer as mobile phase and a porous polymeric matrix, such as Sepharose CL-2B, as stationary phase. The pores in the polymeric matrix have a specific molecular weight cut-off that allows a differential elution, since smaller molecules (e.g., proteins) enter the pores and slow down, whereas bigger particles (e.g., EVs) will flow outside the pores and elute first (Monguió-Tortajada et al., 2019a). SEC may result in EV preparations with higher purity, intact structure and activity, besides being userfriendly, scalable and less time-consuming [START_REF] Nordin | Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties[END_REF][START_REF] Benedikter | Ultrafiltration combined with size exclusion chromatography efficiently isolates extracellular vesicles from cell culture media for compositional and functional studies[END_REF][START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a). Some of the drawbacks of SEC are the limitation in the input volume, possible sample dilution and possible co-purification of EVs with viruses, since they have similar sizes [START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF].

Besides size-exclusion chromatography and ultracentrifugation, other methods have been used for EV separation, such as ultrafiltration, precipitation, immunoaffinity capture and microfluidics [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF][START_REF] Sidhom | A Review of Exosomal Isolation Methods: Is Size Exclusion Chromatography the Best Option?[END_REF]. Although these alternative methods have not been extensively and individually applied in bacterial EV separation, each one has its own set of advantages and disadvantages, holding promise for future applications [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF][START_REF] Sidhom | A Review of Exosomal Isolation Methods: Is Size Exclusion Chromatography the Best Option?[END_REF]. Some methods may be used in combinations and the best methodological approach for EV separation depends on the scientific question and the desired downstream applications [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF].

Biophysical characterization of bacterial EVs

EV obtention must be validated with the characterization by multiple and complementary techniques, to assure the quality of separation and to assess the likelihood that certain biomarkers or activities are associated with EVs, and not coisolated contaminants [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF]. The physical characterization of EVs focus mainly in the determination of morphology and size distribution, and may be achieved by direct high-resolution imaging or indirect optical or electrical detection [START_REF] Szatanek | The Methods of Choice for Extracellular Vesicles (EVs) Characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF].

Microscopy techniques

Electron microscopy is one of the most conventional methodologies for EV imaging, employing an electron beam for the obtention of high-resolution images, including inner structure analysis with transmission electron microscopy (TEM) (Figure 7) and topographic analysis with scanning electron microscopy (SEM) [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. These techniques have been successfully applied for the morphology and size characterization of isolated EVs and EVs being released by bacteria (Lee et al., 2009;[START_REF] Elhenawy | LPS Remodeling Triggers Formation of Outer Membrane Vesicles in Salmonella[END_REF][START_REF] Valguarnera | Surface Exposure and Packing of Lipoproteins into Outer Membrane Vesicles Are Coupled Processes in Bacteroides[END_REF]. Nevertheless, sample preparation includes fixation and drying steps that may change native EV structure, and it may be difficult to obtain representative size measures, due to the low number of analyzed EVs [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. Another commonly used image technique is atomic force microscopy (AFM), which records the interactions between a thin probing tip at the end of a microcantilever and the sample surface [START_REF] Kikuchi | Diversity of physical properties of bacterial extracellular membrane vesicles revealed through atomic force microscopy phase imaging[END_REF]. As the system scans the surface, the tip changes its vertical position in response to the topography of the sample, and the voltage used to move the tip up or down corresponds to the height data that is used in the construction of a topographic image with nanometer resolution [START_REF] Allison | Atomic force microscopy of biological samples[END_REF]. As advantages of this approach, there are minimum deformation during sample preparation, as it does not require labeling; and the measurement of unique properties, such as EV stiffness and elasticity [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. However, it is a technique with low throughput and that requires specific operation skills, which has limited its wide application in EV research [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF].

Other techniques applied to bacterial EV research include electron cryotomography, that allowed three-dimensional characterizations and reconstructions, providing insights into mechanisms of biogenesis [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF]Tartaglia et al., 2018); confocal laser scanning microscopy and confocal fluorescence microscopy, that allowed the monitoring of EV uptake by receptor cells [START_REF] Ellis | Virulence and immunomodulatory roles of bacterial outer membrane vesicles[END_REF][START_REF] Ling | Escherichia Coli Outer Membrane Vesicles Induced DNA Double-Strand Breaks in Intestinal Epithelial Caco-2 Cells[END_REF].

Light scattering techniques

Some techniques rely on the Brownian motion of EVs in suspension in order to measure their size distribution. That is the case of dynamic light scattering (DLS), which is based on the incidence of a monochromatic laser beam in the suspension, and the scattering of the light that finds freely defusing particles, such as EVs. The temporal fluctuations of scattered light intensity allow the calculation of particles sizes [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. The advantages of this method are the simplicity, speed and detection of a wide range of sizes, whereas the disadvantages include the limitation to monodisperse samples and the low resolution for subpopulations detection [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF].

The light scattered by particles undergoing Brownian motion is also the basis of nanoparticle tracking analysis (NTA), extensively used in EV research. In this case, scattered light is used to track and record individual particles movement over time. Thus, it is possible to reconstruct individual particles trajectories and calculate their sizes through the application of Stokes-Einstein equation [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. The advantages include the possibility to analyze polydisperse samples, high resolution, possibility of integrating fluorescence detection, speed and simplicity, whereas the limitations include limited concentration ranges for reliable measures and the impact of physical parameters, such as temperature and viscosity [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF].

Other techniques

Although initially applied to cells, flow cytometry is another technique that finds applications in EV research. As cells (or EVs) pass individually through a flow chamber, they are illuminated by laser beams, and the scattered light allows the determination of size, granularity and composition of the particles. Although this is a high-throughput methodology, extensively applied in cellular biology, most of the conventional flow cytometers are unreliable for the detection of smaller EVs [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF][START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. Fluorescent labeling by monoclonal antibodies or in the interior of EVs have been proposed to overcome these limitations [START_REF] Szatanek | The methods of choice for extracellular vesicles (EVs) characterization[END_REF].

Finally, tunable resistive pulse sensing (TRPS) has been increasingly applied for measurements of EV size, concentration and surface charge. The methodology is based on the recording of changes in electrical current as particles pass through a nanopore, which can be related to particle volume [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF]. The technique does not require EV labeling and it is accurate, although the application to heterogeneous samples is still limited [START_REF] Hartjes | Extracellular vesicle quantification and characterization: Common methods and emerging approaches[END_REF].

Bioinformatics approaches in EV research

Several fields in Cellular and Molecular Biology, including EV research, have meaningfully benefited from the development and cost reduction in high-throughput methodologies, which established the so-called "omics" sciences [START_REF] Aizat | Recent development in omics studies[END_REF][START_REF] Chitoiu | Multi-omics data integration in extracellular vesicle biology-Utopia or future reality?[END_REF]. These advances included the next generations of DNA and RNA sequencing, as well as improvements of mass spectrometry for analysis of proteins and metabolites [START_REF] Aizat | Recent development in omics studies[END_REF]. This breakthrough allowed a whole new approach: rather than focusing in individual genes, transcripts, proteins or metabolites, research transitioned to a holistic perspective of biology, considering entire and heterogeneous sets of biological entities and their interactions [START_REF] Aizat | Recent development in omics studies[END_REF]. At the same time, heterogeneity among organisms, cells and EVs subtypes is now being addressed with emergent approaches that account for local variability in large-scale contexts [START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF][START_REF] Chitoiu | Multi-omics data integration in extracellular vesicle biology-Utopia or future reality?[END_REF]. The immense volume of produced data, as well as their intrinsic meanings and relationships, relies on Bioinformatics analysis to dissect properties, find patterns, compare groups, organize and interpret biological information [START_REF] Aizat | Recent development in omics studies[END_REF].

Exploratory analysis

The omics high-throughput data represent an enormous challenge for analysis, since the results are generally expressed as long lists of biological entities (e.g., genes or proteins) and possibly their quantitative measures (e.g., expression), which turns individual literature search and interpretation into an impractical approach (Reimand et al., 2019). Bioinformatics offers diverse tools to deal with this complexity, attempting to find intrinsic patterns and relationships to make sense of omics-derived big data [START_REF] Bessarabova | Knowledge-based analysis of proteomics data[END_REF][START_REF] Carnielli | Functional annotation and biological interpretation of proteomics data[END_REF][START_REF] Keerthikumar | Bioinformatics tools for extracellular vesicles research[END_REF].

One of such tools is functional enrichment analysis, which implements the reduction of long lists of biological entities into smaller functional categories, such as biochemical pathways (Reimand et al., 2019). That is possible because several individual genes, transcripts or proteins are involved in broader, yet less numerous, biological processes [START_REF] Bessarabova | Knowledge-based analysis of proteomics data[END_REF]Reimand et al., 2019). In pathway enrichment analysis, the overrepresentation of pathways in an experimental-derived list of genes, transcripts or proteins is statistically tested relative to what is expected by chance (Reimand et al., 2019). For each pathway, the analysis considers both the number of elements detected in the experiment and the total of annotated elements, as well as quantitative measures, such as relative ranking (Reimand et al., 2019). The visualization of the results allow the identification of the main enriched biological themes and their relationships (Reimand et al., 2019). Several tools are freely available to conduct and visualize enrichment analysis, including g-Profiler (Raudvere et al., 2019), GSEA [START_REF] Subramanian | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles[END_REF], FunRich [START_REF] Pathan | FunRich: An open access standalone functional enrichment and interaction network analysis tool[END_REF] and Enrichment map [START_REF] Merico | Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation[END_REF].

The lists of biological entities derived from high-throughput approaches may also be described according to their classification into categories, such as the Clusters of Orthologous Groups (COGs) of proteins [START_REF] Galperin | Microbial genome analysis: The COG approach[END_REF]. This clusterization approach was first proposed in 1997, with the delineation of 720 functional clusters, each comprising orthologous proteins or proteins sets from at least three lineages [START_REF] Tatusov | A Genomic Perspective on Protein Families[END_REF]. As orthologs typically share the same function, that allowed functional transfer and it was largely adopted for genome-wide functional annotation, particularly in poorly characterized genomes [START_REF] Galperin | Expanded Microbial genome coverage and improved protein family annotation in the COG database[END_REF]. Therefore, a gene or protein set can be analyzed regarding the frequencies of COG categories that occur within that set.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) has also been traditionally used for the interpretation of high-throughput molecular datasets [START_REF] Kanehisa | KEGG: Kyoto Encyclopedia of Genes and Genomes[END_REF][START_REF] Kanehisa | KEGG for integration and interpretation of large-scale molecular data sets[END_REF]. KEGG is a resource that integrates multiple databases of systems information (e.g., pathways, functional modules), genomic information (e.g., genes, genomes and orthologs), and chemical information (e.g., enzymes, reactions, compounds) [START_REF] Kanehisa | KEGG for integration and interpretation of large-scale molecular data sets[END_REF]. It offers manually curated resources and it is widely used for ortholog-based high-throughput annotation of genes and proteins and dissection of functions in a determined dataset [START_REF] Kanehisa | KEGG as a reference resource for gene and protein annotation[END_REF]. Similarly, other relevant tools are used to interpret high-throughput molecular data, including Gene Ontology (Gene Ontology Consortium, 2019) and Reactome [START_REF] Jassal | The reactome pathway knowledgebase[END_REF].

Finally, exploratory analysis of proteomic data may be complemented with categorical classification of subcellular localizations. The predictors of subcellular localization are generally based on the analysis of signal peptides, transmembrane motifs, evolutionary relationships, amino acid compositions or integrative approaches [START_REF] Nielsen | A Brief History of Protein Sorting Prediction[END_REF]. Commonly used tools include CELLO [START_REF] Yu | Predicting subcellular localization of proteins for Gramnegative bacteria by support vector machines based on n -peptide compositions[END_REF], PSORTb [START_REF] Yu | PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes[END_REF]), SignalP (Almagro Armenteros et al., 2019), LipoP [START_REF] Juncker | Prediction of lipoprotein signal peptides in Gram-negative bacteria[END_REF], among others. These predictions are useful because subcellular localization is generally related to protein function [START_REF] Hu | Predicting Gram-positive bacterial protein subcellular localization based on localization motifs[END_REF]. In the case of EV research, this is particularly important, because it might improve the understanding of biogenesis and content sorting in the different types of EVs (Toyofuku et al., 2019).

Interactions networks

Another way to make sense of high-throughput data is to analyze how individual biological entities connect with each other, allowing the construction of interactomes, i.e., a representation of the molecular interactions in certain contexts that provides an holistic view on cellular processes [START_REF] Koh | Analyzing protein-protein interaction networks[END_REF][START_REF] Hawe | Inferring interaction networks from multi-omics data[END_REF]. Interactomes may be represented as biological interactions networks, comprising a set of nodes (or points), which represent proteins, DNA, RNA or small molecules, and a set of edges (or lines), which represent the interactions between these molecules [START_REF] Chisanga | Network tools for the analysis of proteomic data[END_REF]. Some examples of biological interactions networks include protein-protein interactions (PPIs) networks (Figure 8), transcriptional regulatory networks, metabolic networks and even heterogeneous networks based on the integration of multi-omics data [START_REF] Chisanga | Network tools for the analysis of proteomic data[END_REF][START_REF] Hawe | Inferring interaction networks from multi-omics data[END_REF]. Since proteins constitute the backbones of cellular processes, including signaling and metabolic pathways, the majority of experimentally and computationally determined interactions are PPIs [START_REF] Koh | Analyzing protein-protein interaction networks[END_REF][START_REF] Chisanga | Network tools for the analysis of proteomic data[END_REF]. PPIs correspond to physical contacts between proteins, that are specific (nonaccidental), non-generic (not corresponding to generic functions, such as protein synthesis and degradation), and context-dependent (depending on cell type, environmental conditions, etc.) (de Las Rivas and Fontanillo, 2010). PPIs may be determined experimentally by pairwise methodologies, such as yeast two-hybrid (Y2H), in which two moieties of a transcription factor are individually linked to the proteins whose interaction will be tested; if those proteins interact, the moieties bind, activating the transcription machinery and a reporter system (e.g., β-galactosidase activity) [START_REF] Koh | Analyzing protein-protein interaction networks[END_REF]. Other methodologies are based on the detection of whole complexes, such as affinity purification coupled to mass spectrometry (AP-MS), in which a single protein is used as bait in a matrix and the passage of a sample through the matrix results in the retention of interacting partners, its preys, later identified by mass spectrometry (de Las Rivas and Fontanillo, 2010; [START_REF] Koh | Analyzing protein-protein interaction networks[END_REF]. These methodologies are also liable of generating high-throughput data, that have been stored in curated interactions databases, such as the Human Protein Reference Database (HPRD) [START_REF] Goel | Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis[END_REF], Molecular Interaction Database (MINT) [START_REF] Zanzoni | MINT: a Molecular INTeraction database[END_REF], the Search Tool for Recurring Instances of Neighbouring Genes (STRING) [START_REF] Snel | STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene[END_REF] and the IntAct molecular interaction database (IntAct) [START_REF] Hermjakob | IntAct: an open source molecular interaction database[END_REF]. However, experimental procedures for PPIs identification have a series of downsides: they are time consuming, labor intensive, and their applicability and scalability are limited, since high-throughput methodologies are still prone to many false positives and false negatives [START_REF] Ding | Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features[END_REF]. A number of computational approaches were developed to circumvent these limitations and complement interactomes via in silico predictions (Figure 9), which consider, for each potentially interacting protein pair, features based on proteins sequences, structures, functions, networks, gene expression measures and comparative genomics [START_REF] Ding | Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features[END_REF]. Among structure-based methods, there are molecular docking and dynamics, which model proteins interactions in atomistic scale by computing interactions forces, yet at a high computational cost and depending on structure availability (Keskin et al., 2016). Other methods, based on statistics and machine learning, use features of known interacting protein pairs to build prediction models for novel pairs, being applicable at large scale (Keskin et al., 2016;Lian et al., 2019). Evolution-based methods are also available, such as interolog methodology, that accounts for the conservation of PPIs among species, transferring known interactions of orthologous genes to the tested proteins [START_REF] Folador | An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage[END_REF]Keskin et al., 2016). Other prediction methods are based on literature text mining, residues coupling, co-occurrence, co-expression and gene/domain fusion information (Keskin et al., 2016). The prediction of PPIs find applications in several fields in microbiology, including virus-host interactions [START_REF] Zhou | A generalized approach to predicting protein-protein interactions between virus and host[END_REF], bacteria-host interactions (Lian et al., 2019), and microbiome-host interactions [START_REF] Coelho | Computational prediction of the human-microbial oral interactome[END_REF]. In EV research, the application of such techniques is still restricted, with few examples in eukaryote-derived EVs, such as the evaluation of EV purification quality (Choi et al., 2020a) and the identification of functional modules for cancer prognosis [START_REF] Cao | An EV-Associated Gene Signature Correlates with Hypoxic Microenvironment and Predicts Recurrence in Lung Adenocarcinoma[END_REF][START_REF] Xu | Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis[END_REF]. However, the application of such methodologies that account for the complexity of EVs cargo is recommended to obtain a complete perspective of their functional roles in diverse contexts [START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF].

Machine learning

Another approach that has helped to unveil the complexity of molecular biology in the big data era is machine learning (ML). ML is a subfield of artificial intelligence (AI), comprising statistical and mathematical models that are able to improve their outcomes through experience [START_REF] Kelchtermans | Machine learning applications in proteomics research: How the past can boost the future[END_REF][START_REF] Hamet | Artificial intelligence in medicine[END_REF]. In that sense, the models improve their performances due to some sort of information received, through the exploitation of regularities and pattern detection [START_REF] Kelchtermans | Machine learning applications in proteomics research: How the past can boost the future[END_REF]. The general ML workflow involves the processing of input data, the training of the model and its use to make predictions on new data [START_REF] Camacho | Next-Generation Machine Learning for Biological Networks[END_REF].

ML algorithms are often classified into categories, such as unsupervised and supervised learning. Unsupervised learning is used when input data is not labeled, meaning that their categories are unknown. In such cases, the goal might be to create groups (or clusters) from subsets of the data, based on similarity patterns. Principal components analysis (PCA) and hierarchical clustering are common examples [START_REF] Camacho | Next-Generation Machine Learning for Biological Networks[END_REF]. Contrastingly, in supervised learning, labels are available and they are used to train the model, in such a way, that it will be able to predict the categories or values of new, unlabeled data. [START_REF] Camacho | Next-Generation Machine Learning for Biological Networks[END_REF]. Supervised learning encompasses regression analysis, when the outputs are continuous variables, and classification analysis, when the outputs are discrete variables [START_REF] Baştanlar | Introduction to Machine Learning[END_REF].

ML finds applications on several fields in bioinformatics, including PPIs predictions [START_REF] Ahmed | Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network[END_REF]Lian et al., 2019), protein function prediction [START_REF] Bonetta | Machine learning techniques for protein function prediction[END_REF] and several aspects of proteomics based on mass spectrometry [START_REF] Kelchtermans | Machine learning applications in proteomics research: How the past can boost the future[END_REF]. Microbiology meets ML in applications as diverse as bacterial species identification [START_REF] Roux-Dalvai | Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning[END_REF], metagenome-based disease prediction [START_REF] Lapierre | MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction[END_REF], bacterial infection diagnosis [START_REF] Rawson | Supervised machine learning for the prediction of infection on admission to hospital: A prospective observational cohort study[END_REF], fighting microbial resistance [START_REF] Macesic | Machine learning: Novel bioinformatics approaches for combating antimicrobial resistance[END_REF] and even bioterrorism [START_REF] Reardon | How machine learning could keep dangerous DNA out of terrorists' hands[END_REF]. EV research has also benefited from ML methods, with applications in motif detection in miRNAs from exosomes [START_REF] Gaur | Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms[END_REF], diagnosis of traumatic brain injury based on EV miRNA signatures [START_REF] Ko | Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles[END_REF], proteomic profiling of EVs from Alzheimer's disease [START_REF] Muraoka | Proteomic and biological profiling of extracellular vesicles from Alzheimer's disease human brain tissues[END_REF] and identification of EV proteins as biomarkers for cancer detection [START_REF] Hoshino | Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers[END_REF].

Dedicated data repositories

In order to store and organize omics data derived from EV research, some freely available repositories were created, including ExoCarta, Vesiclepedia and EVpedia [START_REF] Keerthikumar | Bioinformatics tools for extracellular vesicles research[END_REF]. ExoCarta is a web-based compendium of exosome cargo, including data mainly of mammal origin, purification protocols and biomarkers identification [START_REF] Mathivanan | ExoCarta: A compendium of exosomal proteins and RNA[END_REF]. It was the first initiative of this kind, launched in 2009 and initially focusing in exosome protein and RNA content [START_REF] Mathivanan | ExoCarta: A compendium of exosomal proteins and RNA[END_REF]. Subsequently, updates in the repository included lipids as a new feature and implemented community involvement in data uploading [START_REF] Mathivanan | ExoCarta 2012: Database of exosomal proteins, RNA and lipids[END_REF]. Although the volume of data increased in the repository, the active participation of the community was still limited, and the heterogeneity in purification protocols required caution for the applicability of the datasets (Simpson et al., 2012). The last improvements in the repository included the adaptation to annotation standards proposed by the International Society for Extracellular Vesicles (ISEV), and the addition of tools for analysis of PPI networks, Gene Ontology and biological pathways [START_REF] Keerthikumar | ExoCarta: A Web-Based Compendium of Exosomal Cargo[END_REF].

As ExoCarta was limited to exosomes, the introduction of Vesiclepedia in 2012 extended data availability, including omics data from other types of EVs and organisms [START_REF] Kalra | Vesiclepedia: A Compendium for Extracellular Vesicles with Continuous Community Annotation[END_REF]. Subsequent updates in Vesiclepedia repository included quality metrics calculated from experimental parameters related to EV-TRACK initiative [START_REF] Van Deun | EV-TRACK: Transparent reporting and centralizing knowledge in extracellular vesicle research[END_REF], as well as the functionality of FunRich, an enrichment analysis tool [START_REF] Pathan | FunRich: An open access standalone functional enrichment and interaction network analysis tool[END_REF][START_REF] Pathan | Vesiclepedia 2019: A compendium of RNA, proteins, lipids and metabolites in extracellular vesicles[END_REF]. Another repository was launched in 2013, named EVpedia, which contained datasets from both prokaryotic and eukaryotic EVs [START_REF] Kim | EVpedia: An integrated database of high-throughput data for systemic analyses of extracellular vesicles[END_REF]. EVpedia also included tools for enrichment, network and ortholog analysis [START_REF] Kim | EVpedia: An integrated database of high-throughput data for systemic analyses of extracellular vesicles[END_REF]. A subsequent update included the category of metabolomics and a personalization function for account creation and storage of private datasets (Kim et al., 2015a).

In summary, the availability of such repositories allowed the centralization and organization of high-throughput datasets from EV cargo of diverse origins, facilitating data acquisition for comparative studies, biomarker discovery and potential insights about cargo sorting and EV biogenesis (Kim et al., 2015a;[START_REF] Keerthikumar | Bioinformatics tools for extracellular vesicles research[END_REF]. Nevertheless, there is still room for improvement, particularly in what refers to community engagement and protocol standardization (Kim et al., 2015a;[START_REF] Keerthikumar | Bioinformatics tools for extracellular vesicles research[END_REF].

Perspectives

As an emerging field, the research on EVs, particularly on those of bacterial origin, has been associated to more traditional bioinformatics analysis, including biological entities identification and quantification on high-throughput datasets and descriptive exploratory analysis [START_REF] Keerthikumar | Bioinformatics tools for extracellular vesicles research[END_REF]. However, the tendency of holistic and integrative approaches that started to be applied mainly in eukaryotic-derived EVs, such as interactions networks analysis and machine-learning, are also promising for EVs of other origins [START_REF] Carnielli | Functional annotation and biological interpretation of proteomics data[END_REF][START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF][START_REF] Gézsi | Systems biology approaches to investigating the roles of extracellular vesicles in human diseases[END_REF]. Furthermore, recent developments hold promises for data generation and bioinformatics analysis in EV research. That includes single-EV sequencing, which will allow the investigation of individual EVs and the identification of cargo and function heterogeneities in EVs subpopulations, currently hidden in bulk approaches [START_REF] Pick | Single-Vesicle Assays Using Liposomes and Cell-Derived Vesicles: From Modeling Complex Membrane Processes to Synthetic Biology and Biomedical Applications[END_REF]. Novel nanotechnological microfluidics-based systems will also contribute to EV isolation, detection and analysis, enriching characterization and improving the quality of downstream analysis, while offering advantages, such as low cost, high throughput and precision [START_REF] Guo | Microfluidics-based on-a-chip systems for isolating and analysing extracellular vesicles[END_REF]. Furthermore, some promising computational methods have extended their application to EV research, such as deep learning, a subfield of machine learning based on several layers of neural networks that can automatically extract features from highthroughput omics data and achieve accurate predictions (Zhang et al., 2019c). Some recent deep learning applications in EV research include the identification of EV transport proteins from sequence features, such as amino acid composition [START_REF] Le | Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture[END_REF], the segmentation of small EVs using transmission electron microscopy images [START_REF] Gómez-De-Mariscal | Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images[END_REF], imaging analyses of EVs in vivo [START_REF] Kranich | In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning[END_REF] and early-stage EV-mediated diagnosis of lung cancer [START_REF] Shin | Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes[END_REF].

Taken together, these and other experimental and computational improvements hold a great potential to bring novel insights into biogenesis, cargo sorting and functional roles of bacterial EVs [START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF].

EVs as mediators of the interaction between beneficial bacteria and the host

As previously discussed, there are more reports of EV occurrence in pathogenic bacteria than in beneficial bacteria (Kim et al., 2015b;Molina-Tijeras et al., 2019). EVs derived from pathogenic bacteria deliver virulence factors and other molecules to host cells, that promote immunomodulation and contribute to pathogenesis [START_REF] Ellis | Virulence and immunomodulatory roles of bacterial outer membrane vesicles[END_REF][START_REF] Macia | Host-and Microbiota-Derived Extracellular Vesicles, Immune Function, and Disease Development[END_REF]. Contrastingly, beneficial bacteria produce EVs that trigger immune tolerance and anti-inflammatory effects, which highlights their therapeutic potential [START_REF] Macia | Host-and Microbiota-Derived Extracellular Vesicles, Immune Function, and Disease Development[END_REF]Molina-Tijeras et al., 2019). Overall, beneficial bacteriaderived EVs present the typical spheric membranous shape, size range of 20 to 400 nm in diameter, and several molecules in their interior or at their surface, depending on the producing bacterium (Figure 10). In the next subsections, the reports of EV production in several Gram-negative and Gram-positive beneficial bacteria are reviewed in detail, with focus on their content and activity.

EVs produced by Gram-negative beneficial bacteria

Studies of EVs derived from Gram-negative beneficial bacteria have focused mainly in the classical probiotic Escherichia coli Nissle 1917. However, the nextgeneration probiotics, such as Akkermansia muciniphila and Bacteroides fragilis, are increasingly investigated for EV production (Molina-Tijeras et al., 2019).

Escherichia coli Nissle 1917-derived EVs

The strain E. coli Nissle 1917 is the most commonly studied Gram-negative probiotic bacterium [START_REF] Sonnenborn | Escherichia coli strain Nissle 1917-from bench to bedside and back: History of a special Escherichia coli strain with probiotic properties[END_REF][START_REF] Behrouzi | Intestinal effect of the probiotic Escherichia coli strain Nissle 1917 and its OMV[END_REF]. It was firstly isolated by Alfred Nissle, in 1917, from the feces of a healthy soldier [START_REF] Nissle | Die antagonistische Behandlung chronischer Darmstörungen mit Colibakterien[END_REF]. Since them, scientific interest has arisen for its role as gut colonizer and its beneficial properties, including the treatment of ulcerative colitis [START_REF] Scaldaferri | Role and mechanisms of action of Escherichia coli Nissle 1917 in the maintenance of remission in ulcerative colitis patients: An update[END_REF], genetic engineering for vaccine development [START_REF] Ou | Genetic engineering of probiotic Escherichia coli Nissle 1917 for clinical application[END_REF] and anticancer therapy [START_REF] Yu | Bioengineered Escherichia coli Nissle 1917 for tumourtargeting therapy[END_REF].

The first study reporting the occurrence of EVs in this probiotic strain performed a comprehensive characterization of their proteome, finding proteins inside EVs that were associated to adhesion to host tissues and bacterial survival, which was suggested to contribute to the ability of this strain to colonize the human gut [START_REF] Aguilera | Proteomic analysis of outer membrane vesicles from the probiotic strain Escherichia coli Nissle 1917[END_REF]. Another study performed a comprehensive proteomic characterization of E. coli Nissle 1917 and an uropathogenic strain (E. coli 536), comparing the method of purification of EVs (ultracentrifugation, density gradient centrifugation or size exclusion chromatography), and growth conditions (iron-restricted and iron-supplemented). The EVs derived from E. coli 536 were enriched in functional terms related to ribosome, while the EVs derived of E. coli Nissle 1917 were enriched in proteins related to glycolysis and ligase activity. Furthermore, some proteins consistently enriched in specific conditions were identified as potential markers of purity and culture conditions of EV preparations (Hong et al., 2019). The proteome of E. coli Nissle 1917-derived EVs and their role in the modulation of mouse macrophage RAW264.7 cells immune response was also investigated. Proteomics allowed the identification of proteins related to probiotic traits, such as intestinal adhesion and colonization (flagellins), bacterial survival (transporters), antimicrobial activities (murein hydrolase), and interaction with the host (outer membrane proteins). Regarding functional roles, EVs were internalized by macrophages, promoted their proliferation, phagocytic and antibacterial functions. EVs also induced anti-inflammatory cytokines (IL-10) and modulated the production of Th1 and Th2-polarizing cytokines [START_REF] Hu | Probiotic Escherichia coli Nissle 1917derived outer membrane vesicles enhance immunomodulation and antimicrobial activity in RAW264.7 macrophages[END_REF].

Other studies further clarified the immunomodulatory roles of E. coli Nissle 1917derived EVs. For instance, EVs could activate cytokine production by peripheral blood mononuclear cells (PBMCs) in co-culture with Caco-2 Cells, which was used as a model of intact intestinal mucosa. Particularly, the increase in the expression of the antiinflammatory cytokine IL-10 was significantly higher for the group stimulated by E. coli Nissle 1917-derived EVs. Colonic mucosa explants were also used as an ex vivo model, confirming that EVs triggered the modulation of the expression of immunomodulatory mediators, evidencing the ability of microbiota-derived EVs to promote signaling through the intestinal epithelial barrier [START_REF] Fábrega | Activation of Immune and Defense Responses in the Intestinal Mucosa by Outer Membrane Vesicles of Commensal and Probiotic Escherichia coli Strains[END_REF]. A follow-up study confirmed that in vivo, showing that the oral administration E. coli Nissle 1917-derived EVs has intestinal anti-inflammatory effects in vivo, in mice with DSS-induced colitis. These results show that EVs reproduce the effects of viable probiotic administration, with the improvement of clinical signs, colonic histology and cytokine profile [START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF].

Epithelial barrier enhancement was another important role associated to probiotic-derived EVs. The EVs and soluble fractions from E. coli Nissle 1917 and ECOR63 regulated the expression of tight junction proteins in intestinal epithelial cells Caco-2 and T-84. That included the upregulation of ZO-1 and claudin-14, and the downregulation of claudin-2, thus reinforcing the epithelial barrier [START_REF] Alvarez | Outer Membrane Vesicles and Soluble Factors Released by Probiotic Escherichia coli Nissle 1917 and Commensal ECOR63 Enhance Barrier Function by Regulating Expression of Tight Junction Proteins in Intestinal Epithelial Cells[END_REF]. A similar role was verified when polarized T-84 and Caco-2 cells were infected with enteropathogenic E. coli, simulating tight junctions' disruption and gut permeability increase. EVs derived from E. coli Nissle 1917 and ECOR63 counteracted the intestinal epithelial barrier dysfunction by modulating occludin and claudin-14 expression, retaining ZO-1 and occludin at tight junctions and ameliorating F-actin disorganization (Alvarez et al., 2019).

Finally, the internalization pathway of EVs was also investigated. EVs derived from strains E. coli Nissle 1917 and ECOR12 were internalized by the human intestinal epithelial cell lines Caco-2 and HT-29, as well as the mucus-secreting HT29-MTX. The internalization occurred by clathrin-mediated endocytosis and that EVs were then sorted to lysosomal compartments. EVs inhibited HT-29 cell growth, but did not affect cell viability, while also inducing DNA damage and repair [START_REF] Cañas | Outer Membrane Vesicles from the Probiotic Escherichia coli Nissle 1917 and the Commensal ECOR12 Enter Intestinal Epithelial Cells via Clathrin-Dependent Endocytosis and Elicit Differential Effects on DNA Damage[END_REF]. A followup study showed that the EVs derived from the same strains were also able to activate immune responses mediated by intracellular receptor NOD1 in intestinal epithelial cells, possibly through the intracellular delivery of peptidoglycan fragments (Cañas et al., 2018).

Akkermansia muciniphila-derived EVs

Recent studies have unraveled beneficial bacteria other than traditional probiotics, namely next generation probiotics, with improved effectiveness and diseasespecificity [START_REF] Chang | Next generation probiotics in disease amelioration[END_REF]Zhang et al., 2019b). One example is A. muciniphila, that is reported to reverse obesity and insulin resistance [START_REF] Chang | Next generation probiotics in disease amelioration[END_REF]. A. muciniphila is a Gram-negative bacterium of oval shape, that can use mucin as its single source of carbon, nitrogen, and energy, being widely distributed in human and animal intestine [START_REF] Zhai | A next generation probiotic, Akkermansia muciniphila[END_REF]Zhang et al., 2019b).

A pioneer study evaluated EVs derived from mice stool by conducting a comprehensive metagenomics characterization. These results were associated to progression of colitis, in order to identify significant changes in EV composition in the disease state, finding that A. muciniphila-derived EVs had an inverse relationship with colitis progression. Therefore, A. muciniphila-derived EVs were further analyzed in vivo, displaying protective effects that ameliorated the severity of colitis induced by dextran sodium sulfate in C57BL/6 mice [START_REF] Kang | Extracellular Vesicles Derived from Gut Microbiota, Especially Akkermansia muciniphila, Protect the Progression of Dextran Sulfate Sodium-Induced Colitis[END_REF]. Another study showed that fecal samples of healthy individuals are enriched in A. muciniphila-derived EVs in comparison to patients with type 2 diabetes. Moreover, the administration of theses EVs to mice with diabetes induced by high-fat diet resulted in enhancement of tight junction function and improved glucose tolerance. Finally, the barrier integrity of human epithelial Caco-2 cells was improved with the administration of EVs, through 5' adenosine monophosphate-activated protein kinase (AMPK) activation [START_REF] Chelakkot | Akkermansia muciniphiladerived extracellular vesicles influence gut permeability through the regulation of tight junctions[END_REF].

Another study indicated that A. muciniphila-derived EVs could be employed in the therapy of obesity, as shown for C57BL/6 mice with obesity induced by a high-fat diet. The EVs derived from A. muciniphila strain MucT reduced the food intake and weight gain in mice, improving intestinal barrier integrity and reducing inflammation. Lipid metabolism and expression of inflammatory markers in adipose tissues were also affected by the treatment with EVs [START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF].

Bacteroides fragilis-derived EVs

B. fragilis are Gram-negative bacteria commonly found in the human gastrointestinal, oral, upper respiratory and genital tract [START_REF] Sun | A potential species of nextgeneration probiotics? The dark and light sides of Bacteroides fragilis in health[END_REF]. The strains in this species are divided in an enterotoxigenic group, which produces B. fragilis toxin (BFT) and triggers energy metabolism and intestinal dysfunction; and a nontoxigenic group, comprising beneficial commensal bacteria that compete with pathogenic bacteria and deliver beneficial molecules, such as polysaccharide A (PSA) [START_REF] Sun | A potential species of nextgeneration probiotics? The dark and light sides of Bacteroides fragilis in health[END_REF][START_REF] Valguarnera | Good Gone Bad: One Toxin Away From Disease for Bacteroides fragilis[END_REF]. Since the beneficial strains of B. fragilis display health-promoting traits, such as anti-inflammatory and anticancer effects, they have also been regarded as next-generation probiotics [START_REF] Chang | Next generation probiotics in disease amelioration[END_REF].

The delivery of beneficial effector molecules derived from B. fragilis to host cells is reported to occur via EVs [START_REF] Sun | A potential species of nextgeneration probiotics? The dark and light sides of Bacteroides fragilis in health[END_REF]. For instance, EVs derived from B. fragilis strain NCTC9343 protected BALB/c mice from 2,4,6-trinitrobenzenesulfonic acidinduced colitis and intestinal inflammation, with the suppression of the production of tumor necrosis factor (TNFα) and the development of Th17 cells. These EVs were internalized by dendritic cells, which induced the differentiation of IL-10-producing Treg cells. One of the main factors in the promoted immunomodulation was capsular polysaccharide (PSA), which was packed inside EVs for its deliverance to the mammalian host [START_REF] Shen | Outer Membrane Vesicles of a Human Commensal Mediate Immune Regulation and Disease Protection[END_REF]. Another study used Caco-2 cell line as a human intestinal epithelial cell model to evaluate the immunomodulatory roles of B. fragilis ATCC 23745derived EVs. Higher concentrations of EVs increased TLR2 and TLR4 gene expressions, as well as modulated cytokines concentrations, decreasing interferon gamma (IFNγ) and increasing IL-4 and IL-10 concentrations [START_REF] Badi | Induction effects of bacteroides fragilis derived outer membrane vesicles on toll like receptor 2, toll like receptor 4 genes expression and cytokines concentration in human intestinal epithelial cells[END_REF].

A comparison of the EVs derived from enteropathogenic B. fragilis BOB25 and non-toxigenic B. fragilis 323-J-86 was also performed, with regard to their proteomic and metabolomic content. B. fragilis 323-J-86-derived EVs had enzymes related to polysaccharide metabolism, suggesting a nutrition role in a community context; whereas B. fragilis BOB25-derived EVs had virulence factors and enzymes comprising whole pathways, related to their stability and persistence. This study suggested that biochemical processes were still active in B. fragilis BOB25-derived EVs after secretion, turning them into an active and self-sufficient biological system [START_REF] Zakharzhevskaya | Outer membrane vesicles secreted by pathogenic and nonpathogenic Bacteroides fragilis represent different metabolic activities[END_REF].

EVs derived from other Gram-negative probiotic bacteria

Other examples of Gram-negative bacteria that were reported to produce EVs with beneficial properties include Lysobacter and Pseudoalteromonas genera. Lysobacter sp. XL1 produced EVs containing components of bacterial outer membrane and bacteriolytic endopeptidases. These EVs showed bacteriolytic activity against Grampositive Staphylococcus aureus 209-P and Gram-negative Erwinia marcescens EC1 [START_REF] Vasilyeva | Secretion of bacteriolytic endopeptidase L5 of Lysobacter sp. XL1 into the medium by means of outer membrane vesicles[END_REF]. The EVs derived from Lysobacter enzymogenes strain C3 showed anti-fungal activity, inhibiting the growth of the yeast Saccharomyces cerevisiae and of the filamentous fungus Fusarium subglutinans [START_REF] Meers | Vesicular delivery of the antifungal antibiotics of Lysobacter enzymogenes C3[END_REF]. Finally, isolates of P. piscicida produced EVs with digestive activity against Vibrio parahaemolyticus, suggesting that EVs may be part of a mechanism of bacterial competition [START_REF] Richards | Mechanisms for Pseudoalteromonas piscicida-Induced Killing of Vibrios and Other Bacterial Pathogens[END_REF]. Some Bacteroides species other than B. fragilis also produces EVs. It is the case of B. thetaiotaomicron, reported to secrete EVs containing large quantities of glycosidases and proteases, which is coherent with its role as a component of human microbiota, helping in the digestion of dietary polysaccharides, and making this digestion products available to other members of the microbiota [START_REF] Valguarnera | Surface Exposure and Packing of Lipoproteins into Outer Membrane Vesicles Are Coupled Processes in Bacteroides[END_REF]. Additionally, the symbiotic commensal Bacteroides vulgatus mpk was reported to produce EVs that are essential for the induction of dendritic cells semi-maturation, resulting in a tolerant dendritic cells phenotype [START_REF] Maerz | Outer membrane vesicles blebbing contributes to B. vulgatus mpk-mediated immune response silencing[END_REF].

EVs produced by Gram-positive beneficial bacteria

Studies concerning EV production in Gram-positive bacteria have emerged only since 2009, with the first comprehensive characterization of Staphylococcus aureusderived EVs (Lee et al., 2009). In the case of Gram-positive beneficial bacteria, there are even fewer reports of EV production, mainly for Lactobacillus and Bifidobacterium species (Molina-Tijeras et al., 2019).

Lactobacillus-derived EVs

Lactobacillus is the main genus of Gram-positive probiotic bacteria that has been studied for EVs production. This is a complex and diverse genus, comprising hundreds of species of pronounced technological and scientific interest, including members of the normal commensal human flora, particularly in the gastrointestinal tract and vagina [START_REF] Goldstein | Lactobacillus species: Taxonomic complexity and controversial susceptibilities[END_REF][START_REF] Hatti-Kaul | Lactic acid bacteria: From starter cultures to producers of chemicals[END_REF]. Some species in this genus have been considered occasional opportunistic pathogens, but reports of serious infections are rare and often associated to comorbidities [START_REF] Goldstein | Lactobacillus species: Taxonomic complexity and controversial susceptibilities[END_REF]. These bacteria also have a long history of use in food production, including cheese, yogurt, and fermented mustard, cabbage, and olives [START_REF] Goldstein | Lactobacillus species: Taxonomic complexity and controversial susceptibilities[END_REF][START_REF] Şanlier | Health benefits of fermented foods[END_REF]. Although a recent reclassification was proposed in the genus [START_REF] Zheng | A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae[END_REF], we kept the traditional nomenclature, as used in the published reports.

Regarding EV production, one of the most studied species in the genus is Lactobacillus plantarum. The strain L. plantarum WCFS1 was showed to produce EVs within the typical shape and sizes (31-200 nm). Their content was investigated, allowing the identification of 31 proteins, half of which were associated to the membrane. When the nematode Caenorhabditis elegans was used as a model of infection with vancomycin-resistant Enterococcus faecalis C68, the pre-treatment with L. plantarumderived EVs resulted in significantly longer survival of C. elegans in comparison with the control group. Furthermore, in addition to this protective effect against infections, EVs were showed to enhance the expression of host defense genes, reproducing the effects of parental bacterial cells (Li et al., 2017). The EVs derived from another strain, L. plantarum KCTC 11401BP, showed antidepressant-like effects in vitro and in vivo. The study investigated the expression of the brain-derived neurotrophic factor (BDNF), an important regulator of synaptic transmission and plasticity. L. plantarum-derived EVs increased the expression of BDNF in hippocampal HT22 cells, reversing the effects of stress hormone glucocorticoid. Furthermore, L. plantarum-derived EVs promoted an increased BDNF expression in the hippocampus of mice with stress-induced depression [START_REF] Choi | Extracellular Vesicles Derived from Lactobacillus plantarum Increase BDNF Expression in Cultured Hippocampal Neurons and Produce Antidepressant-like Effects in Mice[END_REF].

The EVs derived from the strain L. plantarum KCTC 11401BP also presented immunomodulatory properties and a protective effect in the context of skin inflammation induced by S. aureus. When keratinocytes and macrophages were treated with S. aureus-derived EVs, the pre-treatment with L. plantarum-derived EVs was able to decrease IL-6 levels and restore cell viability. In vivo, the administration of L. plantarum-derived EVs to mouse with atopic dermatitis induced by S. aureus resulted in the reduction of epidermal thickening and IL-4 levels (Kim et al., 2018a). Finally, the strain L. plantarum APsulloc 33126 produced EVs that induced M2 macrophage polarization in vitro and an anti-inflammatory phenomenon in the human skin cells, via modulation of the secretion of IL-10, IL-1β and GM-CSF (Kim et al., 2020e).

Other studies focused on the species L. rhamnosus, which also produces EVs. In the case of strain L. rhamnosus JB-1, EVs were spherical as typical, ranging in size from 50 to 150 nm. Their protein cargo consisted of 13 EV-exclusive proteins and 12 proteins shared with bacterial cells, which were mainly related to stress response and glycolysis. Regarding biological activity, the EVs derived from L. rhamnosus JB-1 showed immune and neuronal effects that were similar to those of the parent bacteria, including an increase in functional regulatory T cell population in a murine model [START_REF] Al-Nedawi | Gut commensal microvesicles reproduce parent bacterial signals to host immune and enteric nervous systems[END_REF]. The EVs derived from strain L. rhamnosus GG, by the other side, showed a concentration-dependent cytotoxic effect against HepG2 hepatic cancer cells, via the intrinsic pathway of apoptosis (Behzadi et al., 2017). The cell-free supernatants and EVs of strains L. rhamnosus GG and L. reuteri DSM 17938 were also evaluated regarding their anti-inflammatory properties, showing a reduction in IFNγ responses from T-cells and NK cells in a monocyte dependent manner [START_REF] Mata Forsberg | Extracellular Membrane Vesicles from Lactobacilli Dampen IFN-γ Responses in a Monocyte-Dependent Manner[END_REF].

The species L. casei and L. paracasei have also been studied for its EVs. L. casei BL23 produced EVs ranging in diameter from 26 to 70 nm, containing DNA, RNA and proteins (Rubio et al., 2017). Regarding proteomic composition, 103 proteins were identified, 13 of which were exclusive from EVs. The protein cargo included stress, metabolic, ribosomal and cell wall-associated proteins, as well as a adhesion protein that was exclusively identified in EVs (Rubio et al., 2017). Another strain, L. casei ATCC 393, produced EVs with anti-inflammatory activity towards colon carcinoma Caco-2 cells, decreasing TLR9 gene expression and the level of IFNγ, while increasing IL-4 and IL-10 levels [START_REF] Vargoorani | Stimulatory effects of Lactobacillus casei derived extracellular vesicles on toll-like receptor 9 gene expression and cytokine profile in human intestinal epithelial cells[END_REF]. L. paracasei EVs also displayed anti-inflammatory activity, both in vitro and in vivo (Choi et al., 2020b). In vitro experiments showed that EVs reduced the expression of pro-inflammatory cytokines and increased the expression of anti-inflammatory cytokines in HT-29 human colorectal cancer cells, whereas in vivo experiments showed EVs promoted in mice a protection against DSS-induced colitis (Choi et al., 2020b).

The strain L. reuteri DSM 17938 has also been reported to produce extracellular vesicles. A study investigated both biofilm and planktonic phenotypes of this strain, finding that extracellular DNA was associated to EVs, especially for the biofilm phenotype (Grande et al., 2017;[START_REF] Puca | Detection and Quantification of eDNA-Associated Bacterial Membrane Vesicles by Flow Cytometry[END_REF]. Another study with the same strain showed that L. reuteri-derived EVs could reproduce the effects of the parental bacteria on gastrointestinal motility in mice [START_REF] West | Microvesicles from Lactobacillus reuteri (DSM-17938) completely reproduce modulation of gut motility by bacteria in mice[END_REF]. By the other hand, the strain L. sakei NBRC15893, isolated from a traditional seed mash used for brewing sake, produced EVs capable of enhancing IgA production by murine Peyer's patch cells and activating the mucosal immune system via TLR2 signaling in vitro [START_REF] Yamasaki-Yashiki | IgA-enhancing effects of membrane vesicles derived from Lactobacillus sakei subsp. sakei NBRC15893[END_REF]. The strain L. gasseri JCM 1131T produced EVs with surface-exposed LTA, which could have dramatic effects in the interactions with the host [START_REF] Shiraishi | Lipoteichoic acids are embedded in cell walls during logarithmic phase, but exposed on membrane vesicles in Lactobacillus gasseri JCM 1131[END_REF].

Other studies of Lactobacillus-derived EVs compared their properties, content and activity according to strain of origin. A comprehensive study compared the EVs produced by L. acidophilus ATCC 53544, L. casei ATCC 393, and L. reuteri ATCC 23272. They shared a similar bimodal size distribution ranging from 20 to 50 nm and from 100 to 150 nm in diameter. Moreover, a proteomic analysis showed there was little consistency of EV protein content among the strains, and that L. acidophilus ATCC 53544 produced EVs enriched in components of the bacteriocin pathway [START_REF] Dean | Isolation and characterization of Lactobacillus-derived membrane vesicles[END_REF]. A follow-up study further investigated the EVs of this last strain, which were reported to transport bacteriocin peptides to L. delbrueckii ATCC 15808, an opportunistic pathogen, inhibiting its growth and compromising its membrane integrity. This finding suggested EVs may have additional roles in competition within the complex communities of the gut microbiome [START_REF] Dean | Lactobacillus acidophilus Membrane Vesicles as a Vehicle of Bacteriocin Delivery[END_REF].

A comparison was also performed with kefir-derived strains Lactobacillus kefir KCTC 3611, Lactobacillus kefiranofaciens KCTC 5075, and Lactobacillus kefirgranum KCTC 5086, that produced EVs with size distribution ranging from 80 and 400 nm. These EVs presented anti-inflammatory effect both on colon carcinoma Caco-2 cells and on a mouse model of inflammatory bowel disease induced by 2,4,6-trinitrobenzene sulfonic acid. Their anti-inflammatory effect was enhanced when EVs from 3 strains were combined in the treatment [START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF]. The strains of human vaginal origin L. crispatus BC3, L. crispatus BC5, L. gasseri BC12, and L. gasseri BC13, were reported to produce EVs with antiviral activity (anti-HIV-1) in human T cells in vitro and/or human tissues ex vivo. EVs showed a differential inhibition of HIV-1 infection, which was attributed to their composition of proteins and metabolites. More active EVs had lactate and high amounts of amino acids, as well as several specific proteins, including enolase, chaperonin and elongation factor Tu [START_REF] Ñahui Palomino | Extracellular vesicles from symbiotic vaginal lactobacilli inhibit HIV-1 infection of human tissues[END_REF].

Bifidobacterium-derived EVs

Bifidobacterium is another important probiotic genus for which EVs occurrence has been reported. Currently, this taxon contains more than 70 species, which have been isolated from different ecological niches, including milk, sewage, human blood, the oral cavity, and the gastrointestinal tract (GIT) of mammals, birds, and insects [START_REF] Alessandri | Bifidobacterial Dialogue With Its Human Host and Consequent Modulation of the Immune System[END_REF]. Particularly, bifidobacteria are among the dominant bacterial populations and the first colonizers of the human gut, with some species being involved in a vertical route of colonization, from mother to offspring [START_REF] Milani | Genomics of the genus Bifidobacterium reveals species-specific adaptation to the glycan-rich gut environment[END_REF][START_REF] Chander Roland | Bifidobacteria and Their Health-Promoting Effects[END_REF][START_REF] Alessandri | Bifidobacterial Dialogue With Its Human Host and Consequent Modulation of the Immune System[END_REF].

Regarding EVs described in this genus, there is much less reports than for Lactobacillus. For instance, the strain Bifidobacterium bifidum LMG13195 produced EVs with immunomodulatory activity in vitro. Dendritic cells isolated from peripheral blood mononuclear cells (PBMCs) of healthy donors and exposed to these EVs induced relatively high IL-10 production and Treg cell differentiation [START_REF] López | Treginducing membrane vesicles from Bifidobacterium bifidum LMG13195 as potential adjuvants in immunotherapy[END_REF]. Moreover, the EVs derived from strain B. longum KACC 91563 induced apoptosis of mast cells and alleviated food allergy symptoms in BALB/c mice with food allergy induced by ovalbumin and alum (Kim et al., 2016a). Finally, another strain, B. longum NCC2705, was reported to produce EVs when grown in supernatants of human fecal fermentation broth. These EVs contained double-stranded DNA and mucin-binding proteins, such as GroEL, elongation factor Tu (EF-Tu) and transaldolase (Tal), suggesting that EVs may promote colonization of bifidobacteria [START_REF] Nishiyama | Extracellular Vesicles Produced by Bifidobacterium longum Export Mucin-Binding Proteins[END_REF].

Applications of beneficial bacteria-derived EVs

As detailed, the EVs derived from Gram-negative and Gram-positive beneficial bacteria presented several beneficial properties towards the host, both in vitro and in vivo (Figure 11), even reproducing properties of the producing bacteria [START_REF] Al-Nedawi | Gut commensal microvesicles reproduce parent bacterial signals to host immune and enteric nervous systems[END_REF][START_REF] West | Microvesicles from Lactobacillus reuteri (DSM-17938) completely reproduce modulation of gut motility by bacteria in mice[END_REF]. Besides the characterization of these beneficial properties, some studies proposed interesting applications for the EVs derived from these bacteria, including vaccines, drug delivery and biosensing [START_REF] Gilmore | Immunomodulatory roles and novel applications of bacterial membrane vesicles[END_REF].

EV-based vaccines are increasingly considered due to interesting features, such as the high immunogenicity and self-adjuvant potential, the simple and cost-effective production, and safety as non-replicative nanoparticles [START_REF] Balhuizen | Outer Membrane Vesicle Induction and Isolation for Vaccine Development[END_REF][START_REF] Gilmore | Immunomodulatory roles and novel applications of bacterial membrane vesicles[END_REF]. As an example, the safety and effectiveness of vaccines based on bacteriaderived EVs were attested with human trials in Cuba, Norway and New Zealand to prevent meningococcal serogroup B disease [START_REF] Nøkleby | Safety review: Two outer membrane vesicle (OMV) vaccines against systemic Neisseria meningitidis serogroup B disease[END_REF][START_REF] Oster | Immunogenicity and safety of a strain-specific MenB OMV vaccine delivered to under 5-year olds in New Zealand[END_REF][START_REF] Holst | Vaccines against meningococcal serogroup B disease containing outer membrane vesicles (OMV): Lessons from past programs and implications for the future[END_REF]. Regarding non-pathogenic bacteria, bioengineered E. coli Nissle 1917 produced EVs expressing exogenous antigens on their surface (referred as pathogen-like particles), capable of inducing self-adjuvanted humoral and TH1 cellular responses in BALB/c mice [START_REF] Rosenthal | Mechanistic insight into the TH1-biased immune response to recombinant subunit vaccines delivered by probiotic bacteria-derived outer membrane vesicles[END_REF]. Moreover, EVs derived from B. bifidum LMG 13195 were suggested to serve as adjuvants for immunotherapy, since they induced T regulatory cells differentiation in vitro [START_REF] López | Treginducing membrane vesicles from Bifidobacterium bifidum LMG13195 as potential adjuvants in immunotherapy[END_REF]. Likewise, EVs derived from E. coli DH5α expressing the outer membrane protein Omp22 of A. baumannii protected mice against challenge with a clinical isolate and confirmed EVs feasibility as vaccine platform (Huang et al., 2016b). Recently, the strain E. coli BL21(DE3)Δ60 was designed with bioinformatics tools to produce a generalized vaccine platform, with minimal endogenous proteome, increased loading capacity for recombinant proteins and enhanced EVs productivity [START_REF] Zanella | Proteomeminimized outer membrane vesicles from Escherichia coli as a generalized vaccine platform[END_REF].

Strains of B. thetaiotaomicron were also bioengineered to produce EVs that incorporated bacterial, viral and human proteins at their interior or surface, serving as both vaccine and drug delivery platforms (Carvalho et al., 2019a). The antigens Fraction 1 (F1) and LcrV (V antigen) from Yersinia pestis were expressed in the membrane or lumen of the EVs and delivered to the gastrointestinal and respiratory tracts of cynomolgus macaques (Carvalho et al., 2019b). The approach resulted in the induction of both humoral and cellular responses, self-adjuvanticity and absence of adverse effects (Carvalho et al., 2019b). In another study, antigens from S. enterica ser. Typhimurium and influenza A virus were also expressed, packaged and delivered by EVs, triggering mucosal and systemic immune responses in C57BL/6 mice (Carvalho et al., 2019a). The human protein keratinocyte growth factor-2 was also delivered by this approach, showing a therapeutic effect against DSS-induced colitis in mice (Carvalho et al., 2019a).

EVs also find applications in drug delivery, bioimaging and diagnosis. For example, the msbB mutant of E. coli W3110 was engineered to produce EVs with an affibody for the human epidermal growth factor receptor 2 (HER2) displayed on their surface and the small interfering RNA (siRNA) targeting kinesin spindle protein in their interior (Gujrati et al., 2014a). These EVs could specifically trigger tumor regression in vivo, with reduced side effects (Gujrati et al., 2014a). In another study, the msbB mutant of E. coli W3110-K12 expressing a tyrosinase transgene was used to produce EVs encapsulating melanin, which was efficient as a platform for both optoacoustic imaging and photothermal therapy targeting tumor cells in mice, thus comprising a promising theranostics application [START_REF] Gujrati | Bioengineered bacterial vesicles as biological nano-heaters for optoacoustic imaging[END_REF]. Likewise, bacterial EVs were purified from human stool and analyzed for their metagenomic and metabolomic profile, allowing the establishment of associations of bacterial taxa and specific metabolites with colorectal cancer, and the development of a diagnostic model (Kim et al., 2020a).

Although many applications were initially proposed for EVs derived from pathogenic bacteria, the tendency is an increase in the applications for EVs derived from beneficial bacteria, since they display several advantages, such as the absence or reduced content of toxins, simple purification and possibility of bioengineering for content and yield modulation [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Liu et al., 2018a).

Propionibacterium freudenreichii: general characteristics and probiotic traits

The denomination "probiotics" comprises living organisms, including bacteria and yeasts, with health-promoting properties and suitable for safe consumption, as confirmed by their dietary uses for thousands of years of human history (Ozen and Dinleyici, 2015;[START_REF] Gasbarrini | Probiotics History[END_REF][START_REF] De Melo Pereira | How to select a probiotic? A review and update of methods and criteria[END_REF]. Lactic acid bacteria and bifidobacteria comprise traditional probiotic bacteria species, widely researched and commercialized [START_REF] De Melo Pereira | How to select a probiotic? A review and update of methods and criteria[END_REF][START_REF] Douillard | Biotechnology of health-promoting bacteria[END_REF]. However, different species have emerged in probiotics landscape, such as the dairy species Propionibacterium freudenreichii (Rabah et al., 2017;[START_REF] Douillard | Biotechnology of health-promoting bacteria[END_REF], which are phylogenetically close to bifidobacteria (Figure 12) [START_REF] Douillard | Biotechnology of health-promoting bacteria[END_REF]. The former Propionibacterium genus encompassed a group of microorganisms with importance in industry and health, due to the production of valuable metabolites, food, cosmetic and pharmacological products (Piwowarek et al., 2018b). Previously, this genus included classic dairy propionibacteria species and skin-associated pathogenic propionibacteria [START_REF] Scholz | The natural history of cutaneous propionibacteria, and reclassification of selected species within the genus Propionibacterium to the proposed novel genera Acidipropionibacterium gen. nov., Cutibacterium gen. nov. and Pseudopropionibacterium gen[END_REF]. However, a genome-based taxonomy reevaluation proposed the reclassification of cutaneous propionibacteria into the Cutibacterium genus, together with the inclusion of two other new genera for classic propionibacteria, Acidipropionibacterium and Pseudopropionibacterium [START_REF] Scholz | The natural history of cutaneous propionibacteria, and reclassification of selected species within the genus Propionibacterium to the proposed novel genera Acidipropionibacterium gen. nov., Cutibacterium gen. nov. and Pseudopropionibacterium gen[END_REF]. P. freudenreichii, which is one of the most notable dairy propionibacteria species, kept its former taxonomic classification [START_REF] Scholz | The natural history of cutaneous propionibacteria, and reclassification of selected species within the genus Propionibacterium to the proposed novel genera Acidipropionibacterium gen. nov., Cutibacterium gen. nov. and Pseudopropionibacterium gen[END_REF].

General characteristics

P. freudenreichii is a Gram-positive, high GC-content, mesophilic, aerotolerant, non-motile, non-spore forming bacterium, that shows low nutritional requirements and survives in several environments [START_REF] Falentin | The complete genome of propionibacterium freudenreichii CIRM-BIA1T, a hardy actinobacterium with food and probiotic applications[END_REF][START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]Rabah et al., 2017). Regarding morphology, it is a pleomorphic rod microorganism, with aggregation tendency, forming clusters that resemble Chinese characters (Figure 13) (Rabah et al., 2017). This bacterium was first described by Orla Jensen and von Freudenreich in 1906, isolated from samples of Emmental cheese [START_REF] Freudenreichii | Uber die in Emmentalerkäse stattfindene Propionsäure-gärung[END_REF]. Recently, P. freudenreichii strains have been identified in fecal samples from a discrete cohort of human preterm breast-fed infants, suggesting that it could be a component of the healthy human gut microbiota (Colliou et al., 2017). P. freudenreichii is able to use several carbon sources (e.g., glycerol, erythriol, Larabinose, adonitol, galactose, D-glucose, D-fructose, D-manose, inositol, arbutine, esculine, lactose, lactate and gluconate) in the fermentation process to produce propionate, together with acetate, succinate and carbon dioxide (CO2) [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]Loux et al., 2015;Ojala et al., 2017). Unlike other species, P. freudenreichii is able to reduce pyruvate into propionate via the transcarboxylase cycle (also referred to as Wood-Werkman cycle), which is a cyclic process coupled to oxidative phosphorylation, that allows a higher ATP yield than in other propionate-producing bacteria [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]. Pyruvate is a metabolic node molecule, which may be used either for the NADH-generating synthesis of acetate, or for the NADH-consuming synthesis of propionate [START_REF] Deborde | Interactions between Pyruvate and Lactate Metabolism in Propionibacterium freudenreichii subsp.shermanii: In Vivo 13C Nuclear Magnetic Resonance Studies[END_REF]. In a strain-dependent manner, the bacterium modulates the proportions of pyruvate that are reduced into propionate or oxidized into acetate and CO2, thus maintaining the redox equilibrium [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF].

Technological importance

The species P. freudenreichii is widely used for the production of Swiss-type cheeses, such as Emmental (Figure 14) (Gagnaire et al., 2015;Rabah et al., 2017). In such dairy matrices, the CO2 gas that is produced during fermentation forms bubbles that diffuse slowly, creating characteristic holes, or "eyes", in cheese architecture [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]Ojala et al., 2017). Cheese flavor is related to propionate and acetate, as well as the products of amino acids catabolism and fat hydrolysis by propionibacteria [START_REF] Poonam | Multifaceted attributes of dairy propionibacteria: A review[END_REF][START_REF] Mukdsi | The Secreted Esterase of Propionibacterium freudenreichii Has a Major Role in Cheese Lipolysis[END_REF]. Importantly, these dairy products containing P. freudenreichii displayed anti-inflammatory properties in vivo [START_REF] Plé | Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: Reverse engineering development of an anti-inflammatory cheese[END_REF]Rabah et al., 2018a[START_REF] Rabah | Beneficial propionibacteria within a probiotic emmental cheese: Impact on dextran sodium sulphate-induced colitis in mice[END_REF], increasing the recognition of this bacterium and of its products as health-promoting. Therefore, propionibacteria are considered 2-in-1 bacteria, with both fermentative and probiotic properties, which makes them ideal for the development of health-promoting fermented food (Rabah et al., 2017[START_REF] Rabah | Beneficial propionibacteria within a probiotic emmental cheese: Impact on dextran sodium sulphate-induced colitis in mice[END_REF]. et al., 2020). Moreover, P. freudenreichii has been genetically engineered to enhance vitamin B12 and propionate production [START_REF] Wang | Engineering Propionibacterium freudenreichii subsp. Shermanii for enhanced propionic acid fermentation: Effects of overexpressing propionyl-CoA: Succinate CoA transferase[END_REF]Piwowarek et al., 2018b).

The production of vitamin B12, organic acids, trehalose and other metabolites, together with the safe use as cheese ripening starter and probiotic characteristics, make this bacterium attractive for several biotechnological and industrial applications (Rabah et al., 2017;Pillai et al., 2018;Piwowarek et al., 2018b;Gaucher et al., 2019a). A wide range of genetic and environmental optimizations have been conducted to improve these properties (Pillai et al., 2018;Piwowarek et al., 2018b). Moreover, some optimizations of the growth and processing conditions allowed the improvement of resistance towards storage and towards several industrial processes, such as freezedrying and spray-drying [START_REF] Gaucher | Taking advantage of bacterial adaptation in order to optimize industrial production of dry propionibacterium freudenreichii[END_REF][START_REF] Gaucher | Propionibacterium freudenreichii CIRM-BIA 129 Osmoadaptation Coupled to Acid-Adaptation Increases Its Viability During Freeze-Drying[END_REF](Gaucher et al., , 2019a(Gaucher et al., , 2020b)).

Strain variability

The interesting properties of this bacterium, such as health-promoting features, and participation in vitamin B12 and cheese production, were showed to be straindependent, suggesting the need for analysis that account for that variability [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]. As an example, some strains presented differences concerning nitrogen and sugar degradation, which had a genetic origin, probably horizontal transfers, duplications, transpositions and other mutations (Loux et al., 2015). This strain diversity was confirmed at the genomic level by another study and attributed to transposable elements, in such a way that genome plasticity enabled bacterial adaptation to several environments (Deptula et al., 2017).

In view of this strain-related variability, there have been efforts to specify criteria for the selection of probiotic strains. These criteria include tolerance to stresses encountered in the gastrointestinal tract, ability of adhesion to host cells, antipathogenic activity, anticancer and immunomodulatory properties, industrial requirements and molecular characterization using omics methodologies [START_REF] De Melo Pereira | How to select a probiotic? A review and update of methods and criteria[END_REF]. Mounting evidence shows that P. freudenreichii fulfills these criteria (Rabah et al., 2017).

Stress tolerance

Regarding stress tolerance in the gastrointestinal tract (GIT), some P. freudenrichii strains presented adaptations, including morphological and proteomic modifications [START_REF] Jan | Changes in Protein Synthesis and Morphology during Acid Adaptation of Propionibacterium freudenreichii[END_REF][START_REF] Leverrier | Susceptibility and adaptive response to bile salts in Propionibacterium freudenreichii: Physiological and proteomic anlysis[END_REF]Leverrier et al., , 2004)). The acid tolerance response in the strain P. freudenreichii SI41 was investigated physiologically and morphologically with a kinetic study of stress proteins during acid adaptation. As a result, biotin carboxyl carrier and proteins involved in DNA synthesis and repair were associated to early stress response, whereas chaperonins GroEL and GroES were associated to late stress response [START_REF] Jan | Changes in Protein Synthesis and Morphology during Acid Adaptation of Propionibacterium freudenreichii[END_REF]. Analysis with the same strain showed that bile salts (a mixture of cholate and deoxycholate) triggered drastic morphological changes and induced proteins related to signal sensing and transduction, general stress and an alternative sigma factor [START_REF] Leverrier | Susceptibility and adaptive response to bile salts in Propionibacterium freudenreichii: Physiological and proteomic anlysis[END_REF]. The same strain was used in a follow-up comprehensive study that included heat, acid, and bile salts conditions to study P. freudenreichii tolerance. As a result, each form of stress induced specific proteins, but six of them were common to all stresses, including chaperones and proteins involved in energetic metabolism and oxidative stress (Leverrier et al., 2004). An in vitro study that involved 13 strains of P. freudenrichii showed that most of them had high capacity of tolerance to simulated gastric juices with varying pH and small intestine conditions [START_REF] Huang | In vitro assessment of the upper gastrointestinal tolerance of potential probiotic dairy propionibacteria[END_REF].

Moreover, this resistance was also evidenced in vivo. The mRNA of P. freudenreichii methylmalonyl-transcarboxylase was detected in human fecal samples using real time reverse transcriptase polymerase chain reaction (RT-PCR), which indicated that the bacterium survives and remains metabolically active, transcribing genes within the human digestive tract (Hervé et al., 2007). A multi-strain study using human microbiota-associated rats monitored intestinal microbiota composition and short-chain fatty acids production, confirming that P. freudenreichii stress tolerance in the GIT is also strain-dependent (Lan et al., 2007a). P. freudenreichii CIRM-BIA1 was showed to adapt metabolically and physiologically to the colon environment of pigs, with changes in carbohydrate metabolism, down-regulation of stress genes and upregulation of cell division genes (Saraoui et al., 2013). Furthermore, the use of food vehicles for P. freudenreichii delivery, such as cheese and fermented milk, improved its resistance towards the GIT stressing environment [START_REF] Cousin | Milk fermented by propionibacterium freudenreichii induces apoptosis of HGT-1 human gastric cancer cells[END_REF]Gagnaire et al., 2015;Rabah et al., 2018bRabah et al., , 2018a[START_REF] Rabah | Beneficial propionibacteria within a probiotic emmental cheese: Impact on dextran sodium sulphate-induced colitis in mice[END_REF].

Other aspects of P. freudenreichii resistance to stress conditions have also been studied, such as long-term nutritional shortage (Aburjaile et al., 2016b(Aburjaile et al., , 2016a)). A screening was performed with eight P. freudenreichii strains, which were incubated for several days after the beginning of the stationary phase, without further supplementation of nutrients. They displayed high survival rates and no lysis, indicating that these strains adapt to long-term nutritional shortage, using a viable, but nonculturable state (Aburjaile et al., 2016a). The strain P. freudenreichii CIRM-BIA138 was further studied in these conditions of incubation, and it was shown that a high population was maintained, even after exhaustion of lactate, the preferred carbon source. RNA-seq analysis showed that several metabolic and information processing pathways were down-regulated (Aburjaile et al., 2016b).

Another important feature of P. freudenreichii during stress response is the accumulation of trehalose. A study that investigated this bacterium during adaptation to osmotic, oxidative and acid stress, showed that the trehalose-6-phosphate synthase/phosphatase (OtsA-OtsB) pathway, related to trehalose synthesis, was enhanced in these conditions [START_REF] Cardoso | Biochemical and genetic characterization of the pathways for trehalose metabolism in Propionibacterium freudenreichii, and their role in stress response[END_REF]. Another study focused in stress response in the cold (4 ºC), a condition that mimicked cheese ripening. As a result, seven P. freudenreichii strains displayed a slowed-down cell machinery, cold stress response and the accumulation of trehalose and glycogen [START_REF] Dalmasso | Accumulation of intracellular glycogen and trehalose by Propionibacterium freudenreichii under conditions mimicking cheese ripening in the cold[END_REF]. The accumulation of trehalose, together with glycine betaine, was also verified in a technological context, when bacteria's viability was increased during spray drying and storage, through the optimization of the growth medium composition and thermal adaptation [START_REF] Gaucher | Taking advantage of bacterial adaptation in order to optimize industrial production of dry propionibacterium freudenreichii[END_REF]. The ratio of the concentrations of these intracellular osmoprotectants, trehalose and glycine betaine, was further shown to modulate the stress tolerance during the technological processes of freeze-drying and of spray-drying (Gaucher et al., 2020b).

Adhesion properties

The ability of adhesion to host cells is another important feature of probiotics, which favors their local beneficial action. In the case of P. freudenreichii, the adhesion of some strains to pig ileum cells (IPEC-J2) was between 25 and 35%, and that proportion was higher with the addition of CaCl2 [START_REF] Campaniello | Screening of Propionibacterium spp. for potential probiotic properties[END_REF]. In the case of human host, several bacterial strains were tested for adhesion to HT-29 colon cells in vitro; the most adhesive strain was P. freudenreichii CIRM-BIA129 and surface layer protein B (slpB) key role in adhesion was demonstrated by using gene inactivation (do Carmo et al., 2017Carmo et al., , 2018)). Surface polysaccharides are also components involved in adhesion properties of the species [START_REF] Guyomarc'h | Surface properties associated with the production of polysaccharides in the food bacteria Propionibacterium freudenreichii[END_REF]. Another study demonstrated that adhesion to immobilized mucus could be improved by administration of strains combinations, such as P. freudenreichii ssp. shermanii JS in combination with Bifidobacterium breve or Lactobacillus rhamnosus strains [START_REF] Collado | Development of new probiotics by strain combinations: Is it possible to improve the adhesion to intestinal mucus?[END_REF].

Anti-pathogenic activity

There are also several evidences of an anti-pathogenic activity in this species. P. freudenreichii JS reduced by 39% the adhesion of S. aureus to human intestinal mucus and by 27% its viability, probably due to the production of organic acids [START_REF] Vesterlund | Staphylococcus aureus adheres to human intestinal mucus but can be displaced by certain lactic acid bacteria[END_REF]. P. freudenreichii PTCC 1674 was reported to secrete a lipopeptide biosurfactant with antimicrobial activity mainly against Rhodococcus erythropolis, and anti-adhesive activity mainly against P. aeruginosa [START_REF] Hajfarajollah | Newly Antibacterial and Antiadhesive Lipopeptide Biosurfactant Secreted by a Probiotic Strain, Propionibacterium Freudenreichii[END_REF]. Moreover, P. freudenreichii DSM 20270 significantly inhibited E. coli O157:H7 growth in vitro [START_REF] Campaniello | Screening of Propionibacterium spp. for potential probiotic properties[END_REF]. P. freudenreichii also showed anti-pathogenic properties in animals. P. freudenreichii B-3523 and B-4327 impacted Salmonella strains multiplication, motility and adhesion to avian epithelial cells in vitro [START_REF] Nair | Effect of Propionibacterium freudenreichii on Salmonella multiplication, motility, and association with avian epithelial cells[END_REF]. The follow up study indicated that the cell-free culture supernatants of the same probiotic strains were bactericidal against multidrug-resistant Salmonella enterica serovar Heidelberg. In vivo assays further showed that the probiotic strains reduced the pathogen cecal colonization and dissemination to the liver in turkey poults [START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF]. Probiotic combinations have also been proposed to improve anti-pathogenic activity, such as a combination of P. freudenreichii JS, L. rhamnosus GG and LC705, and B. breve 99, which promoted the inhibition, displacement and competition with several pathogenic species [START_REF] Collado | Protection mechanism of probiotic combination against human pathogens: in vitro adhesion to human intestinal mucus[END_REF]. In another study, P. freudenreichii JS decreased the adhesion of H. pylori to Caco-2 intestinal cells when used individually, but also inhibited membrane leakage, improved epithelial barrier function and modulated inflammatory cytokines when used in combination with L. rhamnosus and B. breve strains [START_REF] Myllyluoma | Effects of multispecies probiotic combination on Helicobacter pylori infection in vitro[END_REF].

Anticancer properties

Anticancer properties have also been reported in this species. A study showed that P. freudenreichii ITGP18 and P. freudenreichii SI41 could induce apoptosis on cultured human colorectal carcinoma cell lines in vitro and this effect was mediated by shortchain fatty acids (SCFAs), such as propionate and acetate (Jan et al., 2002). Following up, it was further clarified that the effect of SCFAs was modulated by extracellular pH shifts; and in acidic pH, cell death mode changed from apoptosis to necrosis in human colon HT-29 cells (Lan et al., 2007b). These effects were confirmed in vivo, with the induction of apoptosis of colon cells in human microbiota-associated rats treated with 1,2dimethylhydrazine (Lan et al., 2008).

Another strain, P. freudenreichii ITG P9, was also employed for the development of a fermented milk with anticancer properties, since it induced apoptosis in cultured HGT-1 human gastric cancer cells in vitro [START_REF] Cousin | Milk fermented by propionibacterium freudenreichii induces apoptosis of HGT-1 human gastric cancer cells[END_REF]. Next, this fermented milk was proposed as an adjuvant in colorectal cancer therapy based on TNF-related apoptosis-inducing ligand (TRAIL), due to possible synergistic effect between the bacterium and TRAIL, which was confirmed with the enhancement of cytotoxic activity in HT-29 cells (Cousin et al., 2016). Another study investigated the crosstalk between bacterium and cancer cells: the later produce lactate as a result of the metabolic shift referred as "aerobic" glycolysis or "Warburg effect"; lactate may then be used by this bacterium as a carbon source, stimulating its production of SCFAs [START_REF] Casanova | Colorectal Cancer Cells Increase the Production of Short Chain Fatty Acids by Propionibacterium freudenreichii Impacting on Cancer Cells Survival[END_REF].

Modulation of microbiota composition

Regarding the modulation of microbiota composition, in P. freudenreichii 7025 cultures, a bifidogenic growth stimulator was observed in cell-free filtrate and cellular methanol extract [START_REF] Kaneko | Growth Stimulator for Bifidobacteria Produced by Propionibacterium freudenreichii and Several Intestinal Bacteria[END_REF]. Following analysis with the same strain allowed the purification of a bifidogenic growth stimulator component, the identification of its chemical structure (2-amino-3-carboxy-1,4-naphthoquinone, ACNQ) and the demonstration of its bifidogenic activity in the concentration of 0.1 ng/mL (Mori et al., 1997). Another strain, P. freudenreichii ET-3 was reported to produce 1,4-dihydroxy-2naphthoic acid (DHNA) in concentrations of 10 µg/mL, which also stimulated the growth of bidobacteria (Isawa et al., 2002). The beneficial effect of DHNA was later confirmed in vivo, using mice with colitis induced by 2.0% dextran sodium sulphate (DSS). DHNA attenuated inflammation, through the modulation of intestinal bacterial microbiota and suppression of lymphocyte infiltration [START_REF] Okada | Propionibacterium freudenreichii component 1.4-dihydroxy-2-naphthoic acid (DHNA) attenuates dextran sodium sulphate induced colitis by modulation of bacterial flora and lymphocyte homing[END_REF].

The bifidogenic growth stimulator derived from P. freudenreichii was also orally administrated to human patients in a pilot study, being promising for the treatment of ulcerative colitis [START_REF] Suzuki | Bifidogenic growth stimulator for the treatment of active ulcerative colitis: a pilot study[END_REF]. Subsequent studies included optimizations of the production of bifidogenic growth stimulators, including an increased production by switching to aerobic growth conditions [START_REF] Furuichi | Aerobic culture of Propionibacterium freudenreichii ET-3 can increase production ratio of 1,4-dihydroxy-2-naphthoic acid to menaquinone[END_REF] and the use of lactic acid as a carbon source in a bioreactor system with a filtration device [START_REF] Kouya | Production of extracellular bifidogenic growth stimulator by anaerobic and aerobic cultivations of several propionibacterial strains[END_REF].

Immunomodulatory properties

There is mounting evidence both in vitro and in vivo, that P. freudenreichii exerts immunomodulatory effects by several mechanisms, in a strain-dependent manner. For example, a screening for IL-10 induction on human peripheral blood mononuclear cells (PBMCs) was performed in 10 strains of P. freudenreichii, resulting in the selection of the 2 most anti-inflammatory strains: P. freudenreichii ITG P20 (equivalent to CIRM-BIA129) and SI48 (Foligné et al., 2010). In the same study, the strain P. freudenreichii SI48 was further tested in vivo, in mice with TNBS-induced acute colitis, lowering significantly inflammatory and histological markers of colitis (Foligné et al., 2010). Another multistrain study employed an integrative strategy encompassing comparative genomics, surface proteomics, transcriptomics, assays of cytokines induction and genes inactivation, in order to identify relevant proteins and strains specificities in immunomodulation (Deutsch et al., 2017). Remarkably, surface proteins were shown to be crucial in immunomodulation, but the immunomodulatory properties varied among strains due to complex combinations of molecular features (Deutsch et al., 2017). In a different subset of P. freudenreichii strains, the secretome was investigated in order to identify proteins that were exported in a strain-specific manner, such as surface proteins, adhesins and moonlighting proteins [START_REF] Frohnmeyer | Secretome profiling of Propionibacterium freudenreichii reveals highly variable responses even among the closely related strains[END_REF]. Additionally, acute colitis induced by dextran sodium sulfate (DSS) in rats was ameliorated by P. freudenreichii KCTC 1063, which stimulated in intestinal cells the expression of MUC2, a main component of mucus (Ma et al., 2020).

The roles of P. freudenreichii in the modulation of host immunological response became even more relevant when a human commensal strain was identified. P. freudenreichii UF1 was demonstrated to be a component of the gut microbiota of preterm infants that were fed with human breast milk and to mitigate intestinal inflammatory diseases (Colliou et al., 2017). Moreover, this strain modulated the intestinal immunity of mice against pathogen challenge, specifically against systemic Listeria monocytogenes infection, by regulating Th17 cells [START_REF] Colliou | Regulation of Th17 cells by P. UF1 against systemic Listeria monocytogenes infection[END_REF]. This beneficial effect was confirmed in newborn mice, which were susceptible to intestinal pathogenic infection, but had their defense enhanced by this strain, particularly by the increase in protective Th17 cells and regulatory T cells [START_REF] Ge | Neonatal intestinal immune regulation by the commensal bacterium, P. UF1[END_REF].

Regarding the bacterial factors involved in immunomodulation, evidence points out mainly to surface proteins (Table 1). The strain P. freudenreichii CIRM-BIA129 had its proteome investigated, with the identification of surface-exposed proteins and their role in induction of IL-10 and IL-6 release by peripheral blood mononuclear cells (PBMCs) (Le Maréchal et al., 2015). Among the identified proteins, there were cell wall-remodeling proteins, transport proteins, moonlighting proteins and other proteins involved in interactions with the host (Le Maréchal et al., 2015). The multi-strain and multi-omics study conducted by Deutsch et al. (2017) clarified that cytoplasmic proteins might also be relevant in immunomodulation, but confirmed the key role of surfacelayer proteins B (SlpB) and E (SlpE), particularly in strain P. freudenreichii CIRM-BIA 129 (Deutsch et al., 2017). SlpB was then showed to be crucial for bacterial adhesion to epithelial intestinal cells (do [START_REF] Carmo | Propionibacterium freudenreichii surface protein SlpB is involved in adhesion to intestinal HT-29 cells[END_REF], and a mutation in its gene had pleiotropic effects, suggesting this protein could have a central role in cellular processes (do Carmo et al., 2018). Additionally, in vivo assays that were conducted in mice with mucositis induced by , showed that SlpB protein is crucial for cytokine modulation triggered by P. freudenreichii CIRM-BIA129 (do [START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF]. Moreover, the glycosylated large surface layer protein A (LspA) of the commensal strain P. freudenreichii UF1 was showed to regulate the interaction with SIGNR1 receptor, which regulates dendritic cells and counteracts pathogenic-driven inflammation, maintaining gut homeostasis [START_REF] Ge | Regulating colonic dendritic cells by commensal glycosylated large surface layer protein A to sustain gut homeostasis against pathogenic inflammation[END_REF].

In addition to surface proteins, DHNA was also associated to immunomodulation. Beside its bifidogenic properties, DHNA inhibited the production of proinflammatory cytokines in intestinal macrophages of IL-10(-/-) mice treated with piroxicam (Okada et al., 2013). Moreover, DHNA was also described as an activator of aryl hydrocarbon receptor (AhR), which is involved in the detoxification of xenobiotics and inflammation regulation [START_REF] Fukumoto | Identification of a probiotic bacteria-derived activator of the aryl hydrocarbon receptor that inhibits colitis[END_REF][START_REF] Cheng | Microbialderived 1,4-Dihydroxy-2-naphthoic acid and related compounds as aryl hydrocarbon receptor agonists/antagonists: Structure-activity relationships and receptor modeling[END_REF].

Functional food

Importantly, the immunomodulatory properties of P. freudenreichii were preserved when food matrices were used as delivery vectors, including cheese (Plé et al., 2015;Rabah et al., 2018aRabah et al., , 2018b[START_REF] Rabah | Beneficial propionibacteria within a probiotic emmental cheese: Impact on dextran sodium sulphate-induced colitis in mice[END_REF] and fermented milk (Cousin et al., 2012a;[START_REF] Foligné | Immunomodulation properties of multi-species fermented milks[END_REF][START_REF] Moslemi | Incorporation of Propionibacteria in Fermented Milks as a Probiotic[END_REF], indicating a great potential for developing probiotic-based functional foods with immunomodulatory properties. As an example, a dairy product fermented by strain CIRM-BIA129 reduced the secretion of proinflammatory cytokines by colonic mucosa, improved food intake and growth of piglets (Cousin et al., 2012a). P. freudenreichii CIRMBIA-129 was also employed in the production of an immunomodulatory cheese, whose consumption by mice ameliorated colitis induced by trinitrobenzenesulphonic acid (TNBS) [START_REF] Plé | Single-strain starter experimental cheese reveals anti-inflammatory effect of Propionibacterium freudenreichii CIRM BIA 129 in TNBS-colitis model[END_REF]. In healthy piglets, the consumption of the same strain associated to a cheese matrix was crucial in the preservation or enhancement of the immunomodulatory properties of the bacterium, including the induction of Th2 and Treg phenotypes (Rabah et al., 2018a). The importance of the cheese matrix was also related to the protection of immunomodulatory protein SlpB against proteolysis in simulated gastrointestinal tract conditions (Rabah et al., 2018b). The strain P. freudenreichii CIRM-BIA129 was also used in combination with strains Lactobacillus delbrueckii CNRZ327 and Streptococcus thermophilus LMD-9 to produce an Emmental cheese that was effective in the improvement of inflammatory markers of DSS-induced colitis in mice [START_REF] Rabah | Beneficial propionibacteria within a probiotic emmental cheese: Impact on dextran sodium sulphate-induced colitis in mice[END_REF]. These examples unveiled how appropriate food matrices protected or enhanced the beneficial properties of these traditional dairy propionibacteria, while establishing perspectives for the design of novel functional foods [START_REF] Moslemi | Incorporation of Propionibacteria in Fermented Milks as a Probiotic[END_REF].

Safety assessments

The long term production of fermented food, such as Emmental cheese, and the bacterium status of "generally recognized as safe" (GRAS) and "qualified presumption of safety" (QPS) assure the safety of P. freudenreichii consumption (Rabah et al., 2017;[START_REF] Dudek-Wicher | Clinical Trials of Probiotic Strains in Selected Disease Entities[END_REF]. However, additional assessments need to be conducted in different matrices and contexts. Probiotics included in humans trials are most frequently from genus Lactobacillus or Bifidobacterium; however, propionibacteria have also been tested [START_REF] Dudek-Wicher | Clinical Trials of Probiotic Strains in Selected Disease Entities[END_REF]. For example, two clinical studies evaluated P. freudenreichii ET-3 culture medium safety in human adult subjects, the first one reported no differences in gastrointestinal symptoms between the groups and the other one reported differences in hematological parameters, although within the normal ranges [START_REF] Uchida | Safety of high doses of Propionibacterium freudenreichii ET-3 culture in healthy adult subjects[END_REF].

Moreover, several clinical trials tested multispecies probiotic supplementation containing propionibacteria. A complex formula that included P. freudenreichii JS, together with L. rhamnosus GG, L. rhamnosus Lc705, B. breve 99, and galacto-oligosaccharides prebiotics has been tested in several randomized, double-blind, placebo-controlled setups. The probiotic intervention was conducted in pregnant women and newborn infants, being safe and effective in the prevention of atopic eczema in children [START_REF] Kukkonen | Probiotics and prebiotic galacto-oligosaccharides in the prevention of allergic diseases: A randomized, double-blind, placebo-controlled trial[END_REF], increased children resistance to respiratory infections [START_REF] Kukkonen | Long-term safety and impact on infection rates of postnatal probiotic and prebiotic (synbiotic) treatment: Randomized, double-blind, placebo-controlled trial[END_REF], protected Caesarean-delivered children from IgEassociated allergic disease [START_REF] Kuitunen | Probiotics prevent IgE-associated allergy until age 5 years in cesarean-delivered children but not in the total cohort[END_REF], restored microbiota composition in children treated with antibiotics or born by caesarean procedure [START_REF] Korpela | Probiotic supplementation restores normal microbiota composition and function in antibiotictreated and in caesarean-born infants[END_REF] and protected Caesarean-delivered children from allergic disease in a 13-year follow-up [START_REF] Kallio | Perinatal probiotic intervention prevented allergic disease in a Caesarean-delivered subgroup at 13-year follow-up[END_REF]. Finally, an integrative study analyzed adverse events associated with this probiotic combination in some of these trials, concluding that there was no association with adverse events in young and elderly subjects [START_REF] Tapiovaara | Absence of adverse events in healthy individuals using probiotics -analysis of six randomised studies by one study group[END_REF].

Importantly, probiotics supplementation is not recommended in cases of immunosuppression, such as during anticancer treatment [START_REF] Dudek-Wicher | Clinical Trials of Probiotic Strains in Selected Disease Entities[END_REF]. Moreover, their beneficial effects and safety are conditioned to a complex interplay between peculiarities of the host and of the probiotic strain or strains, which both encourages further research and suggests caution in some of its applications [START_REF] Dudek-Wicher | Clinical Trials of Probiotic Strains in Selected Disease Entities[END_REF].

Postbiotics and beyond

As previously detailed, P. freurenreichii probiotic effect has been associated to several factors, including cytoplasmic and surface-exposed proteins (Le Maréchal et al., 2015;Deutsch et al., 2017;do Carmo et al., 2017do Carmo et al., , 2019)), short chain fatty acids (Jan et al., 2002;Lan et al., 2007b), metabolites (Isawa et al., 2002;[START_REF] Okada | Propionibacterium freudenreichii component 1.4-dihydroxy-2-naphthoic acid (DHNA) attenuates dextran sodium sulphate induced colitis by modulation of bacterial flora and lymphocyte homing[END_REF][START_REF] Kouya | Production of extracellular bifidogenic growth stimulator by anaerobic and aerobic cultivations of several propionibacterial strains[END_REF] and culture supernatants [START_REF] Uchida | Safety of high doses of Propionibacterium freudenreichii ET-3 culture in healthy adult subjects[END_REF][START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF] (Figure 15). These probiotic-derived factors, which exert a beneficial effect on the host, have been referred as postbiotics (Żółkiewicz et al., 2020).

Postbiotic is an emerging denomination that encompasses probiotic-derived cellfree metabolic products with health-promoting properties, including proteins, lipids, organic acids, vitamins, supernatants, among others [START_REF] Tsilingiri | Postbiotics: what else? Benef[END_REF][START_REF] Nataraj | Postbiotics-parabiotics: the new horizons in microbial biotherapy and functional foods[END_REF][START_REF] Żółkiewicz | Postbiotics-A Step Beyond Pre-and Probiotics[END_REF]. The advantages of postbiotics over probiotics include purity, easy production and storage, industrial scalability, higher specificity in the mechanism of action and less adverse effects [START_REF] Nataraj | Postbiotics-parabiotics: the new horizons in microbial biotherapy and functional foods[END_REF][START_REF] Żółkiewicz | Postbiotics-A Step Beyond Pre-and Probiotics[END_REF]. Another example that would fit into postbiotics definition and whose importance is increasing are extracellular vesicles (EVs). In probiotic bacteria, such as several Lactobacillus and Bifidobacteriaum strains, EVs have been reported as immunomodulatory (Molina-Tijeras et al., 2019). In the case of P. freudenreichii, we recently described the production of EVs by the strain CIRM-BIA129 (Rodovalho et al., 2020b), which has been the first report with physicochemical, proteomic and functional characterization of EVs in the species. We identified relevant proteins in their cargo and demonstrated their anti-inflammatory activity via the modulation of NF-κB pathway in cultured human intestinal epithelial cells in vitro (Rodovalho et al., 2020b).

Postbiotics, including EVs, hold promising perspectives for developing novel probiotic-derived products with enhanced safety and functionality (Molina-Tijeras et al., 2019). Moreover, yield and cargo loading optimization are promising for modulating bacterial EVs properties, enhancing their beneficial effect and biotechnological applications [START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]. Finally, clinical trials should be conducted in the near future to assure the suitability of postbiotics, as well as probiotics themselves, in therapy and prophylaxis, since they might exert a great impact in human health (Molina-Tijeras et al., 2019;[START_REF] Dudek-Wicher | Clinical Trials of Probiotic Strains in Selected Disease Entities[END_REF][START_REF] Rad | Potential Pharmaceutical and Food Applications of Postbiotics: A review[END_REF].

Chapter 2 -Research question, objectives and strategy 1. Context and research question

EV production is now considered an ubiquitous phenomenon, although its identification in particular contexts was delayed due to technical limitations or misconceptions (Woith et al., 2019). That was the case of Gram-positive bacteria, whose thick peptidoglycan cell wall seemed an unsurmountable barrier (Brown et al., 2015). Similarly, EV research on beneficial bacteria is only in its infancy, if compared to pathogenic bacteria [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. Nevertheless, mounting evidence suggested that secreted factors mediated some of the beneficial effects associated to bacteria, sometimes at distant sites from where they are usually found in the human body, such as the brain (Haas-Neill and Forsythe, 2020). EVs could explain these observations, since they are natural vehicles of intercellular exchange, that promote the protection of their internal cargo, being able to cross the mucus layer in the gastrointestinal tract, migrate to other tissues and spread to distant sites [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Molina-Tijeras et al., 2019).

Likewise, EVs are also technologically promising, with a great potential for costeffective and large-scale production, long-term stability and safety [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. EVs were safely used as human-targeted vaccines and they are considered an appropriate alternative for probiotics in the case of immunocompromised individuals [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Molina-Tijeras et al., 2019). Furthermore, the production of EVs derived from beneficial bacteria, as well as their properties, have also been optimized to fulfill specific criteria, such as yield improvement, and content and activity modulation [START_REF] Nakayama-Imaohji | DNA inversion regulates outer membrane vesicle production in Bacteroides fragilis[END_REF][START_REF] Pérez-Cruz | Membrane Vesicles Released by a hypervesiculating Escherichia coli Nissle 1917 tolR Mutant Are Highly Heterogeneous and Show Reduced Capacity for Epithelial Cell Interaction and Entry[END_REF][START_REF] Hirayama | Glycine significantly enhances bacterial membrane vesicle production: a powerful approach for isolation of LPS-reduced membrane vesicles of probiotic Escherichia coli[END_REF][START_REF] Kuhn | Probiomimetics-Novel Lactobacillus-Mimicking Microparticles Show Anti-Inflammatory and Barrier-Protecting Effects in Gastrointestinal Models[END_REF]. All these features make of EVs a very interesting route of content delivery, both in natural and technological contexts.

With the improvement in purification methods and omics characterization, the tendency is a great increase in the reports of widespread EV occurrence and the characterization of their contents, with a special interest in long overlooked, yet very important, beneficial bacteria. One of such beneficial bacteria is P. freudenreichii, a Gram-positive dairy probiotic traditionally used in the production of Swiss-type cheese, organic acids and vitamin B12 (Rabah et al., 2017). As reviewed in detail in Chapter 1, P. freudenreichii was recognized as an important probiotic species due to several beneficial traits, including the adhesion to human epithelial cells, the anti-pathogenic activity, the production of bifidogenic factors that modulate microbiota composition, the anticancer properties mediated by short-chain fatty acids and immunomodulation. These effects are mediated by surface-exposed or secreted factors (Jan et al., 2002;Okada et al., 2013;do Carmo et al., 2017), and they were sometimes attributed to cell-free culture supernatants [START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF]Ma et al., 2020). Furthermore, a secretome profiling of the species included TEM images of P. freudenreichii strain JS22 indicating the presence of membrane-bulging structures that resembled EVs [START_REF] Frohnmeyer | Secretome profiling of Propionibacterium freudenreichii reveals highly variable responses even among the closely related strains[END_REF].

As EVs are being increasingly recognized as a general export system, including in Gram-positive and beneficial bacteria (Brown et al., 2015;Molina-Tijeras et al., 2019), we hypothesized that P. freudenreichii could produce EVs involved in the mediation of its beneficial roles, particularly the immunomodulation of human intestinal cells (Figure 1). Therefore, we formulated the following research question: Does the probiotic P. freudenreichii produce EVs with immunomodulatory roles in different conditions?

Objectives and strategy

The overall objective of this work was to determine whether P. freudenreichii produces EVs with immunomodulatory roles towards human cells. Therefore, an interdisciplinary approach was adopted, integrating nanoparticle characterization, proteomics, interactomics and functional assays to disclose the characteristics and roles of P. freudenreichii-derived EVs obtained in different conditions. In order to investigate this central question, it has been divided in the following specific objectives:

1. Determine if P. freudenreichii produces EVs in a standard growth condition, using size exclusion chromatography as a purification method. 2. Investigate the biophysical characteristics (size, concentration, morphology) of P. freudenreichii-derived EVs. 3. Investigate the protein content of P. freudenreichii-derived EVs. 4. Apply in silico approaches to predict interactions between proteins from P.

freudenreichii-derived EVs and human proteins. 5. Study the immunomodulatory activity of P. freudenreichii-derived EVs towards human intestinal cellular models. 6. Characterize P. freudenreichii-derived EVs obtained from cultures in a different growth medium. 7. Compare growth conditions for the modulation of the properties of P.

freudenreichii-derived EVs, including biophysical parameters, protein content, immunomodulatory activity and interactions patterns. 8. Characterize the properties of P. freudenreichii-derived EVs obtained by a different purification method: ultracentrifugation with density gradient. 9. Analyze the conservation of P. freudenreichii-derived EVs protein content across different growth conditions and purification methods.

Objectives 1-5 will be addressed in Chapter 3, which presents an original research article reporting the purification of P. freudenreichii-derived EVs from cultures in milk ultrafiltrate (UF) medium by size-exclusion chromatography (SEC). This work also presents the biophysical characteristics of these EVs, in addition to the investigation of their protein content, interaction network and immunomodulatory activity.

Objectives 6-7 will be addressed in Chapter 4, which presents an original research article reporting the purification of P. freudenreichii-derived EVs by SEC from cultures in either UF or yeast extract-lactate (YEL) medium. This work presents the possibility of modulating EVs properties, including protein content and immunomodulatory activity, through the optimization of culture conditions.

Objectives 8-9 will be addressed in Chapter 5, which introduces the characterization of P. freudenreichii-derived EVs obtained by ultracentrifugation (UC) with density gradient, from cultures in either UF or YEL medium. Then, the protein content of EVs derived from the different culture media and purification methods is compared.

A general discussion of the results, the conclusions and perspectives will be presented in Chapter 6.

Chapter 3 -Research article 1 Extracellular Vesicles Produced by the Probiotic Propionibacterium freudenreichii CIRM-BIA 129 Mitigate Inflammation by Modulating the NF-κB Pathway

Some aspects of the probiotic effects of P. freudenreichii have been attributed to surface-exposed or secreted factors and cell-free supernatants. Although the importance of specific proteins for the immunomodulation exerted by the species was demonstrated, it remained unclear if the functionality of those proteins was mediated exclusively by surface exposure or conventional export systems; or if there were other important export systems, such as EVs. Additionally, structures resembling EVs were previously identified in TEM images of this species. However, those structures were not purified or further characterized. Therefore, we aimed to investigate whether P. freudenreichii produced EVs, what were their properties and their functional roles, particularly regarding immunomodulation.

Accordingly, the anti-inflammatory strain P. freudenreichii CIRM-BIA129 was applied in this investigation, and hence grown in UF medium until the beginning of the stationary phase. Then, as a protocol for EV purification, the cultures were centrifuged and filtered, and the resulting supernatants were concentrated and applied to a column of SEC. The resulting samples were characterized by TEM and NTA, unveiling nano-sized EVs with typical spherical and cup-shaped morphology. A content investigation by shotgun proteomics showed that EVs contained a diverse set of proteins, including those of cytoplasmic origin, as reported for other Gram-positive bacteria. Some of the identified proteins, such as SlpB, were previously associated to the immunomodulatory roles exerted by P. freudenreichii. Furthermore, in silico interactions predictions revealed potential interactions of these vesicular proteins with a subunit of human transcription factor NF-κB, which is implicated in inflammatory processes. Therefore, the immunomodulatory activity of these EVs was evaluated in intestinal cellular models, with EV treatment reducing the release of pro-inflammatory cytokine IL-8 and the activation of NF-κB transcription factor, specifically when the cells were induced to an inflammatory state by bacterial LPS. This effect was partly mediated by SlpB protein, as the mutant produced EVs with a less intense anti-inflammatory effect.

Therefore, this chapter shows that not only P. freudenreichii CIRM-BIA129 produces EVs, but they have an anti-inflammatory role towards human epithelial cells, which is related to the immunomodulatory proteins carried by these EVs. This is the first comprehensive characterization of the EVs derived from P. freudenreichii and resulted in an original research article published in the journal Frontiers in Microbiology (DOI: 10.3389/fmicb.2020.01544), which is presented in the next pages. Extracellular vesicles (EVs) are nanometric spherical structures involved in intercellular communication, whose production is considered to be a widespread phenomenon in living organisms. Bacterial EVs are associated with several processes that include survival, competition, pathogenesis, and immunomodulation. Among probiotic Gram-positive bacteria, some Propionibacterium freudenreichii strains exhibit antiinflammatory activity, notably via surface proteins such as the surface-layer protein B (SlpB). We have hypothesized that, in addition to surface exposure and secretion of proteins, P. freudenreichii may produce EVs and thus export immunomodulatory proteins to interact with the host. In order to demonstrate their production in this species, EVs were purified from cell-free culture supernatants of the probiotic strain P. freudenreichii CIRM-BIA 129, and their physicochemical characterization, using transmission electron microscopy and nanoparticle tracking analysis (NTA), revealed shapes and sizes typical of EVs. Proteomic characterization showed that EVs contain a broad range of proteins, including immunomodulatory proteins such as SlpB. In silico protein-protein interaction predictions indicated that EV proteins could interact with host proteins, including the immunomodulatory transcription factor NF-κB. This potential interaction has a functional significance because EVs modulate inflammatory responses, as shown by IL-8 release and NF-κB activity, in HT-29 human intestinal epithelial cells. Indeed, EVs displayed an anti-inflammatory effect by modulating the NF-κB pathway; this was dependent on their concentration and on the proinflammatory inducer (LPSspecific). Moreover, while this anti-inflammatory effect partly depended on SlpB, it was not abolished by EV surface proteolysis, suggesting possible intracellular sites of action for EVs. This is the first report on identification of P. freudenreichii-derived

INTRODUCTION

Intercellular communication is an essential biological process that involves several soluble biomolecules that may be secreted, surface-exposed or packed inside extracellular vesicles (EVs) [START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF]Toyofuku, 2019). EVs are lipid bilayer nanoparticles which range in size from 20 to 300 nm and are released by cells from all living kingdoms (Brown et al., 2015;Kim et al., 2015a;Liu et al., 2018a). They play a pivotal role in cell-to-cell communication through their ability to transport bioactive molecules (proteins, nucleic acids, lipids, metabolites) from donor to recipient cells. Bacterial EVs are implicated in virulence factor delivery, antibiotic resistance, competition, survival, and host cell modulation (Kim et al., 2015b;Toyofuku et al., 2019).

The participation of EVs in the beneficial roles of probiotic bacteria has been increasingly reported (Molina-Tijeras et al., 2019). The release of EVs by Lactobacillus species is well documented; Lactobacillus reuteri DSM 17938-derived EVs are associated with extracellular DNA-dependent biofilm formation (Grande et al., 2017) and EVs secreted by Lactobacillus casei BL23 have also been reported and shown to contain diverse biomolecules which include nucleic acids and proteins previously associated with its probiotic effects, such as p40 and p75 (Rubio et al., 2017). Lactobacillus rhamnosus GG-derived EVs have been associated with the apoptosis of hepG2 cancer cells (Behzadi et al., 2017), and Lactobacillus plantarum WCFS1-derived EVs modulated the response of human cells to vancomycinresistant enterococci (Li et al., 2017). Moreover, EVs derived from other probiotic species, such as Bifidobacterium longum KACC 91563, impact host cell responses by inducing mast cell apoptosis, which has implications for the treatment of food allergies (Kim et al., 2016). Furthermore, probiotic strains of Escherichia coli release outer membrane vesicles (OMVs) that are involved in reinforcement of the gastrointestinal epithelial barrier [START_REF] Alvarez | Outer Membrane Vesicles and Soluble Factors Released by Probiotic Escherichia coli Nissle 1917 and Commensal ECOR63 Enhance Barrier Function by Regulating Expression of Tight Junction Proteins in Intestinal Epithelial Cells[END_REF], the regulation of inflammatory responses and intestinal homeostasis, via the NOD1-signaling pathway (Cañas et al., 2018).

Propionibacterium freudenreichii has also been regarded consistently as a probiotic species, mainly because of its immunomodulatory properties and protective effects against experimentally induced inflammation in vivo (Lan et al., 2007a(Lan et al., , 2008;;Foligne et al., 2010;Cousin et al., 2012;Rabah et al., 2018a;Do Carmo et al., 2019). P. freudenreichii is a Gram-positive, pleiomorphic, microaerophilic dairy bacterium that is generally recognized as safe (GRAS) and has a qualified presumption of safety (QPS) status (Loux et al., 2015;Deutsch et al., 2017;Rabah et al., 2017). This species is also known for its involvement in the ripening, texture, and flavor of cheese (Ojala et al., 2017) and in vitamin B12 synthesis (Deptula et al., 2017). A P. freudenreichii strain was recently isolated from the gut microbiota of a human breast milk-fed preterm infant, suggesting that this species could also be considered as a commensal inhabitant of the human digestive tract (Colliou et al., 2017).

As for the molecular mechanisms underlying its probiotic effects, some studies have focused on identifying the surface proteins of P. freudenreichii and their role in cytokine induction (Le Maréchal et al., 2015;Deutsch et al., 2017;Do Carmo et al., 2019). Notably, cell wall-related proteins, S-layer type proteins, moonlighting proteins and proteins related to interactions with the host have been identified as important actors in immunomodulation of P. freudenreichii strain CIRM-BIA 129 (Le Maréchal et al., 2015). Specifically, recent studies reported the role of surface-layer protein B (SlpB) from this strain in bacterial adhesion to intestinal HT-29 cells and immunomodulation (Do Carmo et al., 2017, 2019;Rabah et al., 2018b), as well as that of large surface layer protein A (LspA) from strain P. UF1 in the regulation of colonic dendritic cells during inflammation via SIGNR1 binding [START_REF] Ge | Regulating colonic dendritic cells by commensal glycosylated large surface layer protein A to sustain gut homeostasis against pathogenic inflammation[END_REF]. As well as surface proteins, additional metabolites may contribute to the probiotic effect, such as 1,4-Dihydroxy-2-naphthoic acid (DHNA) from P. freudenreichii ET3, which is linked to AhR pathway activation [START_REF] Fukumoto | Identification of a probiotic bacteria-derived activator of the aryl hydrocarbon receptor that inhibits colitis[END_REF]. DHNA has also been implicated in colitis regression (Okada et al., 2013). Moreover, short-chain fatty acids (SCFAs) from strains TL133 and TL142 have been demonstrated to play a role in inducing apoptosis of tumor cell lines (Lan et al., 2007b(Lan et al., , 2008;;Cousin et al., 2016).

In view of the fact that EVs are emerging as important carriers of biologically active cargos and that vesiculogenesis is a generally occurring phenomenon, we hypothesized that they might explain some of the probiotic properties of P. freudenreichii. For the first time, our findings have shown that this species produces EVs, and we have characterized their physiochemical, biochemical and functional features. We report that P. freudenreichii CIRM-BIA 129-derived EVs are implicated in its anti-inflammatory properties, via modulation of the NF-κB pathway, thus building on knowledge regarding this important probiotic bacterium.

MATERIALS AND METHODS

Bacterial Strain and Growth Conditions

Propionibacterium freudenreichii CIRM-BIA 129 (equivalent to the ITG P20 strain) was supplied, stored and maintained by the CIRM-BIA Biological Resource Center (Centre International de Ressources Microbiennes-Bactéries d'Intérêt Alimentaire, INRAE, Rennes, France). P. freudenreichii CIRM-BIA 129 and its isogenic P. freudenreichii CIRM-BIA 129 slpB mutant strain (Do Carmo et al., 2017) were cultured in cow milk ultrafiltrate (UF) supplemented with 100 mM sodium lactate and 5 g L -1 casein hydrolysate at 30 • C and without agitation, until stationary phase (72 h of incubation, 2 × 10 9 CFU mL -1 ), as reported previously (Cousin et al., 2012).

Purification of EVs

Cells were pelleted by the centrifugation (6000 g, 15 min, room temperature) of cultures in UF (500 mL) and the supernatant fraction was filtered using 0.22 µm Nalgene top filters (Thermo Scientific) to remove any remaining bacterial cells. The supernatant was then concentrated 1000 times using Amicon ultrafiltration units with a 100 kDa cut-off point in successive centrifugations at 2500 g. The concentrated suspension of EVs was recovered in TBS buffer (Tris-Buffered Saline, 150 mM NaCl; 50 mM Tris-HCl, pH 7.5) and further purified by size exclusion chromatography (qEV original 70 nm; iZON), as recommended by the manufacturer [START_REF] Böing | Single-step isolation of extracellular vesicles by sizeexclusion chromatography[END_REF]. Briefly, 0.5 mL of EV samples was applied to the top of the chromatographic column, followed by TBS buffer for elution. Then, fractions of 0.5 mL were recovered in separate tubes. Fractions 1-6 were discarded as void, EVscontaining fractions (fractions 7-9) were pooled together and the remaining fractions were discarded due to protein contamination or low EV content.

Negative Staining for Transmission Electron Microscopy

To characterize the shape of purified EVs, negative staining electron microscopy was conducted as previously described (Tartaglia et al., 2018). Briefly, a drop of EV solution was applied on a glow-discharged formvar-coated copper EM grid and blotted with a filter paper to remove excess solution. A drop of 2% uranyl acetate was applied to the EM grid, blotted again and finally dried before imaging under a Jeol 1400 transmission electron microscope (JEOL Ltd.) operating at 120 Kv.

Nanoparticle Tracking Analysis for EV Size and Concentration Assessment

To measure the size and concentration of EVs, nanoparticle tracking analysis (NTA) was performed at 25.0 • C using a NanoSight NS300 instrument (Malvern Panalytical) with a sCMOS camera and a Blue488 laser [START_REF] Mehdiani | An innovative method for exosome quantification and size measurement[END_REF]. Samples were applied in constant flux with a syringe pump speed of 50. For each measurement, 5×60-s videos were recorded with camera level 15. Other parameters were adjusted accordingly to achieve image optimization.

Proteomic Analysis

Three independent biological replicates of purified EVs from P. freudenreichii CIRM-BIA 129 (approximately 1 µg per sample) and the whole cell proteome were separated and visualized using 12% SDS-PAGE [START_REF] Laemmli | Cleavage of structural proteins during the assembly of the head of bacteriophage T4[END_REF] and silver staining (Switzer et al., 1979). Next, EV proteins were hydrolyzed with trypsin for NanoLC-ESI-MS/MS analysis, as previously described (Gagnaire et al., 2015;Huang et al., 2016). Briefly, gel pieces were washed with acetonitrile and ammonium bicarbonate solution and dried under a vacuum. Next, in-gel trypsin digestion was performed overnight at 37 • C and stopped with trifluoroacetic acid (Sigma-Aldrich). After digestion, the peptides were identified from the MS/MS spectra using X!TandemPipeline software (Langella et al., 2017) and searches were performed against the genome sequence of P. freudenreichii CIRM-BIA 129. The database search parameters were specified as follows: trypsin cleavage was used and the peptide mass tolerance was set at 10 ppm for MS and 0.05 Da for MS/MS. Methionine oxidation was selected as a variable modification. For each peptide identified, a maximum e-value of 0.05 was considered to be a prerequisite for validation. A minimum of two peptides per protein was imposed, resulting in a false discovery rate (FDR) of 0.15% for protein identification.

Proteomic data were further analyzed and visualized using Python libraries Pandas, NumPy, Matplotlib, and Seaborn. Functional annotations and Clusters of Orthologous Groups (COGs) were obtained using the eggNOG-mapper v2 web tool (Huerta-Cepas et al., 2017, 2019), while proteins and gene data were retrieved from NCBI and Uniprot (Bateman, 2019). Subcellular location prediction was performed with CELLO2GO (Yu et al., 2014) and the prediction of lipoproteins was conducted using PRED_LIPO (Bagos et al., 2008).

Prediction of Protein-Protein Interactions

In order to screen for potential biological functions of EVs, the prediction of interactions between EV proteins and human proteins was carried out. The reference human proteome was retrieved from Uniprot (UP000005640) and contained 74,788 protein sequences. For the first method of prediction, EVs and human proteins were submitted to the InterSPPI web server (Lian et al., 2019), a machine-learning-based predictor. For the second method of prediction, a interologbased approach was used [START_REF] Folador | An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage[END_REF], establishing homology relationships with the interactions described in the String and Intact databases [START_REF] Kerrien | The IntAct molecular interaction database in 2012[END_REF]Szklarczyk et al., 2017). The resulting interactions were filtered according to the prediction scores (intersppi: minimum score of 0.9765, for a specificity 0.99; interolog: minimum score of 500 out of 1000). Next, the dataset was reduced to a canonical representation, only retaining the human protein isoform appearing in highest-scoring interactions and removing nonreviewed human proteins. For the predicted interactions, the human counterpart was programmatically mapped to KEGG pathways in order to identify the associated functional modules. Data analysis and graphic representations were obtained using Python libraries Pandas, Seaborn, Matplotlib, Matplotlib_venn, and Cytoscape software [START_REF] Shannon | Cytoscape: a software environment for integrated models of biomolecular interaction networks[END_REF].

Culture of Eukaryotic Cells

HT-29 human epithelial cells were used for immunomodulation assays; either the parental lineage (HT-29, colon adenocarcinoma; ATCC HTB-38) or a lineage transfected with the secreted alkaline phosphatase (SEAP) reporter gene for NF-kB activation monitoring (HT-29/kb-seap-25) (Lakhdari et al., 2010). The reporter HT-29/kb-seap-25 cells were cultured in RPMI-Glutamine medium (Sigma-Aldrich), supplemented with 10% fetal bovine serum (Corning), 1% non-essential amino acids, 1% sodium pyruvate, 1% HEPES buffer (Thermofisher Scientific), and 1% penicillin-streptomycin (Lonza) according to Lakhdari et al. (2010). The parental HT-29 cells were cultured in high-glucose DMEM medium (Dominique Dutscher) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (Do Carmo et al., 2017). For subcultures, cells were rinsed with DPBS (Thermofisher Scientific) and detached with a trypsin (0.05%) -EDTA (0.02%) solution (Sigma). Periodically, 100 µg/mL Zeocin (Invivogen) was applied to the HT-29/kb-seap-25 cell culture in order to maintain selective pressure on the cells containing the transfected plasmid.

NF-κB Modulation Assays

HT-29/kb-seap-25 cells were seeded on 96-well plates at 3 × 10 4 cells/well and incubated for 24 h at 37 • C under 5% CO 2 prior to stimulation (Lakhdari et al., 2010). Monolayer confluence was checked under the microscope before and after every stimulation. TNFα (1 ng mL -1 ; PeproTech), IL-1β (1 ng mL -1 ; Invivogen), and LPS from E. coli O111:B4 (1 ng mL -1 ; L3024-5MG, Sigma-Aldrich) were used to induce inflammation. The cells were stimulated with the samples (controls and EV preparations) and inflammation inducers for 24 h. The supernatants from all the wells were then revealed with Quanti-Blue TM reagent (Invivogen) to assess SEAP activity. Cell proliferation was evaluated under all conditions using the CellTiter 96 R AQueous One Solution Cell Proliferation Assay (MTS, Promega), according to the manufacturer's instructions. Absorbance was read at 655 nm for the SEAP activity assay and at 490 nm for the MTS assay using a Xenius (SAFAS Monaco) microplate reader. For surface protein assays, 10 9 EV ml -1 were applied directly or after incubation at 37 • C for 1 h in the presence or absence of proteinase K (20 µg mL -1 ; Qiagen), in order to evaluate a possible role for EV surface proteins in immunomodulation.

ELISA Cytokine Assay

Enzyme-linked immunosorbent assay (ELISA) tests tests were performed under the same conditions as the NF-κB modulation assays using HT-29 parental cells. The human IL-8/CXCL8 DuoSet (R&D Systems) kit was used to evaluate cell culture supernatants after stimulation, according to the manufacturer's instructions. Absorbance was read at 450 nm using a Xenius (SAFAS Monaco) microplate reader.

Statistical Analysis

All experiments were conducted independently and in triplicate at least, and the results are expressed as means ± standard deviations of biological replicates. For absorbance measurements, the values were normalized by the control condition. The differences between groups were verified using one-way ANOVA followed by Tukey's multiple comparisons test with GraphPad Prism (GraphPad Software, San Diego, CA, United States).

RESULTS

Propionibacterium freudenreichii Produces Extracellular Vesicles

In order to determine whether P. freudenreichii produced EVs, strain CIRM-BIA 129 was cultured in cow milk UF medium and EVs were purified from the cell-free supernatants of stationary phase cultures. As a control, we checked that EVs were absent from the UF medium before being used for bacterial culture. Visualization by electron microscopy revealed that P. freudenreichii strain CIRM-BIA 129 produced EVs of a typical shape, i.e., spherical cup-shaped structures (Figure 1A). Size characterization by NTA showed that the EVs presented a monodisperse profile with modal diameter of 84.80 ± 2.34 nm (Figure 1B).

P. freudenreichii-Secreted EVs Contain a Functionally Diverse Set of Proteins, Including Immunomodulatory Proteins

Cargo proteins associated with P. freudenreichii-secreted EVs were determined by Nano LC-ESI-MS/MS analysis from three biological replicates of EVs. A total of 319 proteins was identified consistently in EVs derived from UF medium cultures (Supplementary Table S1), which corresponds to 11% of the whole theoretical proteome of P. freudenreichii CIRM-BIA 129. Figure 2A confirmed a much more complex proteome in the whole cell P. freudenreichii extract than in the EV extract.

The proteins associated with these EVs were distributed between most of the COG categories (Figure 2B). The majority of proteins could be assigned to COGs related to the general category of "metabolism, " e.g., energy production and conversion (C, 14.8%), amino acid transport and metabolism (E, 10.4%), and carbohydrate transport and metabolism (G, 8%). The most common COGs in the general category of "information, storage and processing" were translation, ribosomal structure and biogenesis (J, 7.1%) and transcription (K, 4.2%). Finally, cell wall/membrane/envelope biogenesis (M, 5.3%) and posttranslational modifications, protein turnover and chaperones (O, 5%), were the most frequently counted COGs in the general category of "cellular processes and signaling." Interestingly, several proteins previously identified as important actors in immunomodulation were packed within EVs: enolase (Eno1, PFCIRM129_06070), aconitase (Acn, PFCIRM129_04640), glutamine synthetase (GlnA1, PFCIRM129_11730), glucose-6phosphate isomerase (Gpi, PFCIRM129_10645), triosephosphate isomerase (Tpi1, PFCIRM129_11290), the surface-layer proteins SlpB (PFCIRM129_00700) and SlpE (PFCIRM129_05460), the BopA solute binding protein (PFCIRM129_08120), internaline A (InlA, PFCIRM129_12235), the hypothetical protein PFCIRM129_10785 and the GroL2 chaperonin (PFCIRM129_10100) (Le Maréchal et al., 2015;Deutsch et al., 2017;Do Carmo et al., 2017).

Regarding predictions of the subcellular localization of the proteins, they were mainly predicted to be cytoplasmic (n = 239), but some membrane (n = 51), and extracellular (n = 29) proteins were also identified (Figure 2C). Lipoprotein signal peptides were also predicted in a small fraction of the proteins (n = 22). Other proteins were predicted to contain a secretory signal peptide I (n = 22) and transmembrane motifs (n = 34) (Figure 2D). Regarding the protein abundance index [START_REF] Ishihama | Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein[END_REF], it displayed a non-normal distribution with a tail of highly expressed proteins, including immunomodulatory SlpB, 60 kDa chaperonin 2 (GroL2) and enolase 1 (Eno1), as well as cold shock-like protein CspA, iron/manganese superoxide dismutase (SodA), cysteine synthase 2 (Cys2), alkyl hydroperoxide reductase subunit C (AhpC), and malate dehydrogenase (Mdh) (Figure 2E).

The Proteins From P. freudenreichii-Secreted EVs Potentially Interact With Human Immunomodulatory Proteins

The proteins found in P. freudenreichii-derived EVs were tested against the human proteome in silico in order to predict their interactions. Machine learning-based (intersppi) and homologybased (interolog) methods were employed for this task. There was a considerable difference regarding the number of predicted interactions which depended on the method used. Intersppi predicted 117,513 interactions, while the interolog method predicted 2,890 interactions; there were 143 common interactions between the two methods (Figure 3A and Supplementary Tables S2-S4). Regarding interacting bacterial proteins, 115 proteins appeared exclusively in intersppi interactions, 51 proteins appeared exclusively in interolog interactions and 90 proteins appeared in interactions predicted by both methods (Figure 3B and Supplementary Table S5). As for interacting human proteins, the majority was predicted by the intersppi method only (6,883 proteins), whereas 747 proteins appeared exclusively in interolog interactions and 611 proteins appeared in interactions shared by both methods of prediction (Figure 3C and Supplementary Table S6).

The predicted interactions mapped to diverse KEGG terms, including metabolism, signal transduction, infectious diseases and the immune system (Supplementary Figure S1). Interestingly, the nuclear factor NF-κB p105 subunit (NFKB1, P19838) was the most frequent interacting human protein considering common and intersppi-exclusive interactions. The subnetwork of interactions mapping to the KEGG NF-κB signaling pathway (Figure 3D) included both interactions with adapter molecules (e.g., TICAM1, TICAM2, TRAF6) and Tolllike receptors (e.g., TLR1, TLR4, TLR5, TL6), as well as NFKB1 itself (Supplementary Figures S2,S3). These results therefore indicated some interesting potential roles for EV proteins in immunomodulation that need to be verified experimentally.

P. freudenreichii-Secreted EVs Modulate the NF-κB Pathway in a Dose and Inducer-Dependent Manner

In view of the finding that in silico predictions showed interactions between EV proteins and human immunomodulatory proteins (and particularly NFKB1), together with previous evidence of immunomodulatory roles for P. freudenreichii, we investigated this potential in vitro. A cellular reporter system regarding modulation of the regulatory activity of the NF-κB transcription factor (HT-29/kb-seap-25) was therefore employed (Lakhdari et al., 2010). Cells were kept in contact with proinflammatory inducers (LPS, TNF-α or IL-1β) and EV preparations, in order to test the ability of EVs to attenuate the induced inflammatory response.

Among cells that were not treated with proinflammatory inducers, their exposure to EVs did not affect the activity of the reporter, keeping a basal level of NF-κB activity (Figure 4A). When HT-29/kb-seap-25 cells were treated with LPS in the absence of EVs, NF-κB activation increased, thus showing LPS-dependent induction of the NF-κB pathway. With the addition of EVs at increasing concentrations, a dosedependent reduction of NF-κB activation was observed. With the highest EV concentration tested (1.0 × 10 9 EVs ml -1 ), NF-κB activation was comparable to that of untreated control cells. Furthermore, when the EV concentration was kept at a constant level, NF-κB modulation was also dependent on the pathway inducer. In the presence of EVs, there was a significant reduction in NF-κB activation in LPS-treated cells, but not in cells treated with other inducers (TNF-α and IL-1β) (Figure 4B).

P. freudenreichii-Secreted EVs Also Modulate IL-8 Release in a Dose and Inducer-Dependent Manner

To further investigate the anti-inflammatory role suggested by the reduction in NF-κB activity, release of the proinflammatory chemokine IL-8 by HT-29 cells was determined in the presence of various EV concentrations and proinflammatory inducers. In the absence of proinflammatory inducers, EVs had no effect on IL-8 release from HT-29 cells (Figure 5A). In the TBS buffer control group, LPS-treated HT-29 cells displayed an increase in IL-8 release when compared to untreated cells, reflecting the LPS-induced proinflammatory effect. In the presence of EVs, a significant and dose-dependent decrease of IL-8 release by LPS-stimulated cells was observed. At the highest concentration (1.0 × 10 9 EVs ml -1 ), IL-8 release was reduced down to a level comparable to that seen in untreated control cells.

Moreover, as in the case of NF-κB activity, the EV-mediated reduction in IL-8 release was specific to treatment with the LPS proinflammatory inducer (Figure 5B). When cells were treated with TNF-α or IL-1β, no effect of EVs could be detected on IL-8 release by HT-29 cells. Taken together, these results showed that P. freudenreichii-derived EVs were endowed with antiinflammatory properties that depended on the concentration and inflammatory stimulus.

P. freudenreichii-Secreted EVs Are Not Cytotoxic Against Intestinal Epithelial Cells

In order to ensure that reductions in NF-κB activation and IL-8 release were associated with regulatory activity and not simply with cell death, an MTS cell proliferation assay was also performed (Figure 6). This assay showed that there was no significant difference in cell viability between the control and test groups in terms of both parental HT-29 and HT-29/kb-seap-25 reporter cells. ). The values are normalized by the control conditions (control buffer or stimulation by an inducer in the absence of EVs). There are no statistically significant differences between group means as determined by one-way ANOVA followed by Tukey's multiple comparisons test.

Influence of Surface Proteins on NF-κB Modulation

Further tests were performed to determine whether the modulation of NF-κB by P. freudenreichii EVs was influenced by a surface-layer protein (SlpB), recognized as being immunomodulatory in the studied strain (Deutsch et al., 2017;Do Carmo et al., 2017; Figure 7). EVs derived from an isogenic mutant P. freudenreichii CIRM-BIA 129 slpB and proven not to produce this specific protein, displayed a partial reduction of NF-κB activation, when compared to wild type-derived EVs. This suggested that SlpB plays a fundamental role in EV modulation of the NF-κB pathway, but it is likely that other important effectors also need to be considered.

Furthermore, in order to determine whether NF-κB pathway modulation by EVs is dependent on proteins that might be exposed on the EV surface, they were treated with proteinase K prior to the cellular assays (Figure 7). The incubation of EVs at 37 • C for 1 h, in the presence or absence of proteinase K, did not result in significant differences in NF-κB activation, when compared to the wild-type untreated control. This suggested that these immunomodulatory effectors may be inside EVs and not at their surface.

DISCUSSION

Although research on bacterial EVs focused initially on Gramnegative and pathogenic bacteria, a major increase in the number of studies involving Gram-positive and probiotic bacteria has been seen during the past decade [START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]Molina-Tijeras et al., 2019). That is in line with the growing recognition of the widespread occurrence and diverse functions of EVs (Brown et al., 2015;Liu et al., 2018a,b). In the case of P. freudenreichii, a Gram-positive dairy probiotic bacterium, a recent study indicated the presence of an extracellular structure resembling a potential EV, which bulged out from the membrane of P. freudenreichii strain JS22 [START_REF] Frohnmeyer | Secretome profiling of Propionibacterium freudenreichii reveals highly variable responses even among the closely related strains[END_REF], suggesting that this species might produce EVs. Nevertheless, ours is the first complete report to have identified the occurrence of EVs in P. freudenreichii strain CIRM-BIA 129, and included their physicochemical, biochemical and functional characterization. The EVs thus identified displayed the basic features of extracellular prokaryotic membrane vesicles, i.e., a nanometric size range, a cup-shaped morphology and a spherical structure (Raposo and Stoorvogel, 2013;[START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]Tartaglia et al., 2018).

CIRM-BIA 129 EVs carry a diverse set of proteins that represent a broad range of biological functions. More than half of these proteins are related to metabolism, so that, initially, they may not appear to exert specific functions outside bacterial cells. However, the transport of metabolism-related proteins by EVs may represent a mechanism of functional exchange and complementation in the context of bacterial communities. For example, members of the Bacteroides genus share enzymes with the microbiome through OMVs, so as to contribute to the degradation of complex polysaccharides [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF]Rakoff-Nahoum et al., 2014;Lynch and Alegado, 2017). On the other hand, some of these metabolism-related proteins, from either P. freudenreichii CIRM-BIA 129, or from other strains in the species, have been implicated in interactions with the host. This is the case of enolase (Eno1, PFCIRM129_06070) and aconitase (Acn, PFCIRM129_04640) (Deutsch et al., 2017). Others, such as glutamine synthetase (GlnA1, PFCIRM129_11730), glucose-6-phosphate isomerase (Gpi, PFCIRM129_10645) and triosephosphate isomerase (Tpi1, PFCIRM129_11290), have also been described as moonlighting proteins, with adhesin functions, in other species [START_REF] Kainulainen | Glutamine synthetase and glucose-6-phosphate isomerase are adhesive moonlighting proteins of Lactobacillus crispatus released by epithelial cathelicidin LL-37[END_REF]; Rodríguez-Bolaños and Perez-Montfort, 2019). As well as metabolism-related proteins, other proteins packed into EVs are also related to interactions between P. freudenreichii and the host: SlpB (PFCIRM129_00700) and SlpE (PFCIRM129_05460) surface-layer proteins, the BopA solute binding protein (PFCIRM129_08120), internaline A (InlA, PFCIRM129_12235), the hypothetical protein PFCIRM129_10785 and the GroL2 chaperonin (PFCIRM129_10100) (Le Maréchal et al., 2015;Deutsch et al., 2017;Do Carmo et al., 2017). It has been shown that SlpB mediates the adhesion of P. freudenreichii CIRM-BIA 129 to intestinal epithelial HT29 cells (Do Carmo et al., 2017), reduces LPS-induced IL-8 expression in HT-29 cells (Do [START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF] and participates in the induction of anti-inflammatory cytokines such as IL-10 in human peripheral blood mononuclear cells, mesenteric lymph nodes cells and epithelial HT29 cells (Foligne et al., 2010;Le Maréchal et al., 2015;Deutsch et al., 2017;Rabah et al., 2018a). Also, inactivation of the gene encoding SlpE suppresses IL-10 induction by P. freudenreichii CIRM-BIA 129 (Deutsch et al., 2017). It is interesting to note that SlpB, Eno1 and GroL2 were found to be some of the most abundant EV proteins, which may be a further indication of the potentially beneficial roles exerted by CIRM-BIA 129 EVs on host cells. This is in accordance with the potential immunomodulatory role suggested for these proteins in a multi-strain and multi-omics study (Deutsch et al., 2017).

In order to investigate potential roles for EVs in the context of host-microorganism interactions, we conducted in silico predictions of interactions between the bacterial proteins identified in EVs and human proteins. We employed a machine learning-based method (intersppi), which relies on protein sequences and network properties, to identify patterns of classification into groups of interacting or non-interacting protein pairs (Lian et al., 2019). We also used a homologybased prediction of interacting pairs (interolog) that relies on the mapping of similarities to interaction databases and interaction transfers among homologs [START_REF] Folador | An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage[END_REF]. Our results revealed a considerable difference between the methods in the number and nature of the interactions predicted. This difference was mainly due to the fact that the interolog methodology depends on data availability in interaction databases, such as STRING and INTACT, from which homology mapping is performed (Wang et al., 2012). The data may therefore have been biased by experimental work focused on specific aspects and organisms, as well as the conservation of proteins, which may affect homology identification. For example, metabolismrelated proteins tend to be more conserved, so they are therefore overrepresented in interactions predicted using the interolog methodology. On the other hand, intersppi aims to capture more general patterns of protein binding, enabling ab initio predictions that are not reliant on the availability of prior data. However, it is still susceptible to training data bias and a certain probability of false positives (Keskin et al., 2016).

The predicted interactions mapped to several immunologyrelated KEGG terms, such as signal transduction, infectious diseases and the immune system, thus shedding light on a possible immunomodulatory role for P. freudenreichii EVs. These predictions corroborate some previous findings which associated the P. freudenreichii bacterium with immunomodulatory roles (Foligne et al., 2010;Deutsch et al., 2017;Do Carmo et al., 2017;[START_REF] Frohnmeyer | Secretome profiling of Propionibacterium freudenreichii reveals highly variable responses even among the closely related strains[END_REF]Rabah et al., 2018a). Interestingly, the predicted data also suggested that this immunomodulation could involve the NF-κB pathway, since the nuclear factor NF-κB p105 subunit (NFKB1, P19838) was the most frequent interacting human protein according to the intersppi predictions and also the interactions shared between the two methods. Regarding bacterial proteins previously reported as being immunomodulatory, Acn, Eno1, GroL2, HtrA1, SlpA, SlpB, and SlpE were predicted to interact directly with NFKB1 using the intersppi method. In addition, the interaction between Eno1 and NFKB1 was also predicted by interolog. Other proteins in the NF-κB pathway were also predicted to interact with Acn: the inhibitor of nuclear factor kappa-B kinase subunits alpha (IKKA) and beta (IKKB), as well as TIR domain-containing adapter molecule 1 (TICAM1). Moreover, interactions with tolllike receptors (e.g., TLR1, TLR4, TLR5, TL6) suggested that the immunomodulatory role of EVs might also occur at the receptor level. Briefly, PPI data (i.e., highly interacting NFKB1 protein, interactions involving bacterial proteins previously demonstrated to be immunomodulatory and interactions mapping to KEGG terms related to the immune response) suggested an ability of EVs produced by P. freudenreichii CIRM-BIA 129 to exert an immunomodulatory effect via the NF-κB pathway, which then needed to be confirmed in vitro.

When tested on HT-29 intestinal epithelial cells, we found that EVs exert an inhibitory effect on LPS-induced IL-8 secretion, as was previously observed with intact P. freudenreichii CIRM-BIA 129 cells (Do Carmo et al., 2019). Their effect was dose-dependent and unrelated to side effects of EVs on cell viability. This immunomodulatory response was mediated through modulation of the regulatory activity of the NF-κB transcription factor. The NF-κB pathway leads to the upregulation of proinflammatory genes, being targeted by diverse pathogens and probiotic bacteria [START_REF] Hayden | NF-B, the first quarter-century: remarkable progress and outstanding questions[END_REF][START_REF] Mitchell | Signaling via the NFκB system[END_REF]. Therefore, some probiotics can downregulate the production of these proinflammatory cytokines, acting at different steps along the NF-κB pathway: L. rhamnosus GG and Lactobacillus delbrueckii subsp. bulgaricus downregulated p38 and IkB expression, respectively (Giahi et al., 2012), L. plantarum LM1004 and Lactobacillus casei DN-114 001 modulated the nuclear translocation of NF-κB (Tien et al., 2006;[START_REF] Lee | Micronized and heat-treated Lactobacillus plantarum LM1004 stimulates host immune responses via the TLR-2/MAPK/NF-κB signalling pathway in vitro and in vivo[END_REF], Bacteroides thetaiotaomicron promoted the nuclear export of RelA [START_REF] Kelly | Commensal anaerobic gut bacteria attenuate inflammation by regulating nuclear-cytoplasmic shutting of PPAR-γ and ReIA[END_REF], VSL#3 inhibited proteasome degrading activity [START_REF] Petrof | Probiotics inhibit nuclear factor-κB and induce heat shock proteins in colonic epithelial cells through proteasome inhibition[END_REF], and Bifidobacterium breve C50 decreased the phosphorylation of p38-MAPK and IkappaB-alpha [START_REF] Heuvelin | Mechanisms involved in alleviation of intestinal inflammation by bifidobacterium breve soluble factors[END_REF]. Probiotic strains were also shown to exhibit immunoregulatory effects via the modulation of TLR negative regulators of the NF-κB pathway [START_REF] Lakhdari | Identification of NF-κB modulation capabilities within human intestinal commensal bacteria[END_REF][START_REF] Kanmani | Functional capabilities of probiotic strains on attenuation of intestinal epithelial cell inflammatory response induced by TLR4 stimuli[END_REF]. Specifically, Lactobacillus helveticus SBT2171 was reported to inhibit NF-κB activation by inducing A20 expression via TLR2 signal in LPS-stimulated peritoneal macrophages [START_REF] Kawano | Lactobacillus helveticus SBT2171 induces A20 expression via toll-like receptor 2 signaling and inhibits the lipopolysaccharide-induced activation of nuclear factor-kappa b and mitogen-activated protein kinases in peritoneal macrophages[END_REF]. Lactobacillus acidophilus was shown to regulate the inflammatory response induced by enterotoxigenic E. coli K88 in piglets, notably through the increased expression of Tollip, IRAK-M, A20, and Bcl-3 [START_REF] Li | Lactobacillus acidophilus alleviates the inflammatory response to enterotoxigenic Escherichia coli K88 via inhibition of the NF-κB and p38 mitogen-activated protein kinase signaling pathways in piglets[END_REF]. Lactobacillus paracasei was associated to the inhibition of pro-inflammatory cytokines production by monocyte-macrophages, via the induction of A20, SOCS1, SOCS3, and IRAK3 [START_REF] Sun | Lactobacillus paracasei modulates LPS-induced inflammatory cytokine release by monocyte-macrophages via the up-regulation of negative regulators of NF-kappaB signaling in a TLR2-dependent manner[END_REF].

The precise mechanism by which EVs produced by P. freudenreichii CIRM-BIA 129 modulate NF-κB activity still needs to be elucidated, but our work has provided some clues. EVs do not exert an immunomodulatory effect when the NF-κB pathway is stimulated by other inducers (such as TNF-α and IL-1β), indicating that EVs mitigate the activation of NF-κB via the LPS signaling pathway. These different ligands bind to specific receptors at the cell surface in order to activate the NF-κB pathway. Once bound, they use the same signal transduction mechanisms to activate the pathway. Therefore, EVs probably act at a level of the NF-κB pathway that is not common to the three inducers: TLR4, CD14, LBP, MD-2 and the TICAM1 and TICAM2 TIR domain-containing adaptor proteins. It is interesting to note that in silico prediction of protein-protein interactions predicted interactions with TLR4, the cell-surface receptor for LPS, but not with the TNF-α and IL-1β receptors. Furthermore, proteinase-treated EVs conserved their immunomodulatory properties. Therefore, the EV-triggered inhibition of NF-κB does not signal through binding between EV surface exposed proteins and the LPS receptor (i.e., LBP, TLR-4, CD14, MD-2). One can suppose that non-proteinaceous inhibitors may also be involved. However, to date, the immunomodulatory properties of P. freudenreichii have been associated with proteins. Moreover, EVs produced by a slpB P. freudenreichii mutant partly lost their immunomodulation effects, indicating that several proteins, including SlpB, play a direct or indirect role in the anti-inflammatory response mediated by EVs. These results also suggest that these immunomodulatory proteins are packed into EVs and are not surface exposed. It cannot be excluded that immunomodulatory proteins interact directly with LPS receptors after their release from the lysis of EVs in the vicinity of cells, but it does seem more likely that they target specific intercellular components of the LPS-induced NF-κB pathway after EV uptake by membrane fusion or endocytosis [START_REF] Mulcahy | Routes and mechanisms of extracellular vesicle uptake[END_REF][START_REF] Jefferies | To infect or not to infect: molecular determinants of bacterial outer membrane vesicle internalization by host membranes[END_REF].

It is now well recognized that EVs act as proxies of their parental cells. Accordingly, P. freudenreichii and its EVs share common features, notably their immunomodulatory effects mediated by SlpB. Whether P. freudenreichii also signals through the same pathway as its EVs constitutes a basis for further studies. Likewise, whether P. freudenreichii EVs exert immunomodulatory effects in vivo, as has been shown for the bacterium (Foligne et al., 2010;Rabah et al., 2018a;Do Carmo et al., 2019) is a challenging question that should be addressed in future research and with respect to potential EVbased probiotic applications. In sum, this study reflects efforts to demonstrate the widespread occurrence and functional diversity of EVs, particularly in a group of emerging EVs research, such as Gram-positive probiotic bacteria. It also contributes to a clearer understanding of the mechanisms associated with the probiotic traits of P. freudenreichii while opening up possibilities of employing bacterial-derived EVs for functional cargo delivery and for the development of novel probiotic products.

Environmental conditions modulate the protein content and immunomodulatory activity of extracellular vesicles produced by the probiotic Propionibacterium freudenreichii

As demonstrated in Chapter 3, P. freudenreichii CIRM-BIA129 cultured in UF medium produced EVs with anti-inflammatory activity towards human epithelial cells. The biophysical, biochemical and functional properties of those UF-derived EVs were comprehensively characterized. However, mounting evidence suggests that several factors, including growth media, growth phase, salts, pH and exposure to antibiotics, can modulate the properties of bacteria-derived EVs. Therefore, we aimed next to investigate the influence of growth media in the modulation of P. freudenreichii-derived EVs properties. Accordingly, P. freudenreichii CIRM-BIA129 was grown in either UF or YEL medium until the beginning of stationary phase. Then, the same protocol for EV isolation was applied. That consisted in culture centrifugation and filtration to recover cell-free supernatants, concentration and application in a SEC column. The resulting samples were characterized by NTA measurements, immunomodulatory activity assays, qualitative and quantitative proteomics. These analyses revealed differences between UF and YEL-derived EVs in abundance, diameter, anti-inflammatory activity, protein identities and abundances. Moreover, it pinpoints how the different abundances of EVassociated proteins could affect the pattern of interactions between bacterial and human proteins, thus affecting immunomodulatory activities.

Therefore, this comparative study allowed a better comprehension of how the optimization of bacterial growth conditions could be applied to modulate EVs properties, even in modulating their content and enhancing their anti-inflammatory properties. This chapter introduces the second original research article derived from this thesis, published in the journal Applied and Environmental Microbiology (DOI: 10.1128/AEM.02263-20), which is presented in the next pages. ABSTRACT Propionibacterium freudenreichii is a probiotic Gram-positive bacterium with promising immunomodulatory properties. It modulates regulatory cytokines and mitigates the inflammatory response in vitro and in vivo. These properties were initially attributed to specific bacterial surface proteins. Recently, we showed that extracellular vesicles (EVs) produced by P. freudenreichii CIRM-BIA129 mimic the immunomodulatory features of parent cells in vitro (i.e., modulating NF-κB transcription factor activity and interleukin-8 release), which underlies the role of EVs as mediators of the probiotic effects of the bacterium. The modulation of EV properties, and particularly of those with potential therapeutic applications, such as the EVs produced by the probiotic P. freudenreichii, is one of the challenges in the field to achieve efficient yields with the desired optimal functionality. Here, we evaluated whether the culture medium in which the bacteria are grown could be used as a lever to modulate the protein content and, hence, the properties of P. freudenreichii CIRM-BIA129 EVs. The physical, biochemical, and functional properties of EVs produced from cells cultivated on laboratory yeast extract lactate (YEL) medium and cow milk ultrafiltrate (UF) medium were compared. UF-derived EVs were more abundant and smaller in diameter, and they displayed more intense anti-inflammatory activity than YEL-derived EVs. Furthermore, the growth media modulated EV content in terms of both the identities and abundances of their protein cargos, suggesting different patterns of interaction with the host. Proteins involved in amino acid metabolism and central carbon metabolism were modulated, as were the key surface proteins mediating host-propionibacterium interactions.

IMPORTANCE Extracellular vesicles (EVs) are cellular membrane-derived nanosized particles that are produced by most cells in all three kingdoms of life. They play a pivotal role in cell-cell communication through their ability to transport bioactive molecules from donor to recipient cells. Bacterial EVs are important factors in hostmicrobe interactions. Recently, we have shown that EVs produced by the probiotic P. freudenreichii exhibited immunomodulatory properties. We evaluate here the impact of environmental conditions, notably culture media, on P. freudenreichii EV production and function. We show that EVs display considerable differences in protein cargo and immunomodulation depending on the culture medium used. This work offers new perspectives for the development of probiotic EV-based molecular delivery systems and reinforces the optimization of growth conditions as a tool to modulate the potential therapeutic applications of EVs.

KEYWORDS EV, NF-κB, anti-inflammatory, comparative proteomics, growth conditions, immunomodulation, membrane vesicle, protein-protein interactions Address correspondence to Eric Guédon, eric.guedon@inrae.fr.

P robiotic organisms are increasingly being used in medical and technological con- texts because of their health benefits (1,2). Among these organisms, dairy bacteria such as Propionibacterium freudenreichii are of particular value because of their longterm safe consumption and economic interest (3,4). P. freudenreichii is a Gram-positive bacterium that is used traditionally as a Swiss-type cheese starter (5) but has also been studied with respect to the production of vitamin B 12 (6,7) and organic acids (8,9). Furthermore, this bacterium can survive for a considerable period in vitro and under cheese-making conditions (10). It can also survive and adapt metabolically to animal and human gastrointestinal tracts (11)(12)(13) and has been identified in the fecal samples from a discrete cohort of human preterm breastfed infants (14,15).

P. freudenreichii has long been studied for its probiotic properties, such as modulating the composition of the microbiota, antitumor activity, and immunomodulation (3). This species mainly modulates the composition of the microbiota through a bifidogenic effect, promoted by abundantly produced metabolites such as 1,4-dihydroxy-2naphthoic acid (DHNA) and 2-amino-3-carboxy-1,4-naphthoquinone (ACNQ) (16)(17)(18)(19). Its antitumor activity is linked to the production of short-chain fatty acids such as propionate and acetate (20)(21)(22)(23)(24). Regarding immunomodulation, some proteins in this species, and particularly surface layer protein B (SlpB), have been associated with a reduction in the proinflammatory cytokines released and/or an increase in the release of anti-inflammatory cytokines both in vitro and in vivo (19,(25)(26)(27)(28)(29).

In addition to bacterial surface exposure, we recently showed that some immunomodulatory P. freudenreichii proteins are found associated with extracellular vesicles (EVs) (30). EVs are spherical nanometric structures produced by cells in the three domains of life (31). They are composed of lipid bilayers and an internal functional cargo, implicated in several biological processes that include interactions with host cells (31)(32)(33)(34). The production of EVs has been demonstrated in several probiotic bacteria species (35)(36)(37)(38)(39)(40)(41). In the case of P. freudenreichii, EVs produced by the CIRM-BIA129 strain had a proteome predicted to interact with key protein components of the human inflammatory response, which was reinforced by the ability of EVs to reduce both the activity of the transcription factor NF-κB and interleukin-8 (IL-8) release in an intestinal epithelial cell model (30). The modulation exerted by ultrafiltrate (UF)-derived EVs occurred when the inflammatory response was induced by bacterial lipopolysaccharide (LPS) but not by other inducers, such as tumor necrosis factor alpha (TNF-a) and IL-1b. In addition, the EV-borne SlpB protein was shown to play a role in this immunomodulation, because EVs secreted by a DslpB mutant displayed reduced anti-inflammatory activity (30).

As well as their functional role in modulating interactions with the host, bacterial EVs are themselves modulated by specific conditions. In other words, environmental shifts impact the production and functional properties of EVs (42). For example, exposure to antibiotics induces vesiculation in several Staphylococcus aureus strains (43) and modifies the protein content of EVs secreted by Acinetobacter baumannii DU202 (44) and Campylobacter jejuni 81-176 (45). Iron-limiting conditions were shown to be associated with quantitative changes to the EV proteome of pathogenic and probiotic Escherichia coli strains (46,47), of Helicobacter pylori 60190 (48), and of Mycobacterium tuberculosis H37Rv (49). Salt stress was also shown to be linked to drastic changes in the protein content and proinflammatory activity of EVs secreted by Listeria monocytogenes 10403S (50,51). The EV proteome or its activity also changed in line with growth phases in Bacillus subtilis 168 (52), Rhizobium etli CE3 (53), Pseudomonas aeruginosa PAO1 (54), and H. pylori 26695 (55) and with the composition of media in Gallibacterium anatis 12656-12 (56) and Pseudomonas putida KT2440 (57). The bacterial properties of EVs were also affected by pH shifts (58) and exposure to epibromohydrin (59) or cannabidiol (60) and to other environmental conditions (61).

The impact of environmental conditions on EV production and function still remains poorly documented in probiotic bacteria, although it may represent a tool to modulate the properties of EVs and have potential therapeutic applications. Therefore, our aim was to investigate whether different growth conditions impact the production of P. freudenreichii-derived EVs as well as modulate their properties. In the present study, we have shown that P. freudenreichii CIRM-BIA129 cultured in milk UF or yeast extractlactate (YEL) growth medium produced EVs with distinct physicochemical, biochemical, and functional properties. In particular, UF-and YEL-derived EVs displayed considerable differences in protein cargo and immunomodulation. This study is a comparative analysis of the properties of the EVs produced by P. freudenreichii under various growth conditions and will contribute to understanding how probiotic traits are affected by different contexts. It also suggests interventional opportunities for the engineering of EV content and activity, enabling improvements to their potential technological and therapeutic roles.

RESULTS

The production of EVs by P. freudenreichii is dependent on growth conditions. During this work, we tried to determine whether environmental conditions, i.e., different growth media, affected the production of EVs by P. freudenreichii CIRM-BIA129. For that purpose, the sizes and concentrations of EVs purified from UF and YEL culture media were evaluated. Both conditions yielded EVs with a monodisperse size distribution, although YEL-derived EVs were less abundant than UF-derived EVs (Fig. 1A). They also displayed differences in diameter: UF-derived EVs had a modal size of 80.06 6 2.606 nm and a mean size of 100.9 6 3.204 nm, whereas YEL-derived EVs were significantly larger (P , 0.05), with a modal size of 89.72 6 6.324 nm and a mean size of 117.9 6 7.856 nm (Fig. 1B andC). The total concentration of EVs was significantly higher (P , 0.01) from UF (2.164 Â 10 12 6 1.383 Â 10 11 EVs ml 21 ) than from YEL (9.394 Â 10 11 6 1.846 Â 10 11 EVs ml 21 ) medium (Fig. 1D). Finally, the EV relative yield of CIRM-BIA129 (i.e., the amount of recovered EVs normalized by the amount of bacterial cells at sampling time) was more than three times higher from UF than from YEL medium (Fig. 1E).

The biological activity of P. freudenreichii-derived EVs is dependent on growth conditions. EVs derived from the two growth conditions were also evaluated in terms of their biological activity in vitro, i.e., NF-κB activation and IL-8 release. UF-derived P. freudenreichii CIRM-BIA129 EVs had previously been shown to exert an immunomodulatory effect on HT-29 human intestinal epithelial cells via the NF-κB pathway, i.e., modulation of transcription factor NF-κB activity and IL-8 release (30). To determine whether growth conditions also modulate EV activity, we compared the ability of the EVs produced from UF and YEL media to modulate the regulatory activity of the NF-κB transcription factor and the release of IL-8 from human intestinal epithelial cells. For that purpose, NF-κB/SEAP HT-29 cells, which are designed to measure NF-κB regulatory activity (62), and HT-29 parental cells were induced to an inflammatory state by different proinflammatory inducers and treated with EV preparations. Whatever the growth medium, EVs exerted an inhibitory effect on the regulatory activity of the NF-κB transcription factor only when the inflammation pathway was induced by LPS (Fig. 2A). Nevertheless, EV-mediated reduction of the LPS-induced proinflammatory effect was growth medium dependent. Indeed, the anti-inflammatory activity of EVs was significantly more intense with UF-derived than with YEL-derived EVs (P , 0.0001) (Fig. 2A). The specific LPS-induced anti-inflammatory effect of EVs was confirmed by the evaluation of IL-8 release from the HT-29 parental cell line. However, in this case, only UFderived EVs were able to reduce LPS-induced IL-8 release (Fig. 2B). Finally, UF-and YELderived EVs had no cytotoxic effect on the two HT-29 cell lines, indicating that reductions in NF-κB activation and IL-8 release were not associated with cell death (Fig. 2C andD).

EVs produced under different growth conditions contain shared and exclusive proteins. To identify potential factors involved in the differences in biological activity between UF-and YEL-derived EVs, we characterized the protein content of EVs produced under the two growth conditions (see Table S4 in the supplemental material). Qualitative proteomics analysis enabled the identification of 391 proteins, 32 of which were exclusively detected in UF-derived EVs, one was exclusively detected in YEL-derived EVs, and 358 were common to both growth conditions (Fig. 3A). Concerning subcellular localization, all exclusive proteins (UF and YEL exclusive) were predicted to be cytoplasmic. The shared proteins were mainly cytoplasmic (n = 256), but some were also predicted to be membrane proteins (n = 65) or extracellular proteins (n = 37) (Fig. 3B). Predicted lipoproteins were only identified among proteins found under both conditions (n = 25) (Fig. 3C). The frequencies of Clusters of Orthologous Groups (COG) categories were well distributed among shared proteins, mainly being related to metabolism and information processing. UF-exclusive proteins were mainly linked to carbohydrate or amino acid metabolism and DNA processing, while the only YEL-exclusive protein had an unknown function (Fig. 3D).

A comparison of growth conditions reveals several differentially abundant EV proteins. Qualitative proteomic analysis pinpointed proteins specific to YEL-and UF-derived EVs. Further, to better grasp the impact of growth conditions on EV content, the relative abundance of the proteins was analyzed under each condition. Quantitative proteomics showed that the relative abundance of EV proteins could vary as a function of culture medium (Fig. S1). Among the total of 391 identified proteins, the abundances of 164 proteins did not differ between YEL-and UF-derived EVs (P . 0.05), 127 proteins were significantly more abundant in UF-derived EVs (P # 0.05, log [fold change] . 0), and 100 proteins were significantly more abundant in YEL-derived EVs (P # 0.05, log[fold change] , 0) (Fig. 4A). Among the proteins more abundant in UF-derived EVs, the most significant included those related to carbohydrate (LacZ, IolC, and IolE1), amino acid (AroH and Hom), and energy (NirA2) metabolism. Among the proteins more abundant in YEL-derived EVs, the most significant were mainly ribosomal proteins (RplC, RplV, RplT, RplB, and RpsG). Differentially abundant proteins under each condition also included those of unknown function or poorly characterized (PFCIRM129_01355, PFCIRM129_01765, PFCIRM129_02410, and PFCIRM129_10740) (Fig. 4B). Interestingly, among EV proteins undergoing significant changes to their abundance as a function of the culture medium, we identified several that had reportedly been associated with a stress response (Fig. 4C) or immunomodulatory properties (Fig. 4D) in P. freudenreichii or other bacterial species. Functional enrichment analysis (Tables S5 andS6) showed that proteins that were more abundant in UF-derived EVs were mainly related to energy, carbohydrate, amino acid, and sulfur metabolism KEGG pathways, together with COG category C (energy production and conversion). On the other hand, proteins more abundant in YEL-derived EVs were mainly related to glycolysis/gluconeogenesis and ribosome KEGG pathways, together with COG category J (translation, ribosomal structure, and biogenesis) (Fig. 4E). Predictions of interactions highlight the relevant bacterial proteins potentially implicated in immunomodulation. The qualitative and quantitative differences in the protein content of EVs revealed factors that might vary with growth conditions. Our next aim was to investigate whether this variation, when translated into different patterns of interaction, might offer insights into the mechanisms underlying the differential activity of EVs. Therefore, 101 human proteins from the NF-κB pathway and 391 bacterial proteins found in EVs were submitted to the prediction of protein-protein interactions. A total of 893 interactions were considered to be valid (minimum score of 0.9765), involving 49 human and 266 bacterial proteins (Table S7). Next, we performed progressive filtering procedures to select meaningful interactions according to experimental criteria. First, in view of the fact that the anti-inflammatory activity of EVs was LPS specific, we filtered out interactions involving human proteins that were not related to the LPS-induced part of the NF-κB pathway, which resulted in a subnetwork of 357 interactions (Fig. 5A). A further filtering was then performed to retain only human proteins that were specific to the LPS-induced NF-κB pathway, i.e., not related to TNF-a or IL-1b induction. This resulted in 23 interactions, involving just two human proteins: TCAM1 and TLR4 (Fig. 5B). A final filtering was performed in bacterial proteins to retain only those whose differential expression was considered significant (P # 0.05) according to quantitative proteomics (Fig. 5C). Therefore, TLR4 and TCAM1 human proteins were identified within the NF-κB signaling pathway as potential targets for immunomodulation. Moreover, novel bacterial proteins were suggested to be responsible for the differential intensity of the anti-inflammatory response resulting from various growth conditions: pyruvate synthase/pyruvate-flavodoxin oxidoreductase (NifJ1), the NADH-quinone oxidoreductase chain G (NuoG), an uncharacterized protein (PFCIRM129_01355), the GtfB glycosyltransferase (GtfB), a putative DNA polymerase I (PolA), and UvrABC system protein A (UvrA3).

DISCUSSION

Several studies have demonstrated that the production, properties, and roles of bacterial EVs are affected by environmental conditions. Thus, monitoring the environmental conditions under which the bacteria are grown offers a tool to modulate the production and properties of EVs and may have some therapeutic applications (42). We recently reported the production of EVs by the probiotic P. freudenreichii CIRM-BIA129, and our study included their physicochemical and functional characterization (30). In particular, we showed that they displayed anti-inflammatory effects throughout modulation of the regulatory activity of the NF-κB transcription factor and IL-8 release in human epithelial cell models. However, whether the production and immunomodulatory effects of P. freudenreichii-secreted EVs could be modulated by environmental conditions still needed to be determined.

Here, we compared the properties of P. freudenreichii EVs purified from two growth media that are routinely used for P. freudenreichii cultures: cow's milk ultrafiltrate (UF) medium and yeast extract-lactate (YEL) medium (29,63,64). YEL is the gold-standard laboratory medium for propionibacteria. This medium, in addition to yeast extract (providing the necessary growth factors) and sodium lactate (the preferred carbon and energy source), contains peptone (as a nitrogen source) (65). This medium was developed to mimic the growth conditions of propionibacteria in Swiss-type cheeses after fermentation of the cheese curd by lactic acid bacteria. We later developed a milk UF medium to mimic the growth of propionibacteria in fermented milk (63,66). This medium represents the aqueous phase of cow milk, added with casein peptone and sodium lactate. UF and YEL were chosen to compare EV properties because they differentially impact the physiology of the bacterium, notably its growth parameters (biomass and generation time), the pH of the extracellular medium at the end of stationary phase, and cell viability after stress challenges (63,64).

We demonstrated that culture media affect the physical, biochemical, and biological properties of P. freudenreichii-secreted EVs. Notably, compared to EVs recovered from YEL cultures, markedly more were recovered from UF cultures. In some bacterial species, an increase in EV production has been linked to an adaptation to stressful conditions (67)(68)(69). In the case of P. freudenreichii, UF could be considered a more stressful growth condition than YEL, since the bacterium accumulated higher proportions of trehalose during growth in UF than in YEL (64), with this sugar being involved in the response to stress, notably during acid adaptation and osmoadaptation (64,70,71). Moreover, some of the proteins that we found more abundant in UF than YEL-derived EVs were associated in the parent cells with the adaptation of the bacterium to stress (Fig. 4C). Among these, chaperonin GroL2 (GroL2), pyruvate synthase/pyruvate-flavodoxin oxidoreductase (NifJ1), and alkyl hydroperoxide reductase protein C22 (AhpC) were notably associated with the response to acid stress in this bacterium (72). Taken together, these results suggested that EV production in P. freudenreichii is affected by stress conditions and showed that the control of growth conditions might offer a lever to modulate EV production in this probiotic bacterium.

As mentioned above, in addition to impacting the physical properties of EVs, the culture medium also modulates EV content in terms of both the identities and abundances of its protein cargo. When the protein contents were compared, modest differences were observed between YEL-and UF-derived EVs. Indeed, more than 90% (n = 358) of EV proteins were found to be common to the YEL and UF media. Only one protein with an unknown function was specific to YEL, and 32 of the predicted cytoplasmic proteins were found to be exclusive to UF-derived EVs. This notably included several proteins with functions assigned to the COG categories of "replication, recombination, and repair" (L, n = 8), "carbohydrate transport and metabolism" (G, n = 7), and "amino acid transport and metabolism" (E, n = 5). The most important impact of the culture medium concerned the abundance of proteins packed into EVs. The abundance of approximately 60% of EV proteins changed significantly as a function of culture medium. In the case of YEL-derived EVs, the more abundant proteins were mainly related to ribosomes, glycolysis, and gluconeogenesis, which may have been linked to the higher growth rate of P. freudenreichii in YEL medium (63,64). In contrast, UFderived EVs were more enriched in proteins related to pentose phosphate, sulfur, secondary metabolites, inositol phosphate, amino acids, and energy metabolism, which are probably related to stress responses. Although the rules for the cargo selection and sorting into bacterial EVs remain elusive, especially in Gram-positive bacteria (73), it is often thought that the content of EVs reflects the physiology, metabolic status, and biological properties of the cells producing them (32,52). Therefore, modulation of both the presence and abundance of proteins in EVs as a function of culture medium might be related to the level of their synthesis by cells. Indeed, it seems consistent that the relative abundance of a protein in a whole cell would affect its availability to be packed into EVs in the absence of, or in addition to, a selective process (34,74). Accordingly, several proteins associated with the adaptation of the bacterium to UF or YEL medium were identified as being more abundant in UF-or YEL-derived EVs, respectively. For example, some of the proteins specifically present or found to be more abundant in UF-derived EVs were involved in the assimilation of the main carbon sources of UF, including beta-galactosidase (LacZ) (which hydrolyzes lactose into glucose and galactose) and inositol assimilation proteins (IolE1, IolC, IolT3, and IolA). However, one cannot exclude that, in addition to the level of synthesis by cells, some of these proteins are also purposely packed into EVs to perform specific functions, such as host cell interactions (75). To date, the molecular mechanisms that drive the recruitment of proteins into bacterial EVs have remained unclear. Determining which proteins are selected, and how, is of crucial value to identifying those purposely packed into EVs, notably to better understand their biological roles. Recently, we showed that abundance, charge, and subcellular localization could influence the protein availability of the vesicle cargo in S. aureus (74). Whether these rules are also applicable to the EVs secreted by P. freudenreichii still needs to be investigated, but our data may offer an additional opportunity to uncover the mechanisms governing the selection of proteins into EVs.

Finally, the culture medium in which bacteria are grown also modulates the biological activity of CIRM-BIA129 EVs. Indeed, UF-and YEL-derived EVs presented remarkable differences in terms of immunomodulation. We showed that EVs from both origins reduced the activity of NF-κB, a key mediator of inflammatory responses, but at different intensities. This reduction occurred specifically when inflammation was induced by bacterial LPS and was achieved in roughly 74% for UF-derived EVs but only 41% for YEL-derived EVs. This differential EV-mediated modulation of NF-κB activity has profound consequences on the cellular inflammatory response. Indeed, LPS-induced IL-8 release by HT-29 cells was significantly reduced by UF-derived EVs but not by YELderived EVs. To date, the bacterial effectors involved in the immunomodulatory activity of P. freudenreichii-secreted EVs are still unknown. We recently showed that SlpB participates in EV-mediated anti-inflammatory effects in vitro, since EVs derived from a DslpB mutant displayed a partial reduction of NF-κB activation compared to wild-typederived EVs (30). However, it is not known whether the role of SlpB is direct or indirect. Among P. freudenreichii-associated immunomodulatory proteins, SlpB, internaline A (InlA), and the chaperonin GroL2 were more abundant in UF-than in YEL-derived EVs. In contrast, surface-layer protein D (SlpD), the solute binding protein of the ABC transport system (BopA), trypsin-like serine protease (HtrA1), and a protein of unknown function, PFCIRM129_10740, were more abundant in YEL-derived EVs. The differences in abundance of at least one of these recognized immunomodulatory proteins may explain the differential anti-inflammatory activity seen for UF-and YEL-derived EVs (27)(28)(29)76).

One can also suppose that other differentially abundant proteins are implicated in the differential activity of EVs triggered by culture media. These include those bacterial proteins predicted to interact with the human proteins Toll-like receptor 4 (TLR4) and TIR domain-containing adapter molecule 1 (TICAM-1), which are of particular interest. Indeed, these two proteins are involved in the NF-κB signaling pathway induced by LPS but not by TNF-a or IL-1b; therefore, they are good candidates as molecular targets for immunomodulation mediated by P. freudenreichii-derived EVs with LPS-specific induction. Consequently, the abundances of their potential bacterial interacting partners might also explain the differences in EV activity as a function of culture medium. Among the predicted TLR4-and TICAM1-interacting bacterial proteins, six displayed a different abundance pattern between UF-and YEL-derived EVs. These included NifJ1, the NADH-quinone oxidoreductase chain G (NuoG), and an uncharacterized protein (PFCIRM129_01355), which were more abundant in UF-derived EVs, and GtfB glycosyltransferase (GtfB), a putative DNA polymerase I (PolA), and the UvrABC system protein A (UvrA3), which were more abundant in YEL-derived EVs. These proteins have not previously been characterized as being immunomodulatory, but our in silico network predictions suggest they can interact with human proteins with key roles in the inflammatory response; their abundances also could explain why UF-and YEL-derived EVs display distinct patterns of biological activity. Whether these proteins as well as other differentially abundant immunomodulatory proteins participate in the modulation of EV biological activity as a function of culture medium certainly deserves further investigation.

To sum up, our study showed that the probiotic P. freudenreichii CIRM-BIA129 produced EVs with distinct properties depending on the culture medium in which the bacterium was grown and reinforced the importance of environmental conditions to the properties of EVs. Although little is known about the mechanisms that underpin the modulation of Gram-positive bacterial EV production, content, and activity, they respond definitively to environmental stimuli in the case of P. freudenreichii. Specifically, in the context of probiotic bacteria, the optimization of medium composition might represent a tool to improve the beneficial activity of EVs and enable the development of EV-based therapeutic applications, such as functional foods with improved properties or pure EV formulations. Comprehensive studies might elucidate the relationships between specific medium components and the vesicular export of immunomodulatory proteins, enabling an improvement to the anti-inflammatory properties of EVs.

MATERIALS AND METHODS

Bacterial cultures. P. freudenreichii CIRM-BIA129 (equivalent to the ITG P20 strain, provided by CNIEL) was supplied, stored, and maintained by the CIRM-BIA Biological Resource Center (Centre International de Ressources Microbiennes-Bactéries d'Intérêt Alimentaire, INRAE, Rennes, France). The bacteria were grown under two conditions: in yeast extract-lactate medium (YEL) or cow milk ultrafiltrate medium (UF), which was further supplemented with 100 mM sodium lactate and 5 g liter 21 casein hydrolysate, as previously described (63,65). For both conditions, incubation was at 30°C, without agitation, until the start of stationary phase (2 Â 10 9 bacteria ml 21 for UF and 3 Â 10 9 bacteria ml 21 for YEL), as reported elsewhere (63). The culture starting volume was 500 ml for each biological replicate.

Purification of EVs. Bacterial cultures (500 ml) were centrifuged at room temperature (6,000 Â g, 15 min) and the supernatants filtered through 0.22-mm top filters (Nalgene, Thermo Scientific). The supernatants were then concentrated 1,000-fold using 100-kDa ultrafiltration units (Amicon, Merck Millipore) by successive centrifugations (2,500 Â g). The concentrated suspension of EVs was recovered in TBS buffer (Tris-buffered saline; 150 mM NaCl, 50 mM Tris-Cl, pH 7.5) and further purified by size exclusion chromatography (qEV original, 70 nm; iZON) as recommended by the manufacturer (30,77). The resulting fractions containing EVs in TBS buffer were pooled (1.5 ml) and further concentrated to achieve approximately 50 ml using 10-kDa centrifugal filter units (Amicon, Merck Millipore). The aliquots were stored at 220°C or used immediately.

Characterization of EVs. The characterization of EVs in terms of size and concentration was performed by nanoparticle tracking analysis using a NanoSight NS300 (Malvern Panalytical), equipped with a sCMOS camera, Blue488 laser, and NTA 3.3 Dev Build 3.3.104 software. The experiments were conducted at 25°C, with a camera level of 15 and a syringe pump speed of 50. Concentration, focus, and other parameters were adjusted accordingly for each experimental group. A detection threshold of 3 was employed to determine valid particles. EV homogeneity and integrity were also confirmed by negative staining electron microscopy (data not shown).

Evaluation of EV activity in vitro. Two HT-29 human colon adenocarcinoma cell lines were employed to characterize the activity of EVs in vitro, as previously reported (30). Briefly, the parental HT-29 intestinal epithelial cells (ATCC HTB-38) were used to measure IL-8 release (IL-8/CXCL8 DuoSet; R&D Systems), and the lineage transfected with the secreted alkaline phosphatase (SEAP) reporter system (HT-29/kb-seap-25) was used to monitor NF-κB activity (Quanti-Blue reagent; Invivogen) (62). HT-29/kbseap-25 cells were cultured in RPMI-glutamine medium (Sigma-Aldrich) supplemented with 10% fetal bovine serum (Corning), 1% nonessential amino acids, 1% sodium pyruvate, 1% HEPES buffer (Thermofisher Scientific), and 1% penicillin-streptomycin (Lonza) (62). The parental HT-29 cells were cultured in high-glucose Dulbecco's modified Eagle medium (DMEM) (Dominique Dutscher) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (76). The cells were treated with TBS buffer as a control or the EV preparations (1.0 Â 10 9 EVs ml 21 ) purified from UF or YEL cultures. To induce inflammation, the cells were also treated with TNF-a (1 ng ml 21 ; PeproTech), IL-1b (1 ng ml 21 ; Invivogen), LPS from Escherichia coli O111:B4 (1 ng ml 21 ; Sigma-Aldrich), or the TBS control. After seeding in 96-well plates (3 Â 10 4 cells/well) and stimulation with the samples, the cells were incubated at 37°C, under 5% CO 2 , for 24 h. Cell confluence was verified under the microscope before and after stimulation. Cell viability after stimulation was investigated using the CellTiter 96 AQueous one-solution cell proliferation assay (MTS; Promega). Absorbance was measured with a Xenius (SAFAS Monaco) microplate reader at 655 nm for the SEAP (NF-κB) activity assay, 450 nm for the IL-8 enzyme-linked immunosorbent assay, and 490 nm for the MTS cell viability assay.

Identification and quantification of proteins using mass spectrometry. One microgram of EV was dissolved in SDS and subjected to SDS-PAGE for a short period, allowing the entry of the protein into 2 to 3 mm of separating gel. The gel pieces were subjected to in-gel trypsinolysis followed by peptide extraction, as described previously (30,72,78). After digestion, the peptides were stored at 220°C until further analysis. Nano-liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments were performed as previously described (30,79). The peptides were identified from MS/MS spectra using X! TandemPipeline software (80), and searches were performed against the genome sequence of P. freudenreichii CIRM-BIA129 (GenBank accession no. NZ_HG975455). The database search parameters were specified as the following: trypsin cleavage was used, and peptide mass tolerance was set at 10 ppm for MS and 0.05 Da for MS/MS. Methionine oxidation was selected as a variable modification. For each peptide identified, an E value lower than 0.05 was considered a prerequisite for validation. A minimum of two peptides per protein was imposed, resulting in a false discovery rate (FDR) of ,0.15% for protein identification. Each peptide identified by tandem mass spectrometry was quantified using the free MassChroQ software (81) before data treatment and statistical analysis with R software (R 3.4; Project for Statistical Computing). A specific R package, called MassChroqR (v0.4.3), was used to automatically filter dubious peptides and group the peptide quantification data into proteins. Two different and complementary analytical methods were used, based on peak counting or XIC (extracted ion current). For peak counting, variance analysis was performed on proteins with a minimum peak ratio of 1.5 between both culture conditions. Proteins with an adjusted P value of ,0.05 were considered significantly different. For XIC-based quantifications, normalization was performed to take account of possible global quantitative variations between LC-MS runs. Peptides shared between different proteins were automatically excluded from the data set, as were peptides present in fewer than two of the three biological replicates. Missing data were then imputed from a linear regression based on other peptide intensities for the same protein. Analysis of variance was used to determine proteins whose abundance differed significantly between growth conditions. Proteomic analysis. Protein subcellular localizations were predicted by Cello2GO (82); lipoproteins were predicted by PRED-LIPO (83) and Clusters of Orthologous Groups (COG) categories, and KEGG Pathways were predicted by eggNOG-mapper v2 (84,[START_REF] Huerta-Cepas | Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper[END_REF]. The Venn diagram, heatmaps, and volcano plot were conceived using Python's Matplotlib-venn 0.11.5, Seaborn 0.10.0, and Bioinfokit 0.7 packages, respectively.

Functional enrichment analysis. For functional enrichment analysis, the lists of up-and downregulated proteins were submitted to the g:Profiler web server [START_REF] Raudvere | g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)[END_REF][START_REF] Reimand | Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap[END_REF], together with an ortholog-based Gene Matrix Transposed (GMT) file (see Files S1 and S2 in the supplemental material) constructed from KEGG pathways and COG whole-proteome annotation categories obtained from eggNOG-mapper v2 (84,[START_REF] Huerta-Cepas | Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper[END_REF]. Protein lists were ordered by decreasing relative level of abundance, and a significance threshold (adjusted P value) of 0.05 was adopted. For visualization, adjusted P values of 1 Â 10 26 or lower were capped to this value.

Prediction of protein-protein interactions. Human and bacterial protein sequences were retrieved from the UniProt and NCBI databases, respectively. Bacterial sequences identified in EVs derived from at least one of the growth conditions, as well as the human sequences corresponding to KEGG's NF-κB pathway, were submitted to the InterSPPI web server for the prediction of interactions [START_REF] Lian | Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties[END_REF]. Only interactions with a score of 0.9765 or higher were considered valid, with a specificity of 0.99. These interactions were gradually filtered so that human counterparts corresponded to, or were specific to, the LPSinduced portion of the NF-κB pathway, and/or bacterial proteins displayed a significant fold change in their expression according to quantitative proteomics. The lists of human proteins used for filtering are available in Tables S1 to S3. Predicted interactions and protein annotations were processed using the Pandas Python package [START_REF] Mckinney | Data structures for statistical computing in Python[END_REF]. Visualization of the interaction network was achieved using Cytoscape software [START_REF] Shannon | Cytoscape: a software environment for integrated models of biomolecular interaction networks[END_REF].

Statistical analysis. All experiments were performed independently and at least in triplicate. The results are presented as means 6 standard deviations. Absorbance measurements were normalized to the control conditions. The differences between experimental groups were analyzed using the Mann-Whitney test or two-way analysis of variance (ANOVA) followed by Tukey's multiple-comparison test.

Chapter 5 -Additional results

The extracellular vesicles from Propionibacterium freudenreichii obtained by different purification methods and growth conditions share a large core proteome

The studies presented in the previous chapters showed that P. freudenreichii produced EVs with anti-inflammatory roles and that their properties depended on whether the bacteria were grown in UF or YEL culture media. Therefore, the optimization of bacteria growth conditions represented a tool for the modulation of EVs properties, including their anti-inflammatory activity towards human epithelial cells. In these previous studies, EVs were obtained by size exclusion chromatography (SEC), which is a size-based purification method. Nevertheless, in spite of the increasing recognition of SEC as a high-quality straightforward method for EV purification, the most widely used methods for bacteria EV purification are still those based on ultracentrifugation (UC). Therefore, we aimed to investigate if P. freudenreichii-derived EVs purified by this method would present similar properties, particularly regarding protein content.

P. freudenreichii was grown in UF and YEL media until the beginning of the stationary phase, when the cultures were centrifuged, filtered and the supernatant were concentrated. Next, purification was conducted with a series of ultracentrifugation steps, including one with a sucrose density gradient. Microscopic and size characterization confirmed EV purification. Shotgun proteomics allowed the identification of a diverse set of proteins, also depending on bacteria growth conditions. A comparative analysis of the protein content of UC-and SEC-derived EVs, isolated from cultures in both UF and YEL showed that, although some proteins were specific of each condition, 308 were shared among all conditions. This core proteome was enriched in proteins related to energy and carbon metabolism, ribosomal structure and biogenesis, quorum sensing, protein export and peptidoglycan biosynthesis.

Therefore, these results offer some clues of the functions conserved in P. freudenreichii-derived EVs, eliminating growth conditions and purification biases. They are presented in detail in the next pages.

Introduction

Extracellular vesicles (EVs) are nano-sized membranous particles that transport biomolecules implicated in intercellular communication (Brown et al., 2015;Woith et al., 2019;[START_REF] Dagnelie | Bacterial extracellular vesicles: A new way to decipher host-microbiota communications in inflammatory dermatoses[END_REF][START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. EVs were consistently reported as an export system in species from all kingdoms of life, including bacteria [START_REF] Deatheragea | Membrane vesicle release in bacteria, eukaryotes, and archaea: A conserved yet underappreciated aspect of microbial life[END_REF]Woith et al., 2019;[START_REF] Nagakubo | Cracking Open Bacterial Membrane Vesicles[END_REF]. A diverse set of functions was attributed to bacterial EVs, including quorum sensing [START_REF] Mashburn | Membrane vesicles traffic signals and facilitate group activities in a prokaryote[END_REF], biofilm formation [START_REF] Flemming | Biofilms: an emergent form of bacterial life[END_REF]Caruana and Walper, 2020), competition [START_REF] Li | Gram-Negative Bacteria Produce Membrane Vesicles Which Are Capable of Killing Other Bacteria[END_REF], nutrition [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF]Prados-Rosales et al., 2014), defense [START_REF] Manning | Contribution of bacterial outer membrane vesicles to innate bacterial defense[END_REF]Lee et al., 2013b), pathogenesis [START_REF] Pathirana | Bacterial membrane vesicles: Biogenesis, immune regulation and pathogenesis[END_REF][START_REF] Cecil | Outer Membrane Vesicle-Host Cell Interactions[END_REF] and probiosis [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Molina-Tijeras et al., 2019). Among beneficial bacteria, EVs with antiinflammatory activity were reported in several species, including Akkermansia muciniphila [START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF], Bacteroides fragilis [START_REF] Shen | Outer Membrane Vesicles of a Human Commensal Mediate Immune Regulation and Disease Protection[END_REF], Echerichia coli Nissle 1917[START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF], Bifidobacterium bifidum [START_REF] López | Treginducing membrane vesicles from Bifidobacterium bifidum LMG13195 as potential adjuvants in immunotherapy[END_REF], Lactobacillus species [START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF][START_REF] Vargoorani | Stimulatory effects of Lactobacillus casei derived extracellular vesicles on toll-like receptor 9 gene expression and cytokine profile in human intestinal epithelial cells[END_REF] and Propionibacterium freudenreichii (Rodovalho et al., 2020b(Rodovalho et al., , 2020a)).

Research on bacterial EVs relies greatly on the available purification methods, since the culture media used to grow bacteria are complex and heterogeneous, containing EVs in varying concentrations, bacterial cells, spent medium components, and secreted molecules, such as proteins, lipids and metabolites [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]Orench-Rivera and Kuehn, 2016). After separation from bacterial cells and concentration, EVs must be further purified to deplete medium contaminants by methods such as ultracentrifugation (UC) and size-exclusion chromatography (SEC) (Brown et al., 2015;Dauros Singorenko et al., 2017;[START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF][START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF]. UC-based methods use high-speed centrifugation to differentially sediment EVs and contaminants [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]Monguió-Tortajada et al., 2019a;[START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF], whereas SEC is based on the differential elution of EVs and contaminants through a size-selective porous polymeric matrix [START_REF] Nordin | Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties[END_REF][START_REF] Benedikter | Ultrafiltration combined with size exclusion chromatography efficiently isolates extracellular vesicles from cell culture media for compositional and functional studies[END_REF][START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a, 2019b). Although SEC is an approach of rising relevance, UC is the most used method for bacterial EV purification to date (Dauros Singorenko et al., 2017;[START_REF] Monguió-Tortajada | Extracellular-Vesicle Isolation from Different Biological Fluids by Size-Exclusion Chromatography[END_REF], 2019a;Zhang et al., 2019a).

Our previous study demonstrated that the Gram-positive probiotic P. freudenreichii CIRM-BIA129 produces EVs with anti-inflammatory activity via NF-κB pathway modulation towards cultured human intestinal epithelial cells, together with EVs biophysical and proteomic characterization (Rodovalho et al., 2020b). We then showed that the properties of P. freudenreichii-derived EVs, including their anti-inflammatory activity and protein content, depended on the culture media in which the bacterium was grown (either UF, milk ultrafiltrate; or YEL, yeast extract-lactate) (Rodovalho et al., 2020a). In both studies, EVs were purified by SEC. However, as UC is the standard method for the purification of bacteria-derived EVs, we aimed to analyze whether this method is also suitable for the purification of P. freudenreichii-derived EVs and to compare their biophysical properties and protein content.

Here, we show that EVs were successfully obtained by the application of UC to the concentrated supernatants of P. freudenreichii CIRM-BIA129 cultures both in UF and YEL media. The comparison with SEC-purified EVs showed that their proteomic content varied, although it was possible to identify a vesicular core proteome of 308 proteins, indicating that some functions are conserved in P. freudenreichii-derived EVs obtained in four different conditions. These conserved functions included carbon metabolism, peptidoglycan biosynthesis, ribosome, protein export, quorum sensing and immunomodulation. In addition to broadening the toolset for the purification of P. freudenreichii-derived EVs, this study identifies EV proteins that are conserved across different conditions of EV isolation, which could be relevant for the identification of biomarkers of purification quality and could also provide insights into mechanisms of EV biogenesis and cargo sorting.

2.

Material and Methods

Culture conditions

The strain Propionibacterium freudenreichii CIRM-BIA 129 (ITG P20) was supplied by the CIRM-BIA Biological Resource Center (Centre International de Ressources Microbiennes-Bactéries d'Intérêt Alimentaire, INRAE, Rennes, France). P. freudenreichii was cultured either in cow milk ultrafiltrate (UF) supplemented with 100 mM sodium lactate and 5 g L -1 casein hydrolysate or in yeast extract-lactate (YEL), both at 30°C, without agitation, until the beginning of the stationary phase (2 x 10 9 bacteria mL -1 for UF and 3 x 10 9 bacteria mL -1 for YEL) (Malik et al., 1968;Cousin et al., 2012c).

Purification of EVs

Bacterial cultures were centrifuged (6000 g, 15 min) and filtered (0.22 mm, Nalgene top filters, Thermo Scientific) at room temperature. Cell-free supernatants were then concentrated in successive centrifugations using Amicon ultrafiltration units (100-fold, 100 kDa, 2500 g). The concentrated supernatants were then submitted to a series of 3 ultracentrifugation rounds: 1) the first one to pellet EVs and discard the supernatant containing contaminant proteins (150,000xg, 120 min, 4°C), 2) the second one for a higher-quality density-based separation, with the application of the resuspended pellets onto the top of a discontinuous sucrose gradient (8-68%) (100,000xg, 150 min, 4°C), and 3) the third one to eliminate the excess of sucrose from pooled EV-containing fractions (150,000xg, 120 min, 4°C), a washing step using TBS buffer (150 mM NaCl; 50 mM Tris-Cl, pH 7.5) (Tartaglia et al., 2018(Tartaglia et al., , 2020)). The final samples were then resuspended in TBS buffer and used immediately or stored at -20°C.

Biophysical characterization of EVs

The size and concentration of EVs were evaluated by nanoparticle tracking analysis (NTA), using a NanoSight NS300 instrument (Malvern Panalytical), equipped with a sCMOS camera and a Blue488 laser. All measures were performed at 25°C, in constant flux, with a syringe pump speed of 50. For each sample, 5 videos of 60 s were recorded, under camera level 15. Other parameters were adjusted accordingly to achieve image optimization [START_REF] Vestad | Size and concentration analyses of extracellular vesicles by nanoparticle tracking analysis: a variation study[END_REF]Rodovalho et al., 2020b). Transmission electron microscopy (TEM) was performed to evaluate the morphology, homogeneity and integrity of EVs, as previously described (Tartaglia et al., 2018;Rodovalho et al., 2020b). Briefly, glow-discharged formvar-coated copper EM grids were used for the application of a drop of EV solution and the negative staining was conducted with the application of 2% uranyl acetate to the grid. Between these steps, the grids were blotted with filter paper to remove the excess of solution. After drying, the grids were imaged using a Jeol 1400 transmission electron microscope (JEOL Ltd.) operating at 120 Kv.

Mass spectrometry and protein identification

The identification of proteins in EV samples was performed by in-gel trypsinolysis, peptide extraction and nano liquid chromatography tandem mass spectrometry (nano-LC-ESI MS/MS) analysis, as previously described (Gagnaire et al., 2015;Huang et al., 2016a;Gaucher et al., 2020a;Rodovalho et al., 2020b). The software X! TandemPipeline was used to identify peptides from MS/MS spectra (Langella et al., 2017) and the searches were performed against the genome of P. freudenreichii CIRM-BIA129 (Accession: NZ_HG975455). The database search parameters included trypsin cleavage, peptide mass tolerance set at 10 ppm for MS and 0.05 Da for MS/MS, and methionine oxidation as a variable modification. The E-value threshold for peptide identification was set to 0.05 and a minimum of two peptides was required for protein identification, resulting in a false discovery rate (FDR) of < 0.15%.

Bioinformatics analysis

Protein sequences were retrieved from NCBI GenBank (Accession: NZ_HG975455). Ortholog-based annotation was obtained with eggNOG-mapper (Huerta-Cepas et al., 2017, 2019), including the assignment to Clusters of Orthologous Groups (COG) categories and KEGG Pathways terms. Subcellular localization and lipoprotein signals were predicted with Cello2GO (Yu et al., 2014) and PRED-LIPO (Bagos et al., 2008), respectively. Venn diagrams, heatmaps and donut plots were constructed with Python libraries Pandas, Seaborn, Matplotlib_venn and Venn. Proteomic data for SEC-purified EVs were retrieved from our previous publication (Rodovalho et al., 2020a). Functional enrichment analysis were performed with g:profiler (Raudvere et al., 2019;Reimand et al., 2019), as previously described (Rodovalho et al., 2020a), using KEGG terms and adopting a significance threshold (adjusted P-value) of 0.05. Enrichment results were represented as an enrichment network, where enriched pathways were represented as the nodes and the overlaps among them (common proteins) as edges. The enrichment network was constructed from g:profiler results and visualized with EnrichmentMap [START_REF] Merico | Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation[END_REF] and Cytoscape (Shannon et al., 2003). For the network construction, a node FDR q-value threshold of 0.05 was applied for functional category filtering and a threshold of 0.375 was applied for the representation of the similarity between functional categories as edges.

Statistical analysis

All experiments were performed at least in triplicate and the numerical results are expressed as mean ± standard deviation, unless specified otherwise. For significance evaluation, Mann Whitney tests or one-way ANOVA followed by Tukey's multiple comparisons tests were performed. In this study, we evaluated whether P. freudenreichii-derived EVs could be purified by UC. Therefore, a series of UC steps were applied and the resulting samples were analyzed by NTA and TEM. The analysis confirmed the purification of EVs with typical characteristics. Regarding size distribution, both UF-and YEL-derived EVs presented a monodisperse profile (Figure 1A-B). Regarding morphology and integrity, TEM images showed intact and spherical structures (Figure 1A-B, inset). EV modal diameter did not varied significatively between UF-derived EVs (80.40 ± 1.308 nm) and YEL-derived EVs (75.87 ± 2.458 nm) (Figure 1C). A similar tendency was verified for EV mean diameter, with 85.83 ± 1.704 for UF-derived EVs and 90.43 ± 2.875 for YEL-derived EVs (Figure 1D). Regarding EV recovery, although no statistically significative differences were verified, the concentration of was higher for YEL-derived EVs (5.950 x 10 11 ± 1.6 x 10 11 ) than UFderived EVs (2.587 x 10 11 ± 4.1 x 10 10 ) (Figure 1E) and the relative ratio between the amount of recovered EVs and bacterial cells was 1.6 times higher for YEL-derived EVs, relative to UF-derived EVs (Figure 1F). UC-purified EVs were then biochemically analyzed, regarding their protein content. A total of 598 proteins were identified in UC-purified EVs, 560 of which were shared among UF-and YEL-derived EVs. Furthermore, 35 proteins were identified exclusively in UF-derived EVs, whereas 3 proteins were identified exclusively in YEL-derived EVs (Figure 2A). Regarding subcellular localization, the exclusive proteins of UF-and YEL-derived EVs were predicted to be cytoplasmic, whereas the proteins that were common to both conditions were mainly cytoplasmic (n=433), but also included some membrane (n=80) and extracellular (n=47) proteins (Figure 2B). Lipoprotein signals were identified only in proteins that were common to both conditions (UF-and YEL-derived EVs) (n=30) (Figure 2C). Regarding COG categories, YEL-exclusive proteins related to translation (n=2) and carbohydrate metabolism (n=1), whereas UFexclusive proteins were distributed among several categories, most notably translation (n=10), replication (n=7) and coenzyme metabolism (n=5). The proteins common to UF-and YEL-derived EVs were well distributed among COG categories, including mainly metabolism, translation and unknown functions (Figure 2D).

Results

UC is a suitable method for the purification of P. freudenreichii-derived EVs

UC-purified EVs contain a diverse set of proteins

UC-purified EVs are less abundant than SEC-purified EVs

The biophysical properties of UC-purified EVs were then compared with those of SECpurified EVs, of our previous study. Size distributions presented a monodisperse profile, although there were differences in modal sizes and particles concentration, particularly for SEC_UF condition (Figure 3A). Modal size for SEC_YEL group was higher than the others (Figure 3B). Particle concentration (Figure 3C) and relative EV yield (Figure 3D) presented similar tendencies, with SEC-purified EVs being more abundant than UC-purified EVs. SEC_UF condition resulted in a particularly large abundance of EVs, being drastically different from the other conditions (Figure 3C-D).

UC-and SEC-purified EVs share a core proteome of hundreds of proteins

Next, we compared the proteomic content of UC-purified EVs from this study with our previous analysis of the same strain, when EVs were purified by SEC. Therefore, we compared 4 conditions: UF-derived EVs purified by UC (UC_UF), YEL-derived EVs purified by UC (UC_YEL), UF-derived EVs purified by SEC (SEC_UF) and YEL-derived EVs purified by SEC (SEC_YEL). We found out that although some proteins were exclusive to each condition or common to some conditions, 308 proteins were identified in all 4 conditions, comprising the core proteome of the EVs derived from P. freudenreichii CIRM-BIA129 (Figure 4). Likewise, 302 proteins were identified in more than one condition, but not in all conditions, comprising the accessory proteome (Figure 4B). Notably, it included 273 growth-condition independent proteins that were specific to the EV purification method (46 for SEC and 227 for UC). Finally, 42 proteins were restricted to one of the conditions, comprising the exclusive proteome (Figure 4B).

The core proteome of P. freudenreichii-derived EVs is mainly related to metabolic functions

In order to understand what are the conserved features of the protein content of the EVs derived from P. freudenreichii CIRM-BIA129, we further analyzed their core proteome. The assignment of COG categories showed that almost half of the proteins were related to metabolic processes, although some were related to cellular processes, like cell envelope biogenesis; and information storage and processing, including replication, transcription and translation (Figure 5A). Regarding subcellular localization, almost three quarters of the proteins were predicted to be cytoplasmic (70.5%), although membrane (17.5%) and extracellular (12%) proteins were also identified (Figure 5B). Lipoprotein signals were present in only 8.1% of the proteins, whereas transmembrane (12.7%) and secretion (7.5%) signals were also identified (Figure 5C). Functional enrichment analysis with KEGG terms demonstrated these proteins were mainly related to central carbon metabolism, but also implicated in peptidoglycan biosynthesis, ribosome, protein export and quorum sensing (Figure 5D). Furthermore, some of the proteins identified in the EV core proteome were previously identified as immunomodulatory in studies with strains of P. freudenreichii (Table 1).

The accessory proteome of P. freudenreichii-derived unveils proteins exclusive to each purification method

In addition to the core proteome, we also evaluated the accessory proteome, which correspond to proteins present in more than one, but not all conditions. We focused on proteins that were found exclusively on UC-purified or SEC-purified EVs, in order to evaluate differences related to purification methods. Regarding COG categories, the proteins exclusive to both conditions of SEC-purified EVs were mostly related to metabolism, particularly energy, carbohydrate and amino acids metabolism (Figure 6A). UC-purified EVs were more distributed among other COG categories (Figure 6A). Furthermore, there were proportionally more cytoplasmic proteins in UC-purified EVs and more membrane proteins in SEC-purified EVs (Figure 6B-C).

Discussion

We previously purified P. freudenreichii-derived EVs from cultures in UF and YEL by SEC method (Rodovalho et al., 2020b(Rodovalho et al., , 2020a)). In this study, we report their purification using UC method, both from cultures in UF and YEL media. UC-based purification uses high-speed centrifugation to separate EVs and contaminants by differential sedimentation, whereas SEC is based on the differential elution of EVs and contaminants through a porous polymeric matrix with specific molecular weight cut-off [START_REF] Klimentová | Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria[END_REF]Monguió-Tortajada et al., 2019a;[START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF]. SEC is an approach of rising relevance, scalable and fast, that preserves EV structure and activity, although it has limitations in sample volume and copurification of particles of similar sizes, such as viruses [START_REF] Nordin | Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties[END_REF][START_REF] Benedikter | Ultrafiltration combined with size exclusion chromatography efficiently isolates extracellular vesicles from cell culture media for compositional and functional studies[END_REF][START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a;[START_REF] Mcnamara | Modern Techniques for the Isolation of Extracellular Vesicles and Viruses[END_REF]. At the same time, UC is time-consuming and operator-dependent, with risks of inducing EV aggregation and damaging, although it results in reliable high-purity samples, being the most used approach for bacterial EVs purification [START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a). Each method presents advantages and drawbacks, and its application should account for the complex tradeoff between higher EV yields and less amounts of contaminants (Dauros Singorenko et al., 2017). Anyhow, as confirmed by TEM images and NTA measurements, it is possible to recover EVs of typical nanometric sizes and spherical cup-shaped morphology using UC-based purification, similarly to what we previously described for SEC-based purification. Therefore, UC is also a suitable method for the purification of P. freudenreichii-derived EVs.

For biophysical properties of UC-purified EVs, there was no difference between UC_UF and UC_YEL EVs regarding modal diameter and only a subtle difference regarding EV abundance relative to bacterial cells, yet not significative. Contrastingly, our study with SEC-purified EVs showed that SEC_YEL EVs were larger and less abundant than SEC_UF EVs (Rodovalho et al., 2020a). Therefore, important differences were identified between EVs purified by different methods, regarding EV abundance. SEC-purified EVs were more abundant than UC-purified EVs, particularly for UF-derived EVs. The differences in abundances between the two purification methods might be a direct consequence of the efficiency of each method for EV recovery, considering that lower EVs yield may be a downside of the efforts for minimization of contaminant proteins in EV preparations. The differences in sizes for SEC_UF condition could be due to factors such as the purification of different EVs subpopulations by each method, with distinct biophysical and biochemical properties (Dauros Singorenko et al., 2017;[START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF], the modulation of EVs properties triggered by UF culture medium, or a combination of both factors. Another possibility is that the purification methodologies could trigger physical deformations, particularly for UC-based methods [START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF]Monguió-Tortajada et al., 2019a), thus altering EVs sizes and abundances. Although the purification methods impacted on EVs biophysical properties, there were no abnormal differences and EVs with typical biophysical properties were retrieved in all the four analyzed conditions.

Similar to what we previously verified for SEC-purified EVs, bacteria growth conditions also modulated the protein content of UC-purified EVs. There were 35 proteins exclusive to UC_UF EVs and 3 proteins exclusive to UC_YEL EVs, although the majority of 560 proteins were common to both conditions of EV preparation. In our previous study with SEC-purified EVs, 32 proteins were exclusive to SEC_UF EVs and 1 protein was exclusive to SEC_YEL EVs, with 358 proteins common to both conditions (Rodovalho et al., 2020a). Therefore, although a similar distribution was verified among exclusive and common proteins, 50% more proteins were identified in UC-purified EVs, in comparison to SEC-purified EVs. The proteins exclusively found associated to UC-purified EVs did not show apparent functional particularities regarding COG categories, but were predicted to be of cytoplasmic origin in a higher proportion than SECpurified EVs. Again, the reason could be the purification of distinct EVs subpopulations by the two methods, which would result in protein content variations (Dauros Singorenko et al., 2017;[START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF]. Another possibility is that UC-based purification could be less efficient for P. freudenreichii-derived EVs, thus resulting in the co-purification of more contaminant proteins relative to SEC-based purification [START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF], as suggested by the increased number of cytoplasmic proteins, which could be a result of cell lysis. However, cell lysis also triggered EV biogenesis in bacteria [START_REF] Turnbull | Explosive cell lysis as a mechanism for the biogenesis of bacterial membrane vesicles and biofilms[END_REF][START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF], which hinders such comparisons. Further studies with the optimization of parameters such as molecular weight cutoff for SEC and density gradient composition for UC, as well as single EV characterization, could elucidate the occurrence of EVs subpopulations and critically evaluate the purity of the samples (Dauros Singorenko et al., 2017;[START_REF] Gho | Emergent properties of extracellular vesicles: A holistic approach to decode the complexity of intercellular communication networks[END_REF][START_REF] Mol | Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation[END_REF].

Nonetheless, 308 proteins were consistently identified in EVs from both purification methods and growth conditions, indicating that a particular set of proteins -the EV core proteome -is invariably present in P. freudenreichii-derived EVs, considering the four analyzed conditions. This is a robust and representative set of proteins to be used in further analysis of P. freudenreichii EV proteome, since it is less susceptible to purification and growth biases. The initial characterization of this core proteome showed that some proteins predicted to be extracellular or associated to the membrane were identified, although the majoritarian composition was of cytoplasmic proteins (more than 70%), which is consistent with several proteomic analysis of EVs derived from Gram-positive bacteria (Lee et al., 2009;Brown et al., 2015;Kim et al., 2015b;[START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF].

Regarding the functional aspects of the core proteome, some of the identified proteins were previously associated to immunomodulation in P. freudenreichii. Those included Enolase 1 (Eno1), Aconitase (Acn), 60 kDa chaperonin 2 (GroL2), Surface-layer proteins B (SlpB) and E (SlpE), and a hypothetical protein (PFCIRM129_10785) (Deutsch et al., 2017;do Carmo et al., 2019). Importantly, we demonstrated that SlpB was partly involved in the immunomodulatory activity of SEC-purified EVs (Rodovalho et al., 2020b), as its key role in the interaction with the host has been demonstrated for the bacterial cells as well (do [START_REF] Carmo | Propionibacterium freudenreichii surface protein SlpB is involved in adhesion to intestinal HT-29 cells[END_REF][START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF]. Whether UC-purified EVs also present an immunomodulatory activity should be further investigated, but the identification of these proteins in the core proteome is a promising evidence. Moreover, proteins with functions related to peptidoglycan metabolism were identified in the core proteome, including the transferases MurG (659917354, CDP49364.1) and MurA (659917556, CDP49091.1); endopeptidases such as the secreted cell-wall peptidase of the NlpC/P60 family (659918631, CDP48034.1) and the hypothetical proteins PFCIRM129_03060 (659918230, CDP48413.1) and PFCIRM129_10650 (659917426, CDP49227.1); the transpeptidases cell division protein FtsI (659917360, CDP49370.1) and penicillin-binding protein A (659916821, CDP49790.1); and the carboxypeptidase (659916853, CDP49718.1). These identifications reinforce a hypothesis for EVs biogenesis in Gram-positive bacteria, that involves cell-wall remodeling via enzymatic action [START_REF] Toyofuku | Prophage-triggered membrane vesicle formation through peptidoglycan damage in Bacillus subtilis[END_REF][START_REF] Briaud | Extracellular Vesicle Biogenesis and Functions in Gram-Positive Bacteria[END_REF]. The core proteome was also enriched in metabolic enzymes and ribosomes components, which could have a role as public goods for the bacterial population, serving as metabolic and structural complements for individual bacterial cells (Rakoff-Nahoum et al., 2014;[START_REF] Valguarnera | Surface Exposure and Packing of Lipoproteins into Outer Membrane Vesicles Are Coupled Processes in Bacteroides[END_REF]. In other environments, such as dairy matrices with multiple strains or the human gastrointestinal tract, the export of such proteins might exert a crucial role in adaptation and interspecies interactions (Rakoff-Nahoum et al., 2014;[START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]. That is also true for other enriched terms in the core proteome, such as protein export and quorum sensing, which also mediate bacterial interactions and comprise transporter and signal recognition proteins with important roles in adaptation.

Overall, we demonstrated that, similar to SEC, UC is also suitable for the purification of EVs from cultures of P. freudenreichii in UF and YEL media. Moreover, UC-purified EVs presented biophysical properties that were less variable according to growth conditions than those of SECpurified EVs. Nonetheless, the protein content varied according to growth conditions in UCpurified EVs, and their protein content was more extensive than SEC-purified EVs. Finally, the combination of the proteomic dataset from the four studied conditions allowed the identification of 308 invariably occurring proteins. This core proteome comprises a more representative dataset of the proteins from P. freudenreichii-derived EVs and probably relates to their essential roles, including immunomodulation, carbon metabolism, peptidoglycan biosynthesis, ribosome, protein export and quorum sensing. The further analysis and complementation of this core proteome are promising for the elucidation of key aspects of P. freudenreichii-derived EVs, including mechanisms of biogenesis, cargo sorting and interactions with the host.

Chapter 6 -General discussion and perspectives

The ubiquitous production of EVs and their role as an export system for interkingdom communication are being increasingly reported in the scientific literature (Gill et al., 2019;Woith et al., 2019). Here, we demonstrated that the Gram-positive probiotic P. freudenreichii also produces EVs with immunomodulatory role in human-bacteria communication. Our study focused on the biophysical and proteomic characterization of P. freudenreichii-derived EVs, as well as their functional investigation with interactomics predictions and in vitro immunological assays. Furthermore, we demonstrated that bacteria growth conditions and EV purification methods impact on EVs properties.

What are the mechanisms of EV-mediated immunomodulation?

Regarding the functional investigations, our data showed that SEC-derived EVs exerted an anti-inflammatory role towards cultured human intestinal epithelial cells, via the reduction of NF-κB activity and of IL-8 release. Moreover, this reduction was verified specifically when cells were induced to an inflammatory state by bacterial LPS. Therefore, we supposed that EVs acted on points of the NF-κB pathway that are induced specifically by LPS, and not by the other tested inductors (TNFα and IL-1β). As members of the LPS-induced NF-κB pathway, the human proteins toll-like receptor 4 (TLR4) and TIR domain-containing adapter molecule 1 (TICAM-1) were regarded as potential targets for EV-mediated immunomodulation. TLR4 is a transmembrane protein, whereas TICAM-1 is a cytoplasmic protein. Thus, it is also important to determine whether EVs act intracellularly or extracellularly, relative to the receptor cells.

EVs were reported to interact with surface proteins from the receptor cells, docking to the cell surface [START_REF] French | Extracellular vesicle docking at the cellular port: Extracellular vesicle binding and uptake[END_REF]. Accordingly, P. freudenreichii-derived EVs could interact with the extracellular domain of TLR4 via EV surface components (e.g., proteins) or other cytoplasmic components after EV lysis. However, our data showed that EV surface proteolysis did not abolish their immunomodulatory roles, suggesting that the second option is more probable. Bacterial EVs were also reported to entry into eukaryotic cells via different mechanisms, including direct membrane fusion and endocytosis (O' Donoghue and Krachler, 2016;[START_REF] Bajic | Proteomic profile of extracellular vesicles released by Lactiplantibacillus plantarum BGAN8 and their internalization by non-polarized HT29 cell line[END_REF]. Therefore, EV cargo could mediate the immunomodulation after entry into the host cell, interacting with the intracellular domain of TLR4 or with the protein TICAM-1. Another possibility is a more indirect role, in which EVs could regulate immunomodulation in other ways and the negative modulation of NF-κB pathway would be rather a consequence. For all that, further studies are necessary to elucidate the precise mechanisms of action of P. freudenreichii-derived EVs, including in silico structural approaches to analyze PPIs in higher resolution (Keskin et al., 2016) and experimental approaches to investigate if EV integrity is required for cargo delivery [START_REF] Bonsergent | Content release of extracellular vesicles in a cell-free extract[END_REF], fluorescence tracking of internalization and cargo delivery [START_REF] Ofir-Birin | Monitoring Extracellular Vesicle Cargo Active Uptake by Imaging Flow Cytometry[END_REF], as well as inhibition of specific uptake pathways in eukaryotic cells [START_REF] Bajic | Proteomic profile of extracellular vesicles released by Lactiplantibacillus plantarum BGAN8 and their internalization by non-polarized HT29 cell line[END_REF]. Nonetheless, as the mechanisms of immunomodulation are not fully elucidated for P. freudenreichii bacterial cells, our study with P. freuderenichii-derived EVs opens new perspectives. Whether P. freudenreichii also signals through the same pathway as its EVs constitutes a basis for further studies.

Which EVs effectors are implicated in immunomodulation?

Regarding the bacterial effectors of EV-mediated immunomodulation, our data indicated that SlpB protein is partly involved, since EVs derived from the slpB mutant presented a less intense reduction of NF-κB activity. That is in accordance with the crucial role of SlpB in the bacterium-mediated immunomodulation (do [START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF]. However, as EV-mediated reduction in NF-κB activity was not completely abolished without SlpB, other proteins might be involved. Our analysis that coupled interactomics predictions, quantitative proteomics and functional assays comparing UFand YEL-derived EVs purified by SEC resulted in a small list of bacterial proteins that could interact directly with TLR4 and/or TICAM-1, thus mediating the inhibition of LPSinduced NF-κB pathway. This list includes pyruvate synthase/pyruvate-flavodoxin oxidoreductase (NifJ1), the NADH-quinone oxidoreductase chain G (NuoG), an uncharacterized protein (PFCIRM129_01355), GtfB glycosyltransferase (GtfB), a putative DNA polymerase I (PolA), and the UvrABC system protein A (UvrA3). Moreover, other proteins previously regarded as immunomodulatory in P. freudenreichii, such as surface layer protein E (SlpE) and chaperonin GroL2 (GroL2) (Deutsch et al., 2017), are part of the vesicular core proteome, indicating it could also be implicated in EV-mediated immunomodulation. Further studies with mutants or antibody targeting could elucidate the importance of specific proteins, as well as bioinformatics structural analysis of the predicted interactions. Moreover, although our analyses focused on proteomic investigations, it is also possible that molecules other than proteins could be implicated in EV-mediated immunomodulation. As an example, the DHNA produced by P. freudenreichii also inhibited the production of proinflammatory cytokines in mice (Okada et al., 2013) and could be exported by EVs. Furthermore, short chain fatty acids could also be implicated in immunomodulation [START_REF] Morrison | Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism[END_REF] and exported via EVs. Nucleic acids were also reported to be associated to bacterial EVs and to participate in host-bacteria interactions, particularly sRNAs with regulatory roles (Dauros- Singorenko et al., 2018;Ahmadi Badi et al., 2020;Rodriguez and Kuehn, 2020). Therefore, other approaches should be applied to fully characterize the content of P. freudenreichii-derived EVs, possibly unveiling the presence of nucleic acids, carbohydrates and lipids.

Regarding the cellular model used for in vitro assays, colon adenocarcinoma HT-29 cells were chosen, since this type of cells is constituent of the human gastrointestinal tract mucosa, modelling the host-bacteria interaction environment. However, other types of cultured intestinal epithelial cells could be equally applied for these investigations, such as human Caco-2, T84, SW480, H4 and other animal cellular models [START_REF] Bourgine | Gene Expression Profiling of Systems Involved in the Metabolism and the Disposition of Xenobiotics : Comparison between Human Intestinal Biopsy Samples and Colon Cell Lines[END_REF][START_REF] Ponce De León-Rodríguez | Intestinal in vitro cell culture models and their potential to study the effect of food components on intestinal inflammation[END_REF]. Furthermore, the effect of P. freudenreichii-derived EVs in systemic inflammation could also be studied with coculture systems including immune cells like human peripheral blood mononuclear cells (PBMC) and macrophage cell lines (THP-1, RAW 264.7) (Ponce de [START_REF] Ponce De León-Rodríguez | Intestinal in vitro cell culture models and their potential to study the effect of food components on intestinal inflammation[END_REF]. Additionally, inflammatory biomarkers other than pro-inflammatory cytokine IL-8 could be monitored, such as IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-12, IL-17a, IFNγ, TNFα and GM-CSF. Those further experiments would allow a better glimpse of the mechanisms of immunomodulation exerted by P. freudenreichii-derived EVs.

Would EVs exert immunomodulatory roles in vivo?

As our functional investigation was restricted to in vitro approaches, it is tempting to wonder what would be the relevance of P. freudenreichii-derived EVs in vivo. Several studies demonstrated that P. freudenreichii cells exerted immunomodulatory roles in vivo (Foligné et al., 2010;do Carmo et al., 2019;Ma et al., 2020), and there is also evidence for in vivo immunomodulation mediated by probiotic-derived EVs [START_REF] Al-Nedawi | Gut commensal microvesicles reproduce parent bacterial signals to host immune and enteric nervous systems[END_REF]Kim et al., 2018b;[START_REF] Yamasaki-Yashiki | IgA-enhancing effects of membrane vesicles derived from Lactobacillus sakei subsp. sakei NBRC15893[END_REF]Choi et al., 2020b), particularly the improvement of inflammatory conditions [START_REF] Kang | Extracellular Vesicles Derived from Gut Microbiota, Especially Akkermansia muciniphila, Protect the Progression of Dextran Sulfate Sodium-Induced Colitis[END_REF][START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF][START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF][START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF]. Moreover, bacterial EVs were successfully applied in vivo for vaccines, drug delivery, bioimaging and diagnosis (Gujrati et al., 2014b[START_REF] Gujrati | Bioengineered bacterial vesicles as biological nano-heaters for optoacoustic imaging[END_REF]Carvalho et al., 2019aCarvalho et al., , 2019b;;Kim et al., 2020a). Therefore, it is possible that P. freudenreichii-derived EVs exert anti-inflammatory roles in vivo and find promising applications as well. That would be relevant for pharmacokinetic and pharmacodynamic aspects of P. freudenreichii probiotic effect, since EVs can reach to distant sites of the organism, relative to the gastrointestinal tract, and mediate probiotic effects in the gut-brain axis [START_REF] Jang | In vivo Kinetic Biodistribution of Nano-Sized Outer Membrane Vesicles Derived from Bacteria[END_REF][START_REF] Yaghoubfar | Modulation of serotonin signaling/metabolism by Akkermansia muciniphila and its extracellular vesicles through the gut-brain axis in mice[END_REF]. Moreover, the vectorization in food matrices has shown a protective effect for P. freudenreichii cells [START_REF] Plé | Single-strain starter experimental cheese reveals anti-inflammatory effect of Propionibacterium freudenreichii CIRM BIA 129 in TNBS-colitis model[END_REF]Rabah et al., 2018bRabah et al., , 2018a)), and that should be considered for in vivo administration of EVs as well, with the potential to enhance EV stability and develop enriched dairy products. Finally, EV concentration might be a key variable for in vivo experiments, and low-yield EV purification methods could hinder these studies. Indeed, our investigations showed important differences in EV recovery yields according to the purification method. Therefore, improved purification methods and nanotechnological strategies for EV accumulation in the desired sites should be developed.

Further studies should also consider comparisons between bacteria-and EVsmediated probiotic effect, notably regarding their anti-inflammatory properties, and what would be the advantages and drawbacks of using EVs for in vivo applications. Accordingly, further studies should be conducted to determine the in vivo relevance of P. freudenreichii-derived EVs, accounting for optimizations of administration and vectorization schemes, higher-yield purification methodologies and possible advantages over bacteria direct use.

What other roles could EVs play in human-bacteria interaction?

Although our study focused in the immunomodulatory role of P. freudenreichiiderived EVs, other potential functionalities should be addressed. Indeed, our interatomics analysis predicted interactions with human proteins involved in diverse functional modules, including metabolism, signal transduction and infectious diseases. The last one could be related to the anti-pathogenic activity of P. freudenreichii, previously associated to cell-free supernatants [START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF]. Indeed, this activity could also be associated to some protein components of P. freudenreichiiderived EVs. Furthermore, some predicted interactions mapped to human proteins related to cancer. Although the anti-cancer activity of P. freudenreichii was attributed to short-chain fatty acids (Jan et al., 2002;Cousin et al., 2016), in addition to possibly contain these molecules, EVs could also contain proteins implicated in this anti-cancer activity. Interestingly, some predicted interactions mapped to the KEGG term "Cellular community -eukaryotes", which comprises focal adhesion, adherence junction, tight junction and gap junction. Therefore, it is also possible that P. freudenreichii-derived EVs have a role in intestinal barrier function, as evidenced for other probiotics or probioticderived EVs [START_REF] Blackwood | Probiotic Lactobacillus Species Strengthen Intestinal Barrier Function and Tight Junction Integrity in Experimental Necrotizing Enterocolitis[END_REF][START_REF] Chelakkot | Akkermansia muciniphiladerived extracellular vesicles influence gut permeability through the regulation of tight junctions[END_REF]. Additionally, some interactions mapped to neurodegenerative diseases and could offer insights of potential distal roles for P. freudenreichii-derived EVs, encompassing the gut-brain axis, as showed for other probiotic-derived EVs [START_REF] Jang | In vivo Kinetic Biodistribution of Nano-Sized Outer Membrane Vesicles Derived from Bacteria[END_REF][START_REF] Yaghoubfar | Modulation of serotonin signaling/metabolism by Akkermansia muciniphila and its extracellular vesicles through the gut-brain axis in mice[END_REF]. In spite of lacking experimental validation, these interactomics predictions offer several new approaches for investigating and understanding some aspects of the probiotic activity of P. freudenreichii.

In addition to in silico interactomics, experimental approaches could also allow the identification of novel aspects of P. freudenreichii EV-mediated beneficial effects. The human cellular models used for in vitro assays, as well as other proposed cellular models and tissues derived from in vivo experiments could be submitted to RNA profiling, in order to detect differentially expressed genes after EV treatment. Then, enrichment analysis would identify regulated functional modules, providing insights into a broader EV effect. For P. freudenreichii cells, there are evidences of the modulation of aryl hydrocarbon receptor (AhR) pathway by DHNA, with implications on metabolic detoxification and inflammation regulation [START_REF] Fukumoto | Identification of a probiotic bacteria-derived activator of the aryl hydrocarbon receptor that inhibits colitis[END_REF]. Moreover, gut microbiota bacteria were also showed to regulate peroxisome proliferator-activated receptor γ (PPAR-γ) pathway, via the production of short-chain fatty acid butyrate, with implications in the improvement of irritable bowel syndrome in mice [START_REF] Byndloss | Microbiota-activated PPAR-γ signaling inhibits dysbiotic Enterobacteriaceae expansion[END_REF][START_REF] Nozu | Butyrate inhibits visceral allodynia and colonic hyperpermeability in rat models of irritable bowel syndrome[END_REF]. Therefore, as a producer of short chain fatty acids and DHNA, it is possible that P. freudenreichii and its EVs may regulate other pathways in the eukaryotic cells, such as AhR and PPAR-γ pathways. Transcriptomics and proteomics profiling of the eukaryotic cells treated with EVs, as well as cellular reporter assays may allow the identification of such regulated pathways.

What roles could EVs play in bacteria-bacteria interaction?

Besides the host-bacteria interactions, EVs may also play a role in bacteria-bacteria interactions. That is evident in the case of intestinal bacteria communities that share EVs containing enzymes for the hydrolysis of complex polysaccharides, in such a way that specific species or strains provide molecules to serve as public goods for the community [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF]Rakoff-Nahoum et al., 2014;Lynch and Alegado, 2017). Since P. freudenreichii was identified in fecal samples of preterm breastfed infants (Colliou et al., 2017), it is possible that this species integrates the human intestinal microbiota and its EVs may provide metabolic enzymes for the community. Accordingly, a great part of the proteins identified in our proteomics approach was related to metabolic functions. Moreover, since P. freudenreichii is known for its bifidogenic property, mediated by DHNA and other bifidogenic factors (Mori et al., 1997;Isawa et al., 2002;[START_REF] Kouya | Production of extracellular bifidogenic growth stimulator by anaerobic and aerobic cultivations of several propionibacterial strains[END_REF], it is reasonable to suppose that these molecules may be exported via EVs to support the growth of bifidobacteria in the human gut. It is also possible that EVs mediate bacteria-bacteria interactions in other environments, such as dairy matrices. Since combinations of strains have been successfully used for dairy products [START_REF] Plé | Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: Reverse engineering development of an anti-inflammatory cheese[END_REF][START_REF] Cordeiro | Whey Protein Isolate-Supplemented Beverage, Fermented by Lactobacillus casei BL23 and Propionibacterium freudenreichii 138, in the Prevention of Mucositis in Mice[END_REF], it is possible that EV-mediated communication occur between the bacterial communities within these products, supporting the growth and fermentation processes of other bacteria. Additionally, EVs may represent a way of nutritional enrichment in such products, if they are demonstrated to contain molecules such as vitamin B12, which are produced by P. freudenreichii (Chamlagain et al., 2018;Piwowarek et al., 2018a). All these potential applications and functional attributions require additional experiments to characterize the diversity of EV content, and their roles and mechanisms of interactions in bacterial communities, including omics profiling and cargo tracking.

What environmental and technological aspects impact EVs properties?

Beyond functional investigations, our data also demonstrated that EV production and properties might be modulated by environmental conditions, specifically bacteria growth media (UF and YEL). That is in accordance with other studies, that demonstrated a great variability of EVs properties obtained from different growth conditions (Choi et al., 2014;Lynch et al., 2019). Moreover, some studies demonstrated varying EVs properties or increased production according to bacteria growth phases (Tashiro et al., 2010b;Kim et al., 2016b;Taboada et al., 2019;Zavan et al., 2019) or stress conditions, such as nutrient shortage, antibiotics, osmotic and acid stress (Prados-Rosales et al., 2014;Chan et al., 2017;Lee et al., 2018;Gerritzen et al., 2019;Jun et al., 2019;Lynch et al., 2019). EVs biogenesis was also sometimes considered a type of stress response (McBroom and Kuehn, 2007;Schwechheimer and Kuehn, 2013;[START_REF] Schwechheimer | Modulation of bacterial outer membrane vesicle production by envelope structure and content[END_REF]. Our data reinforce these observations, since the condition with higher EV production (UF medium), also presented higher abundance of EV-associated stress proteins. Therefore, it is possible that stress factors could enhance EV production in P. freudenreichii, also modulating their properties. Altogether, these observations provide the opportunity to rationally optimize EVs content and improve their anti-inflammatory roles via the management of environmental conditions during bacteria growth, which could facilitate large-scale and inexpensive production of therapeutic EVs. Nevertheless, further experiments should be conducted to study how other growth variables and stressful environmental conditions relate to the properties of P. freudenreichii-derived EVs.

The method of choice for EV purification also impacted on the properties of P. freudenreichii-derived EVs, particularly the protein content. We first used SEC as a purification method, which is a size-based, high-quality, straightforward and gentle method, that preserves EVs integrity and properties [START_REF] Gámez-Valero | Size-Exclusion Chromatography-based isolation minimally alters Extracellular Vesicles' characteristics compared to precipitating agents[END_REF]Monguió-Tortajada et al., 2019a). However, UC-based methods are the most used in bacterial EV purification, particularly those incorporating ultracentrifugation rounds with density gradients, although ultracentrifugation may be time-consuming and cause damages and aggregation in EVs (Monguió-Tortajada et al., 2019a). We compared the protein content of EVs obtained in both ways. As no purification method is perfect, there might always be a tradeoff between the recovery of EVs and a certain number of contaminating proteins. Moreover, as the methods are based in different approaches (size or density separation), each one might recover different subpopulations of EVs. Therefore, it is not unexpected that some proteins were exclusive to certain conditions. Nonetheless, hundreds of proteins were conserved in EVs obtained in all the studied conditions, indicating that some proteins (the EV core proteome) might be invariably sorted into EVs. Indeed, these proteins might be involved in essential processes for EVs biogenesis and for their functions in intercellular communication. Although we identified a conservation in EV protein content across different conditions of EV obtention, is also important to account for that variability at the species level, investigating how strain variability affects EV properties. That is particularly important because the immunomodulation exerted by P. freudenreichii is strain-dependent (Deutsch et al., 2017), and that could also be the case of P. freudenreichii-derived EVs.

Other important technological aspects that should be considered for further studies on P. freudenreichii-derived EVs include the evaluation of their stability during digestion throughout the gastrointestinal tract, what could be accomplished with in vitro digestion assays. It is equally important to characterize the stability of EVs during storage, and technological methods for conservation, such as lyophilization. Furthermore, other biophysical features should be characterized, such as rigidity, elasticity, interaction forces, surface charges and agglomeration potential, using techniques that include atomic force microscopy and dynamic light scattering. All these investigations could shed light on how EVs properties are influenced by environmental and technological parameters, and provide hints for different optimizations.

Conclusion

Overall, the present study comprises the first comprehensive analysis of P. freudenreichii-derived EVs, including their biophysical properties, protein content and immunomodulatory roles in vitro. It also demonstrates the importance of bacterial growth conditions and EV purification methods as analytical variables for interpreting bacterial EV properties. Furthermore, the anti-inflammatory role of P. freudenreichiiderived EVs and the viability of modulating their content and functions provide novel tools for the rational design of improved and safe therapeutic products. Although there are several gaps in our knowledge about P. freudenreichii-derived EVs, such as their effects in vivo, the molecules that they contain in addition to proteins, other pathways that they might modulate in eukaryotic cells, the impact of stress conditions and strain variability; still, this pioneer study has certainly initiated an exciting journey into the inner workings of EV-mediated probiotic effect of P. freudenreichii.

Thesis outputs

The work developed during this Ph.D. thesis resulted in the scientific products enumerated below: Résumé étendu (Extended abstract)

Introduction

La production de vésicules extracellulaires (VEs) est désormais reconnue comme un phénomène omniprésent dans les trois branches du vivant et constituant une voie de sécrétion impliquée dans la communication intercellulaire (Gill et al., 2019;Woith et al., 2019). Les VEs sont des particules biologiques sphériques de taille nanométrique, composées d'une bicouche lipidique et d'une lumière interne transportant une cargaison biologique [START_REF] Théry | Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines[END_REF]Gill et al., 2019). Elles sont produites par la plupart des cellules (procaryotes, eucaryotes), principalement à partir du bourgeonnement de la membrane, et sécrétées dans le milieu environnant. Elles jouent un rôle essentiel dans la communication entre les cellules (intra-et inter-espèce) et dans la vectorisation des propriétés biologiques des cellules, de par leur capacité à transporter des molécules bioactives (protéines, acides nucléiques, lipides, enzymes, toxines, métabolites) d'une cellule productrice à une cellule receveuse. Ces médiateurs de la communication agissent par transfert de leur contenu dans les cellules cibles ou par une interaction spécifique entre des ligands présents à leur surface et des récepteurs exprimés par les cellules cibles. La production de ces vecteurs d'information a été démontrée chez de nombreuses bactéries, y compris des bactéries pathogènes, probiotiques et commensales [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF][START_REF] Cecil | Outer Membrane Vesicle-Host Cell Interactions[END_REF]Molina-Tijeras et al., 2019;Caruana and Walper, 2020). Bien que la production de VEs de bactéries ait été principalement étudiée chez des bactéries à Gram négatif et pathogènes, ces dernières années, on a constaté une augmentation, dans la littérature scientifique, des travaux sur la production de VEs par plusieurs bactéries bénéfiques, notamment les bactéries à Gram négatif Akkermansia muciniphila, Bacteroides spp., Escherichia coli Nissle 1917, Lysobacter spp., et à Gram positif Lactobacillus spp. et Bifidobacterium spp (Brown et al., 2015;[START_REF] Kudryakova | Outer membrane vesicles of Lysobacter sp. XL1: biogenesis, functions, and applied prospects[END_REF]Cañas et al., 2018;[START_REF] Stentz | Fantastic voyage: The journey of intestinal microbiota-derived microvesicles through the body[END_REF][START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF][START_REF] Mata Forsberg | Extracellular Membrane Vesicles from Lactobacilli Dampen IFN-γ Responses in a Monocyte-Dependent Manner[END_REF]Molina-Tijeras et al., 2019;[START_REF] Nishiyama | Extracellular Vesicles Produced by Bifidobacterium longum Export Mucin-Binding Proteins[END_REF]. Les VEs produites par ces bactéries exercent divers rôles bénéfiques, notamment des activités anti-pathogènes, immunomodulatrices, antiobésités et antidépressives, ainsi que l'amélioration de la fonction barrière de l'épithélium intestinal et de la mobilité intestinale [START_REF] López | Treginducing membrane vesicles from Bifidobacterium bifidum LMG13195 as potential adjuvants in immunotherapy[END_REF][START_REF] Shen | Outer Membrane Vesicles of a Human Commensal Mediate Immune Regulation and Disease Protection[END_REF][START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF][START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF][START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF][START_REF] Behrouzi | Intestinal effect of the probiotic Escherichia coli strain Nissle 1917 and its OMV[END_REF][START_REF] Vargoorani | Stimulatory effects of Lactobacillus casei derived extracellular vesicles on toll-like receptor 9 gene expression and cytokine profile in human intestinal epithelial cells[END_REF]. Les travaux sur les VEs de bactéries bénéfiques aideront à décoder la communication entre les micro-organismes et leurs hôtes et à mieux comprendre les relations qu'ils peuvent entretenir, en particulier dans le cadre des interactions entre le microbiote intestinal et les cellules humaines [START_REF] Macia | Host-and Microbiota-Derived Extracellular Vesicles, Immune Function, and Disease Development[END_REF]. Par conséquent, la caractérisation des propriétés des VEs dérivés de bactéries bénéfiques présente un grand potentiel pour diverses applications cliniques et technologiques [START_REF] Toyofuku | Bacterial membrane vesicles, an overlooked environmental colloid: Biology, environmental perspectives and applications[END_REF][START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF]. L'une de ces bactéries bénéfiques dont l'importance est de plus en plus reconnue est Propionibacterium freudenreichii, une espèce probiotique laitière à Gram positif qui est utilisée depuis longtemps comme ferment dans la production de fromage de type suisse [START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]Rabah et al., 2017). Dans un contexte technologique, cette bactérie a été étudiée pour l'optimisation de la production de fromage, notamment l'affinage et la production de composés aromatiques, mais aussi pour la production de vitamine B12 et d'acides organiques [START_REF] De Freitas | New insights about phenotypic heterogeneity within Propionibacterium freudenreichii argue against its division into subspecies[END_REF]Gagnaire et al., 2015;Rabah et al., 2017;Piwowarek et al., 2018a). En outre, elle est apparue dans le panorama des probiotiques en raison de plusieurs caractéristiques bénéfiques, notamment la tolérance au stress dans le tractus gastro-intestinal, la capacité d'adhésion aux cellules hôtes, l'activité antipathogène, les propriétés anticancéreuses et l'immunomodulation (Jan et al., 2002;[START_REF] Cousin | Milk fermented by propionibacterium freudenreichii induces apoptosis of HGT-1 human gastric cancer cells[END_REF]do Carmo et al., 2017do Carmo et al., , 2019;;[START_REF] Nair | Effect of Propionibacterium freudenreichii on Salmonella multiplication, motility, and association with avian epithelial cells[END_REF]Rabah et al., 2017). Ces propriétés bénéfiques ont été confirmées par des études in vitro et in vivo (Foligné et al., 2010;Colliou et al., 2017;do Carmo et al., 2019;[START_REF] Ge | Neonatal intestinal immune regulation by the commensal bacterium, P. UF1[END_REF]Ma et al., 2020). Plusieurs approches en sciences omiques ont été utilisées pour étudier les mécanismes moléculaires impliqués dans ces processus, ce qui a permis d'identifier des acteurs moléculaires importants, tels que les protéines de surface, les acides gras à chaîne courte et les facteurs bifidogéniques (Jan et al., 2002;[START_REF] Okada | Propionibacterium freudenreichii component 1.4-dihydroxy-2-naphthoic acid (DHNA) attenuates dextran sodium sulphate induced colitis by modulation of bacterial flora and lymphocyte homing[END_REF]Deutsch et al., 2017;do Carmo et al., 2017). Il a été démontré que la diversité des souches est un aspect important à prendre en considération, car plusieurs de ces propriétés sont souche-dépendantes (Foligné et al., 2010;[START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF][START_REF] Foligné | Immunomodulation properties of multi-species fermented milks[END_REF]Deutsch et al., 2017). Plusieurs études ont réalisé un criblage multi-souches pour identifier des souches remarquables et des optimisations ont été proposées pour comprendre ou améliorer les propriétés technologiques et probiotiques dans des contextes spécifiques [START_REF] Foligné | Immunomodulation properties of multi-species fermented milks[END_REF][START_REF] Plé | Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: Reverse engineering development of an anti-inflammatory cheese[END_REF]Deutsch et al., 2017). De nouvelles études devraient permettre d'approfondir les mécanismes moléculaires liés aux propriétés bénéfiques de cette espèce et de ses produits, tout en considérant la diversité phénotypique des souches et les interactions avec l'hôte et le microbiote.

Question de recherche

La production de VEs est désormais considérée comme un phénomène ubiquitaire, même si son identification dans des contextes particuliers ait été retardée en raison de limitations techniques ou d'idées reçues (Woith et al., 2019). Ceci a été notamment le cas des bactéries à Gram positif, dont l'épaisse paroi cellulaire de peptidoglycane semblait constituer une barrière infranchissable (Brown et al., 2015). De même, la recherche sur les VEs de bactéries bénéfiques n'en est qu'à ses débuts, si on la compare aux bactéries pathogènes [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. Néanmoins, de plus en plus de preuves suggèrent que des facteurs sécrétés sont à l'origine de certains des effets bénéfiques associés à ces bactéries, parfois à des endroits éloignés de ceux où elles se trouvent habituellement dans le corps humain, comme le cerveau [START_REF] Haas-Neill | A Budding Relationship: Bacterial Extracellular Vesicles in the Microbiota-Gut-Brain Axis[END_REF]. Les VEs pourraient donc expliquer en partie ces observations. En effet, ces véhicules naturels d'échange intercellulaire, qui assurent la protection de leur cargaison interne, sont capables de traverser la couche de mucus dans le tractus gastro-intestinal, de migrer vers d'autres tissus et de se propager vers des sites distants [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Molina-Tijeras et al., 2019).

De même, les VEs sont également prometteurs sur le plan technologique, avec un grand potentiel de production rentable et à grande échelle, de stabilité et de sécurité à long terme [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]. Les VEs ont été utilisés en toute sécurité comme vaccins ciblés sur l'homme et sont considérés comme une alternative appropriée aux probiotiques dans le cas de personnes immunodéprimées [START_REF] Bitto | The therapeutic benefit of bacterial membrane vesicles[END_REF]Molina-Tijeras et al., 2019). En outre, la production de VEs dérivés de bactéries bénéfiques, ainsi que leurs propriétés, ont également été optimisées pour répondre à des critères spécifiques, tels que l'amélioration du rendement, et la modulation du contenu et de l'activité [START_REF] Nakayama-Imaohji | DNA inversion regulates outer membrane vesicle production in Bacteroides fragilis[END_REF][START_REF] Pérez-Cruz | Membrane Vesicles Released by a hypervesiculating Escherichia coli Nissle 1917 tolR Mutant Are Highly Heterogeneous and Show Reduced Capacity for Epithelial Cell Interaction and Entry[END_REF][START_REF] Hirayama | Glycine significantly enhances bacterial membrane vesicle production: a powerful approach for isolation of LPS-reduced membrane vesicles of probiotic Escherichia coli[END_REF][START_REF] Kuhn | Probiomimetics-Novel Lactobacillus-Mimicking Microparticles Show Anti-Inflammatory and Barrier-Protecting Effects in Gastrointestinal Models[END_REF]. Toutes ces caractéristiques font des VEs un système très intéressant de livraison de contenus, à la fois dans des contextes naturels et technologiques.

Avec l'amélioration des méthodes de purification et de la caractérisation omique, la tendance est à une forte augmentation des rapports scientifiques sur la prévalence des VEs et la caractérisation de leur contenu, avec un intérêt particulier pour les bactéries bénéfiques, longtemps négligées, mais très importantes. L'une de ces bactéries bénéfiques est P. freudenreichii, un probiotique laitier à Gram positif traditionnellement utilisé dans la production de fromage de type suisse, d'acides organiques et de vitamine B12 (Rabah et al., 2017). P. freudenreichii a été reconnu comme une espèce probiotique importante en raison de plusieurs caractéristiques bénéfiques, notamment l'adhésion aux cellules épithéliales humaines, l'activité antipathogène, la production de facteurs biogéniques qui modulent la composition du microbiote, les propriétés anticancéreuses médiées par les acides gras à chaîne courte et l'immunomodulation. Il a été montré que ces effets sont médiés par des facteurs exposés à la surface ou sécrétés (Jan et al., 2002;Okada et al., 2013;do Carmo et al., 2017), et parfois associés à des surnageants de culture sans cellules [START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF]Ma et al., 2020). Par ailleurs, la présence de structures ressemblant à des VEs dans le surnageant de culture de la souche JS22 de P. freudenreichii suggère que l'espèce puisse produire des VEs [START_REF] Frohnmeyer | Secretome profiling of Propionibacterium freudenreichii reveals highly variable responses even among the closely related strains[END_REF].

Pour ces raisons, et comme les VEs sont de plus en plus reconnus comme un système général d'exportation, y compris pour les bactéries à Gram positif et bénéfiques (Brown et al., 2015;Molina-Tijeras et al., 2019), nous avons émis l'hypothèse que P. freudenreichii pourrait produire des VEs impliqués dans la médiation de ses rôles bénéfiques, en particulier l'immunomodulation de cellules intestinales humaines. Nous avons donc formulé la question de recherche suivante :

La bactérie probiotique P. freudenreichii produit-elle des VEs ayant un rôle immunomodulateur dans différentes conditions ?

Objectifs

L'objectif général de ces travaux est de déterminer si P. freudenreichii produit des VEs au rôle immunomodulateur sur des cellules humaines. Pour cela, nous avons adopté pour une approche interdisciplinaire, intégrant la caractérisation biophysique des VEs, l'analyse protéomique de leur contenu, l'analyse interactomique de leurs protéines avec celles des cellules humaines, et des analyses fonctionnelles pour dévoiler les caractéristiques et les rôles des VEs dérivés de P. freudenreichii obtenues dans différentes conditions. Afin de répondre à cette question centrale, elle a été divisée en plusieurs objectifs spécifiques :

1. Déterminer si P. freudenreichii produit des VEs dans une condition standard de croissance, en utilisant la chromatographie par exclusion de taille comme méthode de purification des VEs. 2. Étudier les caractéristiques biophysiques (taille, concentration, morphologie) des VEs dérivés de P. freudenreichii.

3. Étudier la composition protéique des VEs dérivés de P. freudenreichii. 4. Appliquer des approches in silico pour prédire les interactions entre les protéines des VEs dérivés de P. freudenreichii et les protéines humaines.

5. Étudier l'activité immunomodulatrice des VEs dérivés de P. freudenreichii à l'égard de modèles cellulaires intestinaux humains.

6. Caractériser les VEs dérivés de P. freudenreichii obtenues à partir de cultures dans un milieu de croissance différent.

7. Comparer les conditions de croissance pour la modulation des propriétés des VEs dérivés de P. freudenreichii, notamment les paramètres biophysiques, la composition protéique, l'activité immunomodulatrice et les profils d'interaction.

8. Caractériser les propriétés des VEs dérivés de P. freudenreichii obtenues par une méthode de purification différente : l'ultracentrifugation avec gradient de densité. 9. Analyser la conservation de la composition en protéines des VEs dérivés de P. freudenreichii obtenus dans différentes conditions de croissance et par différentes méthodes de purification.

Ces objectifs ont été abordés dans trois différents chapitres (3, 4 et 5) de cette thèse, comprenant des articles de recherche publiés et des résultats supplémentaires.

Article de recherche 1

Certains aspects des effets probiotiques de P. freudenreichii ont été attribués à des facteurs exposés à la surface ou sécrétés et à des surnageants de culture sans cellules. Même si l'importance de protéines spécifiques pour l'immunomodulation exercée par l'espèce a été démontrée, il n'est pas certain que la fonctionnalité de ces protéines soit exclusivement médiée par une exposition en surface ou par des systèmes d'exportation conventionnels. En effet, il est envisageable que ces facteurs puissent être exportés par d'autres systèmes cellulaires, tels que les VEs. La présence de structures ressemblant à des VEs, récemment identifiées à partir d'images de microscopie électronique à transmission (TEM) de cette espèce, pourrait appuyer cette hypothèse. Toutefois, ces structures n'ont pas été purifiées, ni caractérisées. Nous avons donc cherché à savoir si P. freudenreichii produisait des VEs, et si ces dernières pouvaient être des médiateurs des propriétés probiotiques, notamment immunomodulatrices, de la bactérie.

Pour répondre à ces questions, nous avons utilisé la souche anti-inflammatoire P. freudenreichii CIRM-BIA129. Pour cela, elle a été cultivée en milieu ultrafiltrat de lait (UF) jusqu'au début de la phase stationnaire. Les cultures ont ensuite été centrifugées et filtrées, et les surnageants résultants ont été concentrés et appliqués à une colonne de chromatographie d'exclusion stérique (SEC) afin de purifier les VEs potentiellement présents. Les échantillons résultants ont été caractérisés par TEM et par l'analyse du suivi individuel de particules (NTA), dévoilant ainsi des VEs de taille nanométrique avec une morphologie typique sphérique et en forme de tasses. Une étude du contenu réalisée par une approche de protéomique shotgun a montré que les VEs contenaient un ensemble diversifié de protéines, dont des protéines associées aux propriétés immunomodulatrices de la bactérie, telle que SlpB. De plus, des prédictions d'interaction in silico ont révélé des interactions potentielles entre des protéines vésiculaires et une sous-unité du facteur de transcription humain NF-κB, qui est impliquée dans les processus inflammatoires. Par conséquent, l'activité immunomodulatrice de ces VEs a été donc évaluée dans des modèles cellulaires intestinaux adaptés. Le traitement de cellules épithéliales intestinales par les VEs de P. freudenreichii CIRM-BIA129 réduit la libération de la cytokine pro-inflammatoire IL-8 et l'activation du facteur de transcription NF-κB, en particulier lorsque les cellules ont été stimulées par le lipopolysaccharide (LPS) bactérien, un inducteur proinflammatoire . Cet effet est partiellement médié par la protéine SlpB, car les VEs produits par un mutant slpB ont un effet anti-inflammatoire moins intense.

Par conséquent, la première partie de cette étude montre que non seulement P. freudenreichii CIRM-BIA129 produit des VEs, mais qu'ils ont un rôle anti-inflammatoire sur des cellules épithéliales intestinales humaines, qui est lié aux protéines immunomodulatrices portées par ces VEs. Il est important de noter qu'il s'agit de la première caractérisation complète des VEs dérivés de P. freudenreichii, ce qui a donné lieu à un article de recherche original publié dans la revue Frontiers in Microbiology (DOI: 10.3389/fmicb.2020.01544), intitulé "Extracellular Vesicles Produced by the Probiotic Propionibacterium freudenreichii CIRM-BIA 129 Mitigate Inflammation by Modulating the NF-κB Pathway".

Article de recherche 2

Comme démontré, P. freudenreichii CIRM-BIA129 cultivé en milieu UF a produit des VEs ayant une activité anti-inflammatoire envers les cellules épithéliales humaines. Les propriétés biophysiques, biochimiques et fonctionnelles de ces VEs dérivées de l'UF ont été caractérisées de manière exhaustive. Toutefois, des preuves de plus en plus nombreuses suggèrent que plusieurs facteurs, notamment le milieu de croissance, la phase de croissance, les sels, le pH et l'exposition aux antibiotiques, peuvent moduler les propriétés des VEs dérivés de bactéries. C'est la raison pour laquelle nous avons cherché, dans un second temps, à étudier l'influence du milieu de croissance la modulation des propriétés des VEs dérivés de P. freudenreichii CIRM-BIA129. La souche P. freudenreichii CIRM-BIA129 a été cultivée en milieu UF ou YEL jusqu'au début de la phase stationnaire. Ensuite, le même protocole d'isolement des VEs a été appliqué. Celui-ci consistait en une centrifugation de la culture et une filtration pour récupérer les surnageants sans cellules, une concentration et une application dans une colonne SEC. Les échantillons résultants ont été caractérisés par des mesures de NTA, des tests d'activité immunomodulatrice, et des expériences de protéomique qualitative et quantitative. Ces analyses ont révélé des différences entre les VEs dérivées de l'UF et de l'YEL en termes d'abondance, de diamètre, d'activité anti-inflammatoire, et d'identité et d'abondance des protéines. En outre, elles ont mis en évidence la manière dont les différentes abondances des protéines associées aux VEs pourraient affecter le profil des interactions entre les protéines bactériennes et humaines, affectant ainsi les activités immunomodulatrices.

Cette étude comparative a permis de mettre en évidence l'impact des conditions de croissance de la bactérie sur les propriétés des VEs qu'elle produit, notamment via une modulation de leur protéome et de ses propriétés anti-inflammatoires. De plus, elle ouvre des perspectives sur un contrôle des propriétés des VEs par l'optimisation des conditions de croissance de la bactérie. Cette partie présente le deuxième article de recherche original issu de cette thèse, publié dans la revue Applied and Environmental Microbiology (DOI: 10.1128/AEM.02263-20), et intitulé "Environmental conditions modulate the protein content and immunomodulatory activity of extracellular vesicles produced by the probiotic Propionibacterium freudenreichii".

Résultats supplémentaires

Les études présentées dans les parties précédentes ont montré que P. freudenreichii produisait des VEs ayant un rôle anti-inflammatoire et que leurs propriétés dépendaient des conditions de croissance utilisées pour cultiver la bactérie. Par conséquent, l'optimisation des conditions de croissance représente un outil pour la modulation des propriétés des VEs, y compris leur activité anti-inflammatoire envers les cellules épithéliales humaines. Dans les études précédentes, les VEs ont été purifiés par chromatographie d'exclusion de taille (SEC), qui est une méthode de purification basée sur la taille. Néanmoins, malgré la reconnaissance croissante de l'approche SEC comme une méthode simple et de haute qualité pour la purification des VEs, les méthodes les plus utilisées pour la purification des VEs bactériennes sont toujours celles basées sur l'ultracentrifugation (UC). Par conséquent, nous avons cherché à savoir si les VEs dérivés de P. freudenreichii purifiés par cette méthode présenteraient des propriétés similaires, notamment en ce qui concerne la composition protéique.

P. freudenreichii CIRM-BIA129 a été cultivé en milieu UF et YEL jusqu'au début de la phase stationnaire, et les cultures ont été centrifugées, filtrées et le surnageant concentré. La purification des VEs a été effectuée par une série d'étapes d'ultracentrifugation, dont une avec un gradient de densité de saccharose. La caractérisation microscopique et la caractérisation de la taille ont confirmé la purification de VEs par cette approche. La protéomique shotgun a permis l'identification d'un ensemble diversifié de protéines, dépendant également des conditions de croissance des bactéries. Une analyse comparative de la composition protéique des VEs dérivés d'UC et de SEC, isolés à partir de cultures d'UF et de YEL, a montré que, bien que certaines protéines soient spécifiques à chaque condition, 308 étaient partagées entre toutes les conditions. Ce protéome central était enrichi en protéines liées au métabolisme de l'énergie et du carbone, à la structure ribosomique et à la biogenèse, au quorum sensing, à l'exportation de protéines et à la biosynthèse de peptidoglycane. Ces résultats offrent donc quelques indices des fonctions conservées dans les VEs dérivés de P. freudenreichii, éliminant les biais des conditions de croissance et de purification.

Discussion

La production de VEs et leur rôle en tant que système d'exportation pour la communication intercellulaire intra-et inter-espèce sont de plus en plus rapportés dans la littérature scientifique (Gill et al., 2019;Woith et al., 2019). Dans cette étude, nous avons démontré que le probiotique à Gram positif P. freudenreichii produit également des VEs ayant un rôle immunomodulateur dans la communication entre certaines cellules de l'homme et la bactérie. Notre étude s'est concentrée sur la caractérisation biophysique et protéomique des VEs dérivées de P. freudenreichii, ainsi que sur leur étude fonctionnelle à l'aide de prédictions interactomiques et d'essais immunologiques in vitro. En outre, nous avons démontré que les conditions de croissance des bactéries et les méthodes de purification des VEs ont un impact sur les propriétés de ces dernières.

En ce qui concerne les investigations fonctionnelles, nos données ont montré que les VEs purifiés par SEC ont un rôle anti-inflammatoire sur les cellules épithéliales intestinales humaines cultivées, via la réduction de l'activité du facteur de transcription NF-κB et de la libération d'IL-8. Cet effet anti-inflammatoire des VEs est observé spécifiquement lorsque les cellules sont dans un état inflammatoire induit par le LPS bactérien. Par conséquent, nous avons supposé que les VEs agissent sur des points de la voie de signalisation NF-κB qui sont induits spécifiquement par le LPS, et non par les autres inducteurs que nous avons pu tester (TNFα et IL-1β). En tant que membres de la voie NF-κB induite par le LPS, les protéines humaines toll-like receptor 4 (TLR4) et TIR domain-containing adapter molecule 1 (TICAM-1) sont des cibles potentielles à tester de l'immunomodulation médiée par les VEs de P. freudenreichii CIRM-BIA129. TLR4 est une protéine transmembranaire, tandis que TICAM-1 est une protéine cytoplasmique. Ainsi, il semble important de déterminer si les VEs agissent de manière intracellulaire ou extracellulaire, par rapport aux cellules réceptrices.

Il a été rapporté que les VEs interagissent avec des protéines de surface des cellules réceptrices, se fixant à la surface de la cellule [START_REF] French | Extracellular vesicle docking at the cellular port: Extracellular vesicle binding and uptake[END_REF]. Par conséquent, les VEs dérivés de P. freudenreichii pourraient interagir avec le domaine extracellulaire de TLR4 via des composants de surface des VEs (par exemple, les protéines) ou d'autres composants cytoplasmiques après leur lyse. Cependant, nos données ont montré que la protéolyse de la surface des VEs n'abolissait pas leur rôle immunomodulateur, ce qui suggère que la seconde option soit plus probable. Il a également été rapporté que les VEs bactériens pouvaient entrer dans les cellules eucaryotes par différents mécanismes, notamment la fusion directe des membranes ou par endocytose [START_REF] O'donoghue | Mechanisms of outer membrane vesicle entry into host cells[END_REF][START_REF] Bajic | Proteomic profile of extracellular vesicles released by Lactiplantibacillus plantarum BGAN8 and their internalization by non-polarized HT29 cell line[END_REF]. Par conséquent, le contenu des VEs pourrait servir de médiateur à l'immunomodulation après leur entrée dans la cellule hôte, en interagissant avec le domaine intracellulaire de TLR4 ou avec la protéine TICAM-1. Une autre possibilité envisageable serait un rôle plus indirect, dans lequel les VEs pourraient réguler l'immunomodulation via une autre voie de signalisation et la modulation négative de la voie NF-κB serait plutôt une conséquence. Pour autant, d'autres études sont nécessaires pour élucider les mécanismes d'action précis des VEs dérivés de P. freudenreichii, y compris des approches structurelles in silico pour analyser les interactions proteíne-proteíne à plus haute résolution (Keskin et al., 2016) et des approches expérimentales pour déterminer si l'intégrité des VEs est nécessaire pour la livraison des biomolécules qu'elles transportent [START_REF] Bonsergent | Content release of extracellular vesicles in a cell-free extract[END_REF], le suivi par fluorescence de l'internalisation et de la libération des biomolécules [START_REF] Ofir-Birin | Monitoring Extracellular Vesicle Cargo Active Uptake by Imaging Flow Cytometry[END_REF], ainsi que l'inhibition de voies d'assimilation spécifiques par les cellules eucaryotes [START_REF] Bajic | Proteomic profile of extracellular vesicles released by Lactiplantibacillus plantarum BGAN8 and their internalization by non-polarized HT29 cell line[END_REF]. Néanmoins, comme les mécanismes de l'immunomodulation ne sont pas entièrement élucidés pour les cellules bactériennes de P. freudenreichii, notre étude sur les VEs dérivés de P. freuderenichii ouvre de nouvelles perspectives. En particulier, déterminer si l'activité anti-inflammatoire de la bactérie P. freudenreichii cible la même voie de signalisation, NF-κB, que les VEs qu'elle produit, constitue une base pour d'autres études.

En ce qui concerne les effecteurs bactériens de l'immunomodulation médiée par les VEs, nos données indiquent que la protéine SlpB est partiellement impliquée, puisque les VEs dérivés du mutant slpB présentent une réduction moins intense de l'activité régulatrice de NF-κB que les VEs produits par la souche sauvage. Ce résultat est en partie en adéquation avec le rôle crucial de SlpB dans l'immunomodulation exercée par la bactérie (do [START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF]. Cependant, comme la réduction de l'activité de NF-κB par les VEs n'est pas complètement abolie en absence de SlpB, d'autres protéines pourraient être impliquées. Notre analyse qui a couplé les prédictions de l'interactomique, la protéomique quantitative et les essais fonctionnels comparant les VEs dérivés de l'UF et de l'YEL purifiés par SEC a abouti à une petite liste de protéines bactériennes candidates qui pourraient interagir directement avec TLR4 et/ou TICAM-1, et ainsi déclencher l'inhibition de la voie NF-κB induite par LPS. Cette liste comprend le pyruvate synthase/pyruvate-flavodoxine oxydoréductase (NifJ1), la chaîne G de la NADH-quinone oxydoréductase (NuoG), une protéine non caractérisée (PFCIRM129_01355), la GtfB glycosyltransférase (GtfB), une ADN polymérase I putative (PolA), et la protéine A du système UvrABC (UvrA3). En outre, d'autres protéines auparavant considérées comme immunomodulatrices chez P. freudenreichii, telles que la protéine de la couche superficielle E (SlpE) et la chaperonine GroL2 (GroL2) (Deutsch et al., 2017), font partie du protéome central vésiculaire, ce qui indique qu'elles pourraient également être impliquées dans l'immunomodulation induite par les VEs. D'autres études à partir de mutants pour ces protéines ou des anticorps pourraient élucider l'importance de protéines spécifiques, ainsi que l'analyse structurale bioinformatique des interactions prédites. De plus, bien que nos analyses se soient concentrées sur les recherches protéomiques, il est également possible que des molécules autres que des protéines puissent être impliquées dans l'immunomodulation médiée par les VEs. Par exemple, le DHNA produit par P. freudenreichii a également la capacité à inhiber la production de cytokines pro-inflammatoires chez la souris (Okada et al., 2013) et pourrait être exporté par les VEs. En outre, les acides gras à chaîne courte pourraient également être impliqués dans l'immunomodulation [START_REF] Morrison | Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism[END_REF] et exporté à l'intérieur des VEs. Il a été récemment montré que des acides nucléiques associés aux VEs bactériens participaient aux interactions hôte-bactérie, en particulier de petits ARNs régulateurs (Dauros-Singorenko et al., 2018;Ahmadi Badi et al., 2020;Rodriguez and Kuehn, 2020). Par conséquent, d'autres approches devraient être mises en oeuvre pour caractériser de manière exhaustive, au-delà de sa cargaison protéique, le contenu des VEs dérivés de P. freudenreichii, en révélant éventuellement la présence d'acides nucléiques, de glucides et de lipides.

En ce qui concerne le modèle cellulaire utilisé pour les essais in vitro, les cellules d'adénocarcinome HT-29 du colon ont été choisies, car ce type de cellules est constitutif de la muqueuse du tractus gastro-intestinal humain, modélisant l'environnement d'interaction hôte-bactérie. Toutefois, d'autres types de cellules épithéliales intestinales cultivées pourraient également être utilisées pour ces recherches, comme les modèles cellulaires Caco-2, T84, SW480, H4 et d'autres modèles cellulaires animaux [START_REF] Bourgine | Gene Expression Profiling of Systems Involved in the Metabolism and the Disposition of Xenobiotics : Comparison between Human Intestinal Biopsy Samples and Colon Cell Lines[END_REF][START_REF] Ponce De León-Rodríguez | Intestinal in vitro cell culture models and their potential to study the effect of food components on intestinal inflammation[END_REF]. En outre, l'effet des VEs dérivés de P. freudenreichii dans l'inflammation systémique pourrait également être étudié avec des systèmes de co-culture comprenant des cellules immunitaires comme les cellules mononucléaires du sang périphérique humain (PBMC) et les lignées de cellules macrophages (THP-1, RAW 264.7) (Ponce de [START_REF] Ponce De León-Rodríguez | Intestinal in vitro cell culture models and their potential to study the effect of food components on intestinal inflammation[END_REF]. En outre, des biomarqueurs inflammatoires autres que la cytokine pro-inflammatoire IL-8 pourraient être évalués, tels que IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-12, IL-17a, IFNγ, TNFα et GM-CSF. Ces nouvelles expériences permettraient de mieux comprendre les mécanismes d'immunomodulation exercés par les VEs dérivées de P. freudenreichii.

Notre étude fonctionnelle s'étant limitée aux approches in vitro, il est tentant de se demander quelle serait la pertinence des VEs dérivés de P. freudenreichii in vivo. Plusieurs études ont démontré que les cellules de P. freudenreichii exerçaient des rôles immunomodulateurs in vivo (Foligné et al., 2010;do Carmo et al., 2019;Ma et al., 2020). Par ailleurs, il existe également des preuves de l'immunomodulation in vivo médiée par des VEs dérivées de probiotiques [START_REF] Al-Nedawi | Gut commensal microvesicles reproduce parent bacterial signals to host immune and enteric nervous systems[END_REF]Kim et al., 2018b;[START_REF] Yamasaki-Yashiki | IgA-enhancing effects of membrane vesicles derived from Lactobacillus sakei subsp. sakei NBRC15893[END_REF]Choi et al., 2020b), en particulier l'amélioration des conditions inflammatoires [START_REF] Kang | Extracellular Vesicles Derived from Gut Microbiota, Especially Akkermansia muciniphila, Protect the Progression of Dextran Sulfate Sodium-Induced Colitis[END_REF][START_REF] Fábrega | Intestinal Anti-inflammatory Effects of Outer Membrane Vesicles from Escherichia coli Nissle 1917 in DSS-Experimental Colitis in Mice[END_REF][START_REF] Seo | Therapeutic effects of kefir grain Lactobacillus-derived extracellular vesicles in mice with 2,4,6-trinitrobenzene sulfonic acidinduced inflammatory bowel disease[END_REF][START_REF] Ashrafian | Akkermansia muciniphila-Derived Extracellular Vesicles as a Mucosal Delivery Vector for Amelioration of Obesity in Mice[END_REF]. Par conséquent, il est possible que les VEs dérivés de P. freudenreichii exercent également des rôles anti-inflammatoires in vivo. Cela serait pertinent pour les aspects pharmacocinétiques et pharmacodynamiques de l'effet probiotique de P. freudenreichii, ainsi que pour sa portée sur des sites éloignés de l'organisme, par rapport au tractus gastro-intestinal, comme les effets probiotiques sur l'axe intestin-cerveau [START_REF] Jang | In vivo Kinetic Biodistribution of Nano-Sized Outer Membrane Vesicles Derived from Bacteria[END_REF][START_REF] Yaghoubfar | Modulation of serotonin signaling/metabolism by Akkermansia muciniphila and its extracellular vesicles through the gut-brain axis in mice[END_REF]. De plus, la vectorisation dans les matrices alimentaires de la bactérie a montré un effet protecteur pour les cellules de P. freudenreichii (Plé et al., 2015;Rabah et al., 2018bRabah et al., , 2018a)). Des expériences similaires pourraient être envisagées pour l'administration in vivo des VEs, notamment pour améliorer leur stabilité et développer des produits laitiers enrichis. Enfin, la concentration en VEs pourrait être un facteur clé pour les expériences in vivo, et les méthodes de purification des VEs à faible rendement pourraient entraver ce type d'études. En effet, nos investigations ont montré des différences importantes dans les rendements de récupération des VEs selon la méthode de purification. Il convient donc de mettre au point des méthodes de purification améliorées et des stratégies nanotechnologiques pour l'accumulation des VEs dans les sites souhaités. En conséquence, des études supplémentaires devraient être menées pour déterminer la pertinence in vivo des VEs dérivés de P. freudenreichii, en tenant compte des optimisations des schémas d'administration et de vectorisation, ainsi que des méthodes de purification de plus haut rendement.

Bien que notre étude se soit concentrée sur le rôle immunomodulateur des VEs dérivés de P. freudenreichii, d'autres fonctionnalités potentielles devraient être abordées. En effet, notre analyse interatomique a prédit des interactions avec des protéines humaines impliquées dans divers modules fonctionnels, notamment le métabolisme, la transduction de signaux et les maladies infectieuses. Cette dernière pourrait être liée à l'activité anti-pathogène de P. freudenreichii, précédemment associée à des surnageants sans cellules [START_REF] Nair | Characterizing the Antimicrobial Function of a Dairy-Originated Probiotic, Propionibacterium freudenreichii, Against Multidrug-Resistant Salmonella enterica Serovar Heidelberg in Turkey Poults[END_REF]. En effet, cette activité pourrait également être associée à certains composants protéiques des VEs dérivés de P. freudenreichii. En outre, certaines interactions prédites ont été associées à des protéines humaines liées au cancer. Bien que l'activité anticancéreuse de P. freudenreichii ait été attribuée aux acides gras à chaîne courte (Jan et al., 2002;Cousin et al., 2016), en plus de contenir éventuellement ces molécules, les VEs pourraient également contenir des protéines impliquées dans cette activité anticancéreuse. Il est intéressant de noter que certaines interactions prédites ont été associées au terme KEGG "Communauté cellulaire -eucaryotes", qui comprend l'adhésion focale, la jonction par adhérence, la jonction serrée et la jonction par fente. Par conséquent, il est également possible que les VEs dérivés de P. freudenreichii jouent un rôle dans la fonction barrière de l'épithélium intestinale, comme l'ont montré d'autres probiotiques ou VEs dérivés de probiotiques [START_REF] Blackwood | Probiotic Lactobacillus Species Strengthen Intestinal Barrier Function and Tight Junction Integrity in Experimental Necrotizing Enterocolitis[END_REF][START_REF] Chelakkot | Akkermansia muciniphiladerived extracellular vesicles influence gut permeability through the regulation of tight junctions[END_REF]. En outre, certaines interactions ont été identifiées pour les maladies neurodégénératives et pourraient permettre de mieux comprendre les rôles distales potentiels des VEs dérivés de P. freudenreichii, qui englobent l'axe intestin-cerveau, comme cela a été démontré pour d'autres VEs dérivés de probiotiques [START_REF] Jang | In vivo Kinetic Biodistribution of Nano-Sized Outer Membrane Vesicles Derived from Bacteria[END_REF][START_REF] Yaghoubfar | Modulation of serotonin signaling/metabolism by Akkermansia muciniphila and its extracellular vesicles through the gut-brain axis in mice[END_REF]. Malgré l'absence de validation expérimentale, ces prédictions de l'interactomique offrent plusieurs nouvelles approches pour étudier et comprendre certains aspects de l'activité probiotique de P. freudenreichii.

Au-delà de l'interactomique in silico, des approches expérimentales pourraient également permettre d'identifier de nouveaux aspects des effets bénéfiques induits par les VEs de P. freudenreichii. Les modèles cellulaires humains utilisés pour les essais in vitro, ainsi que d'autres modèles cellulaires proposés et des tissus issus d'expériences in vivo pourraient être soumis à un profilage de l'ARN, afin de détecter les gènes exprimés de manière différentielle après un traitement aux VEs. Ensuite, l'analyse de l'enrichissement permettrait d'identifier les modules fonctionnels régulés, ce qui donnerait un aperçu de l'effet plus large des VEs. Pour les cellules de P. freudenreichii, il existe des preuves de la modulation de la voie du récepteur d'hydrocarbure arylique (AhR) par DHNA, avec des implications sur la détoxification métabolique et la régulation de l'inflammation [START_REF] Fukumoto | Identification of a probiotic bacteria-derived activator of the aryl hydrocarbon receptor that inhibits colitis[END_REF]. En outre, il a également été démontré que les bactéries du microbiote intestinal régulent la voie du récepteur activé par les proliférateurs de peroxysomes γ (PPAR-γ), via la production de butyrate, avec des implications dans l'amélioration du syndrome du côlon irritable chez la souris [START_REF] Byndloss | Microbiota-activated PPAR-γ signaling inhibits dysbiotic Enterobacteriaceae expansion[END_REF][START_REF] Nozu | Butyrate inhibits visceral allodynia and colonic hyperpermeability in rat models of irritable bowel syndrome[END_REF]. Par conséquent, en tant que producteur d'acides gras à chaîne courte et de DHNA, il est possible que P. freudenreichii et ses VEs puissent réguler d'autres voies des cellules eucaryotes, comme les voies AhR et PPAR-γ. Le profilage transcriptomique et protéomique des cellules eucaryotes traitées aux VEs, ainsi que des tests avec des systèmes cellulaires rapporteurs pourraient permettre l'identification de telles voies régulées.

Au-delà des interactions hôte-bactérie, les VEs peuvent également jouer un rôle dans les interactions bactéries-bactéries. Cela est évident dans le cas des communautés de bactéries intestinales qui partagent des VEs contenant des enzymes pour l'hydrolyse de polysaccharides complexes, de telle sorte que des espèces ou souches spécifiques fournissent des molécules utiles à d'autres espèces de la communauté [START_REF] Elhenawy | Preferential Packing of Acidic Glycosidases and Proteases into Bacteroides Outer Membrane Vesicles[END_REF]Rakoff-Nahoum et al., 2014;Lynch and Alegado, 2017). Comme P. freudenreichii a été identifié dans des échantillons de matières fécales de nourrissons prématurés allaités (Colliou et al., 2017), il est possible que cette espèce fasse partie du microbiote intestinal humain et que ses VEs puissent fournir des enzymes métaboliques à la communauté. Il est à noter qu'une grande partie des protéines des VEs identifiées dans notre approche protéomique correspondent à des fonctions métaboliques. De plus, puisque P. freudenreichii est connu pour ses propriétés bifidogènes, médiées par le DHNA et d'autres facteurs bifidogéniques (Mori et al., 1997;Isawa et al., 2002;[START_REF] Kouya | Production of extracellular bifidogenic growth stimulator by anaerobic and aerobic cultivations of several propionibacterial strains[END_REF], il est raisonnable de supposer que ces molécules puissent être exportées via des VEs pour soutenir la croissance des bifidobactéries dans l'intestin humain. Il est également possible que les VEs servent de médiateurs pour les interactions entre bactéries dans d'autres environnements, tels que les matrices laitières. Étant donné que des combinaisons de souches ont été utilisées avec succès pour les produits laitiers [START_REF] Plé | Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: Reverse engineering development of an anti-inflammatory cheese[END_REF][START_REF] Cordeiro | Whey Protein Isolate-Supplemented Beverage, Fermented by Lactobacillus casei BL23 and Propionibacterium freudenreichii 138, in the Prevention of Mucositis in Mice[END_REF], il est possible qu'une communication par les VEs se produise au sein de ses communautés dans ces produits, soutenant la croissance et les processus de fermentation d'autres bactéries. En outre, les VEs peuvent représenter un moyen d'enrichissement nutritionnel dans ces produits, s'il est démontré qu'ils contiennent des molécules telles que la vitamine B12, qui sont produites par P. freudenreichii (Chamlagain et al., 2018;Piwowarek et al., 2018a). Toutes ces applications potentielles et attributions fonctionnelles nécessitent des expériences supplémentaires pour caractériser la diversité du contenu des VEs, ainsi que leurs rôles et mécanismes d'interaction dans les communautés bactériennes, notamment via des profilage omique et le suivi des cargaisons biologiques.

Au-delà des investigations fonctionnelles, nos données ont également démontré que la production et les propriétés des VEs pouvaient être modulées par les conditions environnementales, en particulier les milieux de croissance des bactéries (UF et YEL). Ceci est en accord avec d'autres études, qui ont démontré une grande variabilité des propriétés des VEs obtenus à partir de différentes conditions de croissance (Choi et al., 2014;Lynch et al., 2019). En outre, certaines études ont démontré que les propriétés des VEs variaient ou que leur production augmentait en fonction des phases de croissance des bactéries (Tashiro et al., 2010b;Kim et al., 2016b;Taboada et al., 2019;Zavan et al., 2019) ou des conditions de stress, telles que la carence en nutriments, les antibiotiques, le stress osmotique et acide (Prados-Rosales et al., 2014;Chan et al., 2017;Lee et al., 2018;Gerritzen et al., 2019;Jun et al., 2019;Lynch et al., 2019). La biogenèse des VEs étant aussi parfois considérée comme une réponse cellulaire face à un stress (McBroom and Kuehn, 2007;Schwechheimer and Kuehn, 2013;[START_REF] Schwechheimer | Modulation of bacterial outer membrane vesicle production by envelope structure and content[END_REF]. Nos données renforcent ces observations, puisque la condition avec une production plus élevée des VEs (milieu UF), présentait également une plus grande abondance de protéines de stress associées aux VEs. Par conséquent, il est possible que les facteurs de stress puissent augmenter la production des VEs chez P. freudenreichii, en modulant également leurs propriétés. Dans l'ensemble, ces observations offrent la possibilité d'optimiser rationnellement le contenu des VEs et d'améliorer leurs rôles anti-inflammatoires par la gestion des conditions environnementales pendant la croissance des bactéries, ce qui pourrait faciliter la production à grande échelle et peu coûteuse de VEs thérapeutiques. Néanmoins, d'autres expériences devraient être menées pour étudier comment d'autres variables de croissance et des conditions environnementales stressantes sont liées aux propriétés des VEs dérivées de P. freudenreichii.

La méthode de choix pour la purification des VEs a également eu un impact sur les propriétés des VEs dérivés de P. freudenreichii, en particulier la composition protéique. Nous avons d'abord utilisé la SEC comme méthode de purification, qui est une méthode basée sur la taille, de haute qualité, simple et délicate, qui préserve l'intégrité et les propriétés des VEs [START_REF] Gámez-Valero | Size-Exclusion Chromatography-based isolation minimally alters Extracellular Vesicles' characteristics compared to precipitating agents[END_REF]Monguió-Tortajada et al., 2019a). Cependant, les méthodes basées sur l'UC sont les plus utilisées pour la purification des VEs de bactéries, en particulier celles incorporant des cycles d'ultracentrifugation avec des gradients de densité, même si l'ultracentrifugation peut être longue et causer des dommages aux VEs et engendrer des phénomènes d'agrégation (Monguió-Tortajada et al., 2019a). Nous avons comparé la composition protéique des VEs obtenus par ces deux approches. Comme aucune méthode de purification n'est parfaite, un compromis entre la récupération de grande quantité de VEs et la présence de protéines contaminantes doit être trouvé. De plus, comme les méthodes sont basées sur des approches différentes (séparation par taille ou par densité), chacune d'entre elles pourrait permettre de recueillir des sous-populations différentes de VEs. Il n'est donc pas surprenant que certaines protéines aient été exclusives à certaines conditions. Néanmoins, des centaines de protéines ont été conservées dans les VEs obtenus dans toutes les conditions étudiées, ce qui indique que certaines protéines (le protéome central des VEs) pourraient être invariablement exportées dans des VEs. En effet, ces protéines pourraient être impliquées dans des processus essentiels pour la biogenèse des VEs et pour leurs fonctions dans la communication intercellulaire. Bien que nous ayons identifié une conservation du contenu en protéines des VEs dans différentes conditions d'obtention des VEs, il est également important de tenir compte de cette variabilité au niveau des espèces, en étudiant comment la variabilité des souches affecte les propriétés des VEs. Cela est particulièrement important parce que l'immunomodulation exercée par P. freudenreichii dépend de la souche (Deutsch et al., 2017), et cela pourrait également être le cas des VEs dérivés de P. freudenreichii.

Conclusion

La présente étude constitue la première analyse complète des VEs dérivés de P. freudenreichii, y compris leurs propriétés biophysiques, leur contenu protéique et leurs rôles immunomodulateurs in vitro. Elle démontre également l'importance des conditions de croissance bactérienne et des méthodes de purification des VEs comme variables analytiques pour l'interprétation des propriétés des VEs de bactéries. En outre, le rôle anti-inflammatoire des VEs dérivés de P. freudenreichii et la modulation de leur contenu et de leurs fonctions fournissent de nouveaux outils pour la conception raisonnée de produits thérapeutiques améliorés et sûrs. Bien que nos connaissances sur les VEs dérivés de P. freudenreichii présentent plusieurs lacunes, telles que leurs effets in vivo, les molécules qu'ils contiennent en plus des protéines, les autres voies qu'ils pourraient moduler dans les cellules eucaryotes, l'impact des conditions de stress et la variabilité des souches, cette étude pionnière a certainement lancé un voyage passionnant dans les rouages internes de l'effet probiotique de P. freudenreichii médié par les VEs. Staphylococcus aureus is a Gram-positive opportunistic pathogen that causes a broad spectrum of infections in humans and animals. In humans, these diseases range from superficial skin and soft tissue infections to life-threatening conditions that require hospitalisation and extensive medical support 1,2 . This bacterium is also one of the main causative agents of nosocomial infections. In animals, S. aureus is notably responsible for ruminant mastitis, an inflammation of the mammary glands that dramatically affects animal health and welfare, milk quality and the economics of milk production 3 . Mastitis is also the principal reason for the use of antibiotics in dairy herds 4 . The wide range of clinical manifestations of S. aureus infections is likely associated with its huge arsenal of virulence factors, which include structural components and extracellular factors such as enzymes and toxins 5 . Despite considerable efforts, the precise mechanisms underlying host adaptation, colonisation and interactions are not yet fully understood 6 . Extracellular vesicles (EVs) are used by many pathogenic bacteria as a secretory route to deliver toxic compounds to infected cells 7,8 . EVs are lipid bilayer nanoparticles that range in size from 20 to 300 nm and are released by almost all cells in all domains of life 9 . In Gram-positive bacteria, they are formed by budding and shedding of the cytoplasmic membrane. They play a pivotal role in cell-to-cell communication through their ability to transport bioactive molecules (proteins, nucleic acids, lipids, metabolites) from donor to recipient cells. The EVs produced by S. aureus can mediate the pathogenesis of infection in a variety of ways. They may be cytotoxic to host cells 10-12 , induce the production of cytokines 13-18 , contribute to biofilm formation 19 , mediate antibiotic 1 INRAE, Institut Agro, STLO, F-35000, Rennes, France. 2 Institute of Biological Sciences, Federal University of Minas Gerais, Minas Gerais, Belo Horizonte, Brazil. 3 Excilone, F-78990, Elancourt, France. 4 Univ Rennes, Inria, CNRS, IRISA, Rennes, France. 5 Univ Rennes, CNRS, Inserm, BIOSIT -UMS 3480, US_S 018, F-35000, Rennes, France. 6 INRAE, Université Paris-Saclay, AgroParisTech, UMR GABI, F-78350, Jouy-en-Josas, France. ✉ e-mail: eric.guedon@inrae.fr open resistance 20 or promote an increase in the in vivo survival of S. aureus 21 . In addition, the immunogenic and adjuvant properties of S. aureus EVs can induce a protective response against S. aureus infection, thus offering new alternatives for the design of vaccines and the control of infections 15,17,21,22 . The broad spectrum of activities associated with S. aureus-derived EVs, as well as their strain-dependency, is mainly related to their protein content 12 . The EV cargo includes a variety of proteins, such as virulence factors and lipoproteins, some of which are known to be potent antigens 12,17,18,21-24 . The EV protein cargo has been shown to vary as a function of environmental conditions 21 and the producer strains 12,17 . The identification of proteins shared between the EVs of several strains has suggested that protein packaging in EVs results more from a conserved selective mechanism than from a random process 12,18 . However, to date, the molecular mechanisms that drive the recruitment into EVs of these proteins remain unclear, thus limiting our understanding of the pathophysiological relevance of EVs in vivo and the development of EV-based applications.

Proteins and their characteristics

Staphylococcus

In eukaryotic cells, the components associated with protein packaging into exosomes, a class of eukaryotic EVs, are found in the exosomes produced by most cell types 25,26 . Likewise, the proteins involved in EV formation in S. aureus are also found in the EVs they produce 22,23 . We therefore postulated that S. aureus strains might share an EV proteome containing essential components for the cargo selection of proteins into EVs. Considering that secreted proteins are key elements in the virulence and host adaptation of S. aureus 27 , we suggested that strains isolated from different hosts (human, bovine, ovine) and involved in different types of infection (from mild to severe) offer the best source of diversity to explore cargo selectivity and determine a robust core EV proteome. During this work, we purified EVs released into the culture supernatant of five S. aureus strains and identified the whole set of proteins packaged into these EVs. This enabled identification of a core EV proteome. The distribution, functions, physicochemical properties and amino acid composition of the proteins packaged into EVs as a function of different strains were analysed and compared with regard to the host origin of strains, the severity of infections the strains thus produced could cause and the selectivity of protein cargo into EVs.

Results

EV production in S. aureus strains of human, bovine and ovine origins. The ability to produce EVs of five S. aureus strains isolated from three types of hosts was investigated. These included two bovine strains, Newbould 305 (hereinafter referred to as N305) and RF122, two ovine strains, S. aureus O11 and O46, and the highly virulent human methicillin-resistant S. aureus (MRSA) strain MW2. S. aureus N305, in which EV production had already been demonstrated, was used as a positive control for the EV preparations 18 . All the S. aureus strains used in this study produced EVs with classical features, including a nanoscale size, spherical structure and cup-shaped morphology when visualised using negative staining electron microscopy (Fig. 1A) 28,29 . In addition to EVs, cylindrical (nanotube-like) structures were observed in the S. aureus N305, RF122, O11 and O46 samples (Supplementary Fig. S1). The size of the EVs secreted was homogeneous for each strain and similar between strains (120-130 nm), with the exception of strain O46 which secreted significantly larger EVs (170 nm, P < 0.0001) (Fig. 1B,C). Although the growth conditions and EV preparation methods were the same, the number of EVs harvested from the MW2 and O11 culture supernatants was five to ten times lower than that of the other strains, suggesting that the yield of EV production is a strain-specific feature (Fig. 1D).

protein composition of S. aureus EV proteomes. The proteome of the EVs produced by S. aureus O11, O46, RF122, and MW2 was determined by Nano LC-ESI-MS/MS analysis on three biological replicates. The dataset was completed by the vesicular proteome of strain N305, which had been obtained during a previous study using the same experimental procedures 18 . A total of 253 proteins associated with S. aureus EVs, and corresponding to the EV pan-proteome of these strains, were identified (Supplementary Table S2). 160, 164, 168, 171 and 218 proteins were associated with EVs produced by S. aureus strains RF122, O11, MW2, O46 and N305, respectively. The majority of these 253 proteins were predicted as cytoplasmic (PRED-LIPO, n = 126; CELLO, n = 118; PSORTb, n = 92) or membrane-associated (PSORTb, n = 80; CELLO, n = 65; PRED-LIPO, n = 67) (Fig. 2A-C). About 10% and 20% of EV proteins are predicted to contain a signal peptide I (SignalP, n = 27; PRED-LIPO, n = 20) and a signal peptide II (SignalP, n = 51; PRED-LIPO, n = 50), respectively (Fig. 2B,D). When considered strain-by-strain, the protein localisation was similar to these overall results. Interestingly, although the predicted signal peptide II-containing proteins (i.e. lipoproteins) represented about 2.5% of the whole proteome of the strains studied here, they accounted for 20% of EV proteomes, which was indicative of their relative enrichment in EVs. In agreement, EV lipoproteins represented about 50% of whole cell lipoproteins. Taken together, these results showed that the predicted subcellular localisation and richness of proteins associated with EVs were markedly similar in the five strains analysed.

Functional classification of S. aureus EV proteomes. The functional characteristics of the 253 S. aureus EV proteins were determined using a Clusters of Orthologous Groups (COG) analysis. Overall, S. aureus EV-associated proteins were spread over 18 COGs (Supplementary Table S2 and Fig. 2E). Of note, 30% of EV proteins were assigned to the "function unknown" category (n = 49) or not assigned to any COG categories (n = 27). The majority of the remaining proteins (42%; 74/177) could be assigned to COGs related to the general "metabolism" category; i.e. energy production and conversion (n = 24, COG C), inorganic ion transport and metabolism (n = 19, COG P), carbohydrate transport and metabolism (n = 9, COG G) and amino acid transport and metabolism (n = 10, COG E). COG J (translation/ribosomal structure and biogenesis) was the most common category, with 38 occurrences. Finally, cell wall/membrane/envelope biogenesis (n = 28, COG M), defence mechanisms (n = 7, COG V) and intracellular trafficking/secretion/vesicular transport (n = 6, COG U) were the most frequent COGs in the general "cellular processes and signalling" category.

Interestingly, 36% (92/253) of the proteins found in EVs could be regarded as virulence-associated proteins (Table 1 andSupplementary Table S3). In particular, these included toxins (e.g. LukMF' and LukGH leukocidins, αand β-class phenol soluble modulins, δ-haemolysin), adherence proteins (e.g. elastin binding protein) and host immune evasion factors (e.g. Sbi immunoglobulin-binding protein) as well as proteins involved in antibiotic resistance, survival during pathogenesis and lipoproteins. It should be noted that these factors were among the most abundant proteins in the EV proteome (Supplementary Table S2 and Supplementary Table S3). The distribution of EV proteins according to their functional characteristics was similar between strains, although some subtle differences were observed (Fig. 2E). In summary, the S. aureus EVs were rich in virulence-associated factors and contained proteins related to metabolic and translation functions.

S. aureus EV proteomes reflect strain properties.

To pinpoint potential correlations between strain properties (i.e. host of origin, severity of infection) and the protein cargo found in their EVs, heatmap and clustering analyses were performed on EV proteins from the five S. aureus strains (Fig. 3). First at all, whatever the dataset and clustering method, biological triplicates clustered together, suggesting that protein loading into an EV is a reproducible and selective process in each S. aureus strain. Based on a matrix for the presence/absence of proteins in vesicles (i.e. qualitative analysis; n = 253 proteins), heatmap clustering revealed a partial organisation of EVs according to the host origin of the strains (Fig. 3A). EVs from the two ovine strains formed a group, and MW2 and bovine EVs were the nearest and most distant from this group, respectively.

Based on the relative abundance of proteins in EVs (i.e. quantitative analysis; n = 70 proteins), their organisation was different and appeared to reflect the severity of the infections that producing strains could cause (Fig. 3B). Indeed, the first cluster contained EV proteins from strains involved in the development of mild mastitis (N305 and O46), while the second cluster was composed of EV proteins from strains involved in gangrenous and severe mastitis (O11 and RF122, respectively). This cluster also included MW2, a human isolate that is responsible for severe soft tissue and bloodstream infections. Several groups of proteins separated strains as a function of the severity of the infections they cause. Two of these contained proteins with an overall lower (group 1) and higher (group 2) relative abundance of the mild strains N305 and O46 (Fig. 3B,C). Group 1 aggregated virulence-associated factors (δ-haemolysin Hld, CamS, MW1483, FtnA, SitA) whereas group 2 mainly included proteins with housekeeping functions. Taken together, these results showed that the EV proteome reflects strain-specific features.

Characterisation of a core EV proteome in S. aureus. 119 of the 253 EV proteins identified (47%)

were common to all strains, while 20% were strain-specific (Fig. 4 and Supplementary Table S2). The core EV proteome, i.e. proteins shared by EVs secreted by all the tested strains, was mostly composed of cytoplasmic proteins (ranging from 36% to 60%, depending on the predictor used) and membrane proteins (ranging from 47 to 51%, depending on the predictor used). 54% of EV proteins with a lipoprotein signal peptide present in the dataset belonged to the core EV proteome (n = 27), which also contained more than 80% of proteins belonging to COG F (nucleotide transport and metabolism, 2/2), G (carbohydrate transport and metabolism, 7/7) and O (post-translational modification/protein turnover/chaperones, 4/5) categories. Other major categories concerned COG C (energy production and conversion, 14/24), J (translation/ribosomal structure and biogenesis, 14/38), M (cell wall/membrane/envelope biogenesis, 16/28) and P (inorganic ion transport and metabolism, 12/19). The core EV proteome notably gathered 59% (54/92) of the previously defined virulence-associated proteins (Table 1 and Supplementary Table S3).

To challenge the core EV proteome established for the MW2, N305, RF122, O11 and O46 strains, its overlap with the EV proteomes of Gram-positive bacteria available in the EVpedia database 30 was analysed (Supplementary Table S4). A large fraction (80%, n = 97) of core EV proteins had homologues present in the EVs of S. aureus RN4220. When compared to phylogenetically distant species, proteins belonging to the core EV proteome were also identified, notably in the EVs of Bacillus subtilis 168 Patterns, amino acid composition and physicochemical properties of the S. aureus EV proteome. The reproducible presence of proteins associated with EVs in biological replicates, and the characterisation of a core EV proteome shared by strains of different host origins, supported the hypothesis regarding the existence of selective processes for cargo sorting. To address these issues, the presence of protein patterns in core EV proteins, and the physicochemical properties and amino acid and nucleotide composition of their encoding sequences were assessed. The search for local sequence conservation within at least 3 core EV proteins, done by partial local multiple alignment of sequences with Protomata-Learner, enabled the detection of conservated motifs in only 44 out of the 119 non-redundant (<70% identity) core EV proteins. The most prevalent motif was present in 22 protein sequences, while the others were seen in between three and eight sequences (Supplementary Table S5). Among the 9 most significant motifs, five overlapped with four matches of known motifs or domains from PROSITE database: Lipoprotein signal peptide (PS51257), ATP/GTP-binding site motif A (PS00017), the iron siderophore/cobalamin periplasmic-binding domain (PS50983), and the ATP-binding cassette of ABC transporter (PS50893) (Supplementary Fig. S2 and Supplementary Table S5). The four other motifs had no counterparts in the PROSITE and pfam databases. Interestingly, all the motifs detected were composed of charged or aliphatic amino acid residues.

The overall physicochemical properties of EV proteins were compared to the whole proteomes of the MW2, N305, RF122, O11 and O46 strains (Supplementary Table S6). These analyses showed that the average isoelectric point, the GRAVY (grand average of hydropathy) value and the net charge at pH 7 of proteins packaged into EVs differed significantly (P < 0.001) from those of the whole cell proteomes (Fig. 5A and Supplementary Fig. S3). Notably, EV proteins were more globular and more positively charged at a physiological pH. In addition, proteins packaged into EVs significantly (P < 0.001) contained more charged, polar and tiny residues and fewer aromatic, aliphatic, hydrophobic and large residues than the whole cell proteome. By contrast, no differences were observed regarding the distribution of proteins according to their number of residues and composition in neutral and small residues, although differences were found at the level of individual amino acids: Ala, Gly and Lys were significantly overrepresented (P < 0.001) in EV proteins, whereas Cys, His, Ile, Leu, Phe and Tyr were underrepresented when compared to whole cell proteomes (Fig. 5A and Supplementary Fig. S4). Finally, a comparison carried out on the Codon Adaptation Index (CAI) of each gene revealed that the CAI values of genes coding for EV proteins were significantly higher (P < 0.0001) compared to those of the whole genome (Fig. 5A). For example, EV proteins in strain MW2 contained 36% more Lys compared to the other cell proteins (10.4% versus 7.6%), and the CAI value of the associated genes was 16% higher (0.68 versus 0.59), although they contained 20% fewer Phe, Ile, and Leu residues.

A discriminant analysis was used to identify the physicochemical properties that best discriminated the proteins according to their detection in EVs or not (Fig. 5B). EV proteins were positively associated with the first axis, which was characterised by charged and polar proteins rich in Glu, Asp and Lys and with high CAI values of the corresponding genes, but were negatively associated with proteins rich in hydrophobic, aliphatic and aromatic amino acids with high GRAVY values.

Overall, these results indicated an absence of conserved motifs within all EV protein sequences, but the existence of specific shared features such as positively charged proteins and high CAI values.

Discussion

The study of EVs produced by S. aureus is an emerging field of research, with pioneering work published in the late 2000s 24 . EVs from this bacterium are certainly some of the best documented among Gram-positive bacteria. They might indeed play a role in S. aureus pathogenesis and, as such, they offer interesting perspectives in medical applications. The characterisation of the EV protein cargo has been the subject of particular study 12,17,18,21-24 . However, most of this work focused on clinical isolates with emphasis on the contribution of EVs to the pathogenetic process. Moreover, variations to both the growth conditions used to produce EVs and the procedures for EV preparation and protein identification rarely enabled a reliable comparison of their protein content. Here, we used identical procedures to compare the proteins in EVs. We chose five S. aureus strains of diverse host origins (ovine, bovine, human) and causing different types of infection (from mild to severe) to reveal a core EV proteome and gain insight into the mechanisms that drive the selection of proteins to be packaged into EVs. Indeed, understanding how these proteins are selectively packaged offers a possible source of innovation regarding both intervention against pathogenic bacteria and the development of drug delivery systems.

Under our experimental conditions, all these S. aureus strains produced and secreted EVs into their surrounding environment. However, some differences between the strains were observed, notably regarding EV production yields and EV size. One remarkable feature was the co-purification of cylindrical (nanotube-like) structures together with EVs in the four animal strain samples. Such nanotubes had previously been observed in Gram-negative and a few Gram-positive bacteria, but with some differences regarding the size of the objects 31 . To the best of our knowledge, ours is the first report to have shown a co-purification of EVs with nanotube-like structures in S. aureus. MRSA strains such as MW2 are known to develop a thicker cell wall than methicillin-susceptible S. aureus strains 32 . A major barrier to the release of EVs by Gram-positive bacteria is the need to cross a thick cell wall. Whether this accounted for the absence of nanotube-like structures in the MW2 MRSA samples deserves further investigations, as does any biological role they may play.

The EVs of the five S. aureus strains studied here displayed similarities in terms of their cargo proteins, and a core EV proteome containing 119 proteins was identified. As previously shown for clinical isolates 12,17,21-24 , S. aureus EVs secreted by animal isolates are characterised by the presence of numerous proteins with roles in bacterial survival and pathogenesis. These include toxins (phenol soluble modulins, haemolysin, leukocidin), invasion factors (elastin binding protein, autolysin, extracellular adherence protein), enzymes (nuclease, proteases), surface antigens and immunoglobulin-binding protein. The EV cargo also includes proteins that are involved in metal ion acquisition and allow bacterial cells to circumvent metal ion starvation within the host during infection 33 . No nutritional requirements have yet been described for EVs. Presence of such proteins in the EV proteome thus suggests that EVs act as a source of additional metal ions made available at distance for other invading bacterial cells 34 . Alternatively, they could disturb metal ion acquisition in competing bacterial cells or host cells. The EV cargo also comprises numerous lipoproteins, some of which contribute to the nutrient transport, Toll-like receptor 2 activation and pathogenicity of S. aureus 35,36 . Most of these proteins belong to the core EV proteome, indicating that the secretion through EVs of proteins involved in many facets of S. aureus pathogenesis represents a common strategy for both animal and human S. aureus strains. Moreover, the EVs analysed here also contained antibiotic resistance-associated proteins such as penicillin-binding proteins (in four of the five strains studied) or β-lactamase (in MW2). It was shown that EVs carrying biologically active antibiotic degrading enzymes such as β-lactamases were able to confer transient resistance on susceptible surrounding bacteria 20,37,38 . Indeed, penicillin-binding proteins may act as lures or catching agents and titrate antibiotics in the surrounding medium. Whether these antibiotic resistance-associated proteins do confer transient resistance on distant bacterial cells still needs to be explored and could have interesting implications for the treatment of staphylococcal infections.

A remarkable overlap (>80%) was noted between the core EV proteins that we identified here and the EV proteins previously described in the human S. aureus strain RN4220, although we could have expected a fall in the number of shared proteins in the core EV proteome as EVs from more S. aureus strains were included. This overlap was even more consistent because different growth conditions and methods for EV preparation and protein identification were used 39 . When compared to EV proteomes from phylogenetically distant Gram-positive bacteria, some proteins also appeared to be conserved. This notably included several moonlighting proteins (e.g. enolase, GAPDH, EF-Tu) with functions in adhesion, membrane fusion, and internalisation 40-43 . Because they are considered as a protein secretion pathway, EVs may thus form part of the mechanism for the sorting of moonlighting proteins 44 . Moreover, conservation of these proteins within the EVs of phylogenetically distant species, including Firmicutes and Actinobacteria, might also account for a generic process involved in intercellular interactions; i.e. the attachment and fusion of EVs to the membrane of targeted cells.

EVs mirror the properties of producing strains despite their compositional and functional similarities so could be considered as potential markers for S. aureus infections in both human and veterinary medicine. Indeed, based on their protein content, they can be stratified with respect to the host origin (bovine, ovine, or human) of the strains and the severity of infections they cause. Notably, the relative abundance of proteins in EVs is a criterion that can discriminate them according to the severity of infections. Two groups of proteins with contrasted relative abundances of mild and severe strains were identified during the present study, one of them aggregating several virulence-associated factors (e.g. δ-haemolysin Hld) that was less abundant in mild strains than severe strains. S. aureus strains of bovine and ovine origins have evolved to adapt to their animal hosts and differ from human strains at the genotypic and genomic levels 45,46 . In some cases, species-specific traits are also observed at the molecular level, as exemplified for example by the bovine variant of von Willebrand factor-binding protein 47 , or ovine-specific exfoliative toxin type E 48 . Here, none of the EV proteins specifically associated with ovine (n = 6), bovine (n = 6) or human (n = 5) strains presented any functions that could be related specifically to their host origin, with the exception of beta-lactamase for strain MW2. Moreover, among the 52 strain-specific EV proteins identified here (i.e. proteins found in the EVs of one strain) only nine were strictly strain-specific, because the others were present in the predicted whole proteome of the other strains. This demonstrates that drivers other than the presence of a protein in the whole proteome can account for the strain-specific cargo of EV proteins.

The hypothesis that specific and conserved rules for protein loading into EVs exist was further supported by the identification of a core EV proteome for a variety of strains and the partial conservation of protein cargo in phylogenetically distant species. The mechanisms that control protein packaging into EVs still remain obscure. Our analysis has provided some information on drivers that might influence this process. We found compositional biases in the nucleotide sequences of genes encoding EV proteins and in the amino acid sequences of EV proteins themselves. When compared to the whole genome, the CAI value of genes coding for EV proteins was significantly 238 higher. Likewise, EVs contained proteins with more positively charged residues (notably Lys) than in the whole predicted proteome. It is often thought that CAI reflects the level of protein expression, with high CAI values being associated with highly expressed proteins 49-51 . Such a feature might reflect the possible enrichment of highly expressed proteins in EVs. In line with this, almost half of the 50 most abundant whole cytoplasmic proteins of S. aureus COL were found in EVs during our study 52 . It seems consistent that the relative abundance of a protein in the whole cell could affect its availability to be packed into EVs: the more abundant a protein, the greater the probability of it being loaded into EVs in the absence of, or in addition to, a selective process. The biases regarding the amino acid composition of EV proteins also suggested the selective packaging of positively charged proteins. In line with this, the theoretical PI value of about half of EV proteins is higher than 8, indicating that they are probably positively charged at a physiological pH. Electrostatic interactions are known to contribute to the subcellular targeting of proteins 53,54 . In particular, the negative charges associated with the inner surface of the membrane can direct cationic and basic proteins to a peripheral membrane localisation 55 . On the other hand, lipid homeostasis of the membrane is a crucial key for EV biogenesis 22,23,56-58 . Notably, an accumulation of negative charges in lipid microdomains contributes to membrane curvature and EV biogenesis. The presence of more negatively charged microdomains at the cytoplasmic surface of the membrane may favour the recruitment of positively charged proteins through electrostatic interactions at the site of EV formation, and hence their selective capture.

Our findings show that EVs act as a "proxy" for producing strains in S. aureus, and that host-specific profiles can be seen in the protein composition of EVs as well as in the producing strains. Our analysis of EV protein features suggested that abundance, charge and subcellular localisation could influence the protein availability of the vesicle cargo in S. aureus. However, not all proteins with high CAI values, highly expressed or with basic domains were packed within EVs, indicating the existence of a selective process following additional rules. The identification of a core EV proteome offers an opportunity to reveal specific signature(s) (e.g. signals, motifs, domains) in protein sequences that might be characteristic of the molecular machinery (or machineries) dedicated to the selection of proteins into EV cargoes. Despite our efforts, no specific signature could be evidenced in EV protein sequences, except for signal peptide and lipoprotein signal peptide. It is interesting to note that proteins known to be associated with the biogenesis of S. aureus-secreted EVs (PSM, autolysin) belong to the core EV proteome. One can suppose that the EV cargo also contains components for the selection of proteins into EVs. Chaperones and protein secretion systems, among others, are good candidates, but they still need to be assessed.

Methods

Bacterial strains and growth conditions. Staphylococcus aureus strains used in this study were chosen, based on their representativeness with regard to the clonal complex (CC) associated to a given host (Supplementary Table S1). The CC of the strains used in this study are reportedly dominant (or at least frequent) in S. aureus strains associated to human (CC1), ovine (CC130), and bovine (CC97, CC151). Furthermore, the studied strains are well documented with genome sequence and phenotype information publicly available (Supplementary Table S1). S. aureus ovine strains O11 and O46 were isolated from gangrenous and subclinical mastitis, respectively, and were shown experimentally to induce severe or mild mastitis in ewes 59,60 . Both were isolated in 2003 in Southeast France and kindly provided by Dr Eric Vautor (ANSES, Sophia Antipolis, France). Bovine strain RF122 was isolated from a case of clinical mastitis 61,62 . Bovine strain Newbould 305 (N305) was shown to reproducibly induce mild and chronic mastitis in bovines 63,64 . RF122 and N305 were kindly provided by Pr Ross J. Fitgerald (Edinburgh Infectious Diseases, Roslin Institute, University of Edinburgh, UK) and Dr Pascal Rainard (ISP, INRAE, Nouzilly France), respectively. The human strain MW2 (ATCC BAA-1707) was isolated from a severe case of hospital acquired infection 65 and is reportedly highly virulent 66 . The human strain MW2 was obtained from the Laboratory of Human Bacterial Pathogenesis, National Institutes of Health (Bethesda, MD, USA). The S. aureus strains were grown in Brain Heart Infusion (BHI) medium (Difco, pH 7.4) at 37 °C under agitation (150 rpm). The concentrations and growth phases of the bacteria were estimated from spectrophotometric measurements of optical density at 600 nm (VWR V-1200 spectrophotometer). They were further confirmed routinely by counting the colony forming units (CFU) on BHI agar using the micromethod 67 .

Purification of S. aureus-secreted

EVs from culture supernatants. EVs were purified from S. aureus culture supernatants as described previously 18 . Briefly, sub-cultured cells at the end of the exponential phase were diluted in 1 L of fresh BHI medium. The cultures were grown until the early stationary phase to optimise the number of EVs that could be recovered. The cells were then pelleted at 6,000 g for 15 min and the supernatant fraction was filtered through a 0.22 µm vacuum filter (PES). The filtrate was concentrated using the Amicon ultrafiltration system (Millipore) with a 100 kDa filter and subjected to ultracentrifugation at 150,000 g for 120 min at 4 °C. The pellet was then re-suspended in 8% sucrose in tris-buffered saline (TBS) (150 mM NaCl, 50 mM Tris-Cl, pH 7.5), overlaid with sucrose dilutions ranging from 8% to 68% in TBS and centrifuged at 100,000 g for 150 min at 4 °C in a SW 55 Ti rotor (Beckman Coulter). The different density fractions were collected, and those containing a similar number of EVs, with a similar particle-size distribution and protein pattern were pooled, centrifuged at 150,000 g for 120 min at 4 °C and re-suspended in TBS. EV fractions and isolated EV samples were routinely verified by electron microscopy and Bradford assay (Bio-Rad) and quantified using Nanoparticle Tracking Analysis before being stored at -20 °C until use. electron microscopy. The purity and quality of EV samples were confirmed by negative staining electron microscopy and cryo-electron tomography, as previously described 18 . Images were acquired and analysed at the Rennes Microscopy Imaging Centre platform (MRic MET) (University of Rennes 1, Rennes, France).

Nanoparticle tracking analysis (NTA). NanoSight NS300 (Malvern Instruments, United Kingdom) with a 488 nm laser module and sCMOS camera type were used for all measurements of S. aureus EVs. All counts were 239 performed in replicates of five videos of 60 s for each sample (n = 3) and measured in flow mode using a syringe pump. The EVs were thawed and diluted in TBS until optimum visualisation of a maximum number of vesicles was achieved. All quantifications were performed at a controlled temperature of 25 °C, and the measurement data captured were analysed using NTA 3.3 software (Malvern Instruments). We confirmed that the TBS was free of contamination with any other nanoparticles prior to all measurements.

Identification of proteins in S. aureus EVs. Three independent biological replicates of each S. aureus purified EV were digested for NanoLC-ESI-MS/MS analysis. Approximately 50 µg of EVs were pelleted at 150,000 g for 2 h at 4 °C and suspended in a solution of 6 M Guanidine-HCl (Sigma-Aldrich), 50 mM Tris-HCl (pH 8.0) (VWR) and 2 mM DTT (Sigma-Aldrich). The EVs were heated at 95 °C for 20 min and cooled in 50 mM NH 4 HCO 3 (pH 7.8) (Sigma-Aldrich). The samples were then digested in solution using sequencing grade-modified trypsin (Promega) at an enzyme:protein ratio of 1:50 for 15 h at 37 °C, as previously reported 24 . After digestion, the peptides were stored at -20 °C until further analysis. NanoLC-MS/MS experiments were performed as previously described 18 . The peptides were identified from the MS/MS spectra using X!TandemPipeline software 68 and searches performed against the genome sequence of S. aureus MW2, N305, RF122, O11 and O46 strains. The database search parameters were specified as follows: trypsin cleavage was used and the peptide mass tolerance was set at 10 ppm for MS and 0.05 Da for MS/MS. Methionine oxidation was selected as a variable modification. For each peptide identified, a minimum e-value lower than 0.05 was considered to be a prerequisite for validation. A minimum of two peptides per protein was imposed, resulting in a false discovery rate (FDR) < 01% for peptide and protein identifications.

For protein quantification, each peptide identified by tandem mass spectrometry was quantified using the free MassChroQ software 69,70 before data treatment and statistical analysis with R software (R 3.2.2, Project for statistical computing). A specific R package called 'MassChroqR' (v0.4.3) was used to automatically filter dubious peptides and group the peptide quantification data into proteins. Two different and complementary analytical methods were used, based on peak counting or XIC (eXtracted Ion Current). For peak counting, variance analysis was performed on proteins with a minimum peak ratio of 1.5 between both culture conditions. Proteins with an adjusted P-value < 0.05 were considered to be significantly different. For XIC based quantifications, normalisation was performed to take account of possible global quantitative variations between LC-MS runs. Peptides shared between different proteins were automatically excluded from the dataset, as were peptides present in fewer than two of the three biological replicates. Missing data were then imputed from a linear regression based on other peptide intensities for the same protein. Analysis of variance was used to determine proteins whose abundance differed significantly between strains.

Bioinformatics analysis.

All proteins were searched in the NCBI (https://www.ncbi.nlm.nih.gov/) and UniProt (http://www.uniprot.org/) databases. In-house bash script embedding NCBI's Entrez Direct tools was used to retrieve locus tags from Uniprot id, and PATRIC 71 was used to find locus tags of orthologous genes between the five strains. Several pipelines based on different methods were used to predict EV protein localisations: CELLO 72 , PredLipo 73 , PsortB version 3.0.2 74 and SignalP 5.0 75 . Clusters of Orthologous Groups of proteins (COGs) were used to categorise S. aureus EV proteins 76 . Motifs in protein sequences were discovered using Protomata learner 2.0 (http://tools.genouest.org/tools/protomata/learn/) 77 , while matches of known PROSITE motifs and profiles were identified with ScanProsite 78 . Amino acid composition, physicochemical properties, the GRAVY (grand average of hydropathy) value and the theoretical isolectric point of proteins from EVs and whole cells in the N305, RF122, O11, O46 and MW2 strains were computed with COPid 79 and the Sequence Manipulation Suite 80 from genomic data present in the MicroScope platform 81 . The Codon Adaptation Index (CAI) values of each gene were obtained from the MicroScope platform. Moonlighting proteins were identified using the MoonProt database 82 . Comparisons of proteins from the EVs of S. aureus strains was performed using InteractiVenn 83 .

Statistical analyses.

The data were presented as mean ± standard error. The differences between groups were verified using one-way ANOVA followed by Dunnett's or Tukey's multiple comparisons test with GraphPad Prism version 8.3.1 for macOS (GraphPad Software, San Diego, California USA). A P-value lower than 0.01 was considered to be significant. A partial least squares discriminant analysis (PLS-DA) was performed using the mixOmics R package to identify the variables that best discriminated the MW2 cell proteins identified in EVs from the other MW2 proteins among the 40 variables that described the amino acid composition and physicochemical properties of the proteins, and the CAI of the corresponding genes. PLS-DA was performed on a dataset made up of 40 variables for the 2759 proteins in S. aureus MW2, including 253 EV proteins and 2506 proteins not identified in EVs.

The roles of bacterial extracellular vesicles (EVs) in cell-to-cell signaling are progressively being unraveled. These membranous spheres released by many living cells carry various macromolecules, some of which influence host-pathogen interactions. Bacterial EVs contain RNA, which may serve in communicating with their infected hosts. Staphylococcus aureus, an opportunistic human and animal pathogen, produces EVs whose RNA content is still poorly characterized. Here, we investigated in depth the RNA content of S. aureus EVs. A highthroughput RNA sequencing approach identified RNAs in EVs produced by the clinical S. aureus strain HG003 under different environmental conditions: early-and late-stationary growth phases, and presence or absence of a sublethal vancomycin concentration. On average, sequences corresponding to 78.0% of the annotated transcripts in HG003 genome were identified in HG003 EVs. However, only ~5% of them were highly covered by reads (≥90% coverage) indicating that a large fraction of EV RNAs, notably mRNAs and sRNAs, were fragmented in EVs. According to growth conditions, from 86 to 273 highly covered RNAs were identified into the EVs. They corresponded to 286 unique RNAs, including 220 mRNAs. They coded for numerous virulence-associated factors (hld encoded by the multifunctional sRNA RNAIII, agrBCD, psmβ1, sbi, spa, and isaB), ribosomal proteins, transcriptional regulators, and metabolic enzymes. Twenty-eight sRNAs were also detected, including bona fide RsaC. The presence of 22 RNAs within HG003 EVs was confirmed by reverse transcription quantitative PCR (RT-qPCR) experiments. Several of these 286 RNAs were shown to belong to the same transcriptional units in S. aureus. Both nature and abundance of the EV RNAs were dramatically affected depending on the growth phase and the presence of vancomycin, whereas much less variations were found in the pool of cellular RNAs of the parent cells. Moreover, the RNA abundance pattern differed between EVs and EV-producing cells according to the growth conditions. Altogether, our findings show that the environment shapes the RNA cargo of the S. aureus EVs. Although the composition of EVs is impacted by the physiological state of the producing cells, our findings suggest a selective packaging of RNAs into EVs, as proposed for EV protein cargo. Our study shedds light to the possible roles of potentially functional RNAs in S. aureus EVs, notably in host-pathogen interactions.

INTRODUCTION

The release of extracellular vesicles (EVs) by living cells is a well-established phenomenon required for intercellular communications and trans-kingdom interactions (Brown et al., 2015;Toyofuku, 2019). These spherical membranous particles vary from 20 to 300 nm in diameter and contain macromolecules such as nucleic acids, proteins, lipids, and small metabolites. Initially considered to be trash bags to eliminate unwanted material outside of the cells, they are now widely recognized as protective delivery shuttles of bioactive molecules from donor to recipient cells (Brown et al., 2015;Kim et al., 2015;Gill et al., 2019). The functional characterization of bacterial EVs is of interest due to their capacities to affect bacteria-host cell interactions and bacterial pathogenesis [START_REF] Kaparakis-Liaskos | Immune modulation by bacterial outer membrane vesicles[END_REF][START_REF] Tsatsaronis | Extracellular vesicle RNA: a universal mediator of microbial communication?[END_REF]. Although the formation of outer membrane vesicles (OMVs) in Gram-negative bacteria was early documented in 1966 [START_REF] Work | THE CHEMISTRY AND ELECTRON MICROSCOPY OF AN EXTRACELLULAR LIPOPOLYSACCHARIDE FROM ESCHERICHIA COLI[END_REF], the formation of such structures was disregarded in Gram-positive bacteria until recently. The production of EVs by a Gram-positive bacterium, Staphylococcus aureus, was demonstrated in 2009 and, ever since, numereous studies confirmed EV release by other Gram-positive bacteria (Lee et al., 2009(Lee et al., , 2013a;;Rivera et al., 2010;Prados-Rosales et al., 2011;[START_REF] Brown | Extracellular vesicles produced by the gram-positive bacterium Bacillus subtilis are disrupted by the lipopeptide surfactin[END_REF][START_REF] Olaya-Abril | Characterization of protective extracellular membrane-derived vesicles produced by Streptococcus pneumoniae[END_REF]Kim et al., 2016a;[START_REF] Liu | Gram-Positive bacterial extracellular vesicles and their impact on health and disease[END_REF].

Staphylococcus aureus commonly colonizes the skin or nasal tract of vertebrates, without causing disease [START_REF] Wertheim | The role of nasal carriage in Staphylococcus aureus infections[END_REF]. However, it is also one of the main opportunistic pathogen in humans, and a frequent cause of multi-drug resistant nosocomial infections [START_REF] Ziebuhr | Staphylococcus aureus and Staphylococcus epidermidis: emerging pathogens in nosocomial infections[END_REF]. S. aureus is responsible for a wide array of diseases, ranging from minor infections in soft tissues to life-threatening diseases, such as sepsis, meningitis, and pneumonia [START_REF] Salgado-Pabón | Models matter: the search for an effective Staphylococcus aureus vaccine[END_REF][START_REF] Tong | Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management[END_REF]. The type and severity of infections depend on strain-specific virulence factors, mostly expressed from accessory genetic elements [START_REF] Gill | Potential associations between severity of infection and the presence of virulence-associated genes in clinical strains of Staphylococcus aureus[END_REF]. Secreted and surface-exposed S. aureus virulence factors weaken the host immune response, leading to bacterial immune evasion and pathogenesis [START_REF] Foster | Immune evasion by staphylococci[END_REF]. EVs could be a vehicle for secretion and surface-display of these molecules and, accordingly, recent studies indicate that S. aureus EVs carry important bacterial survival and virulence factors, such as β-lactamases, toxins, and proteins involved in adhesion to host cells (Lee et al., 2009;[START_REF] Gurung | Staphylococcus aureus produces membrane-derived vesicles that induce host cell death[END_REF][START_REF] Jeon | Variation among Staphylococcus aureus membrane vesicle proteomes affects cytotoxicity of host cells[END_REF][START_REF] Askarian | Staphylococcus aureus membrane-derived vesicles promote bacterial virulence and confer protective immunity in murine infection models[END_REF]Tartaglia et al., 2018Tartaglia et al., , 2020;;Wang et al., 2018).

Biologically active β-lactamase in S. aureus EVs can confer a transient resistance against ampicillin to surrounding sensible bacteria (Lee et al., 2013b). Furthermore, the presence of α-hemolysin inside EVs accelerates host cell death (Thay et al., 2013;[START_REF] Hong | An important role of α-hemolysin in extracellular vesicles on the development of atopic dermatitis induced by Staphylococcus aureus[END_REF], and EV-associated exfoliative toxin A (ETA) induces a characteristic toxicity onto human epithelial cells [START_REF] Jeon | Variation among Staphylococcus aureus membrane vesicle proteomes affects cytotoxicity of host cells[END_REF]. Moreover, S. aureus-derived EVs facilitate the induction and exacerbation of skin and pulmonary inflammations [START_REF] Hong | Extracellular vesicles derived from Staphylococcus aureus induce atopic dermatitis-like skin inflammation[END_REF][START_REF] Hong | An important role of α-hemolysin in extracellular vesicles on the development of atopic dermatitis induced by Staphylococcus aureus[END_REF][START_REF] Kim | Staphylococcus aureus-derived extracellular vesicles induce neutrophilic pulmonary inflammation via both Th1 and Th17 cell responses[END_REF][START_REF] Jun | Staphylococcus aureus-derived membrane vesicles exacerbate skin inflammation in atopic dermatitis[END_REF]. EVs-associated molecules can be more efficient than cytoplasmic proteins to elicit an immune response and host-cell toxicity [START_REF] Hong | An important role of α-hemolysin in extracellular vesicles on the development of atopic dermatitis induced by Staphylococcus aureus[END_REF]. In response to antibiotics exposures, EVs increase S. aureus adhesion and cell aggregation, and contribute to biofilm formation (He et al., 2017). Recent data highlight the importance of EVs in staphylococcal pathogenesis since EVs derived from various human and animal strains of S. aureus share a conserved EV proteome (Tartaglia et al., 2020).

The vast majority of functional studies on bacterial EVs, however, challenged their proteome. Regarding the presence of DNAs and RNAs in EVs, most studies have been conducted on Gram-negative bacteria [START_REF] Perez Vidakovics | B cell activation by outer membrane vesicles -a novel virulence mechanism[END_REF]Blenkiron et al., 2016;Koeppen et al., 2016;Bitto et al., 2017;Choi et al., 2017;[START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF][START_REF] Yu | Versatile effects of bacteriumreleased membrane vesicles on mammalian cells and infectious/inflammatory diseases[END_REF][START_REF] Han | Extracellular RNAs in periodontopathogenic outer membrane vesicles promote TNF-α production in human macrophages and cross the blood-brain barrier in mice[END_REF]. OMV-associated RNAs can include messenger RNAs (mRNA), transfer RNAs (tRNA), ribosomal RNAs (rRNA), or small regulatory RNAs (sRNA; Biller et al., 2014;Ghosal et al., 2015;Ho et al., 2015;Sjöström et al., 2015;Blenkiron et al., 2016;Koeppen et al., 2016;Choi et al., 2017;Dauros-Singorenko et al., 2018;Liu et al., 2018a;[START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF][START_REF] Tsatsaronis | Extracellular vesicle RNA: a universal mediator of microbial communication?[END_REF]Frantz et al., 2019). EV-associated RNA cargo, notably sRNAs, can influence host-pathogen interactions, cell-to-cell communications, and bacterial pathogenesis (Dauros-Singorenko et al., 2018;[START_REF] Tsatsaronis | Extracellular vesicle RNA: a universal mediator of microbial communication?[END_REF][START_REF] Lee | Microbe-host communication by small RNAs in extracellular vesicles: vehicles for transkingdom RNA transportation[END_REF]Ahmadi Badi et al., 2020;Lécrivain and Beckmann, 2020). For instance, OMVs from Pseudomonas aeruginosa can transfer an sRNA into the human airway cells, resulting in IL-8 decrease (Koeppen et al., 2016). Likewise, transfection of OMV-associated sRNAs from the periodontal pathogens Aggregatibacter actinomycetemcomitans, Phorphyromonas gingivalis, and Trepanema denticola into human cells reduced host interleukine release (Choi et al., 2017). The presence of RNAs within Gram-positive EVs has been reported for fewer species (Resch et al., 2016;Dauros Singorenko et al., 2017;Frantz et al., 2019;Rodriguez and Kuehn, 2020). Interestingly, Frantz et al. (2019) recently reported that the EV-associated rli32 sRNA of Listeria monocytogenes can trigger the induction of a type I IFN response in host cells. This finding supports that Gram-positive EVs can also participate to host-pathogen interactions by dedicated vesicular RNAs. Data about RNA cargo in EVs released by S. aureus are scarce, with only two recent reports. While the first provided a partial RNA profile of S. aureus MSSA476 EVs without functional analyses [START_REF] Joshi | Transcriptome profiling of Staphylococcus aureus associated Frontiers in Microbiology | www.frontiersin.org 15[END_REF], the second showed that the uncharacterized RNA content of S. aureus Newman EVs likely stimulate the potent IFN-β response observed in cultured macrophage cells (Rodriguez and Kuehn, 2020).

As far as we know, our work is the first example that provides a detailed RNA profile associated to EVs from a reference clinical S. aureus strain, HG003. The staphylococcal EV RNA cargo was unveiled by high-throughput RNA sequencing from purified EVs after release by cells grown under various environmental conditions. They include early-and late-stationary growth phases, with or without a sublethal concentration of vancomycin, an antibiotic used to treat multidrug-resistant infections and that influences S. aureus EV biogenesis and functions [START_REF] Hsu | Vancomycin promotes the bacterial autolysis, release of extracellular DNA, and biofilm formation in vancomycin-non-susceptible Staphylococcus aureus[END_REF]He et al., 2017). The RNA cargo from the EVs was analyzed and compared to the RNA content of the HG003 parental cells.

MATERIALS AND METHODS

Bacterial Strain and Growth Conditions

The S. aureus strain used in this work was the model strain HG003 [START_REF] Herbert | Repair of global regulators in Staphylococcus aureus 8325 and comparative analysis with other clinical isolates[END_REF], a NCTC8325 derivative, isolated in 1960 from a sepsis patient. HG003 contains functional rsbU and tcaR genes, two global regulators that are missing in the NCTC8325 parent strain. The HG003 genome is well documented [START_REF] Sassi | Draft genome sequence of Staphylococcus aureus subsp. aureus strain HG003, an NCTC8325 derivative[END_REF], and this strain is widely used as a reference to investigate staphylococcal regulation and virulence (Liu et al., 2018a). HG003 strain was pre-inoculated in BHI broth and grown overnight at 37°C under 150 rpm/min agitation, and then inoculated 0.1% in 500 ml of fresh BHI (125 rpm/min, at 37°C) on a 1 L Scott flask. Bacterial cultures were retrieved after 6 h and 12 h for early-and late-stationary phases, respectively, in the presence or absence of a sub-inhibitory concentration (0.5 μg/ml) of vancomycin (Supplementary Figure S1).

S. aureus EVs Isolation and Purification

Cultures were submitted to EVs isolation and purification, as previously described (Tartaglia et al., 2018(Tartaglia et al., , 2020)). In brief, for each condition 1 L of bacterial cell culture was centrifuged at 6,000 × g for 15 min and filtered through 0.22 μm Nalgene top filters (Thermo Scientific). Then, the culture supernatant fraction was concentrated around 100-fold using the Amicon ultrafiltration systems (Millipore) with a 100kDa filter, and ultra-centrifuged for 120 min at 150,000 × g to eliminate the soluble proteins. Next, the suspended pellet was applied to a discontinuous sucrose gradient (8-68%) and ultra-centrifuged at 100,000 × g for 150 min. Fractions containing EVs were recovered and washed in TBS (150 mM NaCl; 50 mM Tris-Cl, pH 7.5) for final ultra-centrifugation at 150,000 × g (120 min). At last, EVs were suspended in cold TBS and kept at -80°C until use.

EVs Visualization by Electron Microscopy

Negative staining electron microscopy was performed as previously described (Rodovalho et al., 2020) to investigate the shape and integrity of purified EVs. EVs samples were diluted, and solutions containing between 10 10 and 10 11 particles per ml were analyzed. For this, samples were applied to glowdischarged copper EM grids (FF200-Cu) for 30 s, followed by excess solution removal with filter paper. The same process was repeated with 2% uranyl acetate, and samples were observed with a Jeol 1400 transmission electron microscope (JEOL Ltd.), operating at 120 kV.

Determination of EVs Sizes and Concentrations

Nanoparticle Tracking Analysis (NTA) using an sCMOS camera and a Blue488 laser (Nano Sight NS300) was performed to assess EVs size and concentration. For that, samples were diluted into TBS to achieve optimal concentration and submitted to a constant flux generated by a syringe pump (speed 50), at 25°C. Results were retrieved from 5 × 60 s videos recorded with camera level at 15 and threshold at 5, while other parameters were adjusted as necessary.

RNA Extraction From S. aureus HG003 Whole Cells and Its Derived EVs

RNA extraction was carried out as similar as possible for both cell and EV samples. Bacterial RNA extraction was performed from 10 ml culture pellet. The samples were mixed with glass beads in 300 μl lysis buffer (0.5% SDS w/v, 30 mM sodium acetate; 1 mM EDTA) and 400 μl phenol (acid buffered at pH 5.0) at 65°C. Mechanical lysis was accomplished with 2 cycles of 30 s in Precellys at 6,500 rpm. For EV sample RNA extraction, particles isolated from the equivalent of 800 ml bacterial culture were mixed with 300 μl of lysis buffer and 400 μl phenol at 65°C, the same volumes used for cell RNA extraction. Since EVs lack the thick layer of peptidoglycan (PGN) found in the bacterial cell wall, mechanic lysis was not necessary and was achieved with lysis buffer. EV and EV-producing cell samples were incubated for 10 min at 65°C, being homogenized by vortex every minute. Next, samples were centrifuged during 10 min 13,000 rpm, 4°C, and the upper phase was recovered to a new tube. All samples were mixed with additional 400 μl of phenol at 65°C, and the previous steps were repeated. Then, 400 μl of phenol:chloroform 1:1 was added, followed by two times addition of 400 μl pure chloroform, repeating the step of upper phase recovery, mixture and centrifugation (5 min at 13,000 rpm, 4°C). Subsequently, 1.5 volumes of ice-cold 100% ethanol and 10% volume of NaAc were added and the mix was stored at -20°C overnight.

Samples were centrifuged at 13,000 rpm for 30 min at 4°C, and the pellets were washed twice with 1 ml of cold 70% ethanol. Finally, the pellets were dried with a SpeedVac concentrator for 2 min and dissolved in RNase-free water.

The quality and quantity of the RNAs were verified by Nano Drop, agarose gel, and Bioanalyzer (Agilent). Samples were kept at -80°C until use. No RNase treatment was applied to bacterial cell or to EV samples before RNA extraction.

RNA Sequencing

The RNA samples were sent to ViroScan3D ® (Lyon, France) for DNA removal, ribosomal RNA depletion and RNA sequencing. Total RNA samples were submitted to a DNase treatment with RNase-Free DNase Set (Qiagen) according to manufacturer's instructions. Then, the samples quantified using the Quantifluor RNA system (Promega), and qualified using RNA Nano Chip on Bioanalyzer 2100 (Agilent) for the EV-producing cell samples, and on the SS RNA system on Fragment Analyzer (AATI) for the EVs samples. RNA samples were then submitted to the standard protocol Ovation Universal Prokaryotic RNA-Seq, Nugen, Anydeplete rRNA, library preparation. RNA quantity used for library preparation are displayed in Supplementary Table S1. The quality of libraries was assessed with the Quantifluor DNA system (Promega) and qualified with the HS-NGS system on Fragment Analyzer (Aati). The insert mean size of the libraries was 0.34 kp for the EV-producing cell samples, and 0. the EV samples (Supplementary Table S1). Sequencing was performed with Illumina, NextSeq500, 75 cycles, single-read, High Output. For each experimental condition, three biological replicates were sequenced. EV-producing cell samples ranged from 9 to 27 million pair-end reads per sample, and EV samples ranged from 30 to 67 million pair-end reads per sample. Reads mapping to the reference genome ranged from 8 to 26 million, and from 0.38 to 27 million reads for EV-producing cells and EV samples, respectively. Basic statistics of the RNA-Seq data are displayed in Supplementary Table S1.

Transcriptome Analysis

The reads were cleaned and trimmed with Trim-Galore [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] using the default parameters. Reads were mapped with Bowtie2 [START_REF] Langmead | Fast gapped-read alignment with bowtie 2[END_REF] in local mode against two staphylococcal genomes used as references: the NCTC 8325 (NC_007795.1) reference genome with sRNA annotation from SRD [START_REF] Sassi | SRD: a staphylococcus regulatory RNA database[END_REF] and the HG003 genome (GCA_000736455.1) for the non-annotated genes in NCTC8325 genome. Genes were counted with FeatureCounts (Liao et al., 2014) with the strand, the multi mapping, and the overlapping options.

A list of differentially expressed RNAs was obtained by EdgeR [START_REF] Robinson | edgeR: a bioconductor package for differential expression analysis of digital gene expression data[END_REF] embedded in SARTools [START_REF] Varet | SARTools: a DESeq2-and edgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data[END_REF]. The threshold of statistical significance was set to 0.05, with the adjustment method of Benjamini-Hochberg. RNA coverage was calculated with Bedtools coverage [START_REF] Quinlan | BEDTools: a flexible suite of utilities for comparing genomic features[END_REF]. RNAs with ≥ 90% coverage in at least one EV condition were kept for further indepth analysis. RNA coverage visualization was performed with the Integrative Viewer Software (IGV; [START_REF] Thorvaldsdóttir | Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration[END_REF] on a log scale.

Subcellular location prediction was performed with SurfG+ [START_REF] Barinov | Prediction of surface exposed proteins in Streptococcus pyogenes, with a potential application to other gram-positive bacteria[END_REF]. Clusters of Orthologous Groups (COGs) and KEGG categories were obtained using the eggNOGmapper v2 web tool (Huerta-cepas et al., 2017(Huerta-cepas et al., , 2019)). Functional enrichment analysis was performed with g:Profiler web-server (Raudvere et al., 2019;Reimand et al., 2019). A maximum value of p 0.05 was set as a threshold for significative categories.

A timepoint clustering study was conducted with the R package maSigPro [START_REF] Conesa | maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments[END_REF][START_REF] Nueda | Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series[END_REF] on highly covered EV RNAs with normalized counts by EdgeR. In this analysis vancomycin treatment is not taken into consideration. The threshold of statistical significance was set to 0.05, with the adjustment method of Benjamini-Hochberg.

RT-qPCR

Reverse transcription quantitative PCR (RT-qPCR) was used to validate RNA-seq results. EVs were isolated from the cellfree supernatants of three new independent S. aureus cultures at late-stationary growth phases (12 h) in the absence of vancomycin. EV RNAs were purified as mentioned above. Around 1.5 μg of RNAs was treated with DNAse I (Amplification Grade, Invitrogen) according to manufacturer's instructions. cDNA synthesis was performed with the high capacity cDNA Reverse Transcription kit (Applied Biosystems). The primers used for quantitative PCR (qPCR) are listed in Supplementary Table S2 and were designed using eprimer3 software (EMBOSS). qPCR was carried out in a 16 μl volume containing 15 ng cDNA, specific primers (300 nM), and 8 μl IQ ™ SYBR Green Supermix (Bio-Rad). Reactions were run on a CFX96 real-time system (Bio-Rad, France) using the following cycling parameters: DNA polymerase activation and DNA denaturation 95°C for 5 min, 40 cycles of denaturation at 94°C for 15 s, and extension at 60°C for 30 s. Melting curve analysis was included to check the amplification of single PCR products. Samples setups included biological triplicates and technical duplicates as well as negative controls corresponding to qPCR reactions performed without cDNA (cDNA negative control) and from RT reactions obtained from EV RNAs without reverse transcriptase (RT negative control). Results were analyzed with the GFX Manager software and Ct values were determined. Results with Ct equal or above 40 were considered negative and only experiments with ΔCt ≥ 4 between negative controls and RT samples were considered.

RESULTS

S. aureus HG003 Produces EVs in Different Growth Conditions

Extracellular vesicles secreted by HG003 were isolated from the cell-free supernatants of bacterial cultures at early-and late-stationary growth phases (6 and 12 h, respectively), as well as in the absence (V-) and presence (V+) of a sublethal concentration of vancomycin (0.5 μg/ml). For that purpose, we used centrifugation, filtration, and density gradient ultracentrifugation, the standard method for EV isolation and purification at high purity [START_REF] Yamada | Comparison of methods for isolating exosomes from bovine milk[END_REF]Dauros Singorenko et al., 2017). EV homogeneity and integrity were evaluated by both negative staining electron microscopy and by NTA. Electron micrographs of purified EVs revealed typical nano-sized vesicular structures, with cup-shaped forms in all tested conditions (Raposo and Stoorvogel, 2013; Figure 1A and Supplementary Figure S2). NTA analyses showed a typical profile of particles for all EV samples (Figure 1B and Supplementary Figure S2). A significant increase of approximately 55% in EV diameter was observed in those purified from late-stationary phase cultures, compared to early-stationary phase cultures (for both 6V-vs. 12V-and 6V+ vs. 12V+), whereas no significant difference was observed in the absence or presence of vancomycin (6V-vs. 6V+ and 12V-vs. 12V+, Figure 1C). EV yield is essentially similar at the two growth phases, irrespective to the presence/absence of vancomycin (Figure 1D). In summary, S. aureus HG003 releases EVs with variable diameters depending on the growth phase. A sublethal concentration of vancomycin, however, does not impact the EV morphology, concentration, or diameter.

The S. aureus EVs Harbor all RNA Functional Classes

Total RNA was extracted from HG003 EVs to investigate their compositions. The quality of the RNA preparations was checked and validated, and the samples sequenced. RNA-seq data were the Hld δ-hemolysin encoded by the multifunctional sRNA RNAIII, the PSMβ1 Phenol Soluble Modulin, the FntA ironstorage ferritin, and the MntABC iron ABC transporter. Among the 20 tRNAs annotated in the genome, 15 were identified into the EVs (tRNA His , tRNA Asn , tRNA Glu , tRNA Arg , and tRNA Asp were absent). Five copies of the 16S and 23S rRNAs were also detected, implying that our rRNA depletion procedure was incomplete. Finally, 28 annotated potential sRNAs were detected within EVs, and among the 50 or so bona fide sRNAs defined for the HG003 strain (Liu et al., 2018a), only RsaC was identified with a ≥90% coverage in this study. Note that despite encoding the highly covered Hld transcript, the sRNA RNAIII presented only 71% gene coverage and therefore was excluded from analysis. Around 196 out of the 286 EV-associated RNAs colocalized at the same loci onto the HG003 chromosome, to form 42 clusters of 2 to 29 contiguous genes that were experimentally shown to belong to the same transcriptional units [START_REF] Mäder | Staphylococcus aureus Transcriptome architecture: from laboratory to infection-mimicking conditions[END_REF]; Supplementary Table S6). Among these transcriptional units, 17 displayed a RNA-Seq coverage ≥90% across the entire operon in both the EVs and the EV-producing bacteria. Figure 6 illustrates the sequencing coverage of various contiguous genes within the EVs and the EV-producing cells. Long mRNA operons, up to ~14,000 nucleotides, were detected as fully covered by reads into the purified EVs, supporting the presence of highly covered RNAs and operons as full-length transcripts.

EV RNA Composition Varies With Growth Conditions

The RNA composition of the purified EVs was compared between early-and late-stationary phases, and with or without vancomycin. Eighteen percentage (n = 51) of all detected RNAs with ≥90% coverage were common to all the EV samples (Figure 4A and Supplementary Table S5), implying that the RNA content of the EVs highly varied according to the growth conditions. The percentage of RNAs shared by all the EV-producing cell samples, however, was much higher (70%, n = 1761). The shared RNAs among the EVs included mRNAs expressing virulence factors and quantitatively when facing different growth conditions (Supplementary Table S8). Note that no significantly differentially expressed RNAs were detected according to the presence of vancomycin. As observed previously for the EV RNA content, the growth conditions, particularly the growth phase, impacted mostly the RNA abundance of the EVs, but much less than of the parental cells. Indeed, 38% of RNAs detected within EVs displayed changes in their abundance between conditions, while the fraction of modulated RNAs counted for only 2% of the RNAs in the EV-producing cells. The abundance pattern of several RNAs differed between the EVs and the EV-producing cells according to the growth conditions (Figure 7). While some RNAs such as agrBCD, psmβ1, and hld mRNAs displayed the same variations of their abundance pattern in EVs and EV-producing cells regardless the growth conditions. Others, such as spa and RsaC, were differentially abundant between the EVs and the EV-producing cells. To evaluate the influence of the growth phase on EV and EV producing cell RNA composition, a negative binomial-based approach, with the R package maSigPro [START_REF] Conesa | maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments[END_REF] was applied. Briefly, maSigPro provides a differentially expressed transcript analysis of serial data between experimental groups (e.g., EV and EV producing cells). maSigPro was applied to the 286 highly covered EV RNAs and identified 91 RNAs with significant temporal profile changes (Padj < 0.05). RNAs were clustered according to their expression profiles (Supplementary Table S9). Figure 8 shows the six RNA clusters obtained. Three clusters grouped transcripts with a similar expression profile between EV and EV-producing cells: cluster 4 with 8 RNAs (including, e.g., agrBD and arcC2) and cluster 3 with 18 RNAs (including, e.g., hld and psmβ1) contained more abundant transcripts over time in both EVs and EV-producing cells, whereas cluster 6 with 14 RNAs (including, e.g., fusA, tuf, and secY1) contained less abundant transcripts at 12 h than at 6 h in both. Interestingly, the three other clusters grouped RNAs that showed opposite expression profiles over time in EVs and EV-producing cells. Cluster 1 with 10 RNAs (including, e.g., RsaC and pdhA), and cluster 2 with 24 RNAs (including, e.g., ldh1, qoxABC, and rpoBC) grouped, similarly, more abundant transcripts at 12 h in EVs, and less abundant transcripts at 12 h in EV-producing cells. On the contrary, cluster 5 with 17 RNAs (including, e.g., ccgR and sbi) grouped less abundant transcripts in EVs and more abundant transcripts in EV-producing cells at 12 h. Altogether, this analysis highlighted that the transcript expression pattern could temporally differ between EVs and EV-producing cells.

DISCUSSION

Extracellular vesicles are universal carriers of macromolecules including extracellular RNAs all along from bacteria, archaea, and fungi to protists. Recent investigations on bacterial EV biogenesis, release, and trafficking showed their functional importance for bacterial communication and survival [START_REF] Tsatsaronis | Extracellular vesicle RNA: a universal mediator of microbial communication?[END_REF]. Information regarding S. aureus EV RNA cargo, however, is lagging behind. Here, we report the first exploratory work on EVs released by S. aureus HG003, the characterization of its EV RNA cargo under different conditions, and an indepth transcriptomic comparison between the EVs and the EV-producing cells.

Environmental conditions, such as growth phase and environmental stresses, reportedly influence the production, content and functions of EVs (Tashiro et al., 2010;Kim et al., 2016b;Orench-Rivera and Kuehn, 2016;[START_REF] Askarian | Staphylococcus aureus membrane-derived vesicles promote bacterial virulence and confer protective immunity in murine infection models[END_REF]; et al., 2018;[START_REF] Andreoni | Antibiotics stimulate formation of vesicles in Staphylococcus aureus in both phage-dependent and -independent fashions and via different routes[END_REF]. Here, to investigate the impact of environmental changes on S. aureus EV production, the selected conditions were the early-and late-stationary growth phases, with or without a sublethal concentration of vancomycin that does not impact growth. Differences were observed regarding EV sizes. EVs derived from the late-stationary growth phase were larger than those collected during the earlystationary phase. This can be due to cell-wall morphology and peptidoglycan structure that are characteristics of the growth stage in S. aureus [START_REF] Zhou | Nutrient-dependent structural changes in Staphylococcus aureus peptidoglycan revealed by solid-state NMR spectroscopy[END_REF]. A correlation between the degree of peptidoglycan cross-linking to the cell wall stiffness and EVs release was observed for both Gramnegative and Gram-positive bacteria (Zhou et al., 1998;Deatherage et al., 2009;[START_REF] Schrempf | Extracellular Streptomyces lividans vesicles: composition, biogenesis and antimicrobial activity[END_REF]Schwechheimer et al., 2015;Wang et al., 2018). Notably, sub-inhibitory concentrations of penicillin decreases peptidoglycan cross-linking, triggering an increase in S. aureus EV yields and sizes (Wang et al., 2018). Here, we show that sublethal concentrations of vancomycin, an antibiotic that also targets peptidoglycan synthesis in S. aureus, does not impact either EV morphology or EV production yields, but does change their RNA content in terms of composition and abundance. Vancomycin affects S. aureus EV activity starting at 1 μg/ml (He et al., 2017). The sub-inhibitory vancomycin concentration used here (0.5 μg/ml) is probably too low to detect any changes in EV morphology and production.

All RNA classes are detected by RNA-Seq within HG003derived EVs. These include rRNAs, which were still detected, suggesting that the rRNA depletion carried out here was incomplete. In the absence of filtering by coverage of sequencing data, on average, 78.0 ± 7.0% of the annotated transcripts in HG003 genome were present in the EVs (91.8 ± 1.0% for EV-producing cells). Of these, a large portion of mapped RNAs corresponded to mRNAs. These results are consistent with RNA-Seq data obtained with similar criteria from OMVs in Salmonella enterica serovar Typhimurium (S. Typhimurium) that harbor around 73% of the annotated transcripts including up to 86% of mRNAs according to growth conditions [START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF]. A recent study addressing the sRNA content of EVs also pointed out that mRNAs are the more abundant RNA species in EVs derived from S. aureus strain MSSA476 after rRNA depletion [START_REF] Joshi | Transcriptome profiling of Staphylococcus aureus associated Frontiers in Microbiology | www.frontiersin.org 15[END_REF]. On the contrary, studies with Escherichia coli revealed that EVs were enriched mainly with short RNAs, such as tRNAs (Ghosal et al., 2015;Blenkiron et al., 2016). Nevertheless, these variations may be a result of different RNA extraction and library preparation protocols, or simply correspond to singular characteristics of EVs derived from different bacterial species. As expected, data filtering of RNAs with ≥90% coverage decreased the number of detected RNAs. However, the RNA content of the EVs, particularly the mRNAs and sRNAs (only 5.1 ± 2.8% of EV RNAs initially detected are still identified after the filtering), was much more affected than that of the EV-producing cells (69.0 ± 7.90% of EV-producing cell RNAs are still detected after the filtering). The low RNA coverage in the HG003 EVs might perhaps reflect the absence of transcription within EVs and, thus, the progressive degradation of a substantial fraction of the EVs-associated RNAs after their formation and/or during their purification. These findings are consistent with a recent report showing that the predominant RNA type in EVs from S. aureus Newman is <300-nucleotide long (Rodriguez and Kuehn, 2020). The presence of numerous processed or degraded RNAs could be a common feature of the bacterial EVs, as Gram-negative S. Typhimurium OMV-associated RNAs are also processed or degraded [START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF]. Since one of the primary physiological functions attributed to EVs is the removal of unwanted materials from cells, such as misfolded or degraded proteins (McBroom and Kuehn, 2007), the S. aureus EVs may also help removing the degraded RNAs from the bacteria [START_REF] Groot | Sorting mechanisms for MicroRNAs into extracellular vesicles and their associated diseases[END_REF]. Two hundred and eighty-six highly covered RNAs can be identified within HG003 EVs. That number of highly covered EV RNAs varies according to growth conditions, from 86 in late-stationay growth phase with vancomycin to 273 in earlystationay growth phase also with vancomycin. As expected, all the EV transcrits are also detected in the EV-producing cells. Among them, 51 transcripts are shared by EVs collected in all tested conditions. The 286 highly covered EV RNAs encompass short transcripts, such as tRNAs (~75 nucleotides), mRNAs (psmβ1, 135 nucleotides; agrD, 141 nucleotides), and some sRNAs (Teg84, 79 nucleotides; Teg46, 124 nucleotides). Yet, these highly covered EV RNAs also comprise long transcripts, including 15 mRNAs with lengths >2,000 nucleotides (e.g., pbp2, copA, rpoB, rpoC, and atl). 67% of these RNAs are organized into 42 gene clusters that are co-transcribed in S. aureus [START_REF] Mäder | Staphylococcus aureus Transcriptome architecture: from laboratory to infection-mimicking conditions[END_REF]. Among them, 17 are full-length transcripts across entire operons in HG003 EVs, with lengths up to ~14,000 nucleotides. As observed for S. Typhimurium OMV RNAs [START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF], these findings support that highly covered RNAs are present as full-length transcripts. These RNAs belong to all annotated classes of RNAs. The mRNAs from the EVs encode proteins involved in transcription, translation, energy production and conversion, carbohydrate metabolism, and cell wall biogenesis. In addition to these housekeeping functions, EVs also harbors mRNAs encoding virulence-associated proteins, such as the agr operon responsible of quorum-sensing, autolysin Atl, protein A (Spa), immunoglobulin-binding protein (Sbi), immunodominant staphylococcal antigen B (IsaB), δ hemolysin (Hld) encoded by the multifunctional sRNA RNAIII, and the PSMβ1 phenolsoluble modulin, as well as several iron acquisition systems. Besides, most tRNA species are detected within the EVs, as well as residual rRNAs and 28 annotated sRNAs, including bona-fide RsaC involved in S. aureus oxidative stress adaptation and nutritional immunity [START_REF] Lalaouna | RsaC sRNA modulates the oxidative stress response of Staphylococcus aureus during manganese starvation[END_REF]. Note that RsaC and RNAIII were also detected within S. aureus EVs from strain MSSA476 [START_REF] Joshi | Transcriptome profiling of Staphylococcus aureus associated Frontiers in Microbiology | www.frontiersin.org 15[END_REF], suggesting their wider occurence in staphylococcal EVs.

The presence of full-length, functional RNAs in EVs raises the question of their biological roles. EVs are produced to transport bioactive molecules to interact and communicate with other cells. So far, most studies on S. aureus EVs investigated the protein cargo. Therefore, the broad spectrum of activities associated with S. aureus-derived EVs was related to their protein content [START_REF] Jeon | Variation among Staphylococcus aureus membrane vesicle proteomes affects cytotoxicity of host cells[END_REF]. In some S. aureus strains, EVs carry β-lactams that confer transient resistance to ampicillinsusceptible E. coli and S. aureus (Lee et al., 2013b). Likewise, mycobactin-containing Mycobacterium tuberculosis EVs can deliver iron to strains deficient for iron-uptake (Prados-Rosales et al., 2014). The delivery of full-length mRNAs via EVs to the surrounding bacterial cells, notably those involved into energetic and metabolic functions, could improve their responses to environmental stimuli to fasten their adaptation. Likewise, rRNAs and tRNAs could boost translation in EV recipient bacterial cells and improve their fitness. In bacteria, sRNAs fine tune target gene expression, usually at the posttranscriptional level in response to changes in the environment, including antibiotic resistance and tolerance [START_REF] Mediati | Networks of resistance: small RNA control of antibiotic resistance[END_REF]. Most bacteria encode dozens of sRNAs that are transcribed as independent transcripts or processed from mRNAs. The presence of sRNAs with regulatory roles within S. aureus EVs could be a relocation strategy in other surrounding bacteria that need more of these sRNAs for adaptation and infection spreading and/or to coordinate bacterial group adaptation, activities and behaviors. The transfer of RsaC could enhance the concentration of that riboregulator to other S. aureus cells when intracellular, especially helping survival within the phagolysosome if the S. aureus EVs are internalized by the host cells together with the bacteria. Transfer of functional RNAs to bacterial cells that do not encode the corresponding genes in their genome could also be part of a transient horizontal phenotype acquisition, which could be of use during infection to disseminate specific virulence-associated factors through the bacterial community. Finally, beside interactions between bacterial cells, S. aureus EV associated RNAs, notably sRNAs, may be involved in the host-pathogen interactions [START_REF] Eberle | Bacterial RNA is recognized by different sets of immunoreceptors[END_REF][START_REF] Li | Sequence specific detection of bacterial 23S ribosomal RNA by TLR13[END_REF][START_REF] Furuse | Search for MicroRNAs expressed by intracellular bacterial pathogens in infected mammalian cells[END_REF][START_REF] Sha | Human NLRP3 Inflammasome senses multiple types of bacterial RNAs[END_REF]Koeppen et al., 2016;[START_REF] Westermann | The chemistry and electron microscopy of an extracellular lipopolysaccharide from Escherichia coli[END_REF]Choi et al., 2017;Frantz et al., 2019;[START_REF] Han | Extracellular RNAs in periodontopathogenic outer membrane vesicles promote TNF-α production in human macrophages and cross the blood-brain barrier in mice[END_REF][START_REF] Lee | Microbe-host communication by small RNAs in extracellular vesicles: vehicles for transkingdom RNA transportation[END_REF]Rodriguez and Kuehn, 2020). The 28 potential annotated sRNA detected within HG003 EVs are potential candidates for further functional characterization, especially during S. aureus-host cell interactions. S. aureus secreted EVs elicit immune responses that mimic those of the EV-producing cells [START_REF] Gurung | Staphylococcus aureus produces membrane-derived vesicles that induce host cell death[END_REF][START_REF] Hong | Extracellular vesicles derived from Staphylococcus aureus induce atopic dermatitis-like skin inflammation[END_REF][START_REF] Hong | An important role of α-hemolysin in extracellular vesicles on the development of atopic dermatitis induced by Staphylococcus aureus[END_REF][START_REF] Kim | Staphylococcus aureus-derived extracellular vesicles induce neutrophilic pulmonary inflammation via both Th1 and Th17 cell responses[END_REF][START_REF] Kim | Staphylococcus aureus-derived extracellular vesicles induce monocyte recruitment by activating human dermal microvascular endothelial cells in vitro[END_REF]Thay et al., 2013;Choi et al., 2015;[START_REF] Jeon | Variation among Staphylococcus aureus membrane vesicle proteomes affects cytotoxicity of host cells[END_REF][START_REF] Jun | Staphylococcus aureus-derived membrane vesicles exacerbate skin inflammation in atopic dermatitis[END_REF][START_REF] Askarian | Staphylococcus aureus membrane-derived vesicles promote bacterial virulence and confer protective immunity in murine infection models[END_REF]Tartaglia et al., 2018;Wang et al., 2018Wang et al., , 2020;;Rodriguez and Kuehn, 2020). Strikingly, within their RNA cargo, several mRNAs encode immunomodulatory proteins, as Sbi, Spa and PSMβ1, and may participate into the immune response triggered by the protein cargo if they are ultimately translated. Yet, such functions remain to be demonstrated for the RNA cargo of The RNA cargo of HG003 EVs, in both identity and abundance, depends on the growth conditions. Similarly, the EV RNA cargo of S. Typhimurium is also sensitive to environmental changes indicating that it reflects the bacterial adaptation to its environment [START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF]. It could be a faster way to transfer information of changes perceived by one cell to surrounding cells even before they sensed the environmental stimuli in order to quickly promote group adaptation. We found, however, that the vancomycin treatment had less impact on RNA abundance compared to the growth phase. Although the composition of HG003 EVs represented the intracellular state of the bacterial transcriptome through global packaging, two main findings, however, reinforce the concept of a potential selective packaging of RNAs into EVs, as proposed for its protein cargo [START_REF] Haurat | Selective sorting of cargo proteins into bacterial membrane vesicles[END_REF][START_REF] Cahill | Klebsiella pneumoniae O antigen loss alters the outer membrane protein composition and the selective packaging of proteins into secreted outer membrane vesicles[END_REF]Tartaglia et al., 2020). First, we measured an enrichment for several functional and subcellular localization RNA categories in EVs when compared to EV-producing cells. Second, the relative abundance of several RNAs between two environmental conditions was different in the EVs and the EV-producing cells. Other studies also found that some RNA populations were enriched in EVs from Gramnegative and Gram-positive pathogenic bacteria (Ghosal et al., 2015;Koeppen et al., 2016;Resch et al., 2016;[START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF][START_REF] Malge | mRNA transcript distribution bias between Borrelia burgdorferi bacteria and their outer membrane vesicles[END_REF][START_REF] Han | Extracellular RNAs in periodontopathogenic outer membrane vesicles promote TNF-α production in human macrophages and cross the blood-brain barrier in mice[END_REF][START_REF] Langlete | Vesicles from Vibrio cholerae contain AT-rich DNA and shorter mRNAs that do not correlate with their protein products[END_REF][START_REF] Zhang | sncRNAs packaged by Helicobacter pylori outer membrane vesicles attenuate IL-8 secretion in human cells[END_REF]. This notably includes sRNAs, which can play regulatory activity in the host (Koeppen et al., 2016;[START_REF] Malabirade | The RNA complement of outer membrane vesicles from Salmonella enterica Serovar Typhimurium under distinct culture conditions[END_REF][START_REF] Langlete | Vesicles from Vibrio cholerae contain AT-rich DNA and shorter mRNAs that do not correlate with their protein products[END_REF][START_REF] Zhang | sncRNAs packaged by Helicobacter pylori outer membrane vesicles attenuate IL-8 secretion in human cells[END_REF]. The enrichment of RNAs associated with bacterial diseases in EVs derived from many pathogenic bacteria reinforces the physiopathological role of these structures in host-pathogen interaction and host cell invasion, which could be borne by their RNA cargo as well as by their protein cargo. The selective mechanisms of EV RNA content packaging have not yet been elucidated. It has been proposed that RNA packing into EVs could depend on RNA size and location (eg., nearby EVs formation site), as well as on their affinity for other molecules (eg., membrane proteins; [START_REF] Langlete | Vesicles from Vibrio cholerae contain AT-rich DNA and shorter mRNAs that do not correlate with their protein products[END_REF]. Nevertheless, such enrichment results should be interpreted with carefulness. Indeed, they could also reflect a difference in RNA half-lives between EVs and EV-producing cells, as well as a difference in RNase activity [START_REF] Langlete | Vesicles from Vibrio cholerae contain AT-rich DNA and shorter mRNAs that do not correlate with their protein products[END_REF]Lécrivain and Beckmann, 2020), pointing out that RNAs with longer half-lives could be protected from degradation, leading to an artifactual accumulation in the EVs over time.

In summary, our exploratory work provides novel insights in S. aureus EVs by the characterization of its RNA cargo and paves the way for further functional studies. Mainly, it sheds light on the possible roles of EV RNA cargo in intra-and inter-species communication, in the virulence and pathogenesis of S. aureus, and as trash bags for degraded RNAs. The study of bacterial EV RNA cargo is an emerging area of research. Evidently, as with all emerging fields, each advance raises further questions: Are the full-length RNAs in HG003 EVs functional, and do they possess similar functions than in the bacterial cytoplasm? What are the rules for RNA sorting into HG003 EVs? What are the roles and functions of the S. aureus EV RNA cargo? These exciting questions, among others, should be adressed in further studies.

Chapter 1 Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .Figure 7 .Figure 8 .Figure 9 .Figure 10 .Figure 11 .Figure 12 .Figure 13 .Figure 14 .Figure 15 . 2 Figure 1 . 3 Figure 1 .Figure 3 .Figure 5 .Figure 6 .Figure 7 . 4 Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 . 5 Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .

 1123456789101112131415213135674123455123456 Figure 1. Schematic representation of an EV.________________________________________________4 Figure 2. Types of EVs produced by Gram-negative and Gram-positive bacteria._____________________6 Figure 3. Factors affecting EV biogenesis in Gram-negative bacteria.______________________________7 Figure 4. Factors affecting EV biogenesis in Gram-positive bacteria.______________________________9 Figure 5. Overview of bacterial EVs roles as mediators of interactions with other bacteria, the environment or mammalian hosts.__________________________________________________________________15 Figure 6. Example of EV purification protocol._______________________________________________19 Figure 7. TEM image showing spherical nanosized EVs derived from S. aureus._____________________22 Figure 8. Graphical representation of PPIs networks._________________________________________27 Figure 9. Examples of computational approaches for PPI prediction._____________________________28 Figure 10. Key aspects of beneficial bacteria-derived EVs._____________________________________32 Figure 11. Summary of potential activities reported for the EVs derived from Gram-negative (G-) and Gram-positive (G+) beneficial bacteria.____________________________________________________42 Figure 12. Phylogenetic tree showing evolutionary relatedness between health-promoting P. freudenreichii species, other probiotic or closely-related species._______________________________43 Figure 13. Optical microscopy image showing the morphological aspect of a P. freudenreichii CIRM-BIA129 culture, with typical aggregates._________________________________________________________44 Figure 14. Emmental cheese produced with P. freudenreichii CIRM-BIA129._______________________45 Figure 15. Schematic summary of P. freudenreichii probiotic traits at the molecular level.____________56 Chapter 2 Figure 1. Schematic representation of the research question, concerning the hypothesis that, in addition to secreted short chain fatty acids and bifidogenic factors, as well as surface proteins, EVs could also mediate the probiotic effects of P. freudenreichii towards human cells.___________________________58 Chapter 3 Figure 1. P. freudenreichii CIRM-BIA 129 secretes extracellular vesicles.__________________________65 Figure 2. P. freudenreichii-secreted EVs contain proteins involved in interactions with the host._______66 Figure 3. Predictions of protein-protein interactions suggest a modulation of the NF-kB signaling pathway by P. freudenreichii-secreted EVs.________________________________________________________67 Figure 4. P. freudenreichii-secreted EVs specifically mitigate LPS-induced NF-kB activation in intestinal epithelial cells._______________________________________________________________________68 Figure 5. P. freudenreichii-secreted EVs mitigate the release of IL-8 induced by LPS._________________68 Figure 6. P. freudenreichii-secreted EVs are not cytotoxic against intestinal epithelial cells.___________69 Figure 7. SlpB is required to achieve the effective mitigation of NF-kB activation in human intestinal epithelial cells._______________________________________________________________________70 Chapter 4 Figure 1. Characteristics of P. freudenreichii-secreted EVs under different growth conditions.________79 Figure 2. P. freudenreichii-secreted EVs play different biological roles depending on growth conditions.__________________________________________________________________________80 Figure 3. Qualitative proteomics can reveal differences in EV protein content as a function of growth conditions.__________________________________________________________________________81 Figure 4. EVs derived from different growth conditions carry proteins with different relative abundances.________________________________________________________________________82 Figure 5. The prediction of protein-protein interactions highlights relevant bacterial proteins.________83

Figure 1 .

 1 Figure 1. Schematic representation of an EV. A) Overview. B) Sectional view. C) Detail of lipid bilayer and embedded protein. Based on Van Niel et al. (2018).

Figure 2 .

 2 Figure 2. Types of EVs produced by Gram-negative and Gram-positive bacteria. OM: outer membrane, PG: peptidoglycan, IM: inner membrane, CM: cytoplasmic membrane, OMVs: outer-membrane vesicles, EOMVs: explosive outer-membrane vesicles, OIMVs: outer-inner membrane vesicles, CMVs or MVs: cytoplasmic membrane vesicles or membrane vesicles. Based on Toyofuku et al. (2019) and Nagakubo et al. (2020).

Figure 3 .

 3 Figure 3. Factors affecting EV biogenesis in Gram-negative bacteria. A) Modulation of PG-membrane crosslinking. B) Turgor pressure mediated by misfolded or fragmented proteins. C) Alterations in membrane homeostasis, including LPS modifications, enrichment of specific lipids and interactions with hydrophobic compounds. Based on Brown et al. (2015), Haurat et al. (2015) and Schwechheimer and Kuehn (2015).

Figure 4 .

 4 Figure 4. Factors affecting EV biogenesis in Gram-positive bacteria. A) PG layer remodeling through enzymatic action. B) Turgor pressure over the cytoplasmic membrane. C) Interactions with membrane-targeting molecules, such as phenol-soluble modulins produced by S. aureus. Based on Brown et al. (2015) and Wang et al. (2018).

Figure 5 .

 5 Figure 5. Overview of bacterial EVs roles as mediators of interactions with other bacteria, the environment or mammalian hosts. Based on Caruana and Walper (2020).

Figure 6 .

 6 Figure 6. Example of EV purification protocol. A) Starting culture. B) Initial step, comprising centrifugation and filtration. C) Concentration of the supernatants with centrifugal ultrafiltration units. D-E) Final separation by D) ultracentrifugation with density gradient or E) size-exclusion chromatography. Adapted from Brown et al. (2015).

Figure 7 .

 7 Figure 7. TEM image showing spherical nanosized EVs derived from S. aureus. Image acquired under a Jeol 1400 transmission electron microscope after uranyl acetate negative coloration by Rodovalho (2018).

Figure 8 .

 8 Figure 8. Graphical representation of PPIs networks. A) Representation of proteins as nodes and interactions as edges. Examples of B) intra-species and C) inter-species networks. Based on Keskin et al. (2016).

Figure 9 .

 9 Figure 9. Examples of computational approaches for PPI prediction. A) Molecular docking, based on threedimensional structures of potentially interacting pairs. B) Interolog methodology, based on interaction transfer for evolutionarily-related proteins. C) Machine learning approaches, which convert potentially interacting pairs in numerical representations for classification. Adapted from Keskin et al. (2016).

Figure 10 .

 10 Figure 10. Key aspects of beneficial bacteria-derived EVs. Based on Liu et al. (2018).

Figure 11 .

 11 Figure 11. Summary of potential activities reported for the EVs derived from Gram-negative (G-) and Gram-positive (G+) beneficial bacteria. Based on Molina-Tijeras et al. (2019).

Figure 12 .

 12 Figure 12. Phylogenetic tree showing evolutionary relatedness between health-promoting P. freudenreichii species, other probiotic or closely-related species. Cortesy of Diego L. N. Rodrigues (2021), constructed with Neighbor-Joining method, using reference sequences of common proteins retrieved from the NCBI.

Figure 13 .

 13 Figure 13. Optical microscopy image showing the morphological aspect of a P. freudenreichii CIRM-BIA129 culture, with typical aggregates. Image acquired with an Olympus BX51TF phase contrast microscope by Rodovalho (2018).

Figure 14 .

 14 Figure 14. Emmental cheese produced with P. freudenreichii CIRM-BIA129. Photography courtesy from the study of Rabah et al. (2018).

Figure 15 .

 15 Figure 15. Schematic summary of P. freudenreichii probiotic traits at the molecular level. Based on results by Isawa et al. (2002); Jan et al. (2002), do[START_REF] Carmo | Propionibacterium freudenreichii surface protein SlpB is involved in adhesion to intestinal HT-29 cells[END_REF][START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF], graphically summarized byRodovalho (2021).

Figure 1 .

 1 Figure 1. Schematic representation of the research question, concerning the hypothesis that, in addition to secreted short chain fatty acids and bifidogenic factors, as well as surface proteins, EVs could also mediate the probiotic effects of P. freudenreichii towards human cells. Based on results byIsawa et al. (2002);Jan et al. (2002), do[START_REF] Carmo | Propionibacterium freudenreichii surface protein SlpB is involved in adhesion to intestinal HT-29 cells[END_REF][START_REF] Carmo | Probiotic Propionibacterium freudenreichii requires SlpB protein to mitigate mucositis induced by chemotherapy[END_REF] and the research question of this study, graphically summarized byRodovalho (2021).

FIGURE 1 |

 1 FIGURE 1 | P. freudenreichii CIRM-BIA 129 secretes extracellular vesicles. (A) Transmission electron microscopy image after the negative staining of P. freudenreichii-secreted EVs purified from UF culture supernatants. (B) Size distribution (diameter) of purified EVs as measured by nanoparticle tracking analysis (NTA). Concentration data are expressed as mean ± standard deviation from three independent biological replicates.

FIGURE 2 |

 2 FIGURE 2 | P. freudenreichii-secreted EVs contain proteins involved in interactions with the host. (A) SDS-PAGE showing the protein profile of the whole proteome (WP) and vesicular proteome (EVs). Molecular weight standards are indicated on the left (Mr, kDa). (B) Functional annotation of EV proteins by the prediction of COG categories: D -Cell cycle control, cell division, chromosome partitioning, M -Cell wall/membrane/envelope biogenesis, O -Post-translational modification, protein turnover, and chaperones, T -Signal transduction mechanisms, U -Intracellular trafficking, secretion, and vesicular transport, V -Defense mechanisms, J -Translation, ribosomal structure and biogenesis, K -Transcription, L -Replication, recombination and repair, C -Energy production and conversion, E -Amino acid transport and metabolism, F -Nucleotide transport and metabolism, G -Carbohydrate transport and metabolism, H -Coenzyme transport and metabolism, I -Lipid transport and metabolism, P -Inorganic ion transport and metabolism, Q -Secondary metabolites biosynthesis, transport, and catabolism, S -Function unknown, ? -Not predicted. (C) Subcellular localization of EV proteins as predicted by Cello2GO. (D) Prediction of lipoproteins by PRED-LIPO: Sec: secretion signal peptide, Lipo: lipoprotein signal peptide, TM: transmembrane, Other: no signals found. (E) Protein abundance index (PAI) frequency distribution for EV proteins, highlighting the most frequently expressed proteins.

FIGURE 3 |

 3 FIGURE 3 | Predictions of protein-protein interactions suggest a modulation of the NF-κB signaling pathway by P. freudenreichii-secreted EVs. (A) Number of interactions predicted by each methodology. (B) Number of bacterial proteins predicted to interact as a function of the prediction method. (C) Number of human proteins predicted to interact as a function of the prediction method, highlighting the top-interacting NFKB1 protein. (D) Subnetwork of predicted interactions mapping to the KEGG NF-κB pathway. Blue square nodes represent human proteins, red round nodes represent bacterial proteins, full thin green lines represent intersppi-predicted interactions, dotted purple lines represent interolog-predicted interactions and full thick black lines represent interactions shared by both methods. Inset: selected interactions involving bacterial proteins previously associated with immunomodulatory roles.

FIGURE 4 |

 4 FIGURE 4 | P. freudenreichii-secreted EVs specifically mitigate LPS-induced NF-κB activation in intestinal epithelial cells. (A) Measure of regulatory activity of the NF-κB transcription factor in HT-29 intestinal epithelial cells untreated or treated with LPS (1 ng mL -1 ) in the presence of TBS buffer control or different concentrations of EVs purified from the supernatants of P. freudenreichii CIRM-BIA 129 culture in UF medium. EV concentrations: 0.01 = 1.0 × 10 7 EVs ml -1 , 0.1 = 1.0 × 10 8 EVs ml -1 , 0.25 = 2.5 × 10 8 EVs ml -1 , 0.5 = 5.0 × 10 8 EVs ml -1 , 1 = 1.0 × 10 9 EVs ml -1 . (B) Percentage NF-κB activation in HT-29 intestinal epithelial cells after stimulation by LPS (1 ng mL -1 ), TNF-α (1 ng mL -1 ) or IL-1β (1 ng mL -1 ) inducers, in the presence or absence of UF-derived EVs (1.0 × 10 9 EVs ml -1 ). The values are normalized by the control conditions (stimulation by the inducer in the absence of EVs). ANOVA with the Tukey's multiple comparison test: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

FIGURE 5 |

 5 FIGURE 5 | P. freudenreichii-secreted EVs mitigate the release of IL-8 induced by LPS. (A) Concentration of IL-8 (pg ml -1 ) released by HT-29 intestinal epithelial cells untreated or treated with LPS (1 ng mL -1 ) in the presence of different concentrations of EVs purified from the supernatants of P. freudenreichii CIRM-BIA 129 culture in UF medium. EV concentrations: 0.01 = 1.0 × 10 7 EVs ml -1 , 0.1 = 1.0 × 10 8 EVs ml -1 , 0.5 = 5.0 × 10 8 EVs ml -1 , 1 = 1.0 × 10 9 EVs ml -1 . (B) Percentage of IL-8 released by HT-29 intestinal epithelial cells after stimulation by LPS (1 ng mL -1 ), TNF-α (1 ng mL -1 ) or IL-1β (1 ng mL -1 ) inducers, in the presence or absence of UF-derived EVs (1.0 × 10 9 EVs ml -1 ). The values are normalized by the control conditions (stimulation by inducer in the absence of EVs). One-way ANOVA followed by Tukey's (left panel) or Dunnett's (right panel) multiple comparisons test: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

FIGURE 6 |

 6 FIGURE 6 | P. freudenreichii-secreted EVs are not cytotoxic against intestinal epithelial cells. Viability percentage of HT-29 intestinal epithelial cells (A) and HT-29/kb-seap-25 cellular reporter cell line (B) before or after stimulation by LPS (1 ng mL -1 ), TNF-α (1 ng mL -1 ) or IL-1β (1 ng mL -1 ) inducers, in the presence or absence of UF-derived EVs (1.0 × 10 9 EVs ml -1). The values are normalized by the control conditions (control buffer or stimulation by an inducer in the absence of EVs). There are no statistically significant differences between group means as determined by one-way ANOVA followed by Tukey's multiple comparisons test.

FIGURE 7 |

 7 FIGURE 7 | SlpB is required to achieve the effective mitigation of NF-κB activation in human intestinal epithelial cells. Percentage of NF-κB activation in HT-29 intestinal epithelial cells after stimulation by LPS (1 ng µL -1 ) in the presence or absence of UF-derived EVs (1.0 × 10 9 EVs ml -1 ). WT: wild type P. freudenreichii CIRM-BIA 129-derived EVs, SlpB: isogenic mutant P. freudenreichii CIRM-BIA 129 SlpB-derived EVs, 37 • C: wild type P. freudenreichii CIRM-BIA 129-derived EVs incubated at 37 • C for 1 h, 37 • C + PK: wild type P. freudenreichii CIRM-BIA 129-derived EVs incubated at 37 • C for 1 h in the presence of proteinase K. The values are normalized by the control conditions (stimulation by an inducer in the absence of EVs). One-way ANOVA followed by Tukey's multiple comparisons test: **P < 0.01, ****P < 0.0001.

  Environmental Conditions Modulate the Protein Content and Immunomodulatory Activity of Extracellular Vesicles Produced by the Probiotic Propionibacterium freudenreichii Vinícius de Rezende Rodovalho, a,b Brenda Silva Rosa da Luz, a,b Aurélie Nicolas, a Fillipe Luiz Rosa do Carmo, a Julien Jardin, a Valérie Briard-Bion, a Gwénaël Jan, a Yves Le Loir, a Vasco Ariston de Carvalho Azevedo, b Eric Guédon a a INRAE, Institut Agro, STLO, Rennes, France b Laboratory of Cellular and Molecular Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil

FIG 1

 1 FIG 1 Characteristics of P. freudenreichii-secreted EVs under different growth conditions. (A) Size distribution of EVs derived from UF (left) and YEL (right) culture media. (B and C) Mode (B) and mean (C) diametric sizes of EVs produced from UF and YEL culture media. (D) Total concentrations of EVs produced from UF and YEL culture media. (E) Relative yields of EVs (normalized by the amount of bacterial cells in each culture medium). Size and concentration measurements were acquired using nanoparticle tracking analysis (NTA). Data are expressed as means 6 standard deviations of values obtained from at least three independent biological replicates. Asterisks indicate statistical significance as evaluated by the Mann-Whitney test: **, P # 0.01; *, P # 0.05.

FIG 2 P

 2 FIG 2 P. freudenreichii-secreted EVs play different biological roles depending on growth conditions. (A) Percent NF-κB transcription factor activity in HT-29/kb-seap-25 cells left untreated or treated with the inflammatory inducer TNF-a (1 ng ml 21 ), IL-1b (1 ng ml 21 ), or LPS (1 ng ml 21 ) in the presence or absence of UF-and YELderived EVs (1.0 Â 10 9 EVs ml 21 ). The values are normalized by the control conditions (control TBS buffer or stimulation by the inducer in the absence of EVs). (B) Percentage of IL-8 released by HT-29 intestinal epithelial cells after stimulation by LPS (1 ng ml 21 ), TNF-a (1 ng ml 21 ), or IL-1b (1 ng ml 21 ) inducer in the presence or absence of UF-and YEL-derived EVs (1.0 Â 10 9 EVs ml 21 ). The values are normalized to the control conditions (control TBS buffer or stimulation by inducer in the absence of EVs). (C and D) Percent viability of HT-29/kbseap-25 reporter (left) and HT-29 parental (right) cells before or after stimulation by LPS (1 ng ml 21 ), TNF-a (1 ng ml 21 ), or IL-1b (1 ng ml 21 ) inducer in the presence or absence of UF-and YEL-derived EVs (1.0 Â 10 9 EVs ml 21 ). The values are normalized by the control conditions (control TBS buffer or stimulation by an inducer in the absence of EVs). Data are expressed as mean 6 standard deviation of values obtained from at least three independent biological replicates. Asterisks indicate statistical significance as evaluated by two-way ANOVA with Tukey's multiple-comparison test: ****, P # 0.0001; ***, P # 0.001.

FIG 3

 3 FIG 3 Qualitative proteomics can reveal differences in EV protein content as a function of growth conditions. (A) Venn diagram showing the number of shared and specific proteins for EVs under the two growth conditions. (B to D) Heatmaps showing the distribution of proteins as a function of growth condition categories and functional predictions. Columns show growth condition categories (UF-only, proteins identified exclusively in UF-derived EVs; YELonly, proteins identified exclusively in YEL-derived EVs; UF-YEL, proteins identified under both growth conditions). Rows show subcellular localization prediction (cytoplasmic, membrane, or extracellular) (B), PRED-LIPO prediction (Sec, secretion signal peptide; Lipo, lipoprotein signal peptide; TM, transmembrane; Other, no signals found) (C), and COG functional categories (D).

FIG 4 FIG 5

 45 FIG 4 EVs derived from different growth conditions carry proteins with different relative abundances. (A) Volcano plot showing the relative abundance of proteins from UF-derived EVs versus YEL-derived EVs (fold change UF/YEL) as well as the significance of their fold changes. The results are presented according to a logarithmic scale, with positive fold change ratios shown in blue, negative fold change ratios shown in red, and nonsignificant ratios shown in gray. The horizontal dotted line shows the threshold of significance, and the vertical line indicates a logarithmic fold change of zero. (B) Relative abundance of proteins with the highest significant UF/YEL fold changes. (C and D) Volcano plots highlighting subsets of proteins reportedly associated with a stress response (C) or immunomodulation (D) in P. freudenreichii or other bacterial strains. (E) Enrichment analysis of KEGG pathways and COG categories for the upregulated (left) and downregulated (right) proteins. Significant results are shown in blue for categories enriched in upregulated proteins, in red for categories enriched in downregulated proteins, and in gray for categories that are not statistically enriched. The lower dotted line represents the minimal threshold of significance, and the higher dotted line indicates a cap for categories with P values of 1 Â 10 26 or lower.

Figure 1 .

 1 Figure 1. Biophysical characteristics of UC-purified EVs. A-B) Size distribution of EVs derived from A) UF and B) YEL media, as acquired from NTA measurements. Inset: TEM images of individual EVs, scale bar: 100 nm. C) Modal and D) mean diameter of UF-and YEL-derived EVs. E) Concentration of EVs recovered from cultures in UF and YEL media. F) Ratio between the amount of recovered EVs (obtained from NTA measurements) and bacterial cells (CFU counting) at sampling time, normalized relative to UF-derived EVs. Mann Whitney tests were performed, but no significative differences were found.

Figure 2 .

 2 Figure 2. Proteomic profile of UC-purified EVs, according to culture media. A) Venn diagram presenting the number of proteins identified exclusively in UF-or YEL-derived EVs, or common to both conditions. B) Proteins subcellular localization, as predicted by Cello2GO. C) Lipoproteins prediction by LIPO-PRED. Sec: secretion signal peptide. Lipo: lipoprotein signal peptide. TM: transmembrane. Other: no signals found. D) COG categories assignment. B-D) UF-only: proteins exclusive to UF-derived EVs, YEL-only: proteins exclusive to YEL-derived EVs, UF-YEL: proteins common to both conditions.

Figure 3 .

 3 Figure 3. Comparison of the biophysical properties of UC-and SEC-purified EVs. A) Size (diameter) distribution of the particles in each condition. B) Modal diameter of the particles in each condition. C) Particles total concentration in the end of each purification condition. D) Particles relative yield in the end of each purification condition. For relative yield calculation, the concentration was divided by CFUs at starting cultures and normalized relative to UC_UF. A-D) UC_UF: UF-derived EVs purified by UC. UC_YEL: YEL-derived EVs purified by UC. SEC_UF: UF-derived EVs purified by SEC. SEC_YEL: YEL-derived EVs purified by SEC. Data were acquired from NTA measurements in this study (UC-purified EVs) and our previous study (SEC-purified EVs). Asterisks indicate statistical significance as evaluated by one-way ANOVA followed by Tukey's multiple comparisons tests: ****, P ≤ 0.0001; ***, P ≤ 0.001; **, P ≤ 0.01; *, P ≤ 0.05.

Figure 4 .

 4 Figure 4. Proteins distribution according to condition of EV obtention. A) Venn diagram presenting the number of proteins per condition and intersections. B) Heatmap representing the presence (non-white) or absence (white) of all the analyzed proteins in each condition of EV obtention. Core: core proteome, proteins present in all 4 conditions. Accessory: accessory proteome, proteins present in 2 or 3 conditions. Exclusive: exclusive proteome, proteins present in only 1 condition. A-B) UC_UF: UF-derived EVs purified by UC. UC_YEL: YEL-derived EVs purified by UC. SEC_UF: UFderived EVs purified by SEC. SEC_YEL: YEL-derived EVs purified by SEC.

Figure 5 .

 5 Figure 5. Functional and spatial features of the core proteome. A) Distribution of COG categories. B) Protein subcellular localizations, as predicted by Cello2GO. C) Lipoproteins prediction by LIPO-PRED. Sec: secretion signal peptide. Lipo: lipoprotein signal peptide. TM: transmembrane. Other: no signals found. D) Enrichment map obtained from functional enrichment analysis of KEGG Pathways terms. Node sizes are proportional to functional category set size. Edges indicate shared proteins among the linked sets. Color is related to statistical significance (Q-value).

Figure 6 .

 6 Figure 6. Characteristics of the proteins exclusive of SEC-purified (left panel) and UC-purified (right panel) EVs. A) Distribution of COG categories. B) Protein subcellular localizations, as predicted by Cello2GO. C) Lipoproteins prediction by LIPO-PRED. Sec: secretion signal peptide. Lipo: lipoprotein signal peptide. TM: transmembrane. Other: no signals found.

2 .

 2 Research articles as co-author TARTAGLIA NR, NICOLAS A, RODOVALHO VR, LUZ BSRD, BRIARD-BION V, KRUPOVA Z, THIERRY A, COSTE F, BUREL A, MARTIN P, JARDIN J, AZEVEDO V, LE LOIR Y, GUÉDON E. Extracellular vesicles produced by human and animal Staphylococcus aureus strains share a highly conserved core proteome. Sci Rep. 2020 May 21;10(1):8467. doi: 10.1038/s41598-020-64952-y. (The contributions to this article included the sequences similarity analysis of the proteins found in EVs derived from different strains of S. aureus, for the core proteome composition and manuscript revision.) LUZ BSR, NICOLAS A, CHABELSKAYA S, RODOVALHO VR, LE LOIR Y, AZEVEDO VAC, FELDEN B, GUÉDON E. Environmental plasticity of the RNA content of Staphylococcus aureus fmicb.2021.634226. (The contributions to this article included the functional enrichment analysis of the coding sequences of nucleic acids found in S. aureus-derived EVs and manuscript revision.)

  aureus is an important opportunistic pathogen of humans and animals. it produces extracellular vesicles (EVs) that are involved in cellular communication and enable inter-kingdom crosstalk, the delivery of virulence factors and modulation of the host immune response. The protein content of EVs determines their biological functions. Clarifying which proteins are selected, and how, is of crucial value to understanding the role of EVs in pathogenesis and the development of molecular delivery systems. Here, we postulated that S. aureus EVs share a common proteome containing components involved in cargo sorting. The EV proteomes of five S. aureus strains originating from human, bovine, and ovine hosts were characterised. The clustering of EV proteomes reflected the diversity of the producing strains. A total of 253 proteins were identified, 119 of which composed a core EV proteome with functions in bacterial survival, pathogenesis, and putatively in EV biology. We also identified features in the sequences of EV proteins and the corresponding genes that could account for their packaging into EVs. Our findings corroborate the hypothesis of a selective sorting of proteins into EVs and offer new perspectives concerning the roles of EVs in S. aureus pathogenesis in specific host niches.

Figure 1 .

 1 Figure 1. Shape, size and yield of EVs produced by S. aureus strains MW2, N305, RF122, O11, and O46. (A) Representative electron microscopy images of negatively stained EVs purified from the culture supernatant of S. aureus strains MW2, N305, RF122, O11 and O46. Scale bar = 200 nm. (B) Representative graphs of the size distribution of EVs purified from culture supernatants of S. aureus strains MW2, N305, RF122, O11 and O46. (C) Mean size of EVs produced by S. aureus strains MW2, N305, RF122, O11 and O46. (D) Numbers of EVs produced by S. aureus strains MW2, N305, RF122, O11 and O46. Numbers of EVs per milliliter were normalized to numbers of CFU per milliliter calculated from cultures of each strain grown in BHI medium at a time when EVs were recovered. Data are presented as mean + SD of values obtained from three independent EV replicates. Asterisks indicate statistical significance (one-way ANOVA followed by Dunnett's multiple comparisons test: **P < 0.01; ***P < 0.001) of the mean value for each strain with that of the strain with the lowest mean value (O11).

  (n = 43), Streptococcus pneumoniae R6 (n = 27), Streptococcus pyogenes M1 (n = 38), Listeria monocytogenes 10403 S (n = 33), Mycobacterium tuberculosis ATCC 25618 (n = 20) and Clostridium difficile 630 (n = 25). Nine proteins (the DnaK chaperone protein, GAPDH, ATP synthase subunit beta, Enolase, the Tu elongation factor and four ribosomal proteins) were shared by all of the Gram-positive species analysed.

Figure 2 .

 2 Figure 2. Subcellular localisation and function of S. aureus EV proteins. (A) CELLO, (B) PredLIPO, (C) PSORTb, and (D) SignalP pipelines were used to predict the subcellular localisation of EV proteins in all the strains tested (pooled dataset; black) and individual strains N305 (green), RF122 (yellow), O11 (blue), O46 (red) and MW2 (grey). C, cytoplasm; E, extracellular; L, lipoprotein; M, membrane; O, other; S, signal peptide I; U, unknown; W, cell-wall. (E) Distribution of EV proteins according to their COG classification and strains. C, energy production and conversion; D, cell cycle control/cell division/chromosome partitioning; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; J, translation/ribosomal structure and biogenesis; K, transcription; L, replication/recombination/repair; M, cell wall/membrane/envelope biogenesis; N, cell motility; NA, not available; O, posttranslational modification/protein turnover/chaperones; P, inorganic ion transport and metabolism; S, function unknown; T, signal transduction mechanisms; U, intracellular trafficking/secretion/vesicular transport; V, defence mechanisms.

Figure 3 .

 3 Figure 3. Hierarchical clustering of EV samples. (A) Heatmap representation of clustering according to presence/absence of proteins in EV samples. (B) Heatmap representation of clustering according to the relative abundance of proteins in EV samples. The group 1 and group 2 proteins with contrasting relative abundance between mild (N305 and O46) and severe (RF122, O11 and MW2) strains are boxed. (C) Proteins from group 1 and group 2 and their relative abundance in EVs according to producing strains.

Figure 4 .

 4 Figure 4. Venn diagram of proteins identified in EVs from S. aureus strains MW2, N305, RF122, O11 and O46. The numbers of unique and shared proteins between EVs from several strains are presented.

Figure 5 .

 5 Figure 5. Mean features of EV proteins. (A) Distribution of EV proteins and whole cell proteins of S. aureus strains MW2, N305, RF122, O11 and O46 according to their isoelectric point, composition in positively (POS) charged residues, composition in lysine (Lys) residues and the codon adaptation index (CAI) of their corresponding genes. Asterisks indicate statistical significance when compared to the EV group (one-way ANOVA followed by Dunnett's multiple comparisons test: *P < 0.0001). (B) Score plot and loading plot of the partial least squares-discriminant analysis (PLS-DA) performed on proteins from Staphylococcus aureus MW2in order to differentiate EV proteins (in orange) from proteins not identified in EV proteins (in blue). Variables are the physicochemical properties of proteins, their amino acid composition and the CAI of each of the corresponding genes. In the loading plot, the most significant features positively and negatively associated with EV proteins are indicated in orange and blue, respectively.

Frontiers

FrontiersFIGURE 2 |FIGURE 3 |

 23 FIGURE 2 | Relative RNA composition of S. aureus HG003 and its secreted EVs. Individual colored bars represent the relative amount of each RNA class for mapped reads (A) and annotated RNAs (B). RNA-Seq data is the average of three independent replicates. Number of reads have been normalized with EdgeR. RNA classes are defined from the S. aureus genome annotation NCTC8325/HG003. Early-and late-stationary growth phases (6 and 12, respectively) in absence (V-) or presence (V+) of vancomycin.

FIGURE 4 |

 4 FIGURE 4 | Staphylococcus aureus HG003 EV RNA cargo and its modulation by different growth conditions. (A) Venn diagrams of RNA composition in EV-producing cells (upper panel) and EVs (lower panel) from different growth conditions. Early-and late-stationary growth phases (6 and 12, respectively) in absence (V-) or presence (V+) of vancomycin. (B) Prediction of Clusters of Orthologous Groups (COG) categories for mRNAs: NA, not predicted; S, function unknown; Q, secondary metabolites biosynthesis, transport, and catabolism; P, inorganic ion transport and metabolism; I, lipid transport and metabolism; H, coenzyme transport and metabolism; F, nucleotide transport and metabolism; E, amino acid transport and metabolism; G, carbohydrate transport and metabolism; C, energy production and conversion; O, post-translational modification, protein turnover, and chaperones; U, intracellular trafficking, secretion, and vesicular transport; M, cell wall/membrane/envelope biogenesis; T, signal transduction mechanisms; V, defense mechanisms; D, cell cycle control, cell division, chromosome partitioning; L, replication, recombination and repair; K, transcription; J, translation, ribosomal structure and biogenesis. (C) COG and KEGG categories enriched (p < 0.05) in EVs compared to EV-producing cells. (D) Subcellular localization of proteins encoded by mRNAs as predicted by SurfG+: Cyt, cytoplasmatic; Mbe, membrane; PSE, surface-exposed; Sec, secreted; NA, Not predicted.

FIGURE 6 |

 6 FIGURE 6 | HG003 EVs contain long mRNA transcripts. Comparison between operon coverages between EV-producing cell and EV samples in different conditions. Early-and late-stationary growth phases (6 and 12, respectively) in absence (V-) or presence (V+) of vancomycin. RNA coverage is visualized with Integrative Viewer Software (IGV) in log scale.

FIGURE 7 |

 7 FIGURE 7 | Differential RNA abundance between the EV-producing cells and the EVs. Colored square shows different RNA abundance patterns between

FIGURE 7 |

 7 FIGURE 7 | EV-producing cells and EVs with a log2 fold change.Comparisons comprised data with at least one of the two samples containing ≥90% coverage. The log2 fold change is displayed as colored squares from -2 (purple) to 10 (red). Early-and late-stationary growth phases(6 and 12, respectively) in absence (V-) or presence (V+) of vancomycin.

Yun

FIGURE 8 |

 8 FIGURE 8 | Timepoint clustering of expression profiles between EVs and EV-producing cells. The analyses were performed with the R package maSigPro from normalized counts by EdgeR. Statistical significance (p < 0.05) was adjusted with Benjamini-Hochberg method. Median expression values are calculated at 6 and 12 h for EVs (blue) and EV-producing cells (red) for the six clusters (from A to F).

Table 1 .

 1 Proteins from P. freudenreichii related to its immunomodulatory properties.

	Strain	Protein	Name / Description	Accession	Evidence level	Reference
			Ensemble of surface proteins		proteomic, in vitro	(Le Maréchal et al., 2015)
		GroL2	60 kDa chaperonin 2	CDP49125	genomic, proteomic	(Deutsch et al., 2017)
		HsdM3	Type I restriction-modification system DNA methylase	CDP48267	genomic, transcriptomic, mutant studies in vitro	(Deutsch et al., 2017)
		Lacl1	arabinose operon repressor	CDP47860	transcriptomic	(Deutsch et al., 2017)
		MerA	Pyridine nucleotide-disulphide oxidoreductase	CDP48574	genomic, proteomic	(Deutsch et al., 2017)
		Pep	Hypothetical protein	CDP48241	genomic, mutant studies in vitro	(Deutsch et al., 2017)
	CIRM-BIA129	PFCIRM129_04790	Hypothetical protein	CDP48736	genomic, transcriptomic	(Deutsch et al., 2017)
	(ITG P20)	PFCIRM129_10785	Hypothetical protein	CDP49252	transcriptomic	(Deutsch et al., 2017)
		PFCIRM129_10930	Hypothetical protein	CDP48242	genomic, transcriptomic	(Deutsch et al., 2017)
					genomic, transcriptomic, proteomic, mutant studies in vitro	(Deutsch et al., 2017)
		SlpB	Surface layer protein B	CDP48273	mutant studies in vitro and in vivo	(do Carmo et al., 2019)
					mutant studies in vitro	(do Carmo et al., 2017)
		SlpE	Surface protein with SLH domain	CDP48858	genomic, transcriptomic, proteomic, mutant studies in vitro	(Deutsch et al., 2017)
		SlpF	Surface protein with SLH domain	CDP49687	proteomic, mutant studies in vitro	(Deutsch et al., 2017)
		Acn	Aconitase, Aconitate hydratase	CEG89374	transcriptomic	(Deutsch et al., 2017)
	CIRM-BIA	DcuA	C4-dicarboxylate transporter	CEG91776	genomic	(Deutsch et al., 2017)
	121	Eno1	Enolase 1	CEG91483	proteomic, mutant studies in vitro	(Deutsch et al., 2017)
		HtrA4	Serine protease	CEG91080	genomic, transcriptomic, proteomic, mutant studies in vitro	(Deutsch et al., 2017)

Legend. n.a.: not available.

  Vinícius de Rezende Rodovalho1,2 , Brenda Silva Rosa da Luz 1,2 , Houem Rabah 1 , Fillipe Luiz Rosa do Carmo 2 , Edson Luiz Folador 3 , Aurélie Nicolas 1 , Julien Jardin 1 , Valérie Briard-Bion 1 , Hervé Blottière 4 , Nicolas Lapaque 4 , Gwenaël Jan 1 , Yves Le Loir 1 , Vasco Ariston de Carvalho Azevedo 2 and Eric Guédon 1 *

	Extracellular Vesicles Produced by
	the Probiotic Propionibacterium
	freudenreichii CIRM-BIA 129
	Mitigate Inflammation by Modulating
	the NF-κB Pathway

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Table 1 .

 1 Proteins from the EV core proteome that were identified as immunomodulatory in other studies with strains of P. freudenreichii.

	Protein ID	GI ID	Accession	Description COG Localization Lipoprotein
	Eno1	659917660 CDP48974.1	Enolase 1	G	Cytoplasmic	Other
	Acn	659918109 CDP48708.1	Aconitase	C	Cytoplasmic	Other
	GroL2	659917458 CDP49125.1	60 kDa	O	Cytoplasmic	Other
				chaperonin 2
	SlpE	659917805 CDP48858.1 Surface layer	O	Extracellular	Sec
				protein E
	SlpB	659918413 CDP48273.1 Surface layer	O	Extracellular	Sec
				protein B
	PFCIRM129_10785 659917415 CDP49252.1 Hypothetical	?	Membrane	Lipo
				protein

Legend: Sec: secretion signal peptide, Lipo: lipoprotein signal peptide, TM: transmembrane, Other: no signals found, ?: unknown. References:

Deutsch et al., 2017; Do Carmo et al., 2017, 2019.

1. Research articles as first author RODOVALHO

  VR, LUZ BSR, RABAH H, do CARMO FLR, FOLADOR EL, NICOLAS A, JARDIN J, BRIARD-BION V, BLOTTIÈRE H, LAPAQUE N, JAN G, LE LOIR Y, de CARVALHO AZEVEDO VA, GUÉDON E. Extracellular

Vesicles Produced by the Probiotic Propionibacterium freudenreichii CIRM-BIA 129 Mitigate Inflammation by Modulating the

	NF-κB	Pathway.	Front	Microbiol.	2020	Jul	7;11:1544.	doi:
	10.3389/fmicb.2020.01544.
	RODOVALHO VR, LUZ BSR, NICOLAS A, do CARMO FLR, JARDIN J, BRIARD-BION V,
	JAN G, LE LOIR Y, de CARVALHO AZEVEDO VA, GUEDON E.

Environmental conditions modulate the protein content and immunomodulatory activity of extracellular vesicles produced by the probiotic Propionibacterium freudenreichii.

  Appl Environ Microbiol. 2020 Dec 11:AEM.02263-20. doi: 10.1128/AEM.02263-20.

Table S1 .

 S1 Proteins found in P. freudenreichii-derived EVs and their main characteristics.Legend: # -number to indicate the order of presentation. GI: GenInfo Identifier. ACCESSION: accession number. LOCUS TAG: genomic locus tag identification. DESCRIPTION: protein brief description. COG: COG category prediction. LOCALIZATION: subcellular localization prediction. LIPO-PROTEIN: lipoprotein signal prediction, Sec: secretion signal peptide, Lipo: lipoprotein signal peptide, TM: transmembrane, Other: no signals found. Conditions: EV obtention condition, UC_UF: UF-derived EVs purified by UC, UC_YEL: YEL-derived EVs purified by UC, SEC_UF: UF-derived EVs purified by SEC, SEC_YEL: YEL-derived EVs purified by SEC. For each condition: 1 -presence, 0 -absence.

	8 19 PFCIRM12 GuaB1 28 Gcp 38 PFCIRM12 49 Hpf 58 PFCIRM12 68 Blmh 80 FtsI 88 NuoG 97 PFCIRM12 107 MurA 120 PFCIRM12 127 BkdA2 136 PFCIRM12 148 RpoA 157 RmlA 167 RplC 179 PFCIRM12 189 InfB 200 PanE 212 FepC2 224 PFCIRM12 236 PurC 246 PonA 259 RplS 270 PFCIRM12 278 PFCIRM12 287 PFCIRM12 298 PFCIRM12 310 Pf456 319 PFCIRM12 328 ProV 338 UgpQ2 351 DkgA 362 GlmU 372 IolC 382 DapL 393 PFCIRM12 405 IolD 414 YghZ 423 DnaA 437 Mpg 449 CysD 459 PFCIRM12 488 RplI 496 RpsL 509 GuaB2 521 PFCIRM12 533 PFCIRM12 544 PurK 554 RsmB 568 PFCIRM12 581 AtpA 592 PFCIRM12 605 PFCIRM12 618 PFCIRM12 629 RluA 639 CbiL 651 RpsO	65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991 65991	CDP478 CDP481 CDP478 CDP485 CDP484 CDP478 CDP485 CDP493 CDP480 CDP485 CDP490 CDP494 CDP480 CDP495 CDP479 CDP485 CDP479 CDP497 CDP492 CDP487 CDP488 CDP487 CDP492 CDP492 CDP475 CDP486 CDP483 CDP483 CDP483 CDP497 CDP493 CDP494 CDP494 CDP496 CDP489 CDP491 CDP494 CDP491 CDP491 CDP488 CDP488 CDP489 CDP498 CDP495 CDP494 CDP479 CDP478 CDP480 CDP475 CDP477 CDP485 CDP485 CDP488 CDP487 CDP482 CDP483 CDP493 CDP477 CDP492	PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129 PFCIRM129	Inosine-5-monophosphate FAD-dependent pyridine Putative O-sialoglycoprotein polar amino acid ABC ribosomal S30AE, sigma 54 zinc-binding dehydrogenase Aminopeptidase C (Bleomycin Cell division protein FtsI NADH-quinone Hypothetical transmembrane UDP-N-acetylglucosamine 1-Hypothetical secreted protein 2-oxoisovalerate glycine betaine/choline DNA-directed RNA polymerase Glucose-1-phosphate 50S ribosomal protein L3 inositol-1-phosphate synthase Translation initiation factor IF-2-dehydropantoate 2-Iron transport system Hypothetical protein Phosphoribosylaminoimidazol Penicillin-binding protein 50S ribosomal protein L19 ABC transporter substrate-D-3-phosphoglycerate Hypothetical secreted and Hypothetical protein Hypothetical protein Hypothetical protein ABC-type choline/glycine Glycerophosphoryl diester Putative aldo/keto reductase UDP-N-acetylglucosamine iolC (Myo-inositol catabolism aminotransferase Regulatory protein iolD (Myo-inositol catabolism Oxidoreductase Chromosomal replication 3-methyladenine DNA Sulfate adenylyltransferase Transcriptional regulator 50S ribosomal protein L9 30S ribosomal protein S12 Inosine-5-monophosphate Hypothetical protein oxidoreductase Phosphoribosylaminoimidazol Sun, tRNA and rRNA cytosine-Transcriptional regulator PadR ATP synthase subunit alpha Putative uncharacterized Putative uncharacterized Zn dependant peptidase Pseudouridylate synthase, CbiL Precorrin-2 C20-30S ribosomal protein S15	F C O ET J E E M C M M M C M K J I J H P M F M J S EH T S T D E C S M G E K E C L L EH K J J F L C F JK K C S C S J H J	Cytoplasmic Cytoplasmic Cytoplasmic Membrane Cytoplasmic Cytoplasmic Cytoplasmic Extracellular Cytoplasmic Membrane Cytoplasmic Extracellular Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Extracellular Cytoplasmic Cytoplasmic Extracellular Cytoplasmic Membrane Cytoplasmic Membrane Extracellular Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic	Other Other Other Lipo Other Other Other TM Other Other Other Lipo Other Lipo Other Other Other Other Other Other Lipo Other Other TM Other Lipo Other TM Sec Other Other TM Other Other Other Other Other Other Other Other Other Other TM Other Other Other Other Other Other Other Other Other Other Sec Other Other Other Other Other	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0	1
	9_09300 9_03850 9_07545 9_03580 9_11875 9_00410 9_01960 9_05105 9_04325 9_02820 9_02885 9_02785 9_11455 9_10275 9_00505 9_09095 9_00115 9_03865 9_04720 9_10855 9_02880	8855 8623 8867 8127 8223 8799 7992 7360 8727 8167 7556 7212 8588 7086 8898 8176 8937 6902 7370 7966 7790 7879 7409 7443 9140 8052 8293 8305 8346 6823 7347 7164 7149 6978 7669 7513 7213 7488 7516 7801 7815 7646 6785 7105 7179 8690 8854 8586 9125 8776 7994 8130 7889 7959 8467 8304 7344 9056 7387	73.1 25.1 85.1 62.1 06.1 17.1 91.1 70.1 01.1 12.1 91.1 38.1 90.1 39.1 16.1 21.1 55.1 67.1 72.1 29.1 88.1 92.1 46.1 12.1 91.1 51.1 66.1 78.1 63.1 92.1 57.1 72.1 57.1 71.1 83.1 80.1 39.1 55.1 83.1 99.1 68.1 60.1 36.1 58.1 87.1 64.1 72.1 88.1 76.1 94.1 93.1 65.1 02.1 22.1 27.1 77.1 54.1 57.1 66.1	_07830 _09300 _07890 _03850 _03025 _07545 _04025 _11520 _08495 _03580 _09765 _11875 _09105 _00410 _08045 _03625 _08240 _01960 _11015 _04755 _05625 _05105 _10755 _10570 _00195 _04325 _02820 _02885 _02785 _02105 _11455 _12105 _12030 _01465 _06115 _10400 _11880 _10275 _10415 _05680 _05510 _05995 _02395 _00505 _12185 _08290 _07825 _09095 _00115 _07425 _04035 _03865 _05155 _04720 _10855 _02880 _11440 _07230 _10985	dehydrogenase (IMP nucleotide-disulphide endopeptidase transporter, binding protein modulation protein hydrolase) (penicillin-binding protein 2) oxidoreductase chain G (NADH protein carboxyvinyltransferase dehydrogenase subunit beta Binding protein of ABC alpha chain (RNAP alpha thymidylyltransferase 2 reductase substrate-binding protein ABC PFCIRM129_05105 e-(Transglycosylase/transpeptid binding protein dehydrogenase / erythronate membrane protein PFCIRM129_02785 PFCIRM129_02105 PFCIRM129_11455 betaine transport,ATP-binding phosphodiesterase (oxidoreductase) pyrophosphorylase iolC protein) iolD protein) (acetolactate initiator protein glycosylase subunit 2 (Sulfate adenylate (Glycerol uptake operon dehydrogenase (IMP PFCIRM129_09095 e carboxylase ATPase subunit C5-methylases family (ATPase subunit alpha) (ATP protein protein Uncharacterized RNA methyltransferase
	# 1 2 3 4 5 6 7 9 10 PFCIRM12 ID PFCIRM12 9_07415 PFCIRM12 9_00620 PFCIRM12 9_11635 OpuCA Hsp202 GroEL1 PFCIRM12 9_07430 PFCIRM12 9_01350 9_07810 11 Ask 12 CysE 13 PFCIRM12 9_10155 14 Pyk1 15 PFCIRM12 9_02975 16 SodA 17 PFCIRM12 9_03805 18 PhoU 20 PFCIRM12 9_07990 21 PspA 22 HtrA1 23 GroL2 24 GuaB3 25 TyrB 26 PFCIRM12 9_00555 27 RplM 29 PFCIRM12 9_03845 30 PFCIRM12 9_03855 31 PFCIRM12 9_03860 32 TrpD 33 PpgK 34 YfmC 35 AceE 36 PFCIRM12 9_09290 37 CydA 39 NifJ1 40 RbsB 41 PFCIRM12 9_04165 42 ArgG 43 CpsA1 44 CarA 45 TetR7 46 PFCIRM12 9_07610 47 SecF 48 AroB 50 MltG 51 SecD 52 SecA 53 AlaS 54 PFCIRM12 9_09060 55 AccA 56 AspS 57 MurG 59 HisS 60 NifJ2 61 Eno1 62 PFCIRM12 9_01130 63 CarB 64 YihN 65 FtsE 66 SppA 67 Pep 69 FtsZ 70 YkgG 71 LutB 72 PFCIRM12 9_01055 73 PFCIRM12 9_01060 74 LutA 75 MetK 76 AroE 77 RsmI 78 MraW 79 LldP 81 PFCIRM12 9_04075 82 PFCIRM12 9_09500 83 TyrS 84 PFCIRM12 9_11720 85 NuoC 86 NuoD 87 NuoF 89 NuoI 90 InlA 91 IdsA 92 RtcB 93 PFCIRM12 9_03560 94 PFCIRM12 9_08935 95 Mrc 96 GlgC 98 XthA 99 SdhC1 100 DapA 101 NuoB 102 Pf279 103 MenJ 104 SkfE 105 BraC 106 Lpd 108 CbiM 109 CbiN 110 Dpm 111 GlpT 112 PFCIRM12 9_12095 113 PFCIRM12 9_08715 114 PFCIRM12 9_11950 115 SdhA 116 PFCIRM12 9_09710 117 Vpr 118 PFCIRM12 9_11930 119 FumC 121 DapD 122 ZnuA 123 SlpA 124 PFCIRM12 9_08670 125 CypA 126 BkdB 128 HtrA2 129 GtfB 130 RpsH 131 RplF 132 PFCIRM12 9_09915 133 RpsE 134 Cys2 135 MoeA1 137 BopA 138 RplE 139 OppD 140 PFCIRM12 9_11735 141 AckA 142 CstA 143 Pta 144 GlnA1 145 CodB 146 RpsK 147 RpsD 149 PFCIRM12 9_00865 150 PFCIRM12 9_08025 151 Gpt 152 PFCIRM12 9_03205 153 PFCIRM12 9_08360 154 BkdA1 155 SecE 156 RplA 158 PFCIRM12 9_09870 159 RplJ 160 Gtf 161 RpoB 162 RpoC 163 RplN 164 FusA 165 SlpD 166 RpsJ 168 RplD 169 RplB 170 RfbB 171 RplV 172 RpsC 173 PFCIRM12 9_11765 174 GtfA 175 RpsG 176 PFCIRM12 9_06000 177 PFCIRM12 9_07390 178 IspG 180 ProS 181 NorB 182 Phk 183 DeoR2 184 GlpC 185 GlpB 186 GlpA 187 HrdB 188 Upp 190 PFCIRM12 9_02060 191 FeoB 192 GlpK 193 GalK2 194 PknB 195 PbpA 196 FtsW2 197 NrdJ 198 RlmN 199 PstS 201 IolT3 202 Dxs 203 HsdM1 204 Acn 205 Mrr 206 PFCIRM12 9_06845 207 Gst 208 YdfJ 209 HsdS-2 210 OdhA 211 Asd 213 LemA 214 PFCIRM12 9_05605 215 PFCIRM12 9_10935 216 Gpm1 217 MalE 218 Tsf 219 FhaB 220 FhaA 221 PFCIRM12 9_00780 222 TrpB2 223 SlgT 225 PFCIRM12 9_02410 226 PFCIRM12 9_10740 227 DeoR3 228 PntA 229 PntB 230 RplT 231 AtpG 232 PFCIRM12 9_10650 233 PFCIRM12 9_05250 234 PFCIRM12 9_05245 235 PFCIRM12 9_10690 237 PFCIRM12 9_06895 238 LepB 239 SdhB3 240 PFCIRM12 9_02195 241 SlpE 242 PnpA 243 RecA 244 PFCIRM12 9_10970 245 PFCIRM12 9_02590 247 SlpB 248 PFCIRM12 9_00705 249 PFCIRM12 9_00955 250 MoxR 251 IclR 252 PFCIRM12 9_05365 253 FtsY 254 Ffh 255 PFCIRM12 9_10785 256 PFCIRM12 9_10780 257 SdhC2 258 SdhA3 260 InhA 261 Rho 262 IolA 263 PFCIRM12 9_09985 264 PFCIRM12 9_05935 265 HemA 266 GsiB 267 PFCIRM12 9_07085 268 AsnB 269 PFCIRM12 9_07105 271 MutA 272 PdxT 273 PyrG 274 Cat 275 PFCIRM12 9_07050 276 UvrA3 277 CycA1 279 CbiP 280 CbiE 281 CbiX 282 PFCIRM12 9_02895 283 MutB 284 XseA 285 MetQ 286 PFCIRM12 9_09980 288 ThyA 289 YqfL 290 PFCIRM12 9_07170 291 PFCIRM12 9_07150 292 RsmC 293 WhiA 294 MmdA 295 NudC 296 Dld 297 SufC 299 Tal2 300 SufD 301 Hpt 302 PFCIRM12 9_07345 303 SufB 304 Mao 305 TrpC 306 RpsA 307 Dac 308 PFCIRM12 9_07365 309 YajC 311 Gpi 312 RplQ 313 PolA 314 AcpP 315 BluB 316 CydB 317 GlyA 318 Mdh 320 FtsX 321 NuoE 322 NuoH 323 NuoJ 324 Ald 325 NuoM 326 CycA2 327 AhpC 9_06040 9_02270 9_02985 9_11795 371 LacZ 9_01355 413 GltA1 422 OxaA 436 PFCIRM12 448 PFCIRM12 9_07845 9_09175 9_00135 567 NadE 580 PlsC1 604 PksA 617 UrvD 638 CysS1 650 PFCIRM12 329 Tal1 330 PFCIRM12 9_08605 331 PFCIRM12 9_09455 332 PFCIRM12 9_03530 333 PFCIRM12 9_00395 334 OppB 335 SecY 336 SdhB 337 PFCIRM12 345 Epi 346 FtsH 347 AtpH 348 AtpF 349 PFCIRM12 9_00335 350 GlgP 358 Gab 359 Rnj 360 DhbC 361 SerS1 367 IolE1 368 DnaN 369 MetF 370 AroH 378 NirA2 379 UvrB 380 GyrB2 381 PFCIRM12 389 SufS 390 FabF 391 PFCIRM12 9_01765 392 PFCIRM12 9_10305 400 SdaA 401 DedD 402 SpoU3 403 PFCIRM12 9_10395 404 PFCIRM12 9_10435 411 IspD 412 PFCIRM12 9_05700 421 GidB 434 Prs 435 Mfd 446 UgpQ1 447 PFCIRM12 9_01155 457 Fbp 458 MshD 495 LipB 508 PFCIRM12 520 PFCIRM12 532 PFCIRM12 9_07435 553 EngA 591 PimA 9_10920 9_02955 9_06870 507 PepC 519 Fic 531 PFCIRM12 9_00140 542 Maf 543 PFCIRM12 565 PFCIRM12 9_03620 566 WcqF 578 HrpA2 579 HrdD2 590 DnaG1 602 PFCIRM12 9_09965 603 PFCIRM12 615 PFCIRM12 9_03045 616 PFCIRM12 627 PFCIRM12 9_01830 628 Sms 636 CobA 637 PFCIRM12 420 LutR 433 PFCIRM12 9_09640 445 PFCIRM12 9_01115 456 PFCIRM12 9_01880 467 PFCIRM12 9_11550 9_07910 506 TsaB 518 SbcC 530 HolA 552 MetH 589 CbiB 9_02740 564 CpsK 577 MiaB1 601 RecN 614 PFCIRM12 626 TatD 649 IlvC 432 ActII-1 444 PFCIRM12 9_01220 466 PlsC3 494 LacI4 505 PFCIRM12 517 PurL 529 PyrC 541 MutM2 551 UvrA2 563 PFCIRM12 9_03650 576 MiaB2 588 CbiA 635 Map 625 TrxC 9_08560 648 RluB 455 TypA 9_11620 504 RplR 9_08825 9_04215 613 GlyQS 634 PFCIRM12 431 RedLA2 9_01710 465 PFCIRM12 493 MgtE 516 PFCIRM12 9_06905 550 Rph 562 PFCIRM12 575 GtfC 587 CobU 600 PpnK 9_03400 624 UvrA4 647 Soj 410 AldB 454 PFCIRM12 503 RpsN1 9_08990 528 ProB 540 PFCIRM12 612 PFCIRM12 633 RplW 399 SrtA 9_10340 419 PrsA 430 Pf3022 443 FixC 9_01720 464 FtsQ 515 PFCIRM12 9_06890 549 MurI 561 DnaE1 574 GtfD 586 GyrB1 599 NagD 623 YchF 646 DnaB 339 PFCIRM12 9_02050 340 Fba2 341 Fba1 342 Gpm2 343 PFCIRM12 9_05480 344 Pgm1 352 PFCIRM12 9_05940 353 Tpi1 354 Pfp 355 MetN 356 Pgk 357 PepN 363 Hom 364 OpcA 365 HelD 366 CysN 373 ThrS 374 Nfo 375 HepA 376 GlgX 377 PurQ 383 PurF 384 PFCIRM12 9_01505 385 Tkt 386 Icd 387 TopA 388 Gnd2 394 CE1826 395 PFCIRM12 9_09820 396 Bccp 397 PFCIRM12 9_10630 398 PFCIRM12 9_10165 406 PFCIRM12 9_10490 407 PFCIRM12 9_10545 408 HemY 409 PFCIRM12 415 UppS 416 ApeB 417 PFCIRM12 9_05845 418 PcnB 424 RecF 425 DtxR 426 PFCIRM12 9_06350 427 PFCIRM12 9_06340 428 Lnt 429 MetW 438 PFCIRM12 9_05980 439 DnaJ1 440 HspR1 441 UhpT 442 AatB 450 PFCIRM12 9_02380 451 DisA 452 FolE 453 PFCIRM12 460 YhbJ 461 Gap 462 Zwf 463 PFCIRM12 9_11470 490 PF47 491 PFCIRM12 9_11755 492 GlnE 502 UbiE 527 Obg 539 PFCIRM12 9_03730 9_00720 514 CoaA 560 GltB 573 PFCIRM12 585 Ldh2 598 NtcA2 611 HisF 622 Mca 632 GltX 645 PFCIRM12 9_08620 526 RplU 548 ThiD2 9_03740 9_00950 498 RplP 499 LipA 500 ArsR2 501 PFCIRM12 510 Cys1 511 LacI1 512 RplO 513 RpsM 523 PFCIRM12 9_06675 524 Tgt 525 Era 538 PspC 559 Lgt 572 PFCIRM12 584 LeuA2 597 PFCIRM12 610 GyrA 631 ThiE2 644 NucS 535 Got 536 ArgB 537 RpmB 546 Cls 547 PFCIRM12 9_07040 556 MetN2 557 PFCIRM12 9_04310 558 TrpA 570 PPA2234 571 Slh2 582 AtpD 583 LplA 594 PFCIRM12 9_04595 595 PFCIRM12 9_03455 596 PFCIRM12 9_00735 607 FabD 608 Fnk 609 PFCIRM12 9_03055 620 PFCIRM12 9_10660 621 HemC 643 YwlC 641 AldC 642 GlnS 489 PFCIRM12 9_11920 497 Tuf 522 PFCIRM12 9_07725 534 PFCIRM12 9_00090 545 PyrD 555 RelA 569 PFCIRM12 9_03840 9_04710 9_03200 630 MurD 593 PFCIRM12 606 PFCIRM12 619 CysS2 640 Orn 652 GlgB	GI 65991 8774 65991 7060 65991 7259 65991 8860 65991 8241 65991 8856 65991 8777 65991 6956 65991 8851 65991 8550 65991 8848 65991 7468 65991 7343 65991 8323 65991 8893 65991 8118 65991 7486 65991 8887 65991 7270 65991 7710 65991 7458 65991 7266 65991 8246 65991 7047 65991 8879 65991 8126 65991 8128 65991 8129 65991 7264 65991 7053 65991 7054 65991 8245 65991 8621 65991 7025 65991 8622 65991 8841 65991 8020 65991 7010 65991 7011 65991 8004 65991 7671 65991 8812 65991 8023 65991 8008 65991 8010 65991 8024 65991 8222 65991 8012 65991 8569 65991 8794 65991 8015 65991 7354 65991 8018 65991 8797 65991 7660 65991 7009 65991 8003 65991 7150 65991 8232 65991 7989 65991 8230 65991 7351 65991 7472 65991 7473 65991 7035 65991 7036 65991 7474 65991 7997 65991 7039 65991 7675 65991 7362 65991 7475 65991 8002 65991 8520 65991 9103 65991 7238 65991 8731 65991 8730 65991 8728 65991 8725 65991 7120 65991 8719 65991 7123 65991 8163 65991 8681 65991 7127 65991 7204 65991 8711 65991 8268 65991 7202 65991 8732 65991 8679 65991 8735 65991 8738 65991 7558 65991 7557 65991 8653 65991 8654 65991 7597 65991 7186 65991 7162 65991 8637 65991 7195 65991 8267 65991 7591 65991 7193 65991 7191 65991 7216 65991 7211 65991 8274 65991 7603 65991 8631 65991 7133 65991 8587 65991 7538 65991 8191 65991 8922 65991 8921 65991 7539 65991 8919 65991 7082 65991 7722 65991 8913 65991 8925 65991 8912 65991 7229 65991 8198 65991 7091 65991 8199 65991 7240 65991 6960 65991 8900 65991 8899 65991 8386 65991 8894 65991 7078 65991 8258 65991 8703 65991 8589 65991 8701 65991 8698 65991 7577 65991 8695 65991 8179 65991 8692 65991 8691 65991 8927 65991 8688 65991 7219 65991 8938 65991 8936 65991 8934 65991 8186 65991 8932 65991 8931 65991 7235 65991 8188 65991 8689 65991 7647 65991 8769 65991 7376 65991 7374 65991 8446 65991 7936 65991 7938 65991 7939 65991 7940 65991 7941 65991 7970 65991 6834 65991 6840 65991 6842 65991 7366 65991 6844 65991 6820 65991 6821 65991 6822 65991 7943 65991 7378 65991 6895 65991 7518 65991 8105 65991 7285 65991 8109 65991 7284 65991 8987 65991 7947 65991 7797 65991 7283 65991 8335 65991 6880 65991 7526 65991 7786 65991 8483 65991 7784 65991 7783 65991 7382 65991 6824 65991 6825 65991 8369 65991 9133 65991 8370 65991 6788 65991 7406 65991 7856 65991 6762 65991 6763 65991 9116 65991 7888 65991 7426 65991 7861 65991 7860 65991 7396 65991 8997 65991 9139 65991 9141 65991 6818 65991 7805 65991 7386 65991 7912 65991 7384 65991 9086 65991 8413 65991 8414 65991 8404 65991 8419 65991 7928 65991 7831 65991 9151 65991 9150 65991 7415 65991 7414 65991 9143 65991 9142 65991 7290 65991 8327 65991 7517 65991 8492 65991 7764 65991 7504 65991 8299 65991 9031 65991 7505 65991 9035 65991 9057 65991 7307 65991 8500 65991 6973 65991 9024 65991 7331 65991 6913 65991 8073 65991 9054 65991 9053 65991 8307 65991 9058 65991 7756 65991 6945 65991 8491 65991 9062 65991 6937 65991 9045 65991 9042 65991 6980 65991 7322 65991 7492 65991 8317 65991 7724 65991 7299 65991 7511 65991 7301 65991 6856 65991 8760 65991 7302 65991 6915 65991 8046 65991 7338 65991 6853 65991 8764 65991 8025 65991 7425 65991 8897 65991 7341 65991 8250 65991 8866 65991 7024 65991 7480 65991 8819 65991 8231 65991 8729 65991 8726 65991 8724 65991 8161 65991 8721 65991 8162 65991 8709 7113 7802 7774 7655 6805 8131 7925 8473 8318 9124 8325 7223 6997 65991 6957 65991 65991 65991 65991 6898 8858 8600 9128 7982 65991 65991 8093 65991 65991 65991 65991 65991 8750 65991 8749 65991 7600 65991 8157 65991 7083 65991 8910 65991 8909 65991 8266 65991 65991 7877 65991 9123 65991 7890 65991 7891 65991 8434 65991 7868 9018 65991 7339 65991 9164 65991 8736 65991 65991 7508 65991 7808 65991 7248 65991 7253 65991 6787 65991 7335 65991 8140 65991 65991 7298 65991 8251 65991 6863 65991 7494 65991 7467 65991 7525 65991 7524 65991 7512 65991 7520 7434 65991 7482 65991 7767 7853 65991 7775 65991 7668 65991 7663 65991 7040 65991 7014 65991 6884 65991 65991 8697 65991 65991 65991 8778 65991 8171 7971 65991 8480 8319 6875 8992 8869 65991 8865 8677 65991 8608 8965 65991 9129 65991 8789 65991 7990 65991 8175 65991 65991 7935 65991 8072 65991 8063 65991 8488 65991 65991 8227 65991 65991 6876 65991 65991 8657 65991 65991 65991 7579 65991 7019 65991 6886 65991 7242 8871 65991 65991 65991 65991 8180 7908 65991 8501 8337 7674 8214 9040 65991 65991 8075 65991 65991 65991 8907 65991 65991 7580 65991 7001 6830 65991 7255 65991 7154 8920 65991 65991 8644 65991 8970 65991 8793 65991 65991 8181 65991 7919 65991 7718 65991 8493 8273 65991 8740 65991 7581 65991 7256 7225 65991 8659 9064 8030 8192 8076 8502 65991 7827 65991 8497 65991 6852 65991 65991 8923 65991 8983 8999 65991 65991 65991 65991 65991 8209 65991 8935 65991 7528 65991 7835 7592 7063 65991 7352 65991 8556 65991 65991 9065 8039 8193 7967 8508 65991 7751 65991 8380 9019 65991 7501 65991 65991 65991 6854 65991 8739 65991 8984 8996 65991 65991 65991 65991 65991 65991 65991 65991 6838 65991 7766 65991 7522 65991 7487 65991 7809 65991 7875 65991 7635 65991 7315 65991 8060 65991 6946 65991 7316 65991 65991 8329 65991 7311 65991 7247 65991 6784 65991 8097 65991 8119 65991 8116 65991 9098 65991 7407 65991 8636 65991 6940 65991 7825 65991 8818 65991 8405 65991 65991 7450 65991 7567 65991 7490 65991 7455 65991 7470 65991 7531 65991 7439 65991 7500 65991 65991 7759 65991 7752 65991 7746 65991 7788 65991 7807 65991 7626 65991 7715 65991 7713 65991 7595 65991 7593 65991 7643 65991 7706 65991 7705 65991 6962 65991 6987 65991 6782 65991 6874 65991 6858 65991 65991 7324 65991 7317 65991 7310 65991 7350 65991 7227 65991 7233 65991 7232 65991 8876 65991 65991 8042 8195 7969 8437 8210 7762 8684 8417 65991 8986 9020 65991 65991 65991 65991 65991 65991 65991 65991 8752 8901 65991 8834 65991 8043 8197 7931 8403 8141 7099 7880 65991 8930 65991 8696 65991 8756 65991 65991 8847 65991 8842 65991 8917 65991 65991 8954 65991 8953 65991 8943 65991 65991 65991 65991 65991 65991 65991 65991 65991 9111 65991 9108 65991 9163 8796 65991 8826 65991 9022 65991 8054 65991 8049 65991 8044 65991 8120 65991 8117 65991 7887 65991 7884 65991 8100 65991 8144 65991 8420 65991 8248 65991 8235 65991 8229 65991 7428 65991 7503 8058 7356 65991 8050 65991 8017 65991 65991 7198 65991 8687 65991 8835 65991 9120 65991 65991 8021 65991 8125 7957 8257 7477 65991 9038 7873 65991 65991 65991 65991 65991	IDENTIFICATION ACCESS ION LOCUS TAG CDP477 92.1 PFCIRM129 _07415 CDP495 75.1 PFCIRM129 _00620 CDP493 92.1 PFCIRM129 _11635 CDP478 78.1 PFCIRM129 _07855 CDP484 24.1 PFCIRM129 _03120 CDP478 74.1 PFCIRM129 _07835 CDP477 95.1 PFCIRM129 _07430 CDP496 49.1 PFCIRM129 _01350 CDP478 69.1 PFCIRM129 _07810 CDP481 37.1 PFCIRM129 _08960 CDP478 66.1 PFCIRM129 _07795 CDP491 35.1 PFCIRM129 _10155 CDP493 53.1 PFCIRM129 _11435 CDP483 96.1 PFCIRM129 _02975 CDP479 11.1 PFCIRM129 _08020 CDP485 53.1 PFCIRM129 _03805 CDP491 53.1 PFCIRM129 _10265 CDP479 05.1 PFCIRM129 _07990 CDP494 03.1 PFCIRM129 _11690 CDP490 24.1 PFCIRM129 _06325 CDP491 25.1 PFCIRM129 _10100 CDP493 99.1 PFCIRM129 _11670 CDP484 29.1 PFCIRM129 _03145 CDP495 62.1 PFCIRM129 _00555 CDP478 97.1 PFCIRM129 _07950 CDP485 61.1 PFCIRM129 _03845 CDP485 63.1 PFCIRM129 _03855 CDP485 64.1 PFCIRM129 _03860 CDP493 97.1 PFCIRM129 _11660 CDP495 68.1 PFCIRM129 _00585 CDP495 69.1 PFCIRM129 _00590 CDP484 28.1 PFCIRM129 _03140 CDP481 23.1 PFCIRM129 _09290 CDP495 92.1 PFCIRM129 _01005 CDP481 24.1 PFCIRM129 _09295 CDP478 59.1 PFCIRM129 _07760 CDP486 19.1 PFCIRM129 _04165 CDP496 14.1 PFCIRM129 _01135 CDP496 15.1 PFCIRM129 _01140 CDP486 03.1 PFCIRM129 _04085 CDP489 85.1 PFCIRM129 _06130 CDP478 30.1 PFCIRM129 _07610 CDP486 22.1 PFCIRM129 _04180 CDP486 07.1 PFCIRM129 _04105 CDP486 09.1 PFCIRM129 _04115 CDP486 23.1 PFCIRM129 _04185 CDP484 05.1 PFCIRM129 _03020 CDP486 11.1 PFCIRM129 _04125 CDP481 56.1 PFCIRM129 _09060 CDP478 12.1 PFCIRM129 _07520 CDP486 14.1 PFCIRM129 _04140 CDP493 64.1 PFCIRM129 _11490 CDP486 17.1 PFCIRM129 _04155 CDP478 15.1 PFCIRM129 _07535 CDP489 74.1 PFCIRM129 _06070 CDP496 13.1 PFCIRM129 _01130 CDP486 02.1 PFCIRM129 _04080 CDP494 58.1 PFCIRM129 _12035 CDP484 15.1 PFCIRM129 _03070 CDP485 88.1 PFCIRM129 _04010 CDP484 13.1 PFCIRM129 _03060 CDP493 61.1 PFCIRM129 _11475 CDP491 39.1 PFCIRM129 _10175 CDP491 40.1 PFCIRM129 _10180 CDP496 02.1 PFCIRM129 _01055 CDP496 03.1 PFCIRM129 _01060 CDP491 41.1 PFCIRM129 _10185 CDP485 96.1 PFCIRM129 _04050 CDP496 06.1 PFCIRM129 _01075 CDP489 89.1 PFCIRM129 _06150 CDP493 72.1 PFCIRM129 _11530 CDP491 42.1 PFCIRM129 _10190 CDP486 01.1 PFCIRM129 _04075 CDP481 68.1 PFCIRM129 _09500 CDP475 54.1 PFCIRM129 _00005 CDP494 07.1 PFCIRM129 _11720 CDP480 05.1 PFCIRM129 _08515 CDP480 04.1 PFCIRM129 _08510 CDP480 02.1 PFCIRM129 _08500 CDP479 99.1 PFCIRM129 _08485 CDP494 97.1 PFCIRM129 _12235 CDP479 93.1 PFCIRM129 _08455 CDP495 00.1 PFCIRM129 _12250 CDP485 08.1 PFCIRM129 _03560 CDP480 84.1 PFCIRM129 _08935 CDP495 04.1 PFCIRM129 _12270 CDP494 30.1 PFCIRM129 _11835 CDP479 85.1 PFCIRM129 _08415 CDP484 51.1 PFCIRM129 _03255 CDP494 28.1 PFCIRM129 _11825 CDP480 06.1 PFCIRM129 _08520 CDP480 82.1 PFCIRM129 _08925 CDP480 09.1 PFCIRM129 _08535 CDP480 12.1 PFCIRM129 _08550 CDP490 93.1 PFCIRM129 _09775 CDP490 92.1 PFCIRM129 _09770 CDP480 56.1 PFCIRM129 _08795 CDP480 57.1 PFCIRM129 _08800 CDP490 86.1 PFCIRM129 _09740 CDP494 52.1 PFCIRM129 _11990 CDP494 70.1 PFCIRM129 _12095 CDP480 40.1 PFCIRM129 _08715 CDP494 49.1 PFCIRM129 _11950 CDP484 50.1 PFCIRM129 _03250 CDP490 80.1 PFCIRM129 _09710 CDP494 47.1 PFCIRM129 _11940 CDP494 45.1 PFCIRM129 _11930 CDP494 42.1 PFCIRM129 _11895 CDP494 37.1 PFCIRM129 _11870 CDP484 57.1 PFCIRM129 _03285 CDP490 65.1 PFCIRM129 _09350 CDP480 34.1 PFCIRM129 _08670 CDP495 10.1 PFCIRM129 _12305 CDP480 89.1 PFCIRM129 _09100 CDP491 18.1 PFCIRM129 _09910 CDP485 36.1 PFCIRM129 _03710 CDP479 40.1 PFCIRM129 _08165 CDP479 39.1 PFCIRM129 _08160 CDP491 19.1 PFCIRM129 _09915 CDP479 37.1 PFCIRM129 _08150 CDP495 35.1 PFCIRM129 _00390 CDP490 36.1 PFCIRM129 _06395 CDP479 31.1 PFCIRM129 _08120 CDP479 43.1 PFCIRM129 _08180 CDP479 30.1 PFCIRM129 _08115 CDP494 10.1 PFCIRM129 _11735 CDP485 43.1 PFCIRM129 _03745 CDP495 44.1 PFCIRM129 _00435 CDP485 44.1 PFCIRM129 _03750 CDP494 09.1 PFCIRM129 _11730 CDP496 53.1 PFCIRM129 _01370 CDP479 18.1 PFCIRM129 _08055 CDP479 17.1 PFCIRM129 _08050 CDP483 06.1 PFCIRM129 _00865 CDP479 12.1 PFCIRM129 _08025 CDP495 31.1 PFCIRM129 _00370 CDP484 41.1 PFCIRM129 _03205 CDP479 77.1 PFCIRM129 _08360 CDP480 91.1 PFCIRM129 _09110 CDP479 75.1 PFCIRM129 _08345 CDP479 72.1 PFCIRM129 _08330 CDP491 12.1 PFCIRM129 _09870 CDP479 69.1 PFCIRM129 _08315 CDP485 24.1 PFCIRM129 _03640 CDP479 66.1 PFCIRM129 _08300 CDP479 65.1 PFCIRM129 _08295 CDP479 45.1 PFCIRM129 _08190 CDP479 62.1 PFCIRM129 _08280 CDP494 18.1 PFCIRM129 _11775 CDP479 56.1 PFCIRM129 _08245 CDP479 54.1 PFCIRM129 _08235 CDP479 52.1 PFCIRM129 _08225 CDP485 31.1 PFCIRM129 _03675 CDP479 50.1 PFCIRM129 _08215 CDP479 49.1 PFCIRM129 _08210 CDP494 16.1 PFCIRM129 _11765 CDP485 33.1 PFCIRM129 _03695 CDP479 63.1 PFCIRM129 _08285 CDP489 61.1 PFCIRM129 _06000 CDP477 87.1 PFCIRM129 _07390 CDP492 78.1 PFCIRM129 _11045 CDP492 76.1 PFCIRM129 _11035 CDP482 54.1 PFCIRM129 _09425 CDP487 39.1 PFCIRM129 _04815 CDP487 41.1 PFCIRM129 _04825 CDP487 42.1 PFCIRM129 _04830 CDP487 43.1 PFCIRM129 _04835 CDP487 44.1 PFCIRM129 _04840 CDP487 33.1 PFCIRM129 _04775 CDP497 78.1 PFCIRM129 _02025 CDP497 84.1 PFCIRM129 _02060 CDP497 86.1 PFCIRM129 _02070 CDP492 68.1 PFCIRM129 _10995 CDP497 88.1 PFCIRM129 _02080 CDP497 89.1 PFCIRM129 _02090 CDP497 90.1 PFCIRM129 _02095 CDP497 91.1 PFCIRM129 _02100 CDP487 46.1 PFCIRM129 _04850 CDP492 80.1 PFCIRM129 _11055 CDP497 60.1 PFCIRM129 _01925 CDP491 85.1 PFCIRM129 _10425 CDP487 04.1 PFCIRM129 _04620 CDP492 95.1 PFCIRM129 _11140 CDP487 08.1 PFCIRM129 _04640 CDP492 94.1 PFCIRM129 _11135 CDP476 88.1 PFCIRM129 _06845 CDP487 10.1 PFCIRM129 _04660 CDP488 95.1 PFCIRM129 _05660 CDP492 93.1 PFCIRM129 _11130 CDP483 52.1 PFCIRM129 _02730 CDP497 45.1 PFCIRM129 _01850 CDP491 93.1 PFCIRM129 _10465 CDP488 84.1 PFCIRM129 _05605 CDP482 43.1 PFCIRM129 _10935 CDP488 82.1 PFCIRM129 _05595 CDP488 81.1 PFCIRM129 _05590 CDP492 84.1 PFCIRM129 _11075 CDP497 93.1 PFCIRM129 _02110 CDP497 94.1 PFCIRM129 _02115 CDP482 89.1 PFCIRM129 _00780 CDP475 84.1 PFCIRM129 _00160 CDP482 90.1 PFCIRM129 _00785 CDP498 39.1 PFCIRM129 _02410 CDP492 43.1 PFCIRM129 _10740 CDP488 26.1 PFCIRM129 _05295 CDP498 45.1 PFCIRM129 _02445 CDP498 46.1 PFCIRM129 _02450 CDP475 67.1 PFCIRM129 _00070 CDP488 01.1 PFCIRM129 _05150 CDP492 27.1 PFCIRM129 _10650 CDP488 17.1 PFCIRM129 _05250 CDP488 16.1 PFCIRM129 _05245 CDP492 33.1 PFCIRM129 _10690 CDP476 98.1 PFCIRM129 _06895 CDP475 90.1 PFCIRM129 _00190 CDP475 92.1 PFCIRM129 _00200 CDP497 99.1 PFCIRM129 _02195 CDP488 58.1 PFCIRM129 _05460 CDP492 65.1 PFCIRM129 _10980 CDP487 58.1 PFCIRM129 _04920 CDP492 63.1 PFCIRM129 _10970 CDP476 17.1 PFCIRM129 _02590 CDP482 73.1 PFCIRM129 _00700 CDP482 74.1 PFCIRM129 _00705 CDP483 24.1 PFCIRM129 _00955 CDP482 79.1 PFCIRM129 _00730 CDP487 74.1 PFCIRM129 _05000 CDP488 40.1 PFCIRM129 _05365 CDP476 02.1 PFCIRM129 _00250 CDP476 01.1 PFCIRM129 _00245 CDP492 52.1 PFCIRM129 _10785 CDP492 51.1 PFCIRM129 _10780 CDP475 94.1 PFCIRM129 _00210 CDP475 93.1 PFCIRM129 _00205 CDP493 00.1 PFCIRM129 _11165 CDP483 44.1 PFCIRM129 _02685 CDP491 84.1 PFCIRM129 _10420 CDP482 02.1 PFCIRM129 _09985 CDP489 48.1 PFCIRM129 _05935 CDP491 71.1 PFCIRM129 _10355 CDP483 72.1 PFCIRM129 _02850 CDP477 32.1 PFCIRM129 _07085 CDP491 72.1 PFCIRM129 _10360 CDP477 36.1 PFCIRM129 _07105 CDP477 58.1 PFCIRM129 _07235 CDP493 17.1 PFCIRM129 _11250 CDP482 10.1 PFCIRM129 _10025 CDP496 66.1 PFCIRM129 _01440 CDP477 25.1 PFCIRM129 _07050 CDP493 41.1 PFCIRM129 _11370 CDP497 08.1 PFCIRM129 _01665 CDP486 72.1 PFCIRM129 _04440 CDP477 55.1 PFCIRM129 _07220 CDP477 54.1 PFCIRM129 _07215 CDP483 80.1 PFCIRM129 _02895 CDP477 59.1 PFCIRM129 _07240 CDP489 40.1 PFCIRM129 _05895 CDP496 84.1 PFCIRM129 _01530 CDP482 01.1 PFCIRM129 _09980 CDP477 63.1 PFCIRM129 _07260 CDP496 76.1 PFCIRM129 _01490 CDP477 46.1 PFCIRM129 _07170 CDP477 43.1 PFCIRM129 _07150 CDP496 73.1 PFCIRM129 _01475 CDP493 32.1 PFCIRM129 _11325 CDP491 59.1 PFCIRM129 _10295 CDP483 90.1 PFCIRM129 _02945 CDP489 08.1 PFCIRM129 _05730 CDP493 09.1 PFCIRM129 _11210 CDP491 78.1 PFCIRM129 _10390 CDP493 11.1 PFCIRM129 _11220 CDP497 21.1 PFCIRM129 _01730 CDP477 78.1 PFCIRM129 _07345 CDP493 12.1 PFCIRM129 _11225 CDP497 10.1 PFCIRM129 _01675 CDP486 45.1 PFCIRM129 _04295 CDP493 48.1 PFCIRM129 _11405 CDP497 18.1 PFCIRM129 _01715 CDP477 82.1 PFCIRM129 _07365 CDP486 24.1 PFCIRM129 _04190 CDP492 26.1 PFCIRM129 _10645 CDP479 15.1 PFCIRM129 _08040 CDP493 51.1 PFCIRM129 _11420 CDP484 33.1 PFCIRM129 _03165 CDP478 84.1 PFCIRM129 _07885 CDP495 91.1 PFCIRM129 _01000 CDP491 47.1 PFCIRM129 _10235 CDP478 37.1 PFCIRM129 _07645 CDP484 14.1 PFCIRM129 _03065 CDP480 03.1 PFCIRM129 _08505 CDP480 00.1 PFCIRM129 _08490 CDP479 98.1 PFCIRM129 _08480 CDP485 06.1 PFCIRM129 _03550 CDP479 95.1 PFCIRM129 _08465 CDP485 07.1 PFCIRM129 _03555 CDP479 83.1 PFCIRM129 _08400 26.1 _12415 00.1 _05685 72.1 _05540 69.1 _06040 13.1 _02270 66.1 _03870 71.1 _04985 33.1 _10885 91.1 _02950 75.1 _00110 98.1 _02985 22.1 _11795 24.1 _01200 CDP495 PFCIRM129 50.1 _01355 CDP489 PFCIRM129 CDP488 PFCIRM129 CDP489 PFCIRM129 CDP498 PFCIRM129 63.1 _01940 76.1 _07845 02.1 _09175 79.1 _00135 81.1 _03975 CDP485 PFCIRM129 CDP487 PFCIRM129 92.1 _04540 CDP482 PFCIRM129 CDP483 PFCIRM129 CDP475 PFCIRM129 CDP483 PFCIRM129 CDP480 24.1 PFCIRM129 _08610 CDP480 23.1 PFCIRM129 _08605 CDP490 67.1 PFCIRM129 _09455 CDP485 02.1 PFCIRM129 _03530 CDP495 36.1 PFCIRM129 _00395 CDP479 28.1 PFCIRM129 _08105 CDP479 27.1 PFCIRM129 _08100 CDP484 49.1 PFCIRM129 _03245 CDP494 PFCIRM129 CDP487 90.1 PFCIRM129 _05095 CDP475 74.1 PFCIRM129 _00105 CDP488 03.1 PFCIRM129 _05160 CDP488 04.1 PFCIRM129 _05165 CDP482 66.1 PFCIRM129 _00335 CDP487 81.1 PFCIRM129 _05050 19.1 _07020 CDP493 49.1 PFCIRM129 _11410 CDP476 15.1 PFCIRM129 _00315 CDP480 10.1 PFCIRM129 _08540 CDP496 PFCIRM129 CDP491 75.1 PFCIRM129 _10375 CDP488 61.1 PFCIRM129 _05475 CDP493 81.1 PFCIRM129 _11580 CDP493 86.1 PFCIRM129 _11605 CDP498 38.1 PFCIRM129 _02405 CDP493 45.1 PFCIRM129 _11390 CDP484 85.1 PFCIRM129 _03435 CDP496 PFCIRM129 CDP493 08.1 PFCIRM129 _11205 CDP484 34.1 PFCIRM129 _03170 CDP497 28.1 PFCIRM129 _01765 CDP491 61.1 PFCIRM129 _10305 CDP491 34.1 PFCIRM129 _10150 CDP491 92.1 PFCIRM129 _10460 CDP491 91.1 PFCIRM129 _10455 CDP491 79.1 PFCIRM129 _10395 CDP491 87.1 PFCIRM129 _10435 03.1 _10520 CDP491 49.1 PFCIRM129 _10245 CDP489 03.1 PFCIRM129 _05700 23.1 _05280 CDP488 73.1 PFCIRM129 _05550 CDP489 82.1 PFCIRM129 _06110 CDP489 77.1 PFCIRM129 _06085 CDP496 07.1 PFCIRM129 _01080 CDP496 18.1 PFCIRM129 _01155 CDP497 49.1 PFCIRM129 _01870 CDP497 PFCIRM129 CDP479 71.1 PFCIRM129 _08325 CDP478 PFCIRM129 CDP481 PFCIRM129 CDP475 PFCIRM129 96.1 _07435 CDP485 PFCIRM129 16.1 _03600 34.1 _04780 CDP486 PFCIRM129 40.1 _10920 92.1 _02955 40.1 _01825 93.1 _06870 87.1 _07900 CDP478 83.1 PFCIRM129 _07880 80.1 _08915 CDP481 10.1 PFCIRM129 _09225 66.1 _06730 CDP475 80.1 PFCIRM129 _00140 CDP478 07.1 PFCIRM129 _07495 CDP477 PFCIRM129 89.1 _04015 CDP485 20.1 PFCIRM129 _03620 CDP485 PFCIRM129 CDP487 38.1 PFCIRM129 _04810 CDP487 PFCIRM129 71.1 _04435 CDP486 62.1 PFCIRM129 _04385 CDP481 98.1 PFCIRM129 _09965 CDP482 PFCIRM129 CDP484 10.1 PFCIRM129 _03045 CDP483 PFCIRM129 CDP497 41.1 PFCIRM129 _01830 CDP497 PFCIRM129 CDP480 60.1 PFCIRM129 _08815 CDP476 PFCIRM129 CDP488 PFCIRM129 CDP490 68.1 PFCIRM129 _09640 CDP496 11.1 PFCIRM129 _01115 CDP497 51.1 PFCIRM129 _01880 CDP493 75.1 PFCIRM129 _11550 89.1 _07910 CDP478 PFCIRM129 CDP480 PFCIRM129 CDP476 PFCIRM129 CDP485 PFCIRM129 25.1 _03645 54.1 _04900 CDP486 PFCIRM129 11.1 _10030 54.1 _02740 88.1 _06145 84.1 _03425 41.1 _07140 CDP485 PFCIRM129 CDP487 PFCIRM129 74.1 _04450 CDP482 PFCIRM129 CDP483 PFCIRM129 CDP489 PFCIRM129 25.1 _08090 CDP484 PFCIRM129 CDP490 69.1 PFCIRM129 _09645 CDP496 28.1 PFCIRM129 _01220 74.1 _01995 CDP493 88.1 PFCIRM129 _11615 CDP494 62.1 PFCIRM129 _12055 38.1 _08155 CDP478 PFCIRM129 CDP480 47.1 PFCIRM129 _08750 CDP476 71.1 PFCIRM129 _06755 CDP478 11.1 PFCIRM129 _07515 CDP477 PFCIRM129 CDP485 26.1 PFCIRM129 _03650 CDP487 65.1 PFCIRM129 _04955 CDP486 PFCIRM129 32.1 _06365 CDP479 PFCIRM129 03.1 _09990 56.1 _03280 CDP490 PFCIRM129 14.1 _08560 CDP482 PFCIRM129 70.1 _09650 CDP497 PFCIRM129 89.1 _11620 24.1 _11805 CDP479 PFCIRM129 62.1 _08825 65.1 _07270 29.1 _04215 37.1 _03715 75.1 _04455 12.1 _10035 CDP484 PFCIRM129 36.1 _05345 CDP480 PFCIRM129 07.1 _10010 CDP490 PFCIRM129 17.1 _01710 CDP493 PFCIRM129 CDP494 PFCIRM129 41.1 _08170 CDP480 PFCIRM129 84.1 _06825 00.1 _06905 CDP477 PFCIRM129 CDP486 PFCIRM129 CDP485 PFCIRM129 CDP486 PFCIRM129 CDP482 PFCIRM129 79.1 _03400 CDP488 PFCIRM129 53.1 _08230 CDP482 PFCIRM129 95.1 _10475 CDP492 PFCIRM129 44.1 _05385 81.1 _09715 78.1 _00635 CDP497 PFCIRM129 62.1 _11480 CDP479 PFCIRM129 43.1 _08990 CDP476 PFCIRM129 CDP477 PFCIRM129 66.1 _07275 38.1 _04260 38.1 _03720 30.1 _04760 18.1 _10065 CDP484 PFCIRM129 35.1 _05870 CDP479 PFCIRM129 00.1 _00835 20.1 _07025 CDP491 PFCIRM129 68.1 _10340 CDP488 PFCIRM129 CDP490 PFCIRM129 CDP495 PFCIRM129 19.1 _01720 CDP493 PFCIRM129 13.1 _08555 CDP481 PFCIRM129 85.1 _06830 97.1 _06890 CDP477 PFCIRM129 CDP486 PFCIRM129 CDP485 PFCIRM129 CDP487 PFCIRM129 CDP482 PFCIRM129 CDP489 PFCIRM129 CDP483 PFCIRM129 CDP497 82.1 PFCIRM129 _02050 CDP489 02.1 PFCIRM129 _05695 CDP491 89.1 PFCIRM129 _10445 CDP491 54.1 PFCIRM129 _10270 CDP488 62.1 PFCIRM129 _05480 CDP487 88.1 PFCIRM129 _05085 CDP489 49.1 PFCIRM129 _05940 CDP493 25.1 PFCIRM129 _11290 CDP486 59.1 PFCIRM129 _04365 CDP496 85.1 PFCIRM129 _01535 CDP493 26.1 PFCIRM129 _11295 CDP477 PFCIRM129 CDP483 46.1 PFCIRM129 _02695 CDP493 21.1 PFCIRM129 _11270 CDP493 80.1 PFCIRM129 _11575 CDP498 35.1 PFCIRM129 _02390 CDP486 96.1 PFCIRM129 _04560 CDP485 54.1 PFCIRM129 _03810 CDP485 51.1 PFCIRM129 _03795 CDP476 29.1 PFCIRM129 _02665 CDP492 44.1 PFCIRM129 _10745 CDP480 39.1 PFCIRM129 _08710 CDP496 79.1 PFCIRM129 _01505 CDP488 34.1 PFCIRM129 _05335 CDP478 36.1 PFCIRM129 _07640 CDP483 25.1 PFCIRM129 _00960 CDP477 PFCIRM129 CDP492 19.1 PFCIRM129 _10605 CDP491 02.1 PFCIRM129 _09820 CDP491 57.1 PFCIRM129 _10285 CDP492 24.1 PFCIRM129 _10630 CDP491 37.1 PFCIRM129 _10165 CDP491 98.1 PFCIRM129 _10490 CDP492 08.1 PFCIRM129 _10545 CDP491 67.1 PFCIRM129 _10335 CDP491 PFCIRM129 CDP489 43.1 PFCIRM129 _05910 CDP489 36.1 PFCIRM129 _05875 CDP489 30.1 PFCIRM129 _05845 CDP488 86.1 PFCIRM129 _05615 CDP488 60.1 PFCIRM129 _05470 CDP490 49.1 PFCIRM129 _06475 CDP490 29.1 PFCIRM129 _06350 CDP490 27.1 PFCIRM129 _06340 CDP490 84.1 PFCIRM129 _09730 CDP490 82.1 PFCIRM129 _09720 CDP489 57.1 PFCIRM129 _05980 CDP490 20.1 PFCIRM129 _06305 CDP490 19.1 PFCIRM129 _06300 CDP496 55.1 PFCIRM129 _01380 CDP496 41.1 PFCIRM129 _01300 CDP498 33.1 PFCIRM129 _02380 CDP497 39.1 PFCIRM129 _01820 CDP497 23.1 PFCIRM129 _01740 CDP497 PFCIRM129 CDP493 34.1 PFCIRM129 _11335 CDP493 27.1 PFCIRM129 _11300 CDP493 20.1 PFCIRM129 _11265 CDP493 60.1 PFCIRM129 _11470 CDP494 26.1 PFCIRM129 _11815 CDP494 14.1 PFCIRM129 _11755 CDP494 13.1 PFCIRM129 _11750 CDP480 PFCIRM129 94.1 _07935 CDP476 PFCIRM129 CDP476 PFCIRM129 41.1 _04275 40.1 _03730 32.1 _04770 60.1 _12370 80.1 _03405 46.1 _05925 58.1 _08260 77.1 _00720 CDP478 PFCIRM129 87.1 _06840 21.1 _07030 CDP486 PFCIRM129 CDP485 PFCIRM129 CDP487 PFCIRM129 CDP482 PFCIRM129 CDP484 PFCIRM129 CDP489 PFCIRM129 CDP479 PFCIRM129 CDP482 PFCIRM129 26.1 _08620 19.1 _08060 CDP476 PFCIRM129 52.1 _07720 CDP477 PFCIRM129 42.1 _04280 42.1 _03740 77.1 _05025 23.1 _00950 86.1 _03440 52.1 _00475 93.1 _05110 CDP479 48.1 PFCIRM129 _08205 CDP479 70.1 PFCIRM129 _08320 CDP480 30.1 PFCIRM129 _08640 CDP480 PFCIRM129 CDP478 65.1 PFCIRM129 _07790 CDP478 60.1 PFCIRM129 _07765 CDP479 35.1 PFCIRM129 _08140 CDP479 PFCIRM129 CDP476 55.1 PFCIRM129 _06675 CDP476 54.1 PFCIRM129 _06670 CDP476 44.1 PFCIRM129 _06620 CDP478 PFCIRM129 CDP486 PFCIRM129 CDP485 PFCIRM129 CDP487 PFCIRM129 CDP483 PFCIRM129 CDP484 PFCIRM129 CDP495 PFCIRM129 CDP487 PFCIRM129 CDP475 62.1 PFCIRM129 _00045 CDP475 59.1 PFCIRM129 _00030 CDP476 14.1 PFCIRM129 _00310 14.1 _07530 CDP478 44.1 PFCIRM129 _07680 CDP477 23.1 PFCIRM129 _07040 CDP486 53.1 PFCIRM129 _04335 CDP486 48.1 PFCIRM129 _04310 CDP486 43.1 PFCIRM129 _04285 CDP485 55.1 PFCIRM129 _03815 CDP485 52.1 PFCIRM129 _03800 CDP488 00.1 PFCIRM129 _05145 CDP487 97.1 PFCIRM129 _05130 CDP486 99.1 PFCIRM129 _04595 CDP484 89.1 PFCIRM129 _03455 CDP482 80.1 PFCIRM129 _00735 CDP484 31.1 PFCIRM129 _03155 CDP484 18.1 PFCIRM129 _03090 CDP484 12.1 PFCIRM129 _03055 CDP492 29.1 PFCIRM129 _10660 CDP491 70.1 PFCIRM129 _10350 57.1 _04355 66.1 _11500 CDP486 PFCIRM129 49.1 _04315 CDP486 16.1 PFCIRM129 _04150 CDP486 PFCIRM129 CDP494 44.1 PFCIRM129 _11920 CDP479 61.1 PFCIRM129 _08275 CDP478 53.1 PFCIRM129 _07725 CDP475 71.1 PFCIRM129 _00090 CDP478 PFCIRM129 CDP486 20.1 PFCIRM129 _04170 CDP485 60.1 PFCIRM129 _03840 20.1 _04710 40.1 _03200 44.1 _10200 CDP493 PFCIRM129 39.1 _07125 86.1 _05075 CDP487 PFCIRM129 CDP484 PFCIRM129 CDP491 PFCIRM129 CDP477 PFCIRM129 CDP487 PFCIRM129	DESCRIPTION Cell envelope-related transcriptional attenuator Hypothetical secreted protein Hypothetical protein PFCIRM129_11635 ATP-binding protein opuCA of Glycine betaine/carnitine/choline ABC transporter Heat shock protein 20 2 (20 kDa chaperone 2) 60 kDa chaperonin 1 (Protein Cpn60 1) (groEL protein 1) (Heat shock protein 60 1) Peptidase S51 family protein, dipeptidase E dehydrogenase) (IMPDH) (IMPD) / GMP reductase) Putative uncharacterized protein Hypothetical protein PFCIRM129_07810 Aspartokinase (Aspartate kinase) Serine acetyltransferase ABC transporter ATP-binding protein Pyruvate kinase 1 Hypothetical protein PFCIRM129_02975 Iron/Manganese superoxide dismutase (Superoxide dismutase [Mn/Fe]) (SODM) Hypothetical protein PFCIRM129_03805 Putative phosphate transport system protein oxidoreductase:4Fe-4S ferredoxin, iron-sulfur binding:Aromatic-ring hydroxylase Hypothetical protein PFCIRM129_07990 Phage shock protein A Trypsin-like serine protease 60 kDa chaperonin 2 (Protein Cpn60 2) (groEL protein 2) (Heat shock protein 60 2) Inosine-5-monophosphate dehydrogenase (IMP dehydrogenase) (IMPDH) (IMPD) / GMP reductase Aspartate transaminase (Aminotransferase) sugar transporter 50S ribosomal protein L13 polar amino acid ABC transporter, binding protein component polar amino acid ABC transporter, inner membrane component polar amino acid ABC transporter, ATP binding component Anthranilate phosphoribosyltransferase Polyphosphate glucokinase ABC transporter, binding lipoprotein pyruvate dehydrogenase E1 component (2-oxo-acid dehydrogenase E1 subunit, homodimeric type) Dihydroorotate dehydrogenase Cytochrome d ubiquinol oxidase subunit I Pyruvate synthase/Pyruvate-flavodoxin oxidoreductase binding protein of ribose ABC transporter Hypothetical protein PFCIRM129_04165 Argininosuccinate synthase Cell envelope-related function transcriptional attenuator common domain Carbamoyl-phosphate synthase small chain Regulatory protein, TetR Hypothetical secreted protein Protein-export membrane protein secF 3-dehydroquinate synthase / shikimate kinase,bifunctionnal enzyme Aminodeoxychorismate lyase Protein-export membrane protein secD Preprotein translocase SecA subunit Alanyl-tRNA synthetase (Alanine--tRNA ligase) (AlaRS) Hypothetical secreted protein Acetyl-CoA carboxylase Aspartyl-tRNA synthetase (Aspartate--tRNA ligase) (AspRS) UDP-N-acetylglucosamine--N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase (Undecaprenyl-PP-MurNAc-pentapeptide-UDPGlcNAc GlcNAc transferase) Histidyl-tRNA synthetase (Histidine--tRNA ligase) (HisRS) Pyruvate:ferredoxin (Flavodoxin) oxidoreductase Enolase 1 NADPH:quinone reductase and related Zn-dependent oxidoreductases Carbamoyl-phosphate synthase large chain (Carbamoyl-phosphate synthetase ammonia chain) Membrane protein, Transporter, MFS superfamily ABC transporter, ATP-binding protein SppA, Periplasmic serine proteases Hypothetical protein PFCIRM129_03060 Cell division protein FtsZ Hypothetical protein PFCIRM129_10175 iron-sulfur protein ATP binding protein of ABC transporter ABC transporter permease Oxidoreductase S-adenosylmethionine synthetase (Methionine adenosyltransferase) (AdoMet synthetase) (MAT) Shikimate 5-dehydrogenase Methylase S-adenosyl-L-methionine-dependent methyltransferase mraW L-lactate permease (Peptidoglycan glycosyltransferase) Dihydroorotate dehydrogenase 2 Protein of unknown function Tyrosyl-tRNA synthetase (Tyrosine--tRNA ligase) (TyrRS) Hypothetical protein PFCIRM129_11720 NADH-quinone oxidoreductase chain C (NADH dehydrogenase I, chain C) NADH-quinone oxidoreductase chain D (NADH dehydrogenase I, chain D) NADH-quinone oxidoreductase chain F (NADH dehydrogenase I, chain F) (NDH-1, chain F) dehydrogenase I, chain G) NADH-quinone oxidoreductase subunit I (NADH dehydrogenase I subunit I) (NDH-1 subunit I) internaline A Heptaprenyl diphosphate synthase component II Hypothetical protein PFCIRM129_12250 ErfK/YbiS/YcfS/YnhG precursor FAD linked oxidase domain protein penicillin-binding protein (peptidoglycan glycosyltransferase) Glucose-1-phosphate adenylyltransferase (ADP-glucose synthase) (ADP-glucose pyrophosphorylase) (ADPGlc PPase) Exodeoxyribonuclease III/exonuclease III Succinate dehydrogenase subunit C Dihydrodipicolinate synthase NADH-quinone oxidoreductase chain B Carboxylic ester hydrolase electron transfer oxidoreductase ABC transporter ATP-binding protein Leucine-, isoleucine-, valine-, threonine-, and alanine-binding protein [Precursor] (LIVAT-BP) Dihydrolipoyl dehydrogenase (E3 component of alpha keto acid dehydrogenase complexes) (Dihydrolipoamide dehydrogenase) (Enoylpyruvate transferase) Cobalt transport protein CbiM Cobalt transport protein CbiN dolichyl-phosphate beta-D-mannosyltransferase glycerol-3-phosphate transporter dehydrogenase/reductase Hypothetical protein PFCIRM129_08715 Hypothetical protein PFCIRM129_11950 Succinate dehydrogenase, subunit A transporter Peptidase Subtilases Hypothetical protein PFCIRM129_11930 Fumarate hydratase, class-II 2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase ABC-transporter metal-binding lipoprotein Surface layer protein A (S-layer protein A) cell-wall peptidases, NlpC/P60 family secreted protein Cytochrome P450 (Heme-thiolate monooxygenase) dihydrolipoyllysine-residue (2-methylpropanoyl)transferase. Lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex (E2) (Dihydrolipoamide branched chain transacylase) (Branched-chain alpha-keto acid dehydrogenase E1 component beta chain) (BCKDH E1-beta) Pyruvate dehydrogenase E1 component subunit beta secreted serine protease, trypsin-like serine proteases Glycosyltransferase 30S ribosomal protein S8 50S ribosomal protein L6 Hypothetical protein PFCIRM129_09915 30S ribosomal protein S5 cysteine synthase 2 Molybdenum cofactor synthesis domain transporter solute binding protein of the ABC transport system 50S ribosomal protein L5 ABC2 protein of oligopeptide ABC transporter (OPN:undef) Hypothetical protein PFCIRM129_11735 Acetate kinase Carbon starvation protein Phosphate acetyltransferase Glutamine synthetase Permease for cytosine/purines, uracil, thiamine,allantoin 30S ribosomal protein S11 30S ribosomal protein S4 subunit) (Transcriptase alpha chain) (RNA polymerase subunit alpha) Hypothetical protein PFCIRM129_00865 Resuscitation-promoting factor RpfB Putative purine phosphoribosyltransferase ATPases MoxR family Hypothetical protein PFCIRM129_08360 2-oxoisovalerate dehydrogenase subunit alpha (Branched-chain alpha-keto acid dehydrogenase E1 component alpha chain) (BCKDH E1-alpha) SecE/Sec61-gamma subunit of protein translocation complex 50S ribosomal protein L1 Hypothetical secreted protein 50S ribosomal protein L10 Glycosyl transferase, family 2 DNA-directed RNA polymerase beta chain (RNAP beta subunit) (Transcriptase beta chain) (RNA polymerase subunit beta) DNA-directed RNA polymerase beta chain (RNAP beta subunit) (Transcriptase beta chain) (RNA polymerase beta subunit) 50S ribosomal protein L14 Elongation factor G (EF-G) Surface protein D with SLH domain 30S ribosomal protein S10 50S ribosomal protein L4 50S ribosomal protein L2 DTDP-glucose 4,6-dehydratase 50S ribosomal protein L22 30S ribosomal protein S3 Hypothetical protein PFCIRM129_11765 Glycosyltransferase, family 2 30S ribosomal protein S7 Hypothetical protein PFCIRM129_06000 Hypothetical protein PFCIRM129_07390 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase) Prolyl-tRNA synthetase (Proline--tRNA ligase) (ProRS) Nitric-oxide reductase subunit B (nitric-oxide reductase) Phosphoketolase pyrophosphate DeoR transcriptional regulator Anaerobic glycerol-3-phosphate dehydrogenase subunit C Anaerobic glycerol-3-phosphate dehydrogenase subunit B Anaerobic glycerol-3-phosphate dehydrogenase subunit A RNA polymerase principal sigma factor HrdB Uracil phosphoribosyltransferase (UMP pyrophosphorylase) (UPRTase) Hypothetical protein PFCIRM129_02060 Ferrous iron uptake protein B 9.A.8.1.x Glycerol kinase (ATP:glycerol 3-phosphotransferase) (Glycerokinase) (GK) Galactokinase Serine/threonine protein kinase Penicillin-binding protein Cell division protein Vitamin B12-dependent ribonucleotide reductase (Ribonucleoside-diphosphate reductase NrdJ) Hypothetical protein PFCIRM129_11055 binding protein of phosphate ABC transporter (BP:MOI:undef:phosphate) iolT3 (myo-inositol transporter iolT3) Deoxyxylulose-5-phosphate synthase Type I restriction-modification system DNA methylase Aconitase, Aconitate hydratase restriction system protein Ribonuclease, Rne/Rng family glutathione S-transferase drug exporters of the RND superfamily Putative uncharacterized protein 2-oxoglutarate dehydrogenase, E1 and E2 components Aspartate-semialdehyde dehydrogenase (Semialdehyde dehydrogenase) transporter transmembrane protein Hypothetical protein PFCIRM129_05605 Protein of unknown function phosphoglycerate mutase/fructose-2,6-bisphosphatase binding protein of maltose ABC transporter Elongation factor Ts (EF-Ts) Forkhead-associated protein Forkhead-associated protein Hypothetical protein PFCIRM129_00780 Tryptophan synthase beta subunit Sodium/glucose cotransporter (Na(+)/glucose symporter) 2.A.21.3.2 Sulfate-binding protein precursor Hypothetical protein PFCIRM129_10740 Putative regulatory protein, DeoR family NAD(P)(+) transhydrogenase (AB-specific) NADH dehydrogenase 50S ribosomal protein L20 ATP synthase gamma chain (ATP synthase F1 sector gamma subunit) Hypothetical protein PFCIRM129_10650 2-dehydropantoate 2-reductase Transcriptional regulator, RpiR family Putative uncharacterized protein succinocarboxamidesynthase thiredoxine like membrane protein Signal peptidase I Succinate dehydrogenase TRNA processing ribonuclease BN Surface protein with SLH domain Polyribonucleotide nucleotidyltransferase (Polynucleotide phosphorylase) (PNPase) (Guanosine pentaphosphate synthetase) RecA (Recombinase A) Hypothetical protein PFCIRM129_10970 Hypothetical transmembrane protein ase) Surface layer protein B (S-layer protein B) Surface protein of unknown function transferase Protein of unknown function IclR transcriptional regulator Hypothetical protein PFCIRM129_05365 Signal recognition particle receptor GTP binding signal recognition particle protein Hypothetical protein PFCIRM129_10785 Hypothetical protein PFCIRM129_10780 Succinate dehydrogenase cytochrome B-558 subunit Succinate dehydrogenase flavoprotein subunit Enoyl-[acyl-carrier-protein] reductase (NADH) Transcription termination factor Rho iolA (Myo-inositol catabolism IolA protein) (Methylmalonic acid semialdehyde dehydrogenase) Putative peptidyl-prolyl cis-trans isomerase,FKBP-type (Precursor) Hypothetical membrane protein Glutamyl-tRNA reductase binding protein of oligopeptide ABC transporter (OPN : undef : Oligopeptides) ABC2 protein of ABC transporter (ART:REG) Asparagine synthase (Glutamine-hydrolyzing) ABC transporter Methylmalonyl-CoA mutase small subunit (Methylmalonyl-CoA mutase beta subunit) (MCB-beta) Glutamine amidotransferase subunit pdxT (Glutamine amidotransferase glutaminase subunit pdxT) CTP synthase (UTP--ammonia ligase) (CTP synthetase) Coenzyme A transferase (Putative succinyl-CoA or butyryl-CoA:coenzyme A transferase) oxidoreductase UvrABC system protein A (UvrA protein) (Excinuclease ABC subunit A) D-serine/D-alanine/glycine transporter 4-phosphate dehydrogenase Cobyric acid synthase CbiP/CobQ precorrin methylase (precorrin-3B C17-methyltransferase) CbiE/G/H fusion protein CysG/CbiX Secreted protease with a PDZ domain Methylmalonyl-CoA mutase large subunit (Methylmalonyl-CoA mutase alpha subunit) (MCM-alpha) (MUTB-(R)-2-Methyl-3-oxopropanoyl-CoA CoA-carbonylmutase) Exodeoxyribonuclease VII large subunit ABC-type transport systems, periplasmic component peptidyl-prolyl cis-trans isomerase Thymidylate synthase Hypothetical protein PFCIRM129_01490 Hypothetical protein PFCIRM129_07170 UDP-N-acetylmuramyl tripeptide synthase (Mur ligase) rRNA (guanine-N2-)-methyltransferase Hypothetical protein PFCIRM129_11325 Methylmalonyl-CoA carboxytransferase 12S subunit (Transcarboxylase 12S subunit). 610 bp NUDIX hydrolase D-lactate dehydrogenase ABC-type transport system involved in Fe-S cluster assembly, ATPase component, SufC Transaldolase 2 FeS assembly protein SufD hypoxanthine phosphoribosyltransferase Hypothetical protein PFCIRM129_07345 FeS assembly protein SufB flavin-containing amine oxidase Indole-3-glycerol phosphate synthase (TrpC) 30S ribosomal protein S1 carboxypeptidase (serine-type D-Ala-D-Ala carboxypeptidase) (D-alanyl-D-alanine-carboxypeptidase) Hypothetical protein PFCIRM129_07365 Preprotein translocase subunit YajC Glucose-6-phosphate isomerase (GPI) (Phosphoglucose isomerase) (PGI) (Phosphohexose isomerase) (PHI) 50S ribosomal protein L17 Putative DNA polymerase I Acyl carrier protein (ACP) Phosphoribosyltransferase/nit roreductase (fusion gene) (Nicotinate-nucleotide-dimethylbenzimidazole phosphoribosyltransferase) Cytochrome d ubiquinol oxidase, subunit II Glycine hydroxymethyltransferase precursor Malate dehydrogenase Cell division protein NADH-quinone oxidoreductase chain E NADH-quinone oxidoreductase subunit H (NADH dehydrogenase I subunit H) NADH-quinone oxidoreductase chain J (NADH dehydrogenase I, chain J) Alanine dehydrogenase NADH dehydrogenase I chain M D-serine/D-alanine/glycine transporter Peroxiredoxin/Alkyl hydroperoxide reductase subunit C /Thioredoxin peroxidase/Alkyl hydroperoxide reductase LacZ regulator MSF1 synthetase acyltransferase (glycosyltransferase) protein dependent DNA helicase domain protein protein 22 PFCIRM129_11795 Beta-galactosidase (Lactase) protein Citrate synthase Conserved membrane protein tetR-family transcriptional Major facilitator super family acetyltransferase transporter epimerase/dehydratase PFCIRM129_00135 Glutamine-dependent NAD(+) 1-acylglycerol-3-phosphate O-mannosyltransferase Putative uncharacterized UvrD/REP helicase / ATP-Cysteine--tRNA ligase DEAD/DEAH box helicase protein C22/General stress protein Transaldolase 1 Hypothetical protein PFCIRM129_08605 Restriction endonuclease PvuRts1 I transporter Membrane protease subunits, stomatin/prohibitin homologs (Membrane protease subunit, IM protein of oligopeptide ABC transporter (OPN:undef) Preprotein translocase SecY subunit Succinate dehydrogenase, subunit B Hypothetical protein Methylmalonyl-CoA epimerase FtsH ATP synthase delta chain ATP synthase B chain (F0F1 ATP synthase subunit B) Protein of unknown function Alpha-glucan phosphorylase aminopeptidase Aldehyde dehydrogenase (Succinate-semialdehyde dehydrogenase) (NAD-dependent aldehyde dehydrogenase) Beta-lactamase-like:RNA-metabolising metallo-beta-lactamase Menaquinone-specific isochorismate synthase Seryl-tRNA synthetase IolE (Myo-inositol catabolism IolAEprotein) (Inosose dehydratase) (2-keto-myo-inositol dehydratase) DNA polymerase III, beta chain 5,10-methylenetetrahydrofolate reductase Phospho-2-dehydro-3-deoxyheptonate aldolase (Phosphoribosylformylglycina Sulfite reductase [ferredoxin] UvrABC system protein B (Protein uvrB) (Excinuclease ABC subunit B) DNA gyrase subunit B Putative uncharacterized Cysteine desulphurases, SufS 3-oxoacyl-[acyl-carrier-protein] synthase (Beta-ketoacyl-ACP synthase) Lsr2-like protein Methylmalonyl-CoA carboxytransferase 5S subunit. (transcarboxylase 5S) 505 bp L-serine dehydratase (L-serine ammonia-lyase) Conserved membrane protein (DedA family) TRNA/rRNA methyltransferase GntR-family protein transcriptional regulator Zinc-containing alcohol dehydrogenase superfamily 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (4-diphosphocytidyl-2C-(MEP cytidylyltransferase) Hypothetical protein PFCIRM129_05700 GntR family protein Methyltransferase gidB (Glucose-inhibited division protein B) Ribose-phosphate pyrophosphokinase Transcription-repair coupling factor Glycerophosphoryl diester phosphodiesterase extracellular protein without function phosphoglycerate mutase/Fructose-2,6-bisphosphatase GCN5-related N-Lipoyltransferase (Lipoyl-[acyl-carrier-protein]-protein-N-lipoyltransferase) (Lipoate-binding protein of choline ABC NAD-dependent Hypothetical protein family GTP binding protein protein sigma factor HrdD Phosphatidylinositol alpha-protein ligase B) Aminopeptidase C, Bleomycin hydrolase exonuclease SbcC Fic protein subunit Hypothetical protein PFCIRM129_00140 Maf-like protein transcriptional regulator TetR methyltetrahydrofolate:L-homocysteine S-methyltransferase) Hypothetical transmembrane protein Putative uncharacterized ATP-dependent helicase HrpA RNA polymerase principal protein DNA primase Regulatory protein AAA ATPase, central region Hypothetical transmembrane protein ATP-dependent DNA helicase Hypothetical protein PFCIRM129_01830 DNA repair protein CobA Uroporphyrinogen III Fe-S oxidoreductases family 2 reductoisomerase) methyltransferase keto-beta-hydroxylacil isomeroreductase) (Alpha-(MCT) Transcriptional Regulator, Prolipoprotein diacylglyceryl transferase Cell envelope-related transcriptional attenuator secreted glycosyl hydrolase Hypothetical membrane protein transcriptional regulator degA) PFCIRM129_07910 Peptidase, family M22 synthase II) ATP-dependent dsDNA (dihydroorotase) DNA polymerase III, delta formamidopyrimidine methionine synthase (5-protein synthetase MiaB protein Cobalamin biosynthesis kinase (Acetohydroxy-acid glycosylase) subunit Putative uncharacterized 2-methylthioadenine synthase, CbiA DNA repair protein Sensory transduction histidine DNase (MAP) (Peptidase M) Ketol-acid reductoisomerase methyl-D-erythritol synthase) (Phosphoribosyl synthetase) Putative uncharacterized protein Hypothetical protein PFCIRM129_01220 (Tyrosine phosphorylated protein A) Acyltransferase PlsC Transcriptional regulator, LacI family (HTH-type Hypothetical protein Phosphoribosylformylglycinam idine synthase II (FGAM Dihydroorotase multifunctional complex type Formamidopyrimidine-DNA glycosylase (DNA-UvrA, Excinuclease ATPase Protein of unknown function 2-methylthioadenine synthetase MiaB protein enzyme Cobyrinic acid A,C-diamide kinase (Poly(P)/ATP NAD protein Methionine aminopeptidase kinase) (Glycine--tRNA ligase) (GlyRS) Putative uncharacterized Pseudouridine synthase Rib-PP synthetase) (PRPP protein GTP-binding protein typA/BipA metalloendopeptidase transporter Ribosomal protein L18 protein (tRNA nucleotidyltransferase) acetyltransferase biosynthesis pyrophosphate polyphosphate/ATP-NAD Glycyl-tRNA synthetase UvrA nuclease (RecB family) pyrophosphate synthetase) (P-Putative uncharacterized protein) Peptidase M23B family / magnesium (Mg2+) type Z Hypothetical transmembrane glutamyl kinase) (GK) dehalogenase-like hydrolase Ribonuclease PH (RNase PH) GCN5-related N-Glycosyltransferase, family 2 CobU Bifunctional cobalamin Probable inorganic Excinuclease ATPase subunit ATPase involved in cell division synthase I) g Lactaldehyde dehydrogenase pyrophosphokinase (RPPK) acetyltransferase quinone oxidoreductase (FixC Inorganic pyrophosphatase 30S ribosomal protein S14 PFCIRM129_08990 Glutamate 5-kinase (Gamma-hydrolase, haloacid subunit Aldo/keto reductase 50S ribosomal protein L23 midine synthase I) (FGAM dehydrogenase,decarboxylatin Sortase family protein ribose-phosphate Homoserine O-Electron transfer flavoprotein-PFCIRM129_01720 Cell division protein FtsQ methyltransferase ubiE Hypothetical protein protein Glutamate racemase DNA polymerase III alpha Glycosyl transferase, family 2 DNA gyrase subunit B Phosphatase GTPase YchF (GluRS) Replicative DNA helicase stomatin/prohibitin homolog) Hypothetical protein PFCIRM129_02050 Fructose-bisphosphate aldolase class I Fructose-bisphosphate aldolase class II phosphoglycerate mutase ABC transporter Phosphoglucomutase Hypothetical protein PFCIRM129_05940 triosephosphate isomerase 1 pyrophosphate phosphofructokinase Methionine import ATP-binding protein metN Phosphoglycerate kinase Aminopeptidase N, Lysyl Homoserine dehydrogenase glucose 6-phosphate dehydrogenase effector OpcA Helicase protein Sulfate adenylyltransferase, large subunit/ ATP-sulfurylase, subunit 1 (ATP:sulfate adenylyltransferase) Threonyl-tRNA synthetase (Threonine--tRNA ligase) (ThrRS) endonuclease IV (deoxyribonuclease IV (phage-T4-induced)) Helicase (SNF2-related protein) Glycogen debranching enzyme GlgX Phosphoribosylformylglycinam idine synthase 1 Amidophosphoribosyltransfer ase Hypothetical protein PFCIRM129_01505 Transketolase Putative isocitrate/isopropylmalate dehydrogenase DNA topoisomerase I 6-phosphogluconate PTS enzyme I Putative uncharacterized protein Methylmalonyl-CoA carboxytransferase, 1.3S subunit (Transcarboxylase, 1.3S subunit). 123bp Hypothetical membrane protein Hypothetical protein PFCIRM129_10165 synthase protein) (pyruvate:pyruvate acetaldehydetransferase (decarboxylating)) Oligopeptide transporter, OPT family protein multicopper oxidase Protoporphyrinogen oxidase (PPO) HemY Fe-S oxidoreductase Undecaprenyl pyrophosphate synthase (di-trans,poly-cis-decaprenylcistransferase) Aminopeptidase Abortive infection bacteriophage resistance protein Abi tRNA nucleotidyltransferase PcnB DNA replication and repair protein recF Iron-dependent repressor Hypothetical protein PFCIRM129_06350 Two-component system response regulator Hypothetical protein PFCIRM129_09730 Methionine biosynthesis protein MetW Hypothetical protein PFCIRM129_05980 Chaperone protein dnaJ 1 (DnaJ1 protein) (Heat shock protein 40 1) Heat shock protein transcriptional repressor HspR1 (Hspr1 protein) Major facilitator superfamily MFS_1 Leucyl/phenylalanyl-tRNA-protein transferase transferase) (SAT) (ATP-sulfurylase small subunit) protein V) Methylase DNA integrity scanning protein disA GTP cyclohydrolase I (GTP-CH-I) Hypothetical protein ATPase Glyceraldehyde-3-phosphate dehydrogenase / erythrose 4 phosphate dehydrogenase Glucose-6-phosphate 1-dehydrogenase Hypothetical protein PFCIRM129_11470 Protein mrp homolog (ATP-binding protein) Hypothetical protein PFCIRM129_11755 Glutamate-ammonia-ligase adenylyltransferase ([Glutamate--ammonia-ligase] Menaquinone biosynthesis (Pantothenic acid kinase) GTPase Hypothetical membrane subunit (Ferredoxin) PFCIRM129_03730 synthase subunit HisF (Glutamate--tRNA ligase) protein adenylyltransferase) (ATase) Pantothenate kinase protein PspC sphomethylpyrimidinekinase Glutamate synthase large Hypothetical protein L-lactate dehydrogenase Protein of unknown function Imidazole glycerol phosphate LMBE-related protein Glutamyl-tRNA synthetase ABC transporter, ATP-binding (Glutamine-synthetase dehydrogenase superfamily 50S ribosomal protein L21 transcriptional regulator hydroxymethylpyrimidine/pho transferase pyrophosphorylase adenylyltransferase) 50S ribosomal protein L16 Lipoic acid synthetase Transcriptional regulator, ArsR family Zinc-containing alcohol Cysteine synthase 1 arabinose operon repressor 50S ribosomal protein L15 30S ribosomal protein S13 oxidoreductase metallophosphoesterase Queuine tRNA-ribosyltransferase GTP-binding protein Possible stress-response Prolipoprotein diacylglyceryl dehydrogenase 2-isopropylmalate synthase helicase DNA gyrase subunit A thiamine-phosphate Nuclease of the RecB family ligase) Aspartate aminotransferase Acetylglutamate kinase Ribosomal protein L28 dehydrogenase Phospholipase D/Transphosphatidylase long-chain fatty-acid CoA ligase (AMP-binding enzyme) (long-chain-fatty-acid---CoA ABC protein of ABC transporter Hypothetical protein PFCIRM129_04310 Tryptophan synthase alpha chain (TrpA) Putative long-chain-fatty-acid--CoA ligase/synthetase S-layer protein precursor ATP synthase subunit beta (ATPase subunit beta) (ATP synthase F1 sector subunit beta) Lipoate-protein ligase A Hypothetical protein PFCIRM129_04595 Hypothetical protein PFCIRM129_03455 Putative uncharacterized protein Carboxylic ester hydrolase fructosamine kinase Hypothetical membrane protein DeaD/DeaH box helicase Porphobilinogen deaminase (PBG) (Hydroxymethylbilane synthase) (HMBS) (Pre-uroporphyrinogen synthase) acetylmuramoylalanine--D-glutamate ligase (UDP-N-acetylmuramoyl-L-alanyl-D-Sua5_yciO_yrdC glutamic acid-adding enzyme) glutamate synthetase) (D-Alpha-acetolactate Glutaminyl-tRNA synthetase glucosyl-transferase) decarboxylase glucan:1,4-alpha-D-glucan 6-enzyme) (BE) (1,4-alpha-D-(Mitomycin biosynthesis antiterminator regulatory protein) secreted transglycosydase Elongation factor Tu dehydrogenase) (IMPDH) (IMPD) / GMP reductase NADPH:quinone reductase related Zn-dependent Hypothetical protein PFCIRM129_00090 Dihydroorotate GTP pyrophosphokinase polar amino acid ABC transporter, binding protein synthase F1 sector subunit fold,tRNA/helicase-type PFCIRM129_03200 (Cysteine--tRNA ligase) UDP-N-enzyme (Glycogen branching alpha) Nucleic acid binding, OB-Hypothetical protein Cysteinyl-tRNA synthetase pseudouridine synthase Oligoribonuclease 1,4-alpha-glucan branching	FEATURES COG LOCALIZATION K Cytoplasmic S Cytoplasmic J Cytoplasmic E Cytoplasmic O Cytoplasmic O Cytoplasmic E Membrane S Cytoplasmic S Extracellular E Cytoplasmic E Cytoplasmic P Cytoplasmic G Cytoplasmic S Cytoplasmic P Cytoplasmic Extracellular P Cytoplasmic Extracellular KT Cytoplasmic O Cytoplasmic O Cytoplasmic F Cytoplasmic E Cytoplasmic EGP Membrane J Cytoplasmic ET Membrane E Membrane E Cytoplasmic E Cytoplasmic GK Cytoplasmic P Membrane C Cytoplasmic F Cytoplasmic C Membrane C Cytoplasmic G Membrane D Cytoplasmic E Cytoplasmic K Cytoplasmic F Cytoplasmic K Cytoplasmic S Extracellular U Membrane E Cytoplasmic S Membrane U Membrane U Cytoplasmic J Cytoplasmic L Extracellular I Cytoplasmic J Cytoplasmic M Cytoplasmic J Cytoplasmic C Cytoplasmic G Cytoplasmic C Cytoplasmic F Cytoplasmic G Membrane D Cytoplasmic OU Membrane M Extracellular D Cytoplasmic S Cytoplasmic C Cytoplasmic V Cytoplasmic Q Membrane C Cytoplasmic H Cytoplasmic E Cytoplasmic H Cytoplasmic M Cytoplasmic C Membrane Cytoplasmic Extracellular J Cytoplasmic S Cytoplasmic C Cytoplasmic C Cytoplasmic C Cytoplasmic C Cytoplasmic Extracellular H Cytoplasmic S Cytoplasmic S Extracellular C Cytoplasmic M Extracellular G Cytoplasmic L Cytoplasmic C Membrane E Cytoplasmic C Cytoplasmic S Membrane C Cytoplasmic V Cytoplasmic E Extracellular C Cytoplasmic P Membrane P Membrane S Cytoplasmic G Membrane IQ Cytoplasmic KLT Extracellular H Cytoplasmic C Cytoplasmic P Membrane O Extracellular S Cytoplasmic C Cytoplasmic E Cytoplasmic P Membrane O Extracellular M Extracellular C Cytoplasmic C Cytoplasmic O Extracellular S Cytoplasmic J Cytoplasmic J Cytoplasmic Membrane J Cytoplasmic E Cytoplasmic H Cytoplasmic E Extracellular J Cytoplasmic P Cytoplasmic Extracellular C Cytoplasmic T Membrane C Cytoplasmic E Cytoplasmic F Membrane J Cytoplasmic J Cytoplasmic S Cytoplasmic M Membrane F Cytoplasmic S Cytoplasmic Membrane C Cytoplasmic U Cytoplasmic J Cytoplasmic Cytoplasmic J Cytoplasmic M Cytoplasmic K Cytoplasmic K Cytoplasmic J Cytoplasmic J Cytoplasmic O Extracellular J Cytoplasmic J Cytoplasmic J Cytoplasmic M Cytoplasmic J Cytoplasmic J Cytoplasmic Extracellular S Cytoplasmic J Cytoplasmic Extracellular Membrane I Cytoplasmic J Cytoplasmic P Membrane G Cytoplasmic K Cytoplasmic C Cytoplasmic E Cytoplasmic C Cytoplasmic K Cytoplasmic F Cytoplasmic S Membrane P Membrane C Cytoplasmic G Cytoplasmic KLT Membrane M Extracellular D Membrane F Cytoplasmic J Cytoplasmic P Membrane EGP Membrane H Cytoplasmic V Cytoplasmic C Cytoplasmic L Cytoplasmic J Cytoplasmic O Cytoplasmic P Membrane V Cytoplasmic C Cytoplasmic E Cytoplasmic S Extracellular NU Membrane Cytoplasmic G Cytoplasmic G Membrane J Cytoplasmic T Cytoplasmic T Cytoplasmic Cytoplasmic E Cytoplasmic E Membrane P Membrane M Extracellular K Cytoplasmic C Cytoplasmic C Membrane J Cytoplasmic C Cytoplasmic M Extracellular H Cytoplasmic K Cytoplasmic Cytoplasmic O Cytoplasmic U Membrane C Cytoplasmic S Cytoplasmic O Extracellular J Cytoplasmic L Cytoplasmic S Membrane Cytoplasmic O Extracellular Membrane J Cytoplasmic S Cytoplasmic K Cytoplasmic Extracellular U Cytoplasmic U Cytoplasmic Membrane Extracellular S Membrane C Cytoplasmic I Cytoplasmic K Cytoplasmic C Cytoplasmic O Membrane S Cytoplasmic H Cytoplasmic E Extracellular S Cytoplasmic E Cytoplasmic S Cytoplasmic I Cytoplasmic H Cytoplasmic F Cytoplasmic C Cytoplasmic C Cytoplasmic L Cytoplasmic E Membrane H Cytoplasmic H Cytoplasmic H Cytoplasmic T Membrane I Cytoplasmic L Cytoplasmic P Membrane O Extracellular F Cytoplasmic S Cytoplasmic S Cytoplasmic M Cytoplasmic J Cytoplasmic K Cytoplasmic I Cytoplasmic L Cytoplasmic C Cytoplasmic O Cytoplasmic H Cytoplasmic O Cytoplasmic F Cytoplasmic S Extracellular O Cytoplasmic E Cytoplasmic E Cytoplasmic J Cytoplasmic M Membrane K Cytoplasmic U Cytoplasmic G Cytoplasmic J Cytoplasmic L Cytoplasmic IQ Cytoplasmic CH Cytoplasmic C Membrane E Cytoplasmic C Cytoplasmic D Membrane C Cytoplasmic C Membrane C Membrane E Cytoplasmic C Membrane E Membrane G Cytoplasmic C Cytoplasmic U Membrane K Cytoplasmic S Membrane H Cytoplasmic I Cytoplasmic K Cytoplasmic L Cytoplasmic J Cytoplasmic L Cytoplasmic O Cytoplasmic G Cytoplasmic Cytoplasmic Cytoplasmic EGP Membrane EP Membrane U Membrane C Cytoplasmic Membrane E Cytoplasmic O Cytoplasmic C Cytoplasmic C Cytoplasmic V Cytoplasmic G Cytoplasmic C Cytoplasmic S Cytoplasmic HQ Cytoplasmic J Cytoplasmic G Cytoplasmic L Cytoplasmic E Cytoplasmic E Cytoplasmic C Cytoplasmic L Cytoplasmic L Cytoplasmic L Cytoplasmic E Cytoplasmic I Cytoplasmic S Cytoplasmic C Cytoplasmic E Cytoplasmic I Membrane J Cytoplasmic K Cytoplasmic C Cytoplasmic Membrane J Cytoplasmic F Cytoplasmic L Cytoplasmic C Cytoplasmic Membrane G Cytoplasmic K Cytoplasmic M Membrane GM Cytoplasmic Cytoplasmic S Cytoplasmic M Cytoplasmic H Cytoplasmic E Cytoplasmic S Cytoplasmic Extracellular D Cytoplasmic K Cytoplasmic S Membrane M Cytoplasmic L Cytoplasmic K Cytoplasmic L Cytoplasmic K Cytoplasmic O Cytoplasmic S Cytoplasmic L Cytoplasmic S Cytoplasmic O Cytoplasmic C Cytoplasmic H Cytoplasmic K Cytoplasmic Membrane K Extracellular G Membrane S Cytoplasmic O Cytoplasmic L Cytoplasmic L Cytoplasmic E Cytoplasmic H Cytoplasmic V Cytoplasmic J Cytoplasmic L Cytoplasmic T Cytoplasmic L Cytoplasmic EH Cytoplasmic I Cytoplasmic K Cytoplasmic EH Cytoplasmic I Cytoplasmic K Cytoplasmic L Cytoplasmic F Cytoplasmic F Cytoplasmic L Cytoplasmic L Cytoplasmic M Cytoplasmic J Cytoplasmic H Cytoplasmic J Cytoplasmic Cytoplasmic J Cytoplasmic T Cytoplasmic J Cytoplasmic J Cytoplasmic V Cytoplasmic IQ Cytoplasmic D Extracellular P Cytoplasmic S Membrane J Cytoplasmic K Cytoplasmic M Cytoplasmic H Cytoplasmic G Cytoplasmic L Cytoplasmic Cytoplasmic C Cytoplasmic Cytoplasmic J Cytoplasmic E Cytoplasmic S Cytoplasmic C Cytoplasmic J Cytoplasmic M Membrane EF Cytoplasmic E Cytoplasmic C Cytoplasmic D Membrane L Extracellular M Cytoplasmic L Cytoplasmic M Cytoplasmic L Cytoplasmic G Cytoplasmic J Cytoplasmic L Cytoplasmic O Cytoplasmic Cytoplasmic G Cytoplasmic G Cytoplasmic G Cytoplasmic Q Membrane G Cytoplasmic Cytoplasmic G Cytoplasmic G Cytoplasmic P Cytoplasmic G Cytoplasmic E Cytoplasmic E Cytoplasmic G Cytoplasmic L Cytoplasmic P Cytoplasmic J Cytoplasmic L Cytoplasmic L Cytoplasmic G Cytoplasmic F Cytoplasmic F Cytoplasmic T Cytoplasmic G Cytoplasmic C Cytoplasmic L Cytoplasmic G Cytoplasmic G Cytoplasmic S Membrane C Cytoplasmic O Membrane T Cytoplasmic S Membrane Extracellular H Cytoplasmic C Cytoplasmic I Cytoplasmic E Cytoplasmic V Cytoplasmic J Cytoplasmic L Cytoplasmic K Cytoplasmic T Cytoplasmic T Cytoplasmic M Membrane Q Cytoplasmic Membrane O Cytoplasmic K Cytoplasmic G Membrane O Cytoplasmic H Cytoplasmic L Cytoplasmic H Cytoplasmic S Cytoplasmic S Cytoplasmic G Cytoplasmic G Cytoplasmic S Cytoplasmic D Cytoplasmic I Cytoplasmic H Cytoplasmic S Cytoplasmic O Cytoplasmic H Cytoplasmic E Cytoplasmic S Membrane C Cytoplasmic K Cytoplasmic E Cytoplasmic S Cytoplasmic J Cytoplasmic P Cytoplasmic J Cytoplasmic H Cytoplasmic H Cytoplasmic J Cytoplasmic H Cytoplasmic K Cytoplasmic C Cytoplasmic E Cytoplasmic K Cytoplasmic J Cytoplasmic J Cytoplasmic S Cytoplasmic J Cytoplasmic S Cytoplasmic KT Cytoplasmic M Membrane S Cytoplasmic E Cytoplasmic L Cytoplasmic L Cytoplasmic H Cytoplasmic L Cytoplasmic E Cytoplasmic E Cytoplasmic J Cytoplasmic I Cytoplasmic I Cytoplasmic S Cytoplasmic C Cytoplasmic E Cytoplasmic I Cytoplasmic O Extracellular C Cytoplasmic H Cytoplasmic S Cytoplasmic C Cytoplasmic S Cytoplasmic I Cytoplasmic G Cytoplasmic S Membrane L Cytoplasmic H Cytoplasmic J Cytoplasmic J Cytoplasmic Q Cytoplasmic M Extracellular J Cytoplasmic C Cytoplasmic Membrane F Cytoplasmic KT Cytoplasmic ET Extracellular M Cytoplasmic L Cytoplasmic S Cytoplasmic J Cytoplasmic L Cytoplasmic G Cytoplasmic	LIPO-PROTEIN Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other TM Other Lipo Other Other Other Other Other TM Other Lipo Other Other Other Other Lipo Other Sec Other Other Lipo Other Other TM Other Other Lipo TM Other Other TM Other Other Sec Other Other Other Other Other Other Other Other TM Other Other TM Other Other Other Other Sec Other Other Other Other Other Other Other Sec Other TM Other Other Other Other Sec Other Other Other Other TM Other Other TM Other Other Sec Other Other Lipo Other TM TM Other TM Other Other Other Sec Lipo Other Sec Other Other Lipo Sec Sec Other Other Other Other Other Other Lipo Other Other Other Lipo Other Other Lipo Other TM Other Other TM Other Other TM Sec Other Other Other Other Other Other Other Other Other Other Other Other Other Sec Other Other Other Other Other Other TM Other Other Lipo TM Other Other Other Other Other Other Other Other Sec Other TM Other Other Other Other Sec TM Other Other Lipo TM Other Other Other Other Other Other TM Other Other Other TM TM Other Other Lipo Other TM Other Other Other TM Lipo Other Other Other TM Other Other Other Other Other TM Sec TM Other Other Sec Other Other Sec Other Sec Sec Other Other Other Lipo TM Other Lipo Lipo TM Other Other Other Other Sec Other Other Lipo Other Other Other Other Other TM Other Other Other TM Other Other Other Sec Other Sec TM Lipo Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other TM Other TM Other Other Other Other Other TM Other Other TM Other TM Other Other TM TM Other Other TM Other TM Other Other Other Other Other Other Other Other TM Other Other TM TM Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other TM Other Other Other TM Other Other Other Other Sec Other Other TM Other Other Other Other Other Other Other Lipo Other Other Sec Other Other Other Other Other Other Other Other Other Other Other Other Other TM Other Lipo TM Other Other Other Other Sec Other Other Other Other Other Other Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Sec Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other Other Sec Other Other Other TM Other Other Other Other Other Other Other Other TM Sec Other Other Other TM Other Other Other Sec Other Other Other Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other Sec TM Other Other Other TM Lipo Other Other Other Other Other Other Other Other Other Other TM Other TM Other Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other TM Other Other TM Other Other Other Other Other Other Other Sec Other Other Other Other Other Other Other Other Other Other Other Other TM Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Sec Other Other Other Other Other Other Other Sec Other Other Other Other Other Sec Other Other TM Other Other Lipo Other Other Other Other Other Other	SEC_ UF 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0	CONDITIONS SEC_ YEL UC_ UF 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0	UC_ YEL 1 1 1 1 1 1 1 1
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extracellular vesicles produced by human and animal Staphylococcus aureus strains share a highly conserved core proteome natayme Rocha tartaglia 1,2 , Aurélie nicolas 1 , Vinícius de Rezende Rodovalho 1,2 , Brenda Silva Rosa da Luz 1,2 , Valérie Briard-Bion 1 , Zuzana Krupova 3 , Anne thierry 1 , françois coste 4 , Agnes Burel 5 , Patrice Martin 6 , Julien Jardin 1 , Vasco Azevedo 2 , Yves Le Loir 1 & eric Guédon 1 ✉

Table 1 .

 1 Selection of virulence-associated factors found in EVs from S. aureus strains MW2, N305, RF122, O11 and 046. a adapted from84 .

	Function a	Label	Gene name	MW2	N305	RF122	O11	O46
	Host immune evasion
	Immunoglobulin-binding protein	MW2341	sbi	+	+	+	+	+
	Bi-component leukocidin LukMF' subunit M	SAB0782	hlyII	-	-	+	+	-
	Bi-component leukocidin LukMF' subunit F'	SAB0783	lukF	-	-	+	-	-
	Delta-hemolysin	MW1959	hld	+	+	+	+	+
	Bi-component leukocidin LukGH subunit G	MW1941	lukG	+	+	+	+	+
	Bi-component leukocidin LukGH subunit H	MW1942	lukH	+	+	+	+	+
	Phenol-soluble modulin alpha 4 peptide	MW0406.1	psmA4	+	+	+	+	+
	Phenol-soluble modulin alpha 3 peptide	MW0406.2	psmA3	+	+	+	+	+
	Phenol-soluble modulin alpha 2 peptide	MW0406.3	psmA2	+	+	+	+	+
	Phenol-soluble modulin alpha 1 peptide	MW0406.4	psmA1	+	+	+	+	+
	Beta-class phenol-soluble modulin	MW1056	psmB1	+	+	+	+	+
	Beta-class phenol-soluble modulin	MW1057	psmB2	+	+	+	+	+
	Exoenzymes
	Glyceraldehyde-3-phosphate dehydrogenase	MW0734	gapA1	+	+	+	+	+
	Sortase	MW2448	srtA	+	+	-	+	+
	Adhesion and cell wall anchored surface proteins
	Elastin binding protein	MW1369	ebpS	+	+	+	+	+
	Enolase	MW0738	eno	+	+	+	+	+
	Extracellular adherence protein Eap/Map	SAO46_01921	map_2	-	-	-	-	+
	Proteins related to biofilm production
	Thermonuclease	MW1211	nuc	+	+	+	+	+
	Regulatory proteins
	Accessory gene regulator protein D	SAB1921	agrD	-	-	+	-	-
	Miscellaneous proteins
	Zinc metalloprotease	MW0466	ftsH	+	+	+	+	+
	Serine protease	MW1670	htrA	+	+	+	+	+
	Zn-dependent membrane protease	MW1350	yugP	+	+	+	+	+
	Bifunctional autolysin	MW0936	atl	+	+	+	+	+
	D-alanyl-lipoteichoic acid biosynthesis protein DltD	MW0817	dltD	+	+	+	+	+
	Lipoteichoic acid synthase	MW0681	ltaS	-	+	+	-	-
	Preprotein translocase subunit SecA	MW0715	secA1	+	+	+	+	+
	Preprotein translocase subunit SecDF	MW1587	secDF	+	+	+	+	+

  Frontiers in Microbiology | www.frontiersin.org 13 March 2021 | Volume 12 | Article 634226 S. aureus EVs. mRNAs expressing PSMβ and hemolysin δ toxins from the EVs, if translated into recipient bacteria or host cells, could perhaps facilitate staphylococcal intracellular survival, but this hypothesis should be experimentally challenged.

This bacterium is also well recognized to encompass a pathway for vitamin B12 (cobalamin) synthesis[START_REF] Falentin | The complete genome of propionibacterium freudenreichii CIRM-BIA1T, a hardy actinobacterium with food and probiotic applications[END_REF][START_REF] Thierry | New insights into physiology and metabolism of Propionibacterium freudenreichii[END_REF]. This vitamin is a complex organic molecule and a co-factor of methylmalonyl-CoA mutase, which catalyzes a crucial step in the fermentative route to produce propionate[START_REF] Takahashi-Iñiguez | Role of Vitamin B12 on methylmalonyl-CoA mutase activity[END_REF]. Therefore, the growth conditions of P. freudenreichii have been optimized for the production of vitamin B12, using substrates such as cereal matrices(Chamlagain et al., 2018; Wang et al., 2020a), waste frying sunflower oil[START_REF] Hajfarajollah | Vitamin B12 biosynthesis over waste frying sunflower oil as a cost effective and renewable substrate[END_REF], tofu wastewater[START_REF] Yu | Enhancing the vitamin B12 production and growth of Propionibacterium freudenreichii in tofu wastewater via a light-induced vitamin B12 riboswitch[END_REF] and soybean agroindustry residue(Assis

INRAE, Institut Agro, STLO, Rennes, France,

Laboratory of Cellular and Molecular Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil,

Biotechnology Center, Federal University of Paraíba, João Pessoa, Brazil,

INRAE, AgroParisTech, Paris-Saclay University, Micalis Institute, Jouy-en-Josas, France

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Received 22 September 2020 Accepted 2 December 2020 Accepted manuscript posted online 11 December 2020 Published 29 January 2021 February 2021 Volume 87 Issue 4 e02263-20 Applied and Environmental Microbiology aem.asm.org 1 FOOD MICROBIOLOGY on March 30, 2021 by guest http://aem.asm.org/ Downloaded from

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Scientific RepoRtS |(2020) 10:8467 | https://doi.org/10.1038/s41598-020-64952-y

Acknowledgements

I learned from a song that we are the borders that we crossed. I had to cross several borders during these years and, fortunately, I was not alone. This is the opportunity to say thank you, obrigado and merci to the people that were with me and helped me to become who I am today.

ACKNOWLEDGMENTS

This work benefited from the facilities and expertise of the MRic-TEM platform (https://microscopie.univ-rennes1.fr). The authors are grateful to Agnes Burel (University of Rennes, BIOSIT -UMS 3480, US_S 018, Rennes, France) and Chantal Cauty for sessions with the microscope. The authors would like to thank Ludovica Marinelli (INRAE, AgroParisTech, Paris-Saclay University, Micalis Institute, Jouy-en-Josas, France) for her technical advice on the NF-κB reporter cell line. The authors would also like to thank Victoria Hawken for English-language editing of the manuscript. The authors thank CNIEL (Centre National Interprofessionnel de l'Economie Laitière) for providing the ITG P20 strain.

ACKNOWLEDGMENTS

We are grateful to Ludovica Marinelli and Nicolas Lapaque (INRAE, AgroParisTech, University Paris-Saclay, Micalis Institute, Jouy-en-Josas, France) for their technical advice on the NF-κB reporter cell line. We also thank Victoria Hawken for English language editing of the manuscript. We thank the CNIEL (Centre National Interprofessionnel de l'Economie Laitière) for providing the ITG P20 strain (alias CIRM-BIA129) of P. freudenreichii.

V.R.R., G.J., Y.L.L., V.A.C.A., and E.G. conceived and designed the experiments. V.R.R., V.B.-B., J.J., and B.S.R.L. performed the experiments. V.R.R., A.N., J.J., and E.G. analyzed the data. F.L.R.C., A.N., J.J., V.B.-B., G.J., and E.G. gave practical suggestions regarding performance of the experiments. V.A.C.A., Y.L.L., and E.G. contributed to funding acquisition. V.R.R. and E.G. wrote the original draft. All authors contributed to data interpretation, drafting the manuscript, critically revising the manuscript, and approving its final version. This work received financial support from INRAE (Rennes, France) and Institut Agro (Rennes, France). V.R.R. and B.S.R.L. were supported by the International Cooperation Program CAPES/COFECUB at the Federal University of Minas Gerais funded by CAPESthe Brazilian Federal Agency for the Support and Evaluation of Graduate Education of the Brazilian Ministry of Education (number 99999.000058/2017-03 and 88887.179897/ 2018-00, respectively).

Acknowledgements

This work benefited from the facilities and expertise of the MRic-TEM platform (https://microscopie.univ-rennes1.fr). We are very grateful to Chantal Cauty (INRAE, Agrocampus Ouest, STLO, F-35000, Rennes, France) for sessions with the microscope and her technical advice. Denis Chrétien (Univ Rennes, CNRS, IGDR -UMR 6290, F-35000 Rennes, France) and Aurélien Dupont (Univ Rennes, CNRS, Inserm, BIOSIT -UMS 3480, US_S 018, F-35000 Rennes, France) are warmly acknowledged for cryo-electron tomography experiments. We would also like to thank Victoria Hawken for English-language editing of the manuscript. This work received financial support from Agrocampus Ouest (Rennes, France) and INRAE (Rennes, France). Vinícius de Rezende Rodovalho and Brenda Silva Rosa da Luz were supported by the International Cooperation Program CAPES/COFECUB Foundation at the Federal University of Minas Gerais funded by CAPES -the Brazilian Federal Agency for the Support and Evaluation of Graduate Education of the Brazilian Ministry of Education (99999.000058/2017-03 and 88887.179897/2018-03, respectively). Natayme Rocha Tartaglia was supported by a PhD fellowship from the Brazilian government through CNPq (National Council for Scientific and Technological Development)/PVE (400721/2013-9).

ACKNOWLEDGMENTS

This work was conducted in the frame of BactInflam International Associated Laboratory between INRAE (France) and UFMG (Brazil). It benefited from the facilities and expertise of the MRic-TEM platform (https://microscopie.univ-rennes1.fr). We are grateful to Agnes Burel (Univ Rennes, BIOSIT -UMS 3480, US_S 018, Rennes, France) for sessions with the microscope. We are also grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul) for providing computing and storage resources. Sandrine Parayre (INRAE, STLO) and Sandrine Péron (INRAE, STLO) are warmly acknowledged for the technical assistance during RT-qPCR experiments.

scientific funding was essential for the development of this thesis. I thank the Brazilian Federal Agency for the Support and Evaluation of Graduate Education (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES) of the Brazilian Ministry of Education, for supporting me both in Brazil and France. I also thank the financial and structural support from INRAE (Rennes, France), Institut Agro (Rennes, France) and LGCM-UFMG (Belo Horizonte, Brazil).

FUNDING

This work has received a financial support from the INRAE (Rennes, France) and Institut Agro (Rennes, France). VR and BL were supported by the International Cooperation Program CAPES/COFECUB at the Federal University of Minas Gerais funded by CAPES -the Brazilian Federal Agency for the Support and Evaluation of Graduate Education of the Brazilian Ministry of Education (number 99999.000058/2017-03 and 88887.179897/2018-00, respectively). FUNDING This work has received financial support from INRAE (Rennes, France) and Institut Agro (Rennes, France). This work was part of the CARAVEL project financed by the MICA division from INRAE. BSRL and VRR were supported by the International Cooperation Program CAPES/COFECUB at the Federal University of Minas Gerais funded by CAPES -the Brazilian Federal Agency for the Support and Evaluation of Graduate Education of the Brazilian Ministry of Education (number 88887.179897/2018-00 and 99999.000058/2017-03, respectively).

DATA AVAILABILITY STATEMENT

The datasets generated for this study can be found here: https:// data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/Q6PPXY. Data availability. The mass spectrometry proteomics data can be found at https://doi.org/10 .15454/Q6PPXY.

Data availability

Raw data are available at this adress: https://doi.org/10.15454/SMFFWK.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ebi.ac.uk/ ena, https://www.ebi.ac.uk/ena/browser/view/PRJEB40502 .

Annexes

Supplementary Figure 1 (Article 1). Frequency of predicted interactions mapping to KEGG terms, according to prediction method.________________________________________________________226 Supplementary Figure 2 (Article 1). Human KEGG NF-κB pathway, highlighting in blue the different human proteins predicted to interact with proteins from the EVs of P. freudenreichii CIRM-BIA129. Noninteracting proteins are in green._______________________________________________________227 Supplementary Figure 3 (Article 1). Subnetwork of predicted interactions mapping to KEGG toll-like receptors (TLRs) pathway._____________________________________________________________228 Supplementary Figure 1 (Article 2). Heatmap representing variations in the relative abundance of identified proteins.__________________________________________________________________229 xvii

List of tables

AUTHOR CONTRIBUTIONS

VR, GJ, YL, VC, and EG conceived and designed the experiments. VR, VB-B, JJ, BL, and HR performed the experiments. VR, EF, AN, JJ, and EG analyzed the data. FC, AN, JJ, VB-B, HB, NL, GJ, and EG gave practical suggestions to perform experiments. VC, YL, and EG contributed to funding acquisition. VR and EG wrote the original draft. All authors contributed to data interpretation, drafting the manuscript, critically revising the manuscript, and approving its final version.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2020.01544/full#supplementary-material

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

SUPPLEMENTAL MATERIAL

Supplemental material is available online only. SUPPLEMENTAL FILE 1, PDF file, 0.2 MB. SUPPLEMENTAL FILE 2, XLSX file, 0.1 MB.

Annexes

This section contains supplementary information for this thesis. For research articles, supplementary images are provided, whereas longer supplementary tables are available online, with their electronic addresses provided.

All Python code used in this work is available online at:

Supplementary figures for Research article 1 (Chapter 3)

Supplementary Figure 1. Frequency of predicted interactions mapping to KEGG terms, according to prediction method.

Supplementary Figure 2. Human KEGG NF-κB pathway, highlighting in blue the different human proteins predicted to interact with proteins from the EVs of P. freudenreichii CIRM-BIA 129. Non-interacting proteins are in green.

Supplementary Figure 3. Subnetwork of predicted interactions mapping to KEGG toll-like receptors (TLRs) pathway. Blue square nodes represent human proteins, red round nodes represent bacterial proteins, full thin green lines represent intersppi-predicted interactions, dotted purple lines represent interolog-predicted interactions and full thick black lines represent interactions shared by both methods. Inset: selected interactions involving bacterial proteins previously associated to immunomodulatory roles.

Supplementary figure for Research article 2 (Chapter 4)

Supplementary Figure 1. Heatmap representing variations in the relative abundance of identified proteins.

Author contributions

competing interests

The authors declare no competing interests. compared between the different growth conditions, and with those obtained from parental HG003 cells that produced the EVs in each condition (i.e., the EV-producing cells). Around 2649 ± 238 RNAs annotated in the HG003 genome were identified in the EVs according to growth conditions, with an average count of over five reads per RNA in each condition, whereas 3120 ± 35 annotated RNAs were identified within the EV-producing cells (Supplementary Tables S3 andS4).

All the four main RNA functional classes (tRNAs, rRNAs, mRNAs, and sRNAs) were identified in both the purified EVs and the EV-producing cells (Figure 2A). In both the EVs and the EV-producing cells, ~84% of the mapped RNAs corresponded to protein-coding genes (mRNAs). The sRNAs were the second most abundant mapped RNA class (~12% of the reads). The remaining 4% of the mapped RNAs are tRNAs and residual rRNAs. Note that most of the rRNAs were voluntarily removed during the RNA purification. Most of the annotated mRNAs, tRNAs, and residual rRNAs were identified in the EV-producing parental cells (from 86 to 96%), while this value dropped to 79.8 ± 2.7% for sRNAs (Figure 2B). These percentages were slightly lower for EV samples (from 72 to 89%), although they remained high for the sRNAs (59.1 ± 10.2%). All these RNAs detected in the purified EVs prompted us to check their coverages, to evaluate their integrity.

The S. aureus Purified EVs Contain Both Fragmented and Intact RNAs From Various Functional Classes

For the EV-producing cells, the median values of mRNAs, tRNAs, and residual rRNAs coverages were between 95 and 100%, and were 74 and 92% for the sRNAs (Figure 3A and Supplementary Table S4), implying that those RNAs were mainly intact, and not degraded. The coverage profile was drastically different for the RNAs recovered from the purified EVs. While the coverage of the residual rRNAs varied from 83 to 92%, the median coverage values for the other RNA functional classes ranged from 61 to 92% for the tRNAs, 15 to 47% for the mRNAs, and 4 to 20% for the sRNAs (Figure 3A). These lower coverages suggested that a substantial fraction of mRNAs and sRNAs were fragmented in the EVs compared to the EV-producing cells. To analyze potentially functional RNAs in EVs, only RNAs with a coverage ≥90% were considered for further analysis (Supplementary Table S5). The distribution of the newly filtered RNAs was depicted in Figures 3B,C. Such a harsh quality criterion impacted mainly the RNAs from the EVs, and particularly mRNAs and sRNAs. Only 3.5 ± 2.7 and 3.4 ± 1.2% of annotated mRNAs and sRNAs, respectively, were identified within EVs with such a threshold, while 67.9 ± 7.2% and 34.9 ± 5.0% of annotated mRNAs and sRNA were identified, respectively, for EV-producing cells (Figure 3C).

Compared to the parental cells, the EVs were slightly depleted into mRNAs (68.0 ± 7.0% for the EVs vs. 88.2 ± 0.3% in the EV-producing cells) but, interestingly, were enriched for the other RNA functional classes including the sRNAs (14.3 ± 3.2% for the EVs vs. 8.6 ± 0.4% in the EV-producing cells, Figure 3B).

Functional Characterization of the RNAs From the EVs

According to experimental conditions, from 86 to 273 RNAs with a ≥90% coverage and an average count of over 5 reads per RNA were identified within EVs from S. aureus HG003 (Figure 4A and Supplementary Table S5). They corresponded to 286 unique RNAs and were either mRNAs (220), tRNAs (28), residual rRNAs (10), and sRNAs (28). The presence of some of these transcrits associated with HG003 EVs and corresponding either to mRNAs or sRNAs was confirmed by RT-qPCR on RNAs extracted from three independent biological replicates (Figure 5). Among the mapped mRNAs, most were implicated in translation, ribosomal structure and biogenesis (17.5%, COG J), energy production and conversion (13.6%, COG C), carbohydrate transport and metabolism (COG G, 7.9%), transcription (5.2%, COG K), and cell wall/membrane/ envelope biogenesis (5.2%, COG M; Figure 4B). Several COG and KEGG categories were notably enriched (p < 0.05) in the EVs compared to the EV-producing bacteria (Figure 4C). mRNAs expressing proteins with a cytoplasmic location prediction were more represented in the EVs (79.5%) than into the producing cells (72.9%; Figure 4D). Interestingly, EVs contained several mRNAs coding for virulence-associated proteins such as the immune evasion protein A and Sbi, the Atl autolysin, (Atl and Spa), metabolic enzymes (pyruvate dehydrogenase and cytochrome c oxidase complexes, glycolytic enzymes) and transcriptional regulators (SpxA, CggR, and GlnR), as well as RNAs involved in translation (ribosomal proteins, rRNAs, and tRNAs; Supplementary Table S5). The 51 common RNAs also included 9 potential sRNAs, notably RsaC involved in S. aureus oxidative stress adaptation and nutritional immunity [START_REF] Lalaouna | RsaC sRNA modulates the oxidative stress response of Staphylococcus aureus during manganese starvation[END_REF].

For both the EVs and EV-producing cells, more RNAs were detected at 6 h (275 and 2443 for EVs and EV-producing cells, respectively) than at 12 h (126 and 2161 for EVs and EV-producing cells, respectively, Figure 4A). Likewise, more RNAs were detected in the EVs in the presence (277 and 2,474 for EVs and EV-producing cells, respectively) than in the absence of vancomycin (140 and 2,279 for EVs and EV-producing cells, respectively; Figure 4A), indicating that the antibiotic modifies the RNA cargo of the EVs. These results also highlighted that the growth phase and the antibiotic stress impacted mostly the RNA content of the EVs, but much less that of the parental cells. Indeed, when we considered the RNAs detected in only one condition (i.e., specific RNAs), their fractions were higher in the EVs than in EV-producing cells, and that for all the tested conditions. For example, 58% of RNAs found within EVs at 6 h were specific to this condition, while specific RNAs represented only 14% of all RNAs detected at 6 h in the EV-producing cells.

EV RNA Abundance Varies With Growth Conditions

In addition to the qualitative variations observed, significant differences (Padj < 0.05) in EV RNA abundance between the experimental conditions were also detected (Supplementary Table S7). Among the 286 EV-associated RNAs, 110 were differentially abundant between two conditions. Variations were detected at all times and in the absence or presence of vancomycin, although the growth phase appeared to have a greater impact on RNA abundance (75 and 64 differentially abundant RNAs were detected between early-and late-stationary phase with or without vancomycin, respectively), than the antibiotic stress (8 and 9 differentially abundant RNAs were detected between presence and absence of vancomycin in early-and late-stationary phase, respectively; Supplementary Table S7). A selection of RNAs with a modulation of their abundance according to the growth conditions is displayed in Figure 7.

The most modulated RNAs into EVs produced from the two growth conditions were mRNAs coding for virulenceassociated factors, such as agrB, agrC, agrD, psmB1, and hld with a 30-to 1300-fold change, two potential annotated sRNAs, srn_0560, and srn_1000 with a 16-and 190-fold change, respectively, tRNA Gly (SAOUHSC_T00025) and tRNA Thr (SAOUHSC_T00054), with fold changes greater than 16. Among the differentially abundant RNAs according to the growth phase, 32 were detected both in presence and absence of vancomycin, with similar fold changes highlighting their reproducible variations into EVs across different environmental conditions (Figure 7 and Supplementary Table S7).

Differentially expressed RNAs were also detected for the EV-producing cells when their expression was compared between early-and late-stationary phase both in absence (n = 136) and in presence of vancomycin (n = 147), which was expected since bacterial transcription differs qualitatively

AUTHOR CONTRIBUTIONS

BSRL, BF, SC, YL, VA, and EG conceived and designed the experiments. VA and EG supervised the study. BSRL and SC performed the experiments. AN performed computational analysis. BSRL, BF, SC, VRR, AN, and EG analyzed the data. VA, YL, and EG contributed to funding acquisition. BSRL and EG wrote the original draft. All authors contributed to data interpretation, drafting the manuscript, critically revising the manuscript, and approving its final version.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.634226/ full#supplementary-material Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.