Milan Thorel
email: milan.thorel59@gmail.com

Lourdes Mateos-Hernandes

Baptiste Mulot

Mouna Naila

Adnan Hodzic

Hugues Gaillot

Yannick Ruel

Guillaume Desoubeaux

Jean-Baptiste Delaye

Dasiel Obregon

Alejandra Wu-Chuang

Jose De La Fuente

Luis G Bermúdez-Humarán

Veronica Risco-Castillo

Antoine Leclerc

Alejandro Cabezas-Cruz
email: alejandro.cabezas@vet-alfort.fr

Assessment of the safety and the anti-α-Gal immune response to an oral vaccine in captive-bred Humboldt penguins (Spheniscus humboldti)

Keywords: Anti-α-Gal immunity, Aspergillosis, Aspergillus fumigatus, Escherichia coli Nissle, penguins, Sphenisciformes, Spheniscus humboldti, vaccine, zoo

Oral administration of Escherichia coli O86:B7 expressing high levels of galactose-α-1,3galactose (α-Gal) was previously shown to protect turkeys from aspergillosis caused by Aspergillus fumigatus. In the present study, we first tested the basal level of anti-α-Gal antibodies (Abs) in healthy Humboldt penguin (Spheniscus humboldti) sera and penguin egg yolks. We then conducted a trial to test whether modulation of α-Gal immunity by oral administration of the probiotic bacterium Escherichia coli Nissle could prevent Humboldt penguins from aspergillosis.

To this aim, E. coli Nissle was administered orally to ten penguins and a placebo was concomitantly given to a control group of ten other penguins. An aspergillosis diagnostic panel (CT scans, capillary zone electrophoresis, 3-hydroxybutyrate levels, anti-A. fumigatus Abs measurements) was implemented before, during and after the interventional procedure in both penguin groups. As results, healthy penguins produced natural anti-α-Gal Abs that were also detected in the egg yolks. Surprisingly, oral administration of E. coli Nissle was associated with lower levels of anti-α-Gal and anti-A. fumigatus Abs compared with the control group. Still, a correlation was seen between anti-E.coli Abs (IgM/IgY) and anti-α-Gal Abs (IgM/IgY), only in the vaccinated penguins. E. coli Nissle is a safe probiotic to use in Humboldt penguins with potential protective effect against aspergillosis to be further tested.

Introduction

Several non-pathogenic or pathogenic agents present the glycan galactose-α-1,3-galactose (α-Gal) epitope at their surface, including Aspergillus fumigatus, Plasmodium sp. or Mycobacterium marinum .Birds, as well as humans or fish, lack endogenous expression of α-Gal, but can naturally produce high levels of anti-α-Gal antibodies (Abs) against the in response to antigenic stimulation from the microorganisms of the gut microbiota (REF). (REF).Then, one could legitimately assume that boost of the antibody production following the vaccination with α-Gal antigen provided by massive input of digestive bacteria could thus prevent from the aforementioned debilitating infectious diseases. Indeed, captive-bred penguins are at risk to develop life-threatening Plasmodium sp. infections (REF); mycobacteriosis occurs sporadically in colonies of captive penguins (REF), and aspergillosis is often acknowledged as the main cause of death in zoological institutions. Regarding the latter, yearly fatal cases concern approximately 10% of the colony at ZooParc de Beauval in France and can represent 27% of all cause-mortality in some penguin colonies. Fungal loads in the environmental of captive-penguin settings are often quite high in, making immunosuppressed or stressed individuals particularly at risk of developing clinical aspergillosis, especially during the summer season (July-September) in the northern hemisphere. Treatment of aspergillosis in clinically infected penguins relies primarily on azole drug-based therapy. Alternatives include terbinafine, amphotericin B, or other antifungals. However, clinical signs are non-specific, the diagnosis in laboratory is challenging, and all together the initiation of the adequate antifungal therapy is consequently often delay.

Besides, voriconazole toxicity has been described in penguin species and resistance of Aspergillus sp. strains to azole molecules is reported from low to high prevalence depending on the zoological institutions. For all these reasons, medical prophylaxis against aspergillosis is paramount in penguins.

Oral administration of the bacterium Escherichia coli O86:B7 with high α-Gal content elicited an anti-α-Gal Abs response associated with protection against malaria transmission in α1,3GT-deficient mice (XXX). In another study, probiotic formulations including Aeromonas veronii and Pseudomonas entomophila with high content of α-Gal, were biosafe, increased the levels of anti-α-Gal IgM and protect against M. marinum in a zebrafish model (XXX). :

Protective mechanisms that regulate immunity and metabolism activated in response to probiotics included modification of the gut microbiota composition, B-cell maturation, anti-α-Gal antibodies-mediated control of mycobacteria, while they induced innate immune responses, beneficial effects on nutrient metabolism, and reduced oxidative stress (XXX). In addition, recent research showed that oral administration of E. coli O86:B7 preventing turkeys from developing aspergillosis after an experimental infection with A. fumigatus (REF). However, the protective effect of E. coli O86:B7 was not associated with an increase in circulating anti-α-Gal IgY levels, but with a striking reduction of anti-α-Gal IgA in the lungs of infected turkeys (REF). Animals treated with E. coli O86:B7 experienced lower lung inflammation, which was associated with no activation of pro-inflammatory cytokine genes (i.e., IFNγ, IL6, IL2) in A. fumigatus-infected turkeys. In the light of this evidence, probiotic-based vaccines have been proposed as an alternative to activate protective mechanisms associated to the α-Gal immunity for the control of infectious diseases in fish, birds and humans (XXXX).

Developing an anti-α-Gal oral vaccine opens the perspective to protect Sphenisciformes against aspergillosis, malaria and mycobacteriosis. (REF). In this study, we used the probiotic E. coli Nissle to modulate the anti-α-Gal immunity in penguins at risk of natural A. fumigatus infection at ZooParc de Beauval. The specific objectives of this study were to determine the baseline levels of anti-α-Gal Abs in Humboldt penguins and penguin eggs, to test whether oral administration of E. coli Nissle is safe and if it modulates the anti-α-Gal immunity in penguin.

Besides, we compared aspergillosis-related morbidity and mortality rates between vaccinated vs. non-vaccinated penguins during a one-year period post-vaccination.

Materials and Methods

Study population

Sera of ten Humboldt penguins (penguins n°1-10, Table 1) were used to develop the serological assay. For the vaccine trial, twenty adult penguins were randomly chosen in the colony of 123 individuals from ZooParc de Beauval (Table 1), and assigned to either the vaccine group (n = 10, penguins n°11-20) or the placebo group (n = 10, penguins n°21-30). Penguins were individually identified with color rings. All study animals were considered healthy at inclusion, based on medical records, initial complete clinical examination and baseline plasma electrophoretic profile.

Probiotic bacteria used in the study

Animals in the vaccine group received orally the Escherichia coli Nissle 1917 contained in gastro-resistant hard capsules (2.5 -25 x 10 9 viable cells (CFU) per capsule) of the product Mutaflor (DSM 6601, Pharma-Zentrale GmbH, Herdecke, Germany). Animals in the placebo group were given vegetable empty capsules made with pullulan. These capsules can hold approximately 680ul for liquid solution or 270-680mg for powder antigens (YourSupplements, Bredbury, Stockport, UK).

Vaccination trial design and oral administration of bacteria

Before starting the vaccine trial, its safety was preliminarily assessed using two supplemental penguins (not included in either aforementioned study groups). One of them was given an oral dose of vaccine and the other one the placebo, and both individuals were closely monitored by zookeepers for three weeks. No adverse effect was noticed. Oral administration of both the vaccine and the placebo were considered safe in the Humboldt penguin and the trial was started.

The drug or the placebo was orally administrated every Monday and Wednesday for three consecutive weeks (Table 1). Free-food sessions in the large enclosure of the Humboldt penguins were used to give the vaccine and placebo to study animals. Penguins from the vaccine group were individually given a vaccine-baited thawed sprat, while penguins of the other groups were given the placebo similarly.

Blood samplings, and blood specimen analyses

Three sampling sessions were performed at T-1mo (1 month before the beginning of the vaccination protocol), T1mo (1 month after), and T3mo (3 months after) (Table 1). Penguins were manually restrained and 2-to-2.5ml of blood were sampled from either the medial metatarsal vein or the dorsal coccygeal vein using a 2.5ml plastic syringe and a 22-g needle. For each penguin, 0.5ml of whole blood was immediately transferred to EDTA-tubes (Tube BD® Vacutainer® Silicone EDTA 4ml), homogenized, then transferred to a dry tube containing 0.5ml of perchloric acid, homogenized again and stored at 4°C until further processing. Another 0.5ml was also immediately transferred to heparin-lithium tubes (Tube BD® Vacutainer® Silicone lithium heparin 4ml) and homogenized. The remaining amount of fresh blood (1-to-1.5ml) was then transferred into a plastic tube for serum with coagulation activator (Tube BD® Vacutainer® Silicone dry tube 4ml). All tubes were centrifuged at 5000rpm for 15 minutes twice. Capillary zone electrophoretic profiles were obtained from the fresh heparin-lithium plasma samples using the Minicap® automated system (Sebia, Lissex, 91008 Evry Cedex, France). For each plasma sample, total protein concentration was determined using a chemistry analyzer (Catalyst One, Idexx Laboratories, Inc., Westbrook, Maine 04092 USA). Serum was also extracted, and stored until further processing.

The supernatants obtained from the EDTA-blood + perchloric acid tubes were stored in cryotubes (Thermo Fischer Scientific, 91140 Les Ulis, France) at -20°C. Once the three sampling sessions were completed, samples were thawed and 3-hydroxybutyrate (3-OHB) concentration was measured using the RX Daytona+® instrument with Randox® reagents (Randox Laboratories, Kearneysville, WV 25430, USA) as previously described.

Computed tomography

The twenty penguins of the trial were brought to the on-site veterinary hospital for computed tomography imaging (PHILIPS Brilliance 64 CT Scanner) at two time points (T-1mo, T3mo) (Table 1). Penguins were placed in a 40cm-high, 18cm-large PVC-tube fixed on a rigid plate.

During the first session, 13 penguins underwent conscious, standing scanning using a device inspired by Rivas et al. (Figure 1), while the remaining seven underwent conscious CT scanning in dorsal recumbency. During the two sessions, only two penguins were too fat to fit the PVCtube and were anesthetized with isoflurane delivered by facemask to perform a dorsal recumbent CT scan (Table 1). A binary scoring system was created to assess aspergillosis-related lesions (Supplementary Table S2). Six reading categories were considered: right lung nodules, left lung nodules, air sac nodules, air sac plaques, trachea nodules, and trachea plaques. For each category, the presence (1) or absence (0) of lesions was recorded for each penguin at T-1mo and T3mo. CT scans were blindly read (vaccination status not available), separately by two ECVDI diplomates.

Both readings were then harmonized to a collegiate, unique score for each CT scan. Size (mm) and number of each lesion were also recorded.

Aspergillus fumigatus culture

The highly germinative A. fumigatus CBS 144.89 (CEA10) clinical strain was used. Aspergillus fumigatus culture was performed on Sabouraud dextrose agar (SDA), supplemented with chloramphenicol (5 mg/L) and incubated at 37 °C for 10 days. Sub-cultures were performed twice a week and A. fumigatus colonies were grown for 2-3 days at 37 °C. Conidia were subsequently harvested by resuspension in PBS with 0.01% tween (PBST), filtered in a 70 µm diameter nylon cell strainer (ClearLine Dominique Dutscher, Brumath, France), washed by centrifugation at 3500× g for 10 min, resuspended in with 0.01% tween and then counted using a Malassez counting chamber. Aspergillus fumigatus suspension containing 4 × 10 7 conidia was resuspended in 500 µl PBST. All reagents used for the fungus preparation were apyrogenic.

Escherichia coli strains culture

The bacterial strains E. coli Nissle 1917 (mutaflor strain), E. coli O86:B7 (ATCC 12701), E. coli ClearColi® BL21(DE3) (Lucigen, Middleton, Wisconsin, USA), and E. coli BL21 (DE3, Invitrogen, Carlsbad, CA, USA) were grown on 20 mL of Luria Broth (Sigma-Aldrich, St. Louis, MO, USA), at 37 °C overnight to OD600 = 0.8. Bacteria were washed twice with phosphate buffer saline (PBS): 10 mM NaH2PO4, 2.68 mM KCl, 140 mM NaCl, pH 7.2 (Sigma-Aldrich, St. Louis, MO, USA), centrifuged at 1000× g for 5 min at 4 °C, and resuspended in 500 µl lysis buffer, PBS with 1% triton (PBST, Sigma-Aldrich, St. Louis, MO, USA). All reagents used for bacterial preparation were apyrogenic.

Determination of α-Gal on E. coli strains by flow cytometry

Different E. coli strains were washed three times in PBS and fixed 30 minutes in 4% paraformaldehide. Then, the bacteria were incubated with monoclonal mouse anti-α-Gal antibody (mAb) M86 to α-Gal (Enzo Life Sciences, Farmingdale, NY, USA) diluted 1:100 in PBS. FITCgoat anti-mouse IgM (Sigma-Aldrich, St. Louis, MO, USA) labelled antibody (diluted 1:1000 in PBS 2 h at RT) was used as a secondary antibody. The mean fluorescence intensity (MFI) was determined by flow cytometry and compared between test and control cells.

Bacterial and fungal protein extraction

Escherichia coli and A. fumigatus proteins were extracted with six steel balls using the homogenizer Precellys®24 Dual (Bertin, France) at 5000× g for 20s, 2 times on lysis buffer. The homogenized was centrifuged at 200xg for 5 minutes and the supernatant were collected and quantified with BCA Protein Assay (Thermo Scientific, Waltham, MA, USA) with bovine serum albumin (BSA) as standard.

Evaluation of the specificity of anti-turkey and anti-chicken IgY for penguin IgY

To evaluate the specificity of anti-turkey and anti-chicken IgY against penguin IgY, plates (Nunc-ImmunoTM Plate, Thermo Scientific, Waltham, MA, USA) were coated with α-Gal linked to HSA (HSA-α-Gal, 50ng/well, Dextra Laboratories, Reading, UK) diluted in carbonate/bicarbonate buffer (0.05 M, pH 9.6) and incubated overnight at 4 °C. Wells were washed three times with 100 µL of PBST and then blocked with 100 μL of 1% HSA/PBS for 1 h at RT. After three washes, serum samples, diluted 1:200 in PBS, were added to the wells and incubated for 1 h at 37 °C. The plates were washed three times and HRP-conjugated goat antiturkey IgY (Mybiosource) or goat anti-chicken IgY (Sigma-Aldrich, St. Louis, MO, USA) Abs were added at dilution 1:1500 in PBS (100 μL/well) and incubated for 1 h at RT. Plates were washed three times and the reaction was developed by adding 100 µL ready-to-use TMB solution (Promega) at RT for 20 min in the dark, and then stopped with 50 µL of 0.5 M H 2 SO 4 . The OD was measured at 450 nm using a Multiskan FC ELISA reader (Thermo Scientific, Waltham, MA, USA). All samples were tested in triplicate and the average value of three blanks (no Abs) was subtracted from the reads. The cut-off was determined as two times a mean OD value of the blank controls.

Total chicken and penguin IgY were loaded on a 4-15% Mini-protean TGS stain-free gel (Bio-Rad, Hercules, USA) and separated by SDS-electrophoresis 1h at 120V by duplicated. Proteins were stained with Coomassie blue on one gel, and the other gel was transferred to Trans-Blot Turbo Transfer Packs membrane (Bio-Rad, Hercules, USA). The membrane was blocked 2 hours with 1% Human Serum Albumin (HSA) (Sigma)/PBS at room temperature (RT), washed three times with PBST 0.05% and incubated at 4°C overnight with HRP-conjugated goat anti-turkey IgY (Mybiosource, San Diego, CA, USA), or goat anti-chicken IgY (Sigma-Aldrich) antibodies (Abs) dilution 1:400 in PBS. Immunoreactive proteins were visualized with TMB solution (Promega, Madison, WI, USA).

Indirect ELISA for quantification of Abs against α-Gal, A. fumigatus and E. coli

To measure the levels of Abs against α-Gal, A. fumigatus and E. coli in penguin sera, plates (Nunc-ImmunoTM Plate, Thermo Scientific, Waltham, MA, USA) were coated with α-Gal-HAS, A. fumigatus or E. coli (50ng/well) proteins, respectively. Sera collected at T-1mo, T1mo and T3mo (Table 1) were diluted to 1:200 with phosphate buffered saline (PBS). Plates were blocked by adding 100 μL of 1% HSA/PBS for 1 h at RT. After three washes, samples diluted were added and incubated for 1 h at 37 °C. The plates were washed three times and horseradish peroxidase (HRP)-conjugated goat anti-chicken IgY or IgM (Sigma-Aldrich, St. Louis, MO, USA) were added at 1:1500 dilution in 1% HSA/PBS (100 μL/well) and incubated for 1 h at RT. The plates were washed three times and the reaction was developed by adding 100 µL ready-to-use TMB solution (Promega, Madison, WI, USA) at RT for 20 min in the dark, and then stopped with 50 µL of 0.5 M H 2 SO 4 . The OD was measured at 450 nm using Multiskan FC ELISA reader (Thermo Scientific, Waltham, MA, USA). All samples were tested in triplicate and the average value of three blanks (no Abs) was subtracted from the reads. The cut-off was determined as two times a mean OD value of the blank controls

Competitive ELISA for anti-α-Gal Abs levels determination

The capacity of penguin sera to inhibit the binding of mAb M86 (Enzo Life Sciences, Farmingdale, NY, USA) was measured by a competitive ELISA (ref), with modifications.

Briefly, dilutions of penguin sera were prepared (1:200, 1:80, 1:20 and 1:5) and added to α-Gal antigen simultaneously with M86. Binding of penguin sera to α-Gal was measured as M86 binding inhibition percentage (%). Penguin sera were mixed with mAb M86 and both Abs were added simultaneously to plates coated with α-Gal-BSA (200 ng/well, Dextra Laboratories, Reading, UK). The goat anti-mouse IgM-HRP (Sigma-Aldrich, St. Louis, MO, USA) was used as secondary antibody and the OD was measured at 450 nm with a Multiskan FC ELISA reader (Thermo Scientific, Waltham, MA, USA). Duplicates were used for each sample. The average value of the blanks was subtracted from all reads before further analysis. The M86 binding inhibition % was calculated as (([OD. M86 mAb alone] -[OD. M86 mAb with penguin sera])/[OD. M86 mAb alone]) × 100.

Total IgY purification from penguin egg yolk

Penguin egg yolk and egg white were separated. Total egg IgY was purified with IgY Purification Kit (Thermo Scientific, Waltham, MA, USA) following the manufacturer's instructions. Briefly, egg yolk was weighted and mixed with a volume of cold delipidation reagent equal to five times the egg yolk weight. The mix was incubated for two hours at 4°C, and then centrifugated 20 minutes at 10,000xg in refrigerated centrifuge. The supernatant was recovered and an equal volume of cold IgY precipitation reagent was added and incubated for two hours at RT. After 20 minutes of centrifugation at 10,000xg in a refrigerated centrifuge, the pellet was collected and a volume of PBS equal to the original volume of the egg yolk was added. Total proteins were quantified using BCA Protein Assay (Thermo Scientific, Waltham, MA, USA) with BSA as standard.

Prediction of α-1,3-galactosyltransferase genes in penguin microbiota

In humans (XX), birds (XX) and fish (XX), the production of anti-α-Gal Abs has associated with the presence of bacteria expressing α-Gal in the gut microbiota. Based on the finding of anti-α-Gal Abs in healthy penguins, we hypothesized that penguin bacterial microbiota has bacteria with α1,3GT genes. To partially test this hypothesis, the presence and abundance of α1,3GT genes in the penguin microbiome was inferred from 16S rRNA data using the bioinformatics pipeline PICRUSt2 (XXX).

For the prediction of the functional profile of the microbiome of penguins, published and publicly accessible 16S datasets were used. We considered to include the Humboldt penguin (Spheniscus humboldti), the penguin species of this study; however, no dataset was found. Four species of penguins were included in this preliminary study: the Macaroni penguin (Eudyptes chrysolophus), the Little penguin (Eudyptula minor), the Gentoo penguin (Pygoscelis papua) and the King penguin (Aptenodytes patagonicus) from Dewar et al. 2013 XX (PRJEB3083). After downloading from SRA repository XX, the raw sequences were introduced and analyzed using the QIIME2 software pipeline (v. 2021.4) XX The fastq files were first demultiplexed and filtered for quality control using DADA2 pipeline XX implemented in QIIME2. After, reads were denoised into amplicon sequence variants (ASVs) and taxonomy was assigned to ASVs using a pre-trained Naive Bayes classifier XX from SILVA database (release 138) XX.

PICRUSt2 XX was employed for predictions of functional metabolic pathways based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO) XX. Briefly, ASVs were placed into a reference tree containing 16S rRNA sequences from prokaryotic genomes, which is then used to infer gene family copy numbers from ASVs dataset. After, gene family abundance per sample and pathway abundances were determined. The analysis was focused on the detection of the presence and abundance of α1,3 galactosyltransferase genes and the bacterial taxa, from penguin microbiome, contributing to these genes. Specifically, we tested the presence and abundance of α1,3GT genes (i.e., K02450, K02847, K03275, K03279, K03276, and K03278) in the microbiomes of the selected penguin species. Sankey diagram was performed in R studio XX using the networkD3 package.

Statistical analyses

Statistical differences of parameters measured in penguins of each group were evaluated using paired and unpaired Student's t-test applied to individual paired and unpaired values, respectively. Pearson correlation was used to assess the correlation between anti-α-Gal and anti-E. coli Abs levels. All statistical analysis were performed in the GraphPad 9 Prism program (GraphPad Software Inc., San Diego, CA, USA). Differences were considered significant when p < 0.05.

Ethics

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Results

Specificity of anti-chicken and anti-turkey Abs for penguin Abs

Considering the lack of commercially available anti-penguin Abs to perform ELISA assays, we assessed the specificity of anti-chicken and anti-turkey Abs for penguin Abs. Western blot analysis showed that anti-chicken IgY recognizes bands of similar size in chicken and penguin IgY (Supplementary figure S1). In contrast, anti-turkey IgY recognized several bands in chicken and penguin IgY (Supplementary figure S1). An indirect ELISA was used to measure the levels of anti-α-Gal Abs using anti-chicken and anti-turkey IgY as secondary Abs. In agreement with a non-specific recognition of anti-turkey IgY for penguin Abs, the OD values using the anti-turkey IgY were higher than those using the anti-chicken IgY (data not shown). Based on the western blot and ELISA results, we decided to use anti-chicken secondary Abs in the indirect ELISA assays to measure the levels of penguin IgM and IgY.

Presence and variation of anti-α-Gal Ab in penguin sera and egg yolks

Two egg yolks and nine sera samples from healthy animals were included for testing the occurrence of natural anti-α-Gal Abs in penguins. Characteristics of penguins included in the study are listed in Table 1. The presence of total anti-α-Gal Abs was measured by competitive ELISA. Total anti-α-Gal Abs were detected in sera samples from healthy animals, and also in a penguin with a suspected natural Aspergillus fumigatus infection (Figure 2A). The levels of total anti-α-Gal Abs were higher in the serum of the penguin with suspected aspergillosis compared with that of the healthy animals (Figure 2A). The levels of penguin anti-α-Gal IgY was also variable in this group of healthy animals (Figure 2B). However, the penguin with suspected aspergillosis did not show the highest level of anti-α-Gal IgY. Total anti-α-Gal Abs (Figure 2C) and anti-α-Gal IgY (Figure 2D) were detected in two penguin eggs. Antibodies IgY specific to Escherichia coli and the pathogen A. fumigatus were also detected in the egg yolks (Figure 2D).

Prediction of α1,3-galactosyltransferase genes in penguin microbiota

More than twenty bacterial taxa harbor α1,3GT genes in the microbiome of the penguin species analyzed (Figure 3). However, the abundance of some of these taxa was low and thus contributed little to the abundance of α1,3GT genes in the microbiome. The taxa with the highest contribution (above 8%) represented 8 % of the total number of taxa with these genes. All the α1,3GT genes were present in P. papua while in E. chrysolophus, E. minor and A. patagonicus, only some genes were found (i.e., K02450, K02847, K03278, K03279) (Figure 3). Among the genes, the most frequently found were K02450 and K02847, especially in the Little penguin (Eudyptula minor) which is the closest of the four species analyzed to the Humboldt penguin from the vaccine trial, from a taxonomical approach. The taxa that most contributed to α1,3GT genes in these birds were the bacterial genera Gallicola, PeH15, Fusobacterium and the bacterial family Peptostreptococcales-Tissierellales. These results suggest a wide distribution of α1,3GT genes in penguin microbiota and we consider these results valid in S. humboldti by extension.

Oral administration of E. coli Nissle expressing α-Gal was associated with modulation of anti-α-Gal Abs levels in penguin sera

Two penguins from the vaccine group took two extra doses (penguins n°12 and n°20, Table 1), while another one missed the last dose due to difficulties in animal handling (penguin n°18, Table 1). These three individuals were kept into the vaccine group for statistical analyses.

However, one penguin (penguin n°24, Table 1) from the placebo group accidentally swallowed a dose of vaccine and was excluded from statistical analysis.

The α-Gal glycan was detected in protein extracts of E. coli Nissle and E. coli O86:B7 as assessed by flow cytometry (Table 2). This was not the case for E. coli 'clean', and E. coli BL21 strains, used as negative controls of α-Gal production (Table 2). The average fluorescein isothiocyanate (FITC) absorption values were higher for E. coli Nissle and E. coli O86:B7 compared to those of E. coli 'clean' and BL21 strains (Table 2).

Sera levels of total Abs against α-Gal were measured by competitive ELISA as M86 binding inhibition % in penguins that received E. coli Nissle or placebo orally. Three months (T3mo) after the oral vaccination (Post-V), the penguins that received E. coli Nissle showed a significant reduction (10.19 SD±12.05, 95 % IC 1.57-18.81) in total anti-α-Gal Abs compared with the levels before (Pre-V) the oral vaccination (20.20 SD±11.31,) (Student's ttest p = 0.03, Figure 5A). No differences were observed in the levels of anti-α-Gal IgM (Figure 5B) and IgY (Figure 5C) Post-V compared with the levels Pre-V. On the other hand, the animals in the placebo group showed no significant difference in the levels of total anti-α-Gal Abs (Figure 5D) and anti-α-Gal IgY (Figure 5F) before and after administering the placebo (Figure 5D), while in the same group of animals the levels of anti-α-Gal IgM were significantly lower Post-V (0.66 SD±0.49, 95 % IC 0.80-1.56) compared to Pre-V (2.22 SD±0.68, 95 % IC 1.69-2.74) (Student's t-test p = 0.003, Figure 5E). No significant differences were found in the levels of total anti-α-Gal (Figure 5G), or anti-α-Gal IgM (Figure 5H) and IgY (Figure 5I) Pre-V or Post-V in any group.

We then asked whether oral administration of E. coli Nissle elicits anti-E. coli Abs and, if yes, whether a correlation existed between anti-E. coli and anti-α-Gal Abs levels. No significant change was found in the levels of anti-E. coli IgM Pre-V and Post-V (Figure 5A), while anti-E.

coli IgY (Figure 5B) showed a significant increase in the penguins of the E. coli Nissle group.

However, the same pattern was found in the placebo group, no significant change in anti-E. coli IgM Pre-V and Post-V (Figure 5C) and a significant increase in anti-E. coli IgY (Figure 5D). The levels of anti-E. coli IgM (Figure 5E) and anti-E. coli IgY (Figure 5F) in the penguins of the E. coli Nissle group were not different to those of the placebo group. Positive correlations between the levels of anti-E. coli IgM and anti-α-Gal IgM Pre-V (Pearson coefficient r=0.46, p = 0.04, Figure 5G) and anti-E. coli IgY and anti-α-Gal IgY in the E. coli Nissle group Post-V (Pearson coefficient r=0.63, p = 0.04, Figure 5H) were found. There was no correlation between levels of anti-E. coli IgM or IgY and anti-α-Gal IgM or IgY in the placebo group Post-V or between anti-E. coli IgM and anti-α-Gal IgM in the E. coli Nissle group Post-V.

Oral administration of E. coli Nissle was not associated with changes in markers of Aspergillus infection in penguin plasma

Oral administration of E. coli Nissle was not associated with significant changes in the prealbumin, albumin, α1, α2, β1, β2, and γ-globulins concentrations and albumin/globulin (A/G) ratio compared with the placebo group (Figure 6). Except for prealbumin, the levels of the above plasma proteins did not change significantly Post-V in the E. coli Nissle or placebo groups compared with the levels Pre-V (Supplementary Figure S2). In the placebo group, the levels of prealbumin were significantly lower Post-V (3.36 SD±2.95, 95 % IC 1.09-5.64) compared with the levels Pre-V (6.56 SD±1.37, 95 % IC 5.50-7.62) (Student's t-test p = 0.01), but the level of this plasma protein did not change significantly in the E. coli Nissle group (Supplementary Figure S2).

The mean concentration of 3-OHB Post-V in penguins of the E. coli Nissle group was lower (540.3 SD±226.9 µmol/L) than that in the placebo group (610 SD±259.1 µmol/L), but the difference was not statistically significant (Student's t-test P > 0.05, Figure 7A). The levels of 3-OHB before and after the oral vaccination did not change significantly in the penguins of E. coli Nissle (Figure 7B) or placebo groups (Figure 7C). Individual values of 3-OHB per penguin are available as Supplementary Table S1.

The levels of IgM (Figure 7D) and IgY (Figure 7E) specific to A. fumigatus were not significantly different between groups (E. coli Nissle vs. placebo) Pre-V or Post-V. Oral administration of E. coli Nissle was not associated with significant changes in anti-A. fumigatus IgM levels before and after the treatment, while the levels of this immunoglobulin Pre-V (2.29 SD±0.45, 95 % IC 1.94-2.63) decreased significantly Post-V (1.29 SD±0.22, 95 % IC 1.12-1.46) in the penguins of the placebo group (Student's t-test p = 0.0001, Figure 7F). No significant changes were observed in the levels of anti-A. fumigatus IgY Pre-V and Post-V in the E. coli Nissle or placebo groups (Figure 7G).

Oral administration of E. coli Nissle was not associated with pulmonary lesions in penguins

No pulmonary lesion was observed in any of the penguins included in the study (Supplementary Table S2).

Morbidity and mortality comparison TO BE COMPLETED AT THE END OR DELETED

Discussion

Aspergillosis and diagnostic tools in penguins

Clinical presentation of aspergillosis varies from acute cases involving the lungs to chronic, disseminated infections of the air sacs in infected birds. Mycelial masses called aspergillomas may develop within the respiratory tract, as well as fungal plaques resulting in thickening of avian air sacs. Consequently, computed tomography (CT) is an interesting diagnostic modality for respiratory issues in penguins and was therefore included in the diagnostic panel for aspergillosis assessment in the study penguins. Physiological alterations in lung (density and volume) and air-sac (volume) readings have been associated with dorsal recumbency in Humboldt penguins undergoing CT, while no difference was seen in conscious vs. anesthetized individuals. Thus, decision was made to perform CT in non-anesthetized penguins in a standing positioning, as previously described. Despite increased motion artifact in some penguins, assessment of the whole respiratory tract and scoring of aspergillosis-related lesions were possible in all but two penguins (Supplementary Table S2). Dorsal recumbency and/or anesthesia were only used as a last resort in very few individuals (Table 1), and this technique is considered safe and reliable and is routinely used for assessment of the respiratory tract of penguins at ZooParc de Beauval.

Definitive diagnosis of aspergillosis relies on definitive fungal identification (by fungal culture or PCR) and typical morphological and histological lesions. Ante-mortem diagnosis remains difficult, and a multi-tools analysis is required. Plasma electrophoresis offers valuable data in infected penguins, especially the β globulin fraction containing the fibrinogen and 3-OHB levels. For these reasons, capillary zone electrophoresis assay and measurement of 3-OHB levels were added to the diagnostic panel in this study (Table 1). Despite being a sometimes useful assay in selected avian species such as psittacine birds, the s=galactomannan assay is not reliable in penguins and was not used in the study.

Three months after the beginning of the trial, vaccinated penguins showed lower plasmatic levels of 3-OHB compared to placebo penguins (Figure 7A, Supplementary Table S1), but no statistical difference was seen. Whether the lack of significance is due to insufficient statistical power and the oral vaccine is associated with lower progression of subclinical Aspergillus sp. infection as reflected by lower 3-OHB values would be overstated. Mean values were below previously reported values in healthy African penguins (Spheniscus demersus), a species closely related to the Humboldt penguin. For accurate aspergillosis assessment in penguins, plasma levels of 3-OHB should be interpreted together with -globulins and -2 globulins in order to have a high specificity (> 90%) and a good negative predictive value (≥ 80%). No significant difference was seen in these electrophoretic fractions between penguin groups (Figure 6, Supplementary Figure S2). These results suggest that none of the penguin had developed aspergillosis during the three months of the trial, which is consistent with CT scan readings throughout the study period. As a consequence, differences observed between penguin groups in Abs levels Post-V were only attributable to the oral vaccine compared to the placebo. However, a significant reduction of the pre-albumin fraction was observed on the electrophoretic profiles of non-vaccinated penguins after the vaccination trial (Supplemental Figure S2). This reduction was not seen in the vaccination group. This finding suggests that prealbumine concentrations decrease when globulin levels rise. The only relevant difference in Abs levels Pre-V vs Post-V was a significant decrease of anti--Gal Abs in vaccinated penguins and of anti--Gal IgM in non-vaccinated individuals (Figure 4). Consequently, a direct link between globulin levels and prealbumine concentrations seems highly unlikely in our study population and the exact clinico-pathological relevance of this finding remains unknown. Still, a link with non-humoral immunity pathways is hypothesized. Significantly lower prealbumin values have been reported in falcons with confirmed aspergillosis. In humans, a similar trend was seen in fatal cases of COVID-19. As a transport protein, the human prealbumin fraction (also so called transthyretin) has various physiological effects, including anti-inflammatory actions and a strongly hypothesized positive effect on the cell-mediated immunity, especially on immune cells from the myeloid compartment. Even if no statistical difference was seen in the prealbumin values between penguin groups Post-V (Figure 6A), the significant reduction of prealbumin levels Pre-V vs. Post-V in the non-vaccinated group (Supplementary Figure S2) may be indicative of a reduced immunity against Aspergillus sp., linked to a decreased cell-mediated immunity compared to vaccinated penguins. Whether the E. coli Nissle probiotic has a positive effect on myeloid immune cells in penguins warrants further research.

Possible association between the occurrence of natural anti-α-Gal Abs and the gut microbiome in penguins

ELISAs have been used to study the impact of the vaccine on the immune system of the penguins. The competitive ELISA test also appears useful as a diagnostic tool in penguins and could be implemented in the diagnostic panel for aspergillosis in penguins for further clinical assessment. The serological assay has been specifically developed for the study, but other assays already exist (XXX).

We found variable levels of total and IgY Abs specific to α-Gal in healthy penguins compared to one aspergillus-suspected penguin. Variation of anti-α-Gal levels in human sera have been found in healthy individuals (XXXX), and it has also been linked to infection by pathogens such as Trypanosoma cruzi expressing α-Gal on their surface (XXX). Sera of patients with chronic Chagas disease caused by T. cruzi contain elevated levels of anti-α-Gal Abs that were lytic to this trypanosome species (XXXX). Based on these findings, a prophylactic α-Galbased glycovaccine was formulated and shown to effectively protect against murine acute Chagas disease (XXXX). The presence of α-Gal on the surface of A. fumigatus (XXXX) may explain variations of anti-α-Gal Abs in A. fumigatus-infected birds. Differences in anti-α-Gal Abs may also be associated with individual bacterial composition in the gut microbiome, which have been linked with the production of these natural Abs in humans (XXX), α1,3GT-deficient mice (XXXX), and fish (XXXX). Production of anti-α-Gal Abs in humans is thought to be driven by exposure to microbiota bacteria of the Klebsiella spp., Serratia spp., and E. coli expressing α-Gal (XXX). In agreement with this hypothesis, α1,3-GT genes were broadly distributed in several bacterial species of the families Enterobacteriaceae and Pasteurellaceae as well as Haemophilus and Lactobacillus genera present in the human gut microbiome (XXX). In this study, some of these bacterial taxa were found to contribute to the pool of α1,3-GT genes in the microbiome of the penguin E. chrysolophus, E. minor, P. papua and A. patagonicus. Due to the close phylogenetic relationship of the genera Eudyptula and Spheniscus (XXXX), we propose that α1,3-GT genes are also broadly distributed in the S. humboldti microbiome, although this remains to be experimentally tested. Individual differences in the gut microbiome of S. humboldti could explain the presence of anti-α-Gal Abs in healthy penguins. The positive correlation between the levels of anti-α-Gal and anti-E. coli IgM before the treatment further suggests a role of penguin gut microbiota in the production anti-α-Gal Abs.

It has been suggested that anti-Aspergillus Abs may be transmitted from females to penguin chicks, even if the role of these Abs in the immune defense against Aspergillus sp. infection remains unclear. Interestingly, we found anti-α-Gal Abs in penguin egg yolks. Our results show the presence and variability of anti-α-Gal Abs in eggs from two individual penguins, like it was reported in Leghorn chicken (XXXX). Moreover, a functional role for these Abs has been related to their binding potential to α-Gal antigens in mammal tissues. Particularly, chicken anti-α-Gal IgY block human anti-α-Gal Abs binding to xenograft endothelial cells and consequently block human blood complement-and antibody-dependent cell-mediated lysis mechanisms that are responsible of hyperacute rejections in xenografts (XXXX). In addition to Abs specific to α-Gal, we also found IgY specific to E. coli and A. fumigatus in penguin eggs.

Maternal IgY transported to the embryonic circulation have been suggested to participate in bird immunity (XXX). Difference in anti-α-Gal Abs in egg yolks may be associated with variations in the gut microbiota and anti-α-Gal Abs levels of mother penguins. Whether anti-α-Gal Abs transmitted from the mother to the eggs and offspring can protect against infectious diseases remains an open question.

The role of microbiota in the induction of anti-α-Gal has been experimentally tested, and gut colonization by E. coli O86:B7 elicited anti-α-Gal Abs in α1,3GT-deficient mice (XXX), humans (XXXX), primates (XXXX), white Leghorn chicks (XXXX) and turkeys (XXX). Here we showed that E. coli Nissle produce α-Gal levels similar to those found in E. coli O86:B7 and higher than those found in α-Gal-negative bacteria, as measured by flow cytometry. In our previous study, we found that oral administration of E. coli O86:B7 elicited an IgY response to the α-Gal disaccharide (Galα1-3Gal), but decreased the IgY levels to the α-Gal trisaccharide (Galα1-3Galβ1-4GlcNAc) in turkeys (XXX). However, in the present study, oral administration of E. coli Nissle was not associated with significant changes in the levels of IgY specific to the α-Gal disaccharide (Figure 5C), but with a significant decrease in the levels of total anti-α-Gal Abs in penguins of the E. coli Nissle group (Figure 5A). Notably, oral administration of E. coli Nissle prevented a sharp decrease in anti-α-Gal IgM observed in the placebo group (Figure 5E).

Possible explanations for this are the difference in species (turkeys vs. penguins), the bacterial dose (three administrations per week for three weeks in turkeys (XXX) vs. two administrations per week for three weeks in penguins), or a differential cellular location of the α-Gal epitope in E. coli Nissle and E. coli O86:B7. The α-Gal epitope of E. coli O86:B7 is expressed on the capsule or glycoprotein portion of the bacterial wall, rather than on the LPS molecules (XXX).

Other E. coli strains were found to express the glycan in the LPS molecules (XXX). These findings suggest that the α-Gal epitope can be located in the LPS, capsule or glycoprotein portion of E. coli. The cellular fraction in which E. coli Nissle express α-Gal is currently unknown. The results of the present study suggest that E. coli Nissle express α-Gal in a location that is not accessible to the immune system to induce a significant increase of anti-α-Gal Abs. However, the positive correlation between the levels of anti-α-Gal and anti-E. coli IgY in the E. coli Nissle group suggests that the immune response to α-Gal is associated to that triggered by E. coli.

Conclusion

E. coli Nissle oral probiotic is safe in the Humboldt penguin. The immune response to α-Gal in the Humboldt penguin is closely related to that of E. coli. The oral vaccine tested failed to produce a significant increase in anti-α-Gal Abs as we suspect E. coli Nissle to express α-Gal in a location that is not accessible to the penguin's immune system. Nevertheless, a positive effect on the cell-mediated immunity of vaccinated penguins may be hypothesized, as suggested by the significant decrease of the pre-albumin fraction in the placebo group only. Further study is needed to assess a positive effect of E. coli Nissle oral probiotic on myeloid immune cells in the Humbold penguin. Sankey diagram showing the presence of α1,3GT genes (i.e., K02450, K02847, K03275, K03279, K03276, K03278) in the microbiome of various penguin species, as well as the bacterial genera harboring these genes in each host. The presence and the contribution of taxa to α1,3GT gens were predicted from 16S rRNA data using PICRUSt2 and the α1,3GT genes were annotated using the KEGG orthologs (KO) database as reference. Nodes represent penguin species (left column), functional genes (middle column) and bacterial taxa (right column). The edges represent the connection between individual host, α1,3GT genes and contributing taxon. The

Figure legends Figure 1 .

 legends1 Figure legends

Figure 2 .

 2 Figure 2. Variation of anti-α-Gal Abs in penguin sera and eggs. The levels of circulating total anti-α-Gal Ab (A) and anti-α-Gal IgY (B) were measured by inhibition and indirect ELISA, respectively. Total and IgY anti-α-Gal Abs were variable in the sera of healthy penguins (white bars, ID 1-9). A serum sample of a penguin with suspicion of aspergillosis (pulmonary granuloma seen on CT scan) was included in the analysis (red bar, ID 10). Total anti-α-Gal Ab (C) and anti-α-Gal IgY (D) were measured by inhibition and indirect ELISA, respectively. Egg anti-α-Gal IgY specific to E. coli and A. fumigatus proteins were measured by indirect ELISA. M86 binding inhibition % (A and C) and OD (C and D) means and standard deviation values of

Figure 3 .

 3 Figure 3. Contribution of bacterial taxa to α1,3GT genes in the microbiome of penguins.

  contribution of each taxon to different α1,3GT genes is represented proportionally by the size of the edges. K02450: gspA, general secretion pathway protein A; K02847: waaL, rfaL, O-antigen ligase [EC:2.4.1.-]; K03275: waaO, rfaI, UDP-glucose:(glucosyl)LPS alpha-1,3glucosyltransferase [EC:2.4.1.-]; K03279: waaJ, rfaJ, UDP-glucose:(galactosyl)LPS alpha-1,2glucosyltransferase [EC:2.4.1.58]; K03276: waaR, waaT, rfaJ, UDP-glucose/galactose:(glucosyl)LPS alpha-1,2-glucosyl/galactosyltransferase [EC:2.4.1.-]; K03278: waaI, rfaI, UDP-D-galactose:(glucosyl)LPS alpha-1,3-D-galactosyltransferase [EC:2.4.1.44].

Figure 4 .

 4 Figure 4. Variation in penguin anti-α-Gal Ab levels after oral administration of E. coli Nissle. The levels of circulating total anti-α-Gal Ab (A) and anti-α-Gal IgM (B) and IgY (C) were measured by inhibition (A, D and G) and indirect (B-C, E-F and H-I) ELISA in the E. coli Nissle (red dots) and placebo (black dots) groups before (Pre-V) and after (Post-V) the vaccination protocol. Total anti-α-Gal Abs decreased in the sera of penguins vaccinated with oral E. coli Nissle (A). Anti-α-Gal IgM decreased in the sera of penguins of the placebo group (E). No significant differences were observed in the levels of anti-α-Gal Ab (G), anti-α-Gal IgM (H) and IgY (I) when the two groups, E. coli Nissle and placebo, were compared Pre-V and Post-V. Individual (A-I) and means and standard deviation (G-I) values are shown. Values were compared by paired (A-F) or unpaired (G-I) student's t-test (* p < 0.05, ** p < 0.005; ns: not significant, 1 experiment, n = 10 in the E. coli Nissle group and n = 9 in the placebo group, and three technical replicates per sample).

Figure 5 .

 5 Figure 5. Levels of anti-E. coli Abs after oral administration of E. coli Nissle and their association with anti-α-Gal Ab level. The levels of anti-E. coli IgM (A, C and E) and IgY (B, D and F) were measured in penguin sera by indirect ELISA in animals of the E. coli Nissle (red dots) and placebo (black dots) groups before (Pre-V) and after (Post-V) the vaccination protocol. No significant differences were observed in the levels of anti-E. coli IgM within (A and C) and between (E) groups. Significant differences were observed in the levels of anti-E. coli IgM within (A and C), but not between (E) groups. Positive correlations between the levels of anti-E. coli IgM and anti-α-Gal IgM Pre-V (Pearson coefficient r=0.46, p = 0.04) (G), and anti-E. coli IgY and anti-α-Gal IgY in the E. coli Nissle group Post-V (Pearson coefficient r=0.63, p = 0.04) (H) were found. Individual (A-H) and means and standard deviation (E and F) values are shown. Values were compared by paired (A-D) or unpaired (E and F) student's t-test (** p < 0.005; ns: not significant, 1 experiment, n = 10 in the E. coli Nissle group and n = 9 in the placebo group,

Figure 6 .

 6 Figure 6. Levels of plasma proteins after oral administration of E. coli Nissle. The percentage of prealbumin (A), albumin (B), α1 (C), α2 (D), β1 (E), β2 (F), and γ (G) -globulins concentrations and the albumin/globulin (H) ratio of animals in the E. coli Nissle (red dots) and placebo (black dots) groups Post-V are shown. Individual, means and standard deviation values are shown. Values were compared by unpaired student's t-test (ns: not significant, 1 experiment, n = 9 in the E. coli Nissle group and n = 8 in the placebo group).

Figure 7 .

 7 Figure 7. 3-hydroxybutyrate plasma levels and anti-A. fumigatus Abs after oral administration of E. coli Nissle. The concentration of 3-hydroxybutyrate (µmol/L) in the E. coli Nissle (red dots) and placebo (black dots) groups were compared between groups (A) and within E. coli Nissle (B) or placebo (C) groups before (Pre-V) and after (Post-V) the vaccination protocol. Anti-A. fumigatus IgM (D and F) and IgY (E and G) levels in penguin sera were measured by indirect ELISA and compared between (D and E) and within (F and G) groups. Individual (A-G) and means and standard deviation (A, D and E) values are shown. Values were compared by paired (B, C, F and G) or unpaired (A, D and E) student's t-test (** p < 0.005; ns: not significant, 1 experiment, n = 10 in the E. coli Nissle group and n = 9 in the placebo group, and three technical replicates per sample in the ELISA).

Table S2 . CT scan scorings of study penguins.

 S2

	Tables
								3OHB (µmol/L)
		Group	Penguin n°	ID	Sex	« T-1mo »	« T1mo »	« T3mo »
				11	MIG12-16898112	F	539		617
				12	19152643	F	268		594
				13	QRD14-03650	F	302		688
				14	QRD14-03648	M	844		590
				15	22197091	M	359		332
	Vaccine group	16	14923345	M	417		554
				17	QRD16-01361	F	775		517
				18	QRD18-04258	M	424		511
				19	QRD17-03052	M	616		468
				20	24773872	F	1029		809
				21	27535275	F	435		278
				22	MIG12-20638625	M	382		584
				23	18806226	F	363		224
				24*	19117651	M	536		208
				25	15226273	F	502		603
	Placebo group	26	QRD16-00621	M	795		774
				27	18806231	F	685		454
				28	24773875	F	405		940
				29	QRD17-02318	M	473		391
				30	QRD15-04789	M	747		600
	663
	664
					Right	Left	Air sac	Air sac	Trachea	Trachea
					lung	lung	nodules	plaques	nodules	plaques
					nodules	nodules
			Penguin	CT exam			Yes = 1 ; No = 0
			n°	n° (date)
			1	23 (17/12/21)	0	0	0	0	0	0
	E. coli	Nissle	group	24 (31/03/21°	0	0	0	0	0	0

* Penguin n°24 accidentally swallowed a dose of vaccine and was removed from statistical analyses 662 Supplementary

Table 1 . Animals included in the preliminary study, vaccination protocol, CT scans, and blood samplings.

 1 dorsal recumbency was needed, as excited penguins refused to stay straight in the PVC tube when standing © flash isoflurane anesthesia (facemask) was performed, as these penguins were too fat to be placed appropriately into the PVC tube

					Date of
	Group	Penguin n°	ID Sex	sampling (frozen				Clinical findings			Suspicion of aspergillosis
					serum)
		1	QRD16-00616	F 16/10/2017		Suspicion of ocular aspergillosis on 29/05/2016		Mild
		2	QRD16-01010	M 16/10/2017		Suspicion of aspergillosis on 10/09/2016		Mild
		3	QRD15-05130	F 16/10/2017				-				Weak
	Prelimary	4	QRD16-00603	F 16/10/2017		Suspicion of ocular aspergillosis on 18/07/2017		Mild
	study:	5	QRD16-00994	F 16/10/2017				-				Weak
	test of the competitive	6	QRD15-05124	F 16/10/2017				-				Weak
	ELISA assay	7	QRD16-01002	M 16/10/2017				-				Weak
		8	QRD16-00615	M 16/10/2017				-				Weak
		9	MIG12-17845737 M 15/08/2020		Mild, recurrent bilateral corneal opacification		Mild
		10	18094427 F 27/10/2020	Suspicion of pulmonary aspergillosis (granuloma seen on the CT Scan)	Strong
					Restraint sessions			Vaccine sessions			Restraint sessions
					1 st	1 st	2 nd	3 rd	4 th	5 th	6 th	2 nd	3 rd
					Blood							Blood	Blood
	Group	Penguin n°	ID Sex	samplings #1 + CT #1	Monday 21/12/20	Wednesday 23/12/20	Monday 28/12/20	Wednesday 30/12/20	Monday 04/01/21	Wednesday 06/01/21	samplings #2	samplings #3 + CT#2
					« T-1mo »	PO	PO	PO	PO	PO	PO	« T1mo »	« T3mo »
		11	MIG12-16898112 F 17/12/2020	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
		12	19152643 F 17/12/2020	ok	2 doses	ok	ok	ok	ok	14/01/2021	31/03/2020
		13	QRD14-03650	F 17/12/2020	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
		14	QRD14-03648	M 16/12/2020*	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
	Vaccine	15	22197091 M 16/12/2020*	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
	group	16	14923345 M 16/12/2020* ©	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
		17	QRD16-01361	F 17/12/2020	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020
		18	QRD18-04258	M 17/12/2020	ok	ok	ok	ok	ok	no	15/01/2021	31/03/2020
		19	QRD17-03052	M 17/12/2020	ok	ok	ok	ok	ok	ok	20/01/2021 31/03/2020* ©
		20	24773872 F 16/12/2020	ok	ok	ok	2 doses	ok	ok	14/01/2021	31/03/2020
	Placebo	21	27535275 F 16/12/2020	ok	ok	ok	ok	ok	ok	15/01/2021	31/03/2020
	group	22	MIG12-20638625 M 16/12/2020*	ok	ok	ok	ok	ok	ok	14/01/2021	31/03/2020

*

Table 2 . Presence of α-Gal in E. coli Nissle measured by flow cytometry.

 2

	E. coli strains	FITC (average)	FITC (SD)	Commentary	Reference
		1536	9417	Test strain
	E. coli Nissle	143	130	Negative control for	N/A**
				E. coli Nissle*
		634	5374	Positive control of
	E. coli O86:B7	123	17	α-Gal production Negative control for	REF
				E. coli O86:B7*
		218	839	Negative control for
				α-Gal production.
	E. coli clean			This strain lacks LPS.	REF
		200	581	Negative control for
				E. coli clean*
		128	47	Negative control for
				α-Gal production.
	E. coli BL21			This strain produces low levels of α-Gal.	REF
		124	38	Negative control for
				E. coli BL21*
	* FITC background value resulting from the secondary antibody alone.

** Not applicable

Supplementary materials

660 Supplementary Table S1. 3-hydroxybutyrate plasma levels in study penguins.


Meriem Mhedhbi

Mira Morcos

Ana Galindo-Serrano

Salah Eddine Elayoubi

Performance Evaluation of 5G Radio Configurations for Industry 4.0

Keywords: Fifth-Generation (5G), URLLC, Industry 4.0, System level simulations

The future Radio Access Network (RAN) is being engineered to accommodate the emergence of Ultra Reliable Low Latency Communications (URLLC) and to handle the possible coexistence between URLLC and enhanced Mobile Broad Band (eMBB) services. To achieve this, new features are being implemented in the future RAN to fulfill the strict requirements of the URLLC traffic in terms of latency and reliability. In this paper, we exploit the radio frame features in the New Radio (NR) and evaluate the users' performances in Downlink (DL) scheme. We conduct extensive system level simulations in realistic network deployment considering the Industry 4.0 use case. We study the impact of 1) different radio configurations and 2) the dedication of a reserved bandwidth to the critical traffic, on the URLLC achieved user plane latency and packet loss probability. The coexistence of URLLC and eMBB traffic is also evaluated by measuring the eMBB users' throughput.

I. INTRODUCTION

The Fifth-Generation (5G) of mobile network [START_REF] Elayoubi | 5G white paper[END_REF] is designed to handle the traffic of diverse devices with heterogeneous requirements in terms of Quality of Service (QoS). In particular, the accommodation of mission critical services on top of enhanced Mobile Broad Band (eMBB) services imposes additional constraints on the design of the Ultra Reliable Low Latency Communications (URLLC) services that are extremely demanding in terms of latency and reliability.

Among the URLLC services, industry automation is a key component to smart manufacturing. This use case has been identified in the 3rd Generation Partnership Project (3GPP) and requires an end-to-end latency lower than 1 ms and a Block Error Rate (BLER) target of 10 -5 [START_REF]1.0.0 Study on Communication for Automation in Vertical Domains[END_REF]. The shift to wireless technologies will help factories increase the production efficiency and also reduce the costs, compared to traditional cabled setups, and by that ease the automation process [START_REF] Chen | Ultra-reliable low latency cellular networks: Use cases, challenges and approaches[END_REF], [START_REF] Gangakhedkar | Use cases, requirements and challenges of 5G communication for industrial automation[END_REF].

Several studies proved that the latency required by URLLC systems could be achieved by optimizing both the core and radio networks [START_REF] Popovski | Ultrareliable low-latency communication (URLLC): principles and building blocks[END_REF]. In particular, at the radio level, as the main contributors to latency are the Physical (PHY) and Medium Access Control (MAC) layers [START_REF] Holfeld | Wireless communication for factory automation: An opportunity for LTE and 5G systems[END_REF], multiple solutions are being proposed to reduce the latency induced at these levels and help achieve the very low latency and high reliability required by URLLC. To mention some of these features we find minislot, DL preemption, flexibility in numerology and also the flexible usage of Frequency Division Duplexing (FDD) and Time Division Duplixing (TDD).

The studies in [START_REF] Li | 5G ultra-reliable and low-latency systems design[END_REF], [START_REF] Pocovi | MAC layer enhancements for ultra-reliable low-latency communications in cellular networks[END_REF] show that shortening both Transmission Time Interval (TTI) and the required time for retransmitting the packet, is suitable to achieve hard URLLC latency requirements. The impact of the DL preemption technique on the user plane latency and its coexistence with eMBB are evaluated in [START_REF] Pedersen | Preemptive scheduling of latency critical traffic and its impact on mobile broadband performance[END_REF], [START_REF] Esswie | Opportunistic spatial preemptive scheduling for URLLC and eMBB coexistence in multi-user 5G networks[END_REF]. The authors in [START_REF] Anand | Resource allocation and HARQ optimization for URLLC traffic in 5G wireless networks[END_REF] focus on optimizing the Hybrid Automatic Repeat Request schemes (HARQ) to achieve a target latency. It has been shown that the saved time for the Grant Request (GR) could be useful for more HARQ retransmissions and thus enhancing both transmission latency and reliability [START_REF] Jacobsen | System level analysis of uplink grant-free transmission for URLLC[END_REF].

In addition to the above-mentioned general scope URLLC works, few papers discussed the factory automation use case. In [START_REF] Han | Periodic Radio Resource Allocation to Meet Latency and Reliability Requirements in 5G Networks[END_REF], a joint resource allocation and Modulation Coding Scheme (MCS) was analyzed while considering reliability and latency constraints. The authors in [START_REF] Ashraf | From radio design to system evaluations for ultrareliable and low-latency communication[END_REF] evaluated the performance at system level for the industrial scenario by considering different waveforms for mixed numerologies. The results show that with the adequate 5G radio configuration, the required low-latency and high reliability for industrial applications can be achieved. Network slicing is presented in [START_REF] Kalør | Network slicing for ultra-reliable low latency communication in industry 4.0 scenarios[END_REF] to handle the various types of industrial communications and their heterogeneous requirements in terms of reliability and latency.

In this paper, we conduct and analyze system level simulations for the industry automation use case. We specifically evaluate the DL user plane latency achieved by exploiting different radio configurations at the PHY and MAC layers. We build on the system level simulator described in [START_REF] Maaz | URLLC User Plane Latency Performance in New Radio in Industry 4.0 Scenarios[END_REF] and focus on the industry automation use case by considering an optimized and more realistic network configuration.

The contributions of this paper are articulated as follows:

• We quantify the achievable latency for the industrial network scenario, based on realistic and complete system level simulations. • We identify NR configurations that are more suitable for Industry 4.0 applications. • We investigate the impact of dedicating a reserved bandwidth to URLLC services. • We study the coexistence of the URLLC and eMBB traffic and evaluate the impact of the dedicated bandwidth on the eMBB users' throughput.

This paper is organized as follows: Section II introduces the system model we based our study on. Section III describes the scenario we evaluated and discusses the results obtained from simulations: achievable latency and packet loss probability for URLLC users' and eMBB users' throughput. Finally Section IV draws useful conclusions and recommendations for designing URLLC systems.

II. SYSTEM MODEL

In this section, we emphasize on the different NR features related to URLLC traffic implemented in our system level simulator. We also present the delay model used to estimate the user plane latency and describe the resource allocation scheme we adopted to schedule URLLC and eMBB users.

A. NR Configurations

As Long Term Evolution (LTE), NR adopts the Orthogonal Frequency Division Multiplexing (OFDM) scheme. Each TTI occupies N OF DM symbols and the transmission bandwidth B is divided into N RB Resource Blocks (RBs). The number of Resource Elements (REs) per RB is N RE = 12. However, unlike LTE legacy where, N OF DM = 14 symbols and the Subcarrier spacing (SCS) ∆f = 15 kHz, NR offers more flexible features to satisfy the URLLC users' requirements in terms of latency. NR enables:

• Flexible numerology: where different SCSs are adopted to satisfy the latency requirements of URLLC. These SCSs are obtained from scaling up the LTE-base SCS ∆f = 15 kHz by 2 µ , where (µ = 1, 2, . . . ). The TTI duration is scaled down by 2 µ , enabling faster transmission and lower processing time [START_REF] Holfeld | Wireless communication for factory automation: An opportunity for LTE and 5G systems[END_REF]. Table I presents the supported SCS and the associated TTI duration. • Mini-slots: where N OF DM can be of 2, 4 or 7, enabling more flexible and faster scheduling opportunities. In addition to the different numerologies, NR allows different radio configurations for the 5G frequency bands. Several frequency bands will be used by operators all around the world: low range bands called FR1 which comprehend both FDD and TDD bands and high range bands called FR2 composed of centimeter and millimeter waves bands, using TDD. We will call the association of a frequency band, a numerology and a duplexing mode a "NR configuration". The configurations supported in the standard are presented in Table I. Note that results in Section III consider only 8 possible configurations. The main objective of this paper is to assess the suitability of these configurations for industry automation scenarios.

Table I: NR configurations

B. Traffic model and resource allocation

The URLLC traffic is modeled as short size packets of L information bits, arriving to the gNodeB (gNB) buffer according to a periodic deterministic traffic model as advocated by the Release 16 of the NR specifications [START_REF]Study on physical layer enhancements for NR ultra-reliable and low latency case (URLLC)[END_REF] for the industry 4.0 use case. URLLC users share resources with eMBB users which we consider to have a full buffer type of traffic. This means that eMBB users have unlimited packets to transmit.

In order to satisfy requirements of both traffic types, we adopt a scheduling algorithm that can perform bandwidth sharing while satisfying the stringent requirements of URLLC users in terms of latency and also guaranteeing a minimum quality of service for eMBB traffic. This is achieved by according a priority to the URLLC traffic by scheduling the URLLC users first with a minimum bandwidth reservation for eMBB users. The scheduling algorithm runs as follows:

• A fraction of the bandwidth, that is equal to αB, is allocated to URLLC users in a cyclic way with respect to their packets arrival time in the gNB buffer. • The same allocation policy is applied to eMBB users in the rest of bandwidth equal to (1 -α)B, at minimum, i.e., RBs in αB not allocated to URLLC users, can be allocated to eMBB users. Figure 1 illustrates the bandwidth sharing strategy we implement in our system level simulator. The resource allocation is performed as follows:

• The scheduler manages a queue where generated packets are stored until they are correctly delivered. • At each time slot, the scheduler checks if there are URLLC packets awaiting service in the buffer. If so, they are allocated resources starting from the oldest packet until all URLLC packets are served or the maximum reserved resources αB is attained. eMBB packets are then served on the remaining resources following a round robin discipline. • When serving a packet, the number of required RBs is determined for each MCS satisfying the target BLER, according to the modulation order, the coding rate, the RB size and the packet size. If several MCS satisfy these conditions, we select the MCS that offers the best spectral efficiency. • In case of no MCS achieving the target BLER requirement with the available RBs, which means the channel quality is bad or the amount of available RBs is low, the packet is delayed until the channel quality becomes better or more resources are available in an upcoming TTI. Note that, for implementing this scheduling scheme in our system level simulator, we use lookup tables providing for each MCS the BLER versus Signal to Interference Noise Ratio (SINR) in an Additive White Gaussian Noise (AWGN) Channel. These link level curves are obtained using link level simulations and are able to feed system level simulator. Classical 4G-like turbo coding is implemented for eMBB, while enhanced turbo coding is used for URLLC for allowing very low BLER [START_REF]contribution R1-167414[END_REF].

Packet

C. Latency model

When evaluating the delay, we focus particularly on the PHY and MAC layers user plane latency which is the contribution of the radio network to the time from when the source sends a packet to when the destination receives and decodes it correctly. It is defined as the one way duration it takes to successfully deliver an application layer packet, assuming the mobile station is in the active state.

Let us denote by ∆ U E and ∆ gN B , the processing delays by the gNB and User Equipment (UE), respectively, for transmitting and processing a packet. At UE side the processing delay is given in terms of OFDM symbols and depends on the SCS. Table II gives the processing delays for UE category 1 [START_REF]UP Latency in NR[END_REF], used in our simulations. The 3GPP standard plans to include a second category of advanced UEs with shorter processing delays in Release 16. Regarding the gNB side the processing delay is assumed to be 1 TTI. Figure 2 depicts the delay model and presents the four component of one way user plane latency T radio which is given by the following equation:

T radio = T T x + T Alg + T OT + T Rx (1)
where, T T x represents the transmitter processing time which in our case is equal to ∆ gN B . T Alg is the frame alignment delay and represents the required time a packet, ready to be transmitted, should wait until the transmission can start. T OT corresponds to the over air transmission time and is exactly the TTI duration. Finally T Rx is the receiver processing time and corresponds here to ∆ U E . In case of HARQ retransmission and additional T HARQ delay should be included.

III. SIMULATION RESULTS

In this section, we start by describing the simulation setup and the radio frame configurations we aim to evaluate. Then, we present the simulation results in terms of URLLC users' latency and packet loss probability and eMBB users' throughput.

A. Simulated scenario: Industry 4.0

We consider the scenario of a typical factory including a set of wirelessly connected robots. Each robot is equipped with an URLLC transceiver that communicates with a central controller via a set of gNBs deployed within the factory. For simulation purposes we consider, as presented in Figure 3, a service area of 160 m × 160 m and 280 robots distributed in 5 production lines, as given in 3GPP scenario [START_REF] Ashraf | Ultra-reliable and low-latency communication for wireless factory automation: From lte to 5g[END_REF]. In addition, 5 eMBB users are randomly located within the service area in order to measure the impact of URLLC traffic over the eMBB ones.

We consider the deployment of 2 tri-sector gNBs with 2 antennas per sector placed on the ceiling at 10 m high. We adopt the pathloss and shadowing models of the 3GPP indoor office, presented in [START_REF][END_REF]. We also take into account the blockage effect induced by the movement of metallic obstacles between robots' transceivers and the gNBs. This effect is introduces by metallic blockers that change their position at every TTI. packets. The URLLC users packet size, L, is 96 bits and the BLER target 10 -5 . Please notice that in our simulations the URLLC traffic has a full priority with α = 1, which means than no eMBB packet can be transmitted unless all URLLC packets in the buffer are scheduled. Regarding the eMBB traffic, we consider full buffer where the packet size depends on the number of users and available RBs. The target BLER for eMBB is 10 -1 .

The NR configurations, given in Table III, are evaluated through system level simulations in order to compare their performances in DL scheme in terms of latency, packet loss and throughput. While the 3GPP traffic scenario of 280 robots is simulated for all frequency bands, a low traffic scenario is also considered for the 700 MHz band as it is a resource-limited band.

B. Performance evaluation 1) Latency results:

The set of results given by Figures 4, 5 and 6, present the percentage of successfully URLLC decoded packets as a function of the user plane latency for the evaluated configurations. The latency axis is divided into sub-intervals, where we present the percentage of correctly received packets in each sub-interval.

Figure 4, top, presents the results achieved at 700 MHz corresponding to configurations 1 and 2 in Table III, considering 280 URLLC users. We notice that the percentage of decoded packets at short delays, i.e., below 1 ms, is lower than 10% per sub-interval. Besides, the decaying behaviour of the latency distribution above 1 ms, suggests a channel congestion i.e., at each TTI a very limited number of packets, compared to the total number of generated packets, is transmitted.

Based on these observations, we reduced the number of URLLC users to 20, where we considered 2 robots per production line. The results are presented in Figure 4, bottom, for configurations 3 and 4 presented in Table III. We can observe that the user plane latency distribution presents a completely different behavior and 100% of the received packets delay is always below 1 ms, for both 2 and 4 OFDM symbols per minislot. Please notice that, at this frequency, the bandwidth is 10 MHz and the available number of RBs is limited. Thus, the FDD 700 MHz band can guarantee low latency but only for a limited number of UEs.

At 3.5 GHz, we face a different behavior in the latency distribution due to the TDD frame structure, which highly impacts the transmission delays. We assume the frame configuration at 3.5 GHz band to be "DDDSU", i.e., three downlink subframes, one special uplink sub-frame and one uplink sub-frame.

In fact, the frame structure has a direct impact on the alignment delay which varies, in this case, from 0 to 3 TTIs. Thus, the distribution spread out is explained by the waiting time for the next DL sub-frame.

Regarding the latency, in case of a stringent latency requirements, we can resort to configurations 5 and 6 to decrease the transmission delays, reaching almost 1 ms. The latency achieved at 3.5 GHz shows that configuration 7 is suitable for use cases with less stringent latency requirement, since the delay goes up to 1.5 ms.

Regarding the channel capacity, configurations 5, 6 and 7 are able to handle the 280 robots. The choice of the configuration at 3.5 GHz will be then related to the latency requirement, i.e., configuration 7 is suitable only for a slightly relaxed latency constraint and if the 1 ms constraint is strict, configurations 5 and 6 are recommended. In order to achieve both capacity and latency objectives, the millimeter waves appears to be an interesting option. They offers larger bandwidth (from 100 MHz up to 200 MHz) and higher SCSs (60 and 120 kHz) which means shorter TTIs. Figure 6 presents the latency results for configurations 8 and 9 in Table III and shows that for both cases, 100% of the packets delays are less than 1 ms. Furthermore configuration 9 is able to achieve a delay lower than 0.5 ms. 2) Packet loss results: The packet loss probability is one of the crucial criterion to take into account in URLLC use cases. In this study, in order to estimate it, we consider three types of packet loss possibilities:

• Retransmission drop: occurring when the number of retransmission of a given packet exceeds a fixed threshold.

In our simulations, for URLLC traffic, we set the retransmission threshold to 3. • Latency drop: occurring when a packet transmission delay is higher than the predefined latency budget. The presented results are estimated for latency budget of 1 ms. • Buffer drop: occurring when the waiting time of a given packet in the buffer exceeds the latency budget. Thus, we define the packet loss probability as the ratio between generated packets and dropped packets.

Figure 7 presents the packet loss probability for the different simulated configurations. It exhibits the average packet loss probability over the URLLC users and the maximum users' packet loss.

The results show a high packet loss probability average around 0.1 and 0.5 for configurations 1 and 2, respectively, which is due to the latency drop, as the delay distributions in Figure 4 show. The problem originates from the limited number of RBs, leading to a high waiting time for the packets to be scheduled and transmitted. The packet loss probability is more reasonable for configuration 3 and 4 being of 4 × 10 -5 and 1.4×10 -4 , respectively. For configurations 1 and 3 mini-slot is of 2 OFDM symbols and all the packets are lost due to latency drop. However when using mini-slot of 4 OFDM symbols, which is the case of configurations 2 and 4, some packets are also dropped in the buffer. In this case, the TTI duration is longer and thus the waiting time of a packet to be transmitted is longer. The packet loss probability results at 3.5 GHz are similar to 700 MHz, where the packets are lost due to the stringent latency budget. By increasing the number of OFDM symbols per time slot, we lengthen the TTI duration. Thus, the transmission delay increases and the number of packets lost due the strict latency budget increases too. The average packet loss probability is 2.8 -4 , 0.006 and 0.6 for configuration 5, 6 and 7 respectively.

For configuration 8 the packet loss probability is 4 × 10 -4 and decreases to be quasi null, when we increase the SCS to 120 kHz, i.e configuration 9, where we dispose of enough resources and very short TTI duration. So, all generated packets are transmitted and correctly received within the latency budget.

3) Throughput results: The main objective of this study is to evaluate the performance relative to URLLC users in terms of the latency and reliability. In our simulations, URLLC traffic is given the highest priority, by fixing α = 1. However, it is of paramount importance of ensuring an eMBB traffic. To do this we introduced 5 eMBB users, with full buffer traffic model.

Figure 8 represents the average throughput of eMBB users for configurations 5 to 9. Please notice that results at 700 MHz are not presented, because we do not have enough resources to serve both URLLC and eMBB traffic. For the rest of configurations, results show that the average throughput can vary from 40 to 75 Mbits/s and thus, the eMBB traffic can be handled with a satisfying quality of service. Figure 8 presents also, in red line, the standard deviation of eMBB users throughput. It shows that the throughput can reach 75 Mbits/s for configurations 5, 6 and 7 and 140 Mbits/s for configuration 8 and 9.

IV. CONCLUSION

In this paper we considered the Industry 4.0 use case and evaluated the performance of different radio configurations in terms of latency, packet loss probability and throughput. Regarding the latency, we studied in particular the user plane latency taking into account the radio, processing and buffer delays. We illustrated the distribution of the latency for different configurations and showed that the deployments at 700 MHz can offer short delays suitable for the industry 4.0 scenario, but only for a limited number of users. At 3.5 GHz, dense scenario is supported but only for use cases more relaxed in terms of latency because of the TDD duplex mode and the frame configuration. Only 2 OFDM symbols per mini-slot configuration could be the solution for strict latency scenarios. Finally, configurations at 26 GHz present the best delay results even for 280 robots. The packet loss probability results are congruent with the latency distribution, since the packets are lost mainly due to the latency budget. The same conclusion holds with eMBB users throughput, where the eMBB traffic is absent at 700 MHz. For the rest of configurations, we showed that we can guarantee a high quality of service for the eMBB traffic.

As for future perspectives, we aim to study further use cases in Wide Area Network (WAN) environment and also to implement and to evaluate advanced scheduling techniques for better eMBB and URLLC traffic coexistence.

Frequency

Figure 1 :

 1 Figure 1: Scheduling strategy of URLLC and eMBB traffic

Figure 2 :

 2 Figure 2: Delay model for one transmission

Figure 3 :

 3 Figure 3: Simulated Industry 4.0 scenario In our scenario, we consider periodic traffic model for URLLC users with 1 ms periodicity between consecutive

Figure 4 :Figure 5 :

 45 Figure 4: Latency results at FDD 700 MHz: top for configurations 1 and 2 and bottom for configurations 3 and 4

Figure 6 :

 6 Figure 6: Latency results at TDD 26 GHz for configuration 8 and 9

Figure 7 :

 7 Figure 7: Packet loss probability

Figure 8 :

 8 Figure 8: eMBB users throughput results

Table II :

 II 5G NR processing delays

	SCS(kHz)	15	30	60	120
	∆ U E (OFDM symbols)	8	10	17	20
	∆ gN B (TTI)		1

Table III :

 III Simulated configurations

	Configuration number	1	2	3	4	5	6	7	8	9
	Carrier frequency (GHz)		0.7				3.5		26
	Bandwidth (MHz)		10				50		100
	Duplex mode		FDD				TDD
	Frame configuration		-				DDDSU		DU
	Sub-carrier spacing (kHz)				30				60	120
	Mini-slots	2	4	2	4	2	4	7	7	7
	Number of URLLC users	280		20				280

ACKNOWLEDGEMENT

This work has been performed in the framework of the H2020 project 5G-SMART co-funded by the EU. The authors would like to acknowledge the contributions of their colleagues. This information reflects the consortium's view, but the consortium is not liable for any use that may be made of any of the information contained therein.


Estelle Fischer

Rémy Cachon

Nathalie Cayot

Impact of ageing on pea protein volatile compounds and correlation with odor

Impact of ageing on pea protein volatile compounds and correlation with odor Possible correlation if specific presentation of the results (Fig. 3). Examples :

-A-3 months, earthy off-flavor (Tab. 1), ↑ proportion of volatile responsible for earthy notes (Fig. 3). -B-3-6 months, slight change in odor, no big changes in ratios of compounds responsible for off-flavor.

A 20°C 30°C

Sampling: 0, 3, 6, 9, 12 months

MEASUREMENTS

Fig. 3 :

 3 Fig. 3: Profile of volatile compounds by sensory descriptors during the ageing (in ratios)

OdorFig. 1 :

 1 Fig. 1: Profile of volatile compounds by chemical family during the ageing

  Fig.2: Reactions involved during the ageing3,4,5,6