Peter L Voyvodic

Jerome Bonnet
email: jerome.bonnet@inserm.fr

Cell-free biosensors for biomedical applications

Keywords: Cell-free, Biosensors, TXTL, Point-of-Care, Diagnostics

diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Over the last twenty years, the number of publications on 'cell-free' protein synthesis systems has steadily increased (Figure 1). Although the history of cell-free systems dates back to the deciphering of the genetic code [START_REF] Nirenberg | The dependence of cell-free protein synthesis in E. coli upon naturally occurring or synthetic polyribonucleotides[END_REF], recent years have seen cell-free protein synthesis systems adopted by the field of synthetic biology, as they can be used to rapidly prototype biological circuit design [START_REF] Takahashi | Rapidly Characterizing the Fast Dynamics of RNA Genetic Circuitry with Cell-Free Transcription-Translation (TX-TL) Systems[END_REF], study the origin of life via a 'bottom-up' approach [START_REF] Kai | Cell Free Protein Synthesis and Its Perspectives for Assembling Cells from the Bottom Up[END_REF], and develop biosensors. Part of this rapid expansion has been fueled by a variety of methods for producing cell-free extract, the cytoplasmic component of the reactions containing the polymerases, ribosomes, and other machinery required for protein synthesis. These methods vary in scale from milliliters to liters of culture and lysis methods ranging from French press and sonication to even simply freeze-thawing [START_REF] Sun | Protocols for implementing an Escherichia coli based TX-TL cell-free expression system for synthetic biology[END_REF][START_REF] Perez | Cell-Free Synthetic Biology: Engineering Beyond the Cell[END_REF][START_REF] Shrestha | Streamlined extract preparation for Escherichia coli-based cell-free protein synthesis by sonication or bead vortex mixing[END_REF][START_REF] Didovyk | Rapid and Scalable Preparation of Bacterial Lysates for Cell-Free Gene Expression[END_REF]. Additionally, the commercial PURE system contains all of the purified components for protein synthesis without the RNases and proteases from extract-based systems, and it can even be rapidly and cheaply produced in-house [START_REF] Lavickova | A Simple, Robust, and Low-Cost Method To Produce the PURE Cell-Free System[END_REF][START_REF] Shimizu | Cell-free translation reconstituted with purified components[END_REF]. Combined with new toolboxes for high protein output and the development of biological circuits [START_REF] Garamella | The All E. coli TX-TL Toolbox 2.0: A Platform for Cell-Free Synthetic Biology[END_REF], there are now a broad range of targets, detection mechanisms, outputs, and stability and robustness techniques implemented in cell-free biosensors (Figure 2). While many other in vivo biosensors have been developed under the domain of synthetic biology, they have already been extensively reviewed [START_REF] Khalil | Synthetic biology: applications come of age[END_REF][START_REF] Chang | Microbially derived biosensors for diagnosis, monitoring and epidemiology[END_REF][START_REF] Slomovic | Synthetic biology devices for in vitro and in vivo diagnostics[END_REF][START_REF] Liu | Cell-based biosensors and their application in biomedicine[END_REF] and will not be included here. Rather, we will focus principally on three aspects of cell-free biosensors. First, their use in detecting small molecules including ions, biomarkers, and antibiotics; second, the development of cell-free technologies for detecting nucleic acid sequences from viruses and other pathogens; and third, the pre-processing procedures involved in using complex samples and the variety of techniques used to improve sensor stability including lyophilization.

Detecting small molecules with cell-free biosensors

Detecting ions, antibiotics, and other small molecules

Transcription factor-based biosensors use allosteric transcription factors discovered in nature to control expression of a reporter gene upon binding to particular small molecules. While used extensively with in vivo biosensors, they have also been utilized to engineer cell-free biosensors capable of responding to a range of inducer molecules. Gräwe et al. used the transcription factors MerR and BlcR to build cell-free biosensors to detect mercury ions and gammahydroxybutyrate [START_REF] Gräwe | A paper-based, cell-free biosensor system for the detection of heavy metals and date rape drugs[END_REF]. Furthermore, they constructed a 3D-printed box setup with filters compatible with cell phone diagnostics, expanding the diagnostic platform toward point-of-care applications. Another approach has been to use cell-free systems to detect ribosome-interfering antibiotics through analyzing how they reduce expression of a constitutively expressed reporter. Duyen et al. added paromomycin, tetracycline, chloramphenicol, and erythromycin to cell-free reactions in the PURE system and were able to detect each at concentrations between 0.5-1 µg/mL at a reaction pH between 6 and 10; additionally, they detected paromomycin spiked into environmental water samples [START_REF] Duyen | Paper-based colorimetric biosensor for antibiotics inhibiting bacterial protein synthesis[END_REF].

While most transcription factor-based biosensors require previously existing transcription factors that respond to a given compound, the field has recently expanded with the incorporation of metabolic engineering strategies. Recent work used the transcription factor BenR to detect benzoic acid, then expanded the range of detectable compounds by incorporation of the metabolic enzymes HipO and CocE, which convert hippuric acid and cocaine, respectively, into benzoic acid [START_REF] Voyvodic | Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors[END_REF] (Figure 3a). This methodology is scalable using the SensiPath and RetroPath algorithms, which search multiple databases to find metabolic enzymes and transcription factors to convert and detect previously undetectable molecules [START_REF] Delpine | SensiPath: computer-aided design of sensing-enabling metabolic pathways[END_REF]19]. This framework has been further expanded to detect benzamide and biphenyl-2,3-diol and combine the relative strength of four different inputs to build a cell-free metabolic perceptron [START_REF] Pandi | Metabolic Perceptrons for Neural Computing in Biological Systems[END_REF].

While most transcription factor-based biosensors monitor detection via transcription and translation of a reporter protein, such as green fluorescent protein, other systems bypass the translation step by using fluorescent aptamers. Alam et al. recently developed the RNA Output Sensor Activated by Ligand Induction (ROSALIND) system that combines freeze-dried polymerases, transcription factors, and DNA templates to produce fluorescent aptamers for ligand detection [START_REF] Alam | Rapid, Low-Cost Detection of Water Contaminants Using Regulated In Vitro Transcription[END_REF] (Figure 3b). They engineered sensors for tetracyclines (tetracycline, doxycycline, oxytetracycline, chlortetracycline), macrolides (erythromycin, azithromycin, clarithromycin, roxithromycin), small molecules (3-hydroxy benzoic acid, salicylate, benzalkonium chloride, uric acid), and metals (zinc[II], copper[I/II], cadmium[II], lead[II]); additionally, the authors were able to increase or decrease the sensitivity of the sensors and demonstrate functionality in environmental water samples. Finally, some research has used cellfree protein synthesis to produce proteins that can be coupled to electronics for increased sensitivity. Chen et al. produced the nematode olfactory receptor ODR-10 via an E. coli extract cell-free system for detection of diacetyl and functionalized it to the surface of electrolyteinsulator-semiconductor sensors via a His6-tag and anti-His6-tag aptamers [START_REF] Chen | Functional expression of olfactory receptors using cell-free expression system for biomimetic sensors towards odorant detection[END_REF]. They showed concentration-dependent linear response to diacetyl from 0.01 nM to 1 nM and specificity vs.

Detecting small molecules from complex samples

While most sensor development is initially conducted by adding purified inducers to reactions, field-deployable biosensors will need to remain functional in complex sample conditions, such as those found in clinical samples like blood and urine. Removing the protein production machinery from within the cell membrane has many advantages in sensor development, but permitting the added sample to directly interact with this machinery while maintaining sensor function is a non-trivial task. Some work has circumvented this interference by including preprocessing steps for added clinical samples. Wen et al. developed a cell-free sensor for the quorum sensing acyl-homoserine lactone (AHL) 3OC12-HSL capable of detection at levels under 10 nM [START_REF] Wen | A Cell-Free Biosensor for Detecting Quorum Sensing Molecules in P. aeruginosa-Infected Respiratory Samples[END_REF]. To detect 3OC12-HSL in the sputum of patients, they extracted the AHLs using an organic solvent that was then dried and rehydrated before testing. The resulting cellfree sensor was able to detect the molecules in patients infected with P. aeruginosa and showed reasonable agreement with LC-MS quantification.

A few cell-free sensors have been tested in the presence of unprocessed clinical samples. Salehi et. al developed a hybrid cell-free fusion protein for a nuclear hormone receptor disruptor, human thyroid receptor beta, capable of functioning in up to 40% raw sewage [24]. Additionally, they tested the inhibition of different environmental samples (tap, pond, snow, storm, and soil water), wastewater treatment samples (raw sewage, post clarifier, post biological, post filter, and effluent) and blood and urine on constitutive GFP expression and found that in particular blood and urine exhibited strong inhibitory effects (25% blood or 4% urine resulted in near total inhibition). However, in a later work they found that adding an RNase inhibitor partially recovered expression in urine and significantly recovered expression in blood [25]. Using this upgraded system they detected an estrogen receptor disruptor, E2, in reactions containing up to 20% blood or 10% urine. Finally, our recent work developing sensors for benzoic acid, hippuric acid, and cocaine produced proof-of-concept detectors for each inducer: benzoic acid was detected in commercial beverages and quantified against LC-MS results, endogenous hippuric acid was detected in human urine samples and similarly quantified, and cocaine could be detected in human urine at previously clinically-detected levels [START_REF] Voyvodic | Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors[END_REF]. These examples illustrate that while conducting cell-free protein synthesis reactions in the presence of complex samples has its challenges, they are not unsurmountable and sensors can be developed to detect endogenous compounds even in complex environments.

Detecting pathogens via nucleic acids with cell-free biosensors

The ability to detect pathogenic organisms in a cheap, fast, and portable fashion is a key engineering problem to bringing diagnostic technologies to much of the developing world. Many new diagnostic platforms being engineered focus on detecting nucleic acid sequences of these pathogens, as they are highly specific and can be pre-processed via amplification steps to facilitate lower detection limits. One such cell-free diagnostic platform uses toehold switches to control the expression of an output gene; these RNA-based switches create a loop upstream of the gene that sequesters the ribosome binding site until it binds to a trigger RNA, which unfolds the loop and permits translation[26] (Figure 4a). Pardee et al. designed toehold switches and trigger RNAs based on Ebola mRNA; additionally, the assay could distinguish between Sudan and Zaire strains. Two years later, the same group developed a rapid detection platform for Zika virus, wherein the workflow permitted the design, validation, and production of cell-free sensors in as little as five days that could detect clinically relevant concentrations of Zika virus in three hours [START_REF] Pardee | Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components[END_REF]. This detection limit was made possible through the use of an isothermal nucleic acid sequence-based amplification (NASBA) step that reverse transcribes the target RNA and then through transcription produces many more copies before detection, allowing detection of trigger RNA at concentrations as low as 3 fM. The fast development and low detection limits of these technologies, along with other toehold-based sensors for detecting genomic DNA of the gut microbiota [START_REF] Takahashi | A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers[END_REF] demonstrate great promise for developing point-of-care sensors.

Another nucleic acid-based cell-free sensor of note is the Specific High Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK) system [START_REF] Gootenberg | Nucleic acid detection with CRISPR-Cas13a/C2c2[END_REF] (Figure 4b). Gootenberg et al. take advantage of the ability of Cas13a to not only bind to and cleave a target RNA strand, but once bound to non-specifically degrade surrounding RNA. Using a reporter/quencher pair separated by RNA, they were able to detect Zika and Dengue virus lentiviral particles, Zika virus in human serum and urine, and bacterial culture genomic DNA. Additionally, the system can differentiate between base pair mutations from viral RNA or human genotyping. The following year, the SHERLOCKv2 improved on this technology with multiplexing using orthogonal enzymes (Cas13, Cas12a, and Csm6), quantitatively measuring down to two attomolar, increasing sensitivity, and incorporating a lateral flow readout [START_REF] Gootenberg | Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6[END_REF]. The extremely low limit of detection provides great promise for these nucleic acid based cell-free biosensors going forward.

Sample processing and sensor stability

Preserving cell-free reactions via lyophilization

One of the most difficult aspects of providing diagnostics to rural areas in low-resource nations is the temperature control required. Many diagnostics require refrigeration or freezing and this cold-chain must be maintained from production to user applications. Additionally, even in areas with much greater access to refrigeration, the development of point-of-care diagnostics would be greatly aided by stability at room temperature storage. Most cell extract and buffer components are traditionally stored at -80°C, potentially producing an obstacle to field deployment. However, Pardee et al. first showed in their paper developing toehold sensors that they could lyophilize the transcriptional and translational machinery along with the DNA as pellets or on paper and then later rehydrate to start the reaction [26]. These freeze-dried components could then be stored in tubes at room temperature for up to one year, although they did show an ~80% decrease in signal after that period of time. Similarly, they and others have shown that freeze-dried components can be used to produce therapeutics, thereby potentially enabling on-demand, on-site manufacturing of both diagnostics and treatments[24, [START_REF] Pardee | Portable, On-Demand Biomolecular Manufacturing[END_REF][START_REF] Stark | On-demand, cell-free biomanufacturing of conjugate vaccines at the point-of-care[END_REF][START_REF] Salehi | Cell-free protein synthesis of a cytotoxic cancer therapeutic: Onconase production and a just-addwater cell-free system[END_REF].

Several groups have looked at a variety of other storage conditions on sensor stability. Salehi et al. tested the stability of standard or lyophilized extract stored at -80°C, -20°C, 4°C, or 25°C for up to one year [START_REF] Salehi | Cell-free protein synthesis of a cytotoxic cancer therapeutic: Onconase production and a just-addwater cell-free system[END_REF]. While the non-lyophilized extract showed a rapid decrease in efficacy at higher temperatures, the lyophilized extract was much more stable; however, it had lost all activity after one year. Karig et al. also investigated using the non-reducing disaccharide trehalose to protect cell-free components during oven drying [START_REF] Karig | Preservation of protein expression systems at elevated temperatures for portable therapeutic production[END_REF]. By separately drying the reaction buffer and extract at 37°C, they could later recombine them to produce pyocin capable of killing P. aeruginosa after 136 days, although at lower dilution levels than fresh reactions. Lyophilization has also been shown as an effective means of killing any remaining bacteria that survive the lysis process [START_REF] Smith | Creating a completely "cell free" system for protein synthesis[END_REF], a vital component for future devices to pass through regulation. Additionally, autolysing cell lines can be lyophilized and rehydrated to create functional extract in situ, and Didovyk et al. demonstrated that such a method can produce a mercury sensor with a detection limit of 25 nM, similar to previous in vivo versions of the same biosensor [START_REF] Didovyk | Rapid and Scalable Preparation of Bacterial Lysates for Cell-Free Gene Expression[END_REF]. Lastly, while the progress made in lyophilizing cell-free biosensors is promising and considerable, it should be noted that many successful sensors rely solely on strong, constitutive protein production through a robust T7 polymerase-based system and that other sensors that rely on activation or repression with native E. coli polymerase may require additional troubleshooting to remain functional after lyophilization.

Pre-processing steps for complex samples

Another challenge for cell-free biosensors is the very fact that they have no physical barrier to shield them from the samples introduced. While this can have many benefits, the delicate calibration of protein synthesis reactions can be greatly altered by the introduction of complex mixtures. A few solutions have been proposed to address these issues. For cell-free protein synthesis systems, work from the Bundy group has shown that the incorporation of RNase inhibitors can greatly reduce sample interference, particularly for blood samples [25]. This can facilitate extraction-free reactions, as we have shown by detecting endogenous hippuric acid in urine that requires only a dilution step in water [START_REF] Voyvodic | Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors[END_REF]. Additionally, the pre-processing procedure HUDSON (heating unextracted diagnostic samples to obliterate nucleases) was developed for the SHERLOCK platform to allow for direct detection in clinical samples [START_REF] Myhrvold | Field-deployable viral diagnostics using CRISPR-Cas13[END_REF]. After a 10-25 minute nuclease and viral inactivation step, Myhrvold et al. were able to detect Zika viral RNA in whole blood, serum, saliva, and urine at clinical concentrations (<100aM). While there still remain several hurdles to improve the robustness of many cell-free biosensors, the steps made thus far are promising.

Conclusion

While in this review article we have focused primarily on cell-free biosensors that are based on transcription and/or translation, there are certainly many other categories that we were not able to include, such as protease sensors that can achieve ultrasensitivity [START_REF] Stein | Ultrasensitive Scaffold-Dependent Protease Sensors with Large Dynamic Range[END_REF]. Additionally, many types of cell-free biosensors have been encapsulated into liposomes, recreating a controllable barrier between the reactions and the surrounding environment. These have been used to create synthetic microreactors, osmotic shock and temperature sensors, and even enable communication between cell-free liposomes and bacterial biosensors [START_REF] Courbet | Computer-aided biochemical programming of synthetic microreactors as diagnostic devices[END_REF][START_REF] Majumder | Cell-sized mechanosensitive and biosensing compartment programmed with DNA[END_REF][START_REF] Jia | Temperature-sensitive protein expression in protocells[END_REF][START_REF] Schwarz-Schilling | Chemical communication between bacteria and cell-free gene expression systems within linear chains of emulsion droplets[END_REF]. Furthermore, cellfree reactions have also been used to functionalize biomaterials, which could lead to additional categories of cell-free biosensors in the future [START_REF] Benítez-Mateos | Expanding one-pot cellfree protein synthesis and immobilization for on-demand manufacturing of biomaterials[END_REF]. Finally, recent work has even brought cellfree biosensors into high schools to teach and inspire the next generation of synthetic biologists. Using freeze-dried cell-free reaction components, teachers can instruct students on how to produce a range of fluorescent proteins [START_REF] Stark | BioBits TM Bright: A fluorescent synthetic biology education kit[END_REF], create biosensors to differentiate genomic DNA between different fruit species [START_REF] Huang | BioBits TM Explorer: A modular synthetic biology education kit[END_REF], learn about the mechanisms of antibiotics [START_REF] Stark | BioBits Health: Classroom Activities Exploring Engineering, Biology, and Human Health with Fluorescent Readouts[END_REF], and even conduct experiments with CRISPR/Cas9 [START_REF] Collias | An educational module to explore CRISPR technologies with a cell-free transcription-translation system[END_REF]. With an increase in the amount of research being performed and inspiring future generations by putting cell-free technology in their hands from a young age, the future looks bright for cell-free biosensors.

Figure 1 .

 1 Figure 1. Publications on "cell-free protein synthesis" systems have steadily increased in recent years. Data from Web of Science (WoS, Thomson Reuters) search using the topic "Cell-free Protein Synthesis".

Figure 2 .

 2 Figure 2. Cell-free biosensors use a variety of methods and systems for detection. Sensors can be developed for small molecules, ions, or viral RNA or bacterial genomic DNA. Detection strategies include transcription factor activation/repression, RNA-based ribosome binding site sequestration in toehold loops, or CRISPR-based RNA recognition. Transcription and/or translation [TX(TL)] can be performed using cell extract, the complete transcription and translation PURE system, or using selectively purified polymerases and transcription factors. Signal output can then be measured as fluorescence, luminescence, visible color change, or even voltage shifts. Finally, cell-free sensors can exhibit increased robustness via lyophilization, addition of RNase inhibitors, or heat inactivation of clinical samples before detection.

  2,3-pentanedione, butanone, and isopentyl acetate. These examples show the diversity of approaches being brought to the development of transcription factor-based cell-free biosensors.

Figure 3 .

 3 Figure 3. Transcription factor (TF)-based cell-free biosensors detect small molecules or ions via transcription/translation (TXTL) or by transcription alone. a) Binding of a ligand to its activating transcription factor results in expression of a reporter protein. Alternatively, metabolic engineering-based sensors can detect additional ligands for which there is no known transcription factor by initially transforming the ligand into a detectable ligand using a metabolic enzyme. b) ROSALIND uses ligand binding to release a repressing transcription factor, thereby permitting transcription of a fluorescent RNA aptamer. Figure 3a and Figure 3b are adapted from Voyvodic et al.[17] and Alam et al.[21], respectively.

Figure 4 .

 4 Figure 4. Nucleic acid cell-free biosensors detect target sequences by releasing ribosome binding site (RBS) sequestration or CRISPR-based cleavage of quenched reporters. a) Transcription of a reporter protein is inhibited by sequestration of the RBS in a toehold loop. Binding to the target trigger RNA permits translation and sensor output. b) SHERLOCK detects genomic DNA extracted from bacterial pathogens or viral RNA freed from lentiviral particles via thermal lysis. Amplification occurs via a combination of recombinase polymerase amplification (RPA), or reversetranscription RPA (RT-RPA), and T7 polymerase transcription. Purified Cas13a proteins then bind to the target sequence via a guide RNA and collaterally cleave a quenched fluorescent reporter for detection.Figure 4a and Figure 4b are adapted from Pardee et al.[26] and Gootenberg et al.[29], respectively.

  Figure 4. Nucleic acid cell-free biosensors detect target sequences by releasing ribosome binding site (RBS) sequestration or CRISPR-based cleavage of quenched reporters. a) Transcription of a reporter protein is inhibited by sequestration of the RBS in a toehold loop. Binding to the target trigger RNA permits translation and sensor output. b) SHERLOCK detects genomic DNA extracted from bacterial pathogens or viral RNA freed from lentiviral particles via thermal lysis. Amplification occurs via a combination of recombinase polymerase amplification (RPA), or reversetranscription RPA (RT-RPA), and T7 polymerase transcription. Purified Cas13a proteins then bind to the target sequence via a guide RNA and collaterally cleave a quenched fluorescent reporter for detection.Figure 4a and Figure 4b are adapted from Pardee et al.[26] and Gootenberg et al.[29], respectively.

Acknowledgement

This work was supported by an ERC starting grant "COMPUCELL" to J.B. J.B. also acknowledges continuous support from the INSERM Atip-Avenir program and the Bettencourt-Schueller Foundation. The CBS acknowledges support from the French Infrastructure for Integrated Structural Biology (FRISBI) ANR-10-INSB-05-01.

Competing Interests

The authors declare no competing interests.


Eric Dugat-Bony

Pascal Bonnarme

Sébastien Fraud

Jessie Catellote

Anne-Sophie Sarthou

Valentin Loux

Olivier Rué

Nadège Bel

Sarah Chuzeville

Sandra Hélinck

Jessie Castelotte

Sandra Helinck
email: sandra.helinck@agroparistech.fr

Effect of sodium chloride reduction or partial substitution with potassium chloride on the microbiological, biochemical and sensory characteristics of semi-hard and soft cheeses

Keywords: sodium chloride reduction, sodium chloride substitution, soft cheese, semi-hard cheese, biochemical composition, cheese microbiota, sensory characteristics, spoilage microorganisms

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Excessive sodium intake is known to cause high blood pressure and to increase the risk of hypertension, cardiovascular disease and gastric cancer [START_REF] Mohan | Salt and high blood pressure[END_REF][START_REF] Song | High Salt Intake Is Associated with Atrophic Gastritis with Intestinal Metaplasia[END_REF]. The World Health Organization has proposed to reduce sodium intake by 30% in order to obtain the WHO guideline of 2 g/day (i.e., 5 g of salt/day) by 2020 [START_REF] Who | Guideline: sodium intake for adults and children[END_REF]. In 2015, 75 countries had developed a national sodium reduction strategy [START_REF] Trieu | Salt Reduction Initiatives around the World -A Systematic Review of Progress towards the Global Target[END_REF]. This strategy is multifaceted and includes, for example, the establishment of sodium content targets for foods as well as consumer education. Since sodium reduction in the human diet is one of the main objectives for public health agencies, it has become a major challenge for the food industries that have to reformulate products. Indeed, processed foods, including cheese products, are estimated to contribute over 70% to dietary sodium intake [START_REF] Appel | Compelling Evidence for Public Health Action to Reduce Salt Intake[END_REF]EFSA, 2006). In France, mean daily salt intakes range from approximately 7 to 10 g of salt and are well in excess of dietary needs. Dietary guidelines recommend a 20% salt reduction in foods to obtain a target of 6.5 to 8 g of salt intake per day [START_REF] Anses | Avis de l'agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail relatif au suivi des teneurs en sel des principaux vecteurs entre 2003 et 2011 et simulation des impacts sur les apports en sel de la population française[END_REF].

Cheese is perceived as being a food source that is highly concentrated in sodium, with amounts that can vary depending on the type of cheese. Soft, semi-hard and hard cheeses contain between 0.5% and 2.5% sodium chloride (NaCl), whereas blue-type cheeses contain between 3% and 5% NaCl. Sodium chloride fulfills many important functions in cheese: it modifies the physical properties of the cheese curd and rind (especially maintaining a low aw), controls the growth of the cheese-ripening microbiota, limits the development of pathogens or spoilers, and acts as a flavor enhancer [START_REF] Cruz | Cheeses with reduced sodium content: Effects on functionality, public health benefits and sensory properties[END_REF][START_REF] Guinee | Salt in Cheese: Physical, Chemical and Biological Aspects[END_REF][START_REF] Johnson | Reduction of Sodium and Fat Levels in Natural and Processed Cheeses: Scientific and Technological Aspects[END_REF]. Thus, the sodium reduction strategy must take these parameters into account. This strategy could be achieved either by a reduction in the NaCl level or a partial replacement of NaCl with other salts such as KCl.

Several studies have been conducted to characterize the effect of lowering NaCl content on the quality of Cheddar cheeses [START_REF] Khetra | Selection and optimization of salt replacer, flavour enhancer and bitter blocker for manufacturing low sodium Cheddar cheese using response surface methodology[END_REF][START_REF] Mccarthy | Effect of fat and salt reduction on the changes in the concentrations of free amino acids and free fatty acids in Cheddar-style cheeses during maturation[END_REF][START_REF] Mccarthy | Effect of salt and fat reduction on the composition, lactose metabolism, water activity and microbiology of Cheddar cheese[END_REF], 2016;[START_REF] Murtaza | Texture, flavor, and sensory quality of buffalo milk Cheddar cheese as influenced by reducing sodium salt content[END_REF][START_REF] Porcellato | Application of ARISA to assess the influence of salt content and cation type on microbiological diversity of Cheddar cheese[END_REF][START_REF] Rulikowska | The impact of reduced sodium chloride content on Cheddar cheese quality[END_REF], hard or semi-hard cheeses [START_REF] Baptista | Reduction of 25% salt in Prato cheese does not affect proteolysis and sensory acceptance[END_REF][START_REF] Sihufe | Effect of sodium chloride reduction on physicochemical, biochemical, rheological, structural and sensory characteristics of Tybo cheese[END_REF][START_REF] Soares | Substitution of sodium chloride by potassium chloride in São João cheese of Pico Island[END_REF][START_REF] Soares | The effect of lowering salt on the physicochemical, microbiological and sensory properties of São João cheese of Pico Island[END_REF][START_REF] Søndergaard | Impact of NaCl reduction in Danish semi-hard Samsoe cheeses on proliferation and autolysis of DL-starter cultures[END_REF] and white brined cheeses [START_REF] Arboatti | The influence of sodium chloride reduction on physicochemical, biochemical, rheological and sensory characteristics of Mozzarella cheese[END_REF][START_REF] Ayyash | The effect of sodium chloride substitution with potassium chloride on texture profile and microstructure of Halloumi cheese[END_REF][START_REF] Faccia | Influence of the different sodium chloride concentrations on microbiological and physico-chemical characteristics of mozzarella cheese[END_REF][START_REF] Felicio | Physico-chemical changes during storage and sensory 25 acceptance of low sodium probiotic Minas cheese added with arginine[END_REF][START_REF] Kamleh | The effect of substitution of sodium chloride with potassium chloride on the physicochemical, microbiological, and sensory properties of Halloumi cheese[END_REF][START_REF] Thibaudeau | Production of brine-salted Mozzarella cheese with different ratios of NaCl/KCl[END_REF]. These studies focused on the sensory properties and physicochemical and biochemical characteristics of cheeses. Moreover, it was shown that a decrease in salt resulted in an increase in starter and non-starter lactic acid bacteria (LAB) growth in Cheddar cheese [START_REF] Rulikowska | The impact of reduced sodium chloride content on Cheddar cheese quality[END_REF]. However, there is still a lack of information regarding the impact of salt reduction on the whole microbiota of surface-ripened cheeses. It was previously demonstrated that reducing NaCl content in an experimental soft smear-ripened cheese resulted in microbial disturbances such as a lesser development of Debaryomyces hansenii and a greater development of the Gram-negative bacteria Hafnia alvei (Dugat-Bony et al., 2016). This community disequilibrium was associated with modifications of both the proteolytic kinetics and the volatile aroma profiles. Other studies also highlighted the impact of salt composition on the growth of other ripening cultures such as Brevibacterium aurantiacum and Geotrichum candidum [START_REF] Marcellino | Diversity of Geotrichum candidum Strains Isolated from Traditional Cheesemaking Fabrications in France[END_REF][START_REF] Mounier | Growth Characteristics of Brevibacterium, Corynebacterium, Microbacterium, and Staphylococcus spp. Isolated from Surface-Ripened Cheese[END_REF].

Consequently, NaCl content represents a major technological factor controlling, at least partially, the main biochemical processes linked with the activity of surface microorganisms, including proteolysis, lipolysis and the production of aroma compounds. Moreover, the microbiological quality of NaCl reduction in cheese has to be further evaluated due to its critical role for controlling spoilage and pathogenic microorganism growth [START_REF] Taormina | Implications of Salt and Sodium Reduction on Microbial Food Safety[END_REF]. Indeed, Cheddar cheeses produced with a 50% reduction in salt content were characterized by higher counts of L. monocytogenes than the controls after 50 days at 4°C [START_REF] Hystead | The effect of sodium reduction with and without potassium chloride on the survival of Listeria monocytogenes in Cheddar cheese[END_REF], and salt was shown to inhibit Pseudomonas spp. growth in model cheeses, mimicking the surface of soft smear-ripened cheeses (E. Dugat-Bony et al., 2016).

Regarding sensory perception, sodium reduction has been reported to cause detrimental effects on consumer acceptability [START_REF] Jaenke | Consumer acceptance of reformulated food products: A systematic review and meta-analysis of salt-reduced foods[END_REF]). One possible approach to limit this pitfall consists of partially replacing sodium chloride with potassium chloride (KCl). Other approaches have been studied: use of flavor enhancers such as monosodium glutamate, hydrolyzed vegetable protein, yeast extract, disodium inosinate and use of bitter blockers (Grummer, Bobowski, Karalus, Vickers, & Schoenfuss, 2013;[START_REF] Khetra | Selection and optimization of salt replacer, flavour enhancer and bitter blocker for manufacturing low sodium Cheddar cheese using response surface methodology[END_REF]. Potassium intake may, in fact, have a positive effect on consumer health [START_REF] He | Beneficial effects of potassium[END_REF], and some studies have reported that partial replacement of NaCl with KCl in soft and semi-hard cheeses did not affect the physicochemical characteristics and organoleptic properties of cheese if the substitution did not exceed a ratio of 30% [START_REF] Costa | Manufacture of reduced-sodium Coalho cheese by partial replacement of NaCl with KCl[END_REF][START_REF] Costa | Effect of partial replacement of sodium chloride with potassium chloride on the characteristics of Minas Padrão cheese[END_REF][START_REF] Grummer | Manufacture of reduced-sodium Cheddar-style cheese with mineral salt replacers[END_REF][START_REF] Soares | Substitution of sodium chloride by potassium chloride in São João cheese of Pico Island[END_REF].

Nevertheless, there is still a lack of information regarding the impact of salt substitution on bacteria, yeast and mold development and metabolic activity during cheese ripening.

Understanding how changes in technological parameters can affect microbial functions and, subsequently, the quality of fermented foods, represents a great challenge. Recently, highthroughput sequencing technologies have become very popular to investigate microbial ecology in dairy products [START_REF] De Filippis | Recent Past, Present, and Future of the Food Microbiome[END_REF][START_REF] Jonnala | Sequencing of the Cheese Microbiome and Its Relevance to Industry[END_REF]. When combined with other techniques such as other omics, it provides the opportunity to understand the mechanisms that can affect the microbial ecosystem and the final characteristics of the products [START_REF] Ferrocino | Current perspectives in food-based studies exploiting multi-omics approaches[END_REF]. Only a few examples of such a global approach to the analysis of cheese products exist, but they still makes it possible to evaluate the effect of the ripening stages [START_REF] Dugat-Bony | Overview of a Surface-Ripened Cheese Community Functioning by Meta-Omics Analyses[END_REF] and of temperature and humidity [START_REF] De Filippis | Metatranscriptomics reveals temperature-driven functional changes in microbiome impacting cheese maturation rate[END_REF].

The objective of this study was to investigate the effect of reduced salt content and partial substitution of NaCl with KCl on the quality of soft ("Camembert"-type) and semi-hard ("Reblochon"-type) cheeses using a combination of multi-omics, biochemical and sensory approaches.

MATERIALS & METHODS

Soft and semi-hard cheese production

Soft ("camembert"-type) and semi-hard ("reblochon"-type) cheeses were produced at the ACTALIA Cheese Processing Unit (La Roche-sur-Foron). Cheeses were prepared from pasteurized milk (85°C, 30 seconds) and inoculated with specific starters and spoilage microorganisms when appropriate. Different salt contents were taken into account: standard level, 1.5 g/100 g of cheese (C cheese); reduced level, 80% of NaCl level (R cheese); and substitution level, 80% of NaCl and 20% of KCl levels (S cheese).

For soft cheese, CaCl2 (470 g/L) was added at 25 mL/100 L of pasteurized milk and mesophilic lactic acid starter (MM100 at 6 DCU/100 L of milk, Danisco) and Streptococus thermophilus (TA052 at 25 DCU/100 L, Danisco), Geotrichum candidum (Geo17, Danisco, 1 dose /1000 L) and Penicillium camemberti (PC neige Lyo, Danisco, 2 doses/1000 L) were inoculated. When appropriate, Yarrowia lipolytica ITFF2145 (Actalia) was inoculated as a spoilage yeast at 10 3 cfu/L of milk. This inoculation dose, chosen in partnership with professionals from the dairy sector, corresponded to representative concentrations of field cases. Y. lipolytica ITFF2145 was isolated from defective soft cheese in 2013 in France. When the milk reached pH 6.45 and a temperature of 37°C, the rennet (530 mg/L of chymosin, Berthelot) was added (55 mL/100 L).

Coagulation was followed by a 40-min hardening in order to trap intragranular water by achieving a long hardening period. It corresponds to 5 times the rennet clotting time (8 minutes).

The cheese curd was then cut (cut size of cheese curd particles: 10 mm x 10 mm) and shaped in polyurethane molds (diameter: 10.5 cm). At the beginning of the molding step, pH was 6.20-6.25 and temperature was 33.5°C. Whey was eliminated and molds were inverted three times.

After 20h, cheeses were removed from their mold at pH 4.9-5.10 with a temperature of 16-18°C (day 0). Dry salting then took place (day 1) in order to obtain a final level of 15 g of NaCl/kg of cheese in the control cheese, and 11.5 g of NaCl /kg in the reduced and substituted cheeses.

The cheeses (approx. 250 g) were maintained at 13°C and 85% relative humidity (RH) in order to dry their surfaces for 4 hours and were then incubated in a ripening chamber with a relative humidity of 95% for 20 days at 11°C. Semi-hard cheeses were inoculated with the following starters: S. thermophilus and Lactobacillus bulgaricus (wild strains, SIR-Flore, Thônes, France, at 1.3 L/100 L), G. candidum (Geo2, SIR-Flore, 2 doses/1000 L), Debaryomyces hansenii (DHlyo, Danisco, 2 doses/1000 L) and Brevibacterium linens (FR22, Danisco, 1 dose/1000 L). Pseudomonas fluorescens strains isolated from either raw dairy products, cheese or the dairy environment (ITFF3965, ITFF3966, ITFF3967, ITFF3969) were inoculated as spoilage bacteria at a final concentration of 10 5 cfu/L of milk. This inoculation dose, chosen in partnership with professionals from the dairy sector, corresponded to representative concentrations of field.

When the pasteurized milk reached pH 6.50-6.55 (temperature of 34°C), the rennet (530 mg/L of chymosin, Berthelot) was added (55 mL/100 L). Then, 30 min after rennet addition, the curd was cut (cut size of cheese curd particles: 20 mm x 20 mm) and gently stirred to eliminate whey.

The curd was molded (34°C, pH 6.30-6.35) in polyurethane molds (diameter: 13 cm) and pressed for 90 min. Molds were inverted one time. After 4h, cheeses were removed from their mold (pH 5.15-5.25 and temperature of 24°C). Then, the cheeses were pickled in sterile brine (density: 1140) at 16°C for a variable length of time, depending on the NaCl level target: 14 g of NaCl/kg of cheese in the control cheese (brining time of 80 min), and 10.5 g/kg in the reduced and substituted cheeses. The brining time was 35 minutes for the reduced cheese. KCl was added by dry salting. This brining step is defined as day 0 of ripening. After incubation for 4 days at 19°C and 100% RH, the cheeses (approx. 500g) were transferred to the ripening room, which was maintained at 13°C and 95-100% RH until day 21. The cheeses were turned every two days.

Soft and semi-hard cheeses were sampled at days 1, 7, 14 and 21. At each sampling day, three cheeses for each salting condition were taken and cut into eight parts. The parts were used for microbiological, 16S rRNA gene metabarcoding, metatranscriptomic and biochemical analyses. Volatile aroma compound profile and sensory analysis were performed at day 21.

Microbiological analyses

The cheese samples (10 g) were homogenized in a stomacher for 2 min (AES Chemunex, Easy Mix) and diluted up to 10 -8 by 10-fold serial dilutions. Total microflora were counted according to NF EN ISO 4833, coliforms with NF V 08-050, yeasts and molds with NF V 08-059, and thermophilic lactic acid bacteria with ISO27205. Lactococci were counted on Elliker medium (Difco), B. linens on SCB medium (1.5% tryptone, 0.5% soytone, 0.6% yeast extract, 1.2% sodium lactate, 0.25% K2HPO4, 4% NaCl and 1.5% agar) and Pseudomonas spp. on GSP medium (Glutamate Starch Phenol red agar, VWR).

Bacterial community structure using 16S rRNA gene metabarcoding

The kinetic evolution of the bacterial community structure was analyzed using samples from days 7, 14 and 21. DNA extraction from the cheese surface, PCR amplification of the V3-V4 region of the 16S rRNA gene, library preparation and sequencing were performed as previously described [START_REF] Dugat-Bony | Highlighting the microbial diversity of 12 French cheese varieties[END_REF]. Paired-end reads were merged using Flash [START_REF] Magoč | FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies[END_REF] and analyzed using FROGS [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF], according to the standard operating procedure. Briefly, operational taxonomic units (OTUs) were built using Swarm with an aggregation distance of 3 [START_REF] Mahé | Swarm: robust and fast clustering method for amplicon-based studies[END_REF], and each OTU that accounted for less than 0.005% of the total set of sequences was discarded, as previously recommended [START_REF] Bokulich | Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing[END_REF]. Lastly, the OTU's affiliation was checked using the EzBiocloud database [START_REF] Yoon | Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies[END_REF]. Merged sequence data were deposited at the Sequence Read Archive of the National Center for Biotechnology Information under the BioProject accession number PRJNA523139.

Metatranscriptomic analysis

Metatranscriptomic analysis was conducted on samples from day 21. RNA extraction and rRNA depletion were performed as previously described [START_REF] Monnet | Investigation of the Activity of the Microorganisms in a Reblochon-Style Cheese by Metatranscriptomic Analysis[END_REF]. Libraries were prepared with the ScriptSeq™ RNA-Seq Library Preparation Kit (Illumina) and sequenced on an Illumina NexSeq 500 instrument. Data analysis was performed according to the workflow presented in Figure S1. Briefly, raw sequences were preprocessed using Cutadapt v1.12 [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] and filtered with SortMeRNA v2.0 [START_REF] Kopylova | SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data[END_REF]. Non-rRNA reads were mapped using TopHat2 v2.1.1 [START_REF] Kim | TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions[END_REF] against a reference database composed of coding DNA sequences (CDS) extracted from the genome sequences of strains belonging to the same species as the microorganisms that were inoculated into the cheese.

Accession numbers of the genomes are available in Table S1 andS2 for soft and semi-hard cheeses, respectively. In order to access the transcriptome of non-inoculated species that develop in cheese as well, unmapped reads were de novo assembled using Trinity [START_REF] Grabherr | Full-length transcriptome assembly from RNA-Seq data without a reference genome[END_REF], and gene prediction was conducted on this assembly using FragGeneScan [START_REF] Rho | FragGeneScan: predicting genes in short and error-prone reads[END_REF]. Unmapped reads were mapped back to these predicted CDS using TopHat2.

Each CDS of the de novo assembly was taxonomically annotated using Kaiju [START_REF] Menzel | Fast and sensitive taxonomic classification for metagenomics with Kaiju[END_REF]. All CDS from both the reference database and the de novo assembly were submitted to the GhostKoala server [START_REF] Kanehisa | BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences[END_REF] for uniformed functional annotation based on KEGG Orthology. The count matrix was processed using custom scripts built under the statistical environment R (http://www.r-project.org/) and based on the following packages: Deseq2 [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] and gplots (https://CRAN.R-project.org/package=gplots). Raw sequence data were deposited at the Sequence Read Archive of the National Center for Biotechnology Information under the BioProject accession number PRJNA523735.

Physicochemical and biochemical analyses

Cheese pH was measured using a penetration pH electrode (Mettler Toledo LE427-S7). Water activity was measured using a water activity meter (Aqualab, series 4TE). Chloride content was measured using a chloride analyzer (Corning Chloride Analyzer 926), and sodium ion concentration was determined using an Orion Dual Star pH/ISE meter (Thermo Scientific, Villebon-sur-Yvette, France). D-and L-lactate concentrations were assayed using a commercially available kit (Biosentec, Auzeville Tolosane, France) according to the manufacturer's instructions. The concentrations of different nitrogen fractions were determined to evaluate the influence of reducing NaCl content on proteolysis activity. Total nitrogen (TN) was measured with the Kjeldhal method according to the NF EN ISO 8968 standard, and the non-protein nitrogen (NPN) content and soluble nitrogen (SN) content were measured according to NF ISO 27871. Lipolysis was evaluated by measuring the acid degree value. The number of mg of KOH necessary to neutralize the free acids in 1 g of sample was determined by titration.

Volatile aroma compound profile

Volatile aroma compounds were quantified at the end of the ripening period (21 days) in three independent cheeses for each condition. The cheese surface was grated and transferred into a 10-mL glass bottle that was tightly sealed before storage at -80°C, prior to aroma compound analysis.

Before analysis, the cheese samples were thawed at 4°C, vigorously shaken and diluted (1/5 for semi-hard cheese and 1/10 for soft cheese) with a cold solution of 2,5-dimethylpyrazine (546 μg/L). This solution was used as the internal standard for the experiment. Moreover, NaCl was added to cheese samples with reduced levels of NaCl to have the same NaCl content in order to prevent a salting out effect on the volatile compounds studied. The mixture was stored at 4°C for one night before analysis. Five milliliters of the mixture were then analyzed using a dynamic headspace analyzer (purge-and-trap concentrator, Tekmar-Dohrmann 3100, Tekmar Inc., Cincinnati, OH, USA) coupled to a gas chromatograph (model 6890, Agilent Technologies, Palo Alto, CA, USA) and a mass spectrometer detector (MSD 5973 inert quadrupole mass spectrometer, Agilent Technologies), and fitted with a 60 m × 320 µm i.d. DB-5 MS capillary column (film thickness: 1 μm). The oven temperature was programmed as follows: 5°C held for 9 min and then heated to 230°C at a rate of 4°C min -1 , held for 1 min. Volatiles were identified by comparison of their mass spectra with those of NIST98 and Wiley libraries. Peak absolute areas (arbitrary units) were calculated from the total ion current (TIC).

Sensory analysis

The sensory evaluation was performed using a sensory profile that evaluates the aspect, aroma, texture and taste properties of cheeses after 21 days of ripening. Attribute generation was carried out by 16 trained panelists during two specific preliminary sessions. After the generation step, the panelists agreed on a list of attributes shown in Table 4. A sensory profile using the descriptive analysis method was then performed according to the NF ISO 13 299 method. The intensity of 22 (for semi-hard cheese) and 24 (for soft cheese) attributes was assessed on 10point structured intensity scales. Panelists had to successively evaluate the aspect, aroma, texture and taste of the cheeses. Three replicates per product and per panelist were performed in two sessions. Cheese samples were placed on white plates labeled with randomly selected three-digit numbers. Sensory analyses were carried out in an air-conditioned room (20°C) with a hygrometry between 50 and 70% according to the NF ISO 8589 method. Samples were at approximately 15°C when they were tested. Cheeses were evaluated in a monadic way. The presentation order of samples was randomized across panelists to make sure that the run order did not introduce a bias in the results. Panelists were provided with mineral water for rinsing and flatbread for palate cleaning. The data were directly collected and recorded with FIZZ software (Biosystèmes).

Data analysis

All results were reported as mean ± standard deviation. All data were subjected to one-way or two-way analysis of variance (ANOVA), and differences were considered significant when p < 0.05. A multiple comparison test (Student-Newman-Keuls test) was then applied. Distinct letters (a, b, c) were assigned to significantly different groups, whereas a combination of the corresponding letters was attributed to those located between several groups. Statistical analysis was carried out with XLStat software (Version 2018.4, Addinsoft, France).

Results

3-1 Physicochemical and biochemical characteristics

The composition profile at day 21 of ripening of soft and semi-hard cheeses is illustrated in Table 1 andTable 2, respectively. For soft cheese, salting conditions had no impact on pH, aw and lactate content at day 21 of ripening (Table 1, two-way ANOVA, p > 0.05). Moreover, dry matter and moisture on fat-free dry matter were not affected by the salting conditions (data not shown). However, salting conditions significantly affected both the SN:TN and the NPN:TN ratios (reflecting primary and secondary proteolysis, respectively), and the lipolysis index (twoway ANOVA, p < 0.05). For cheeses produced without Y. lipolytica, the SN:TN ratio reached 43.8% in the control cheese, and 55.4% and 50.5% in R and S cheeses, respectively (Table S3).

The lipolysis index was higher in R (10.8) and S cheeses (10.7) than in controls (9.1). This result indicated that reducing NaCl content in soft cheese leads to an increase in both proteolytic and lipolytic activities. The lipolysis index was also significantly modified by the presence of Y. lipolytica, independently of the salting conditions (Table 1). For example, it was higher in control cheeses produced without Y. lipolytica than in spoiled cheeses (9.1 versus 7.7) (Table S3). Finally, the presence of Y. lipolytica also significantly affected the final pH detected in the cheese core (Table 1). For semi-hard cheese salting conditions had no impact on pH, aw, lactate content and proteolysis at day 21 of ripening (Table 2). Moreover, dry matter and moisture on fat-free dry matter were not affected by the salting conditions (data not shown). The lipolysis index was affected by the interaction of the salting condition and the presence of the spoilage microflora (two-way-ANOVA, p <0.05). It was higher in the R cheeses than in the C and S cheeses and higher in the non-spoiled cheeses than in the spoiled cheeses. The presence of Pseudomonas spp., significantly affected the final pH detected in the cheese core, lactate content and proteolysis, considering both SN:TN and NPN:TN ratios. The final pH values obtained in the cheese core and proteolysis were lower in spoiled cheeses than in non-spoiled cheeses, whatever the salting condition (Table S4). On the opposite, the lactate content was higher in spoiled cheeses than in non-spoiled cheeses.

3-2 Microbial composition and transcriptome analyses of the cheese surface microbiota

Cheese samples were analyzed once a week using classical cultivation-based counts (Table 3).

In parallel, 16S rRNA gene metabarcoding was used to more deeply observe the composition of the bacterial community (Figures 1A and1B). Finally, RNA-Seq was used at the end of ripening (day 21) in order to assess the effect of salt content modification on the overall activity of the whole microbial community (Figures 1C and1D).

Regarding soft cheeses, no significant difference was observed for the counts of inoculated lactic acid starter, P. camemberti and G. candidum between C, R and S cheeses during ripening (Table 3A). RNA sequencing revealed that the relative activity of G. candidum increased in R cheeses -at the expense of other dominant microorganisms, namely Penicillium, Debaryomyces, Streptococcus and Lactobacillus -in comparison to C cheeses (Figure 1C). Partial substitution of NaCl with KCl (S cheeses) restored an RNA-seq profile highly similar to C cheeses. Differential expression analysis did not reveal important changes in the expression of genes for the major microorganisms present in soft cheeses (data not shown), suggesting a limited impact of salting conditions on the functioning of the individual microbial species that compose the ecosystem. Reducing the NaCl content in soft cheeses had no impact on the development of the inoculated spoilage microorganism (Sp cheeses), i.e., Y. lipolytica (data not shown). Moreover, no significant difference was observed for the counts of coliform in control, R and S cheeses during ripening (Table 3A). However, 16S rRNA gene metabarcoding analysis highlighted an increase in the relative abundance of an OTU affiliated with the non-inoculated Enterobacter group aerogenes in both R and S cheeses at days 14 and 21 when compared to C cheeses (Figure 1A).

For semi-hard cheese, the microbial counts of both R and S cheeses were similar to those of the C cheeses (Table 3B). The composition of bacterial communities, as revealed by 16S rRNA gene metabarcoding (Figure 1B) and RNA-Seq profiles (Figure 1D) were also similar for the three salting conditions. Regarding cheeses deliberately inoculated with Pseudomonas spp. (Sp cheeses), we observed significantly higher Pseudomonas counts in both R and S cheeses than in C cheeses at day 7 (Figure 2). From day 14, the relative proportion of the OTU affiliated with Pseudomonas group gessardi was also higher in R and S cheeses than in C cheeses (Figure 1B).

Finally, the expression of Pseudomonas genes increased at day 21 in R cheeses and, to a lesser extent, in S cheeses (Figure 1D). Among them, those encoding proteases and peptidases were overexpressed in R cheeses when compared to C cheeses (Figure 3). This was partially corrected in S cheeses. Altogether, these results showed that reducing the NaCl content in semihard cheeses caused a higher development and activity of Pseudomonas spp., and that partial substitution of NaCl with KCl partially counterbalanced this effect.

3-3 Sensory characteristics

Regarding soft cheeses, significant differences in flavor perception were highlighted by the panel between the control, R and S cheeses. Indeed, 13/24 attributes were perceived as being significantly different between cheeses (Table 4). Surprisingly, no significant salty taste difference was observed between the C cheeses and the R and S cheeses. Both R and S cheeses were perceived as being significantly more flowing, acidic and bitter, and having more color, a stronger ammonia flavor, animal/cowshed odor and global intensity of odor and taste than C cheeses. In contrast, mushroom flavor was perceived to be higher in C cheeses than in R and S cheeses. The total areas measured by gas chromatography after 21 days of ripening in C and R cheeses were significantly different (Figure 4). No significant difference was noticed between C and S cheeses. High levels of alcohols and esters were measured in R cheeses. For example, the area of ethanol, 2-propanol, 2-butanol and ethylhexanoate were three to seven times higher in R cheeses than in control cheeses (Table S5).

Regarding semi-hard cheeses, no significant differences were observed in texture perception, global aroma, taste intensity and salty perception (data not shown). Salt reduction only affected bitterness, which was increasingly noticed in both R and S cheeses (medium values of the bitterness attribute were 1.28, 1.8 and 2.03 for C, R and S cheeses, respectively). There was no difference in the volatile compound levels in any of the semi-hard cheeses (data not shown).

Discussion

The current study investigated the effect of salt reduction (about 20%) and partial substitution of NaCl with KCl (about 20%) on all microbiological, biochemical and sensory facets of soft and semi-hard cheeses.

The sensory evaluation indicated that saltiness intensity was the same in C, R and S cheeses for both soft and semi-hard cheeses. This result is in contradiction with other studies in which the authors observed a decrease in the perception of the salty attribute with 15 to 30% salt content reduction in Edam [START_REF] Czarnacka-Szymani | Impact of nutritional information on consumers' acceptance of cheese with reduced sodium chloride content[END_REF] and Tybo cheese [START_REF] Sihufe | Effect of sodium chloride reduction on physicochemical, biochemical, rheological, structural and sensory characteristics of Tybo cheese[END_REF]. However, both studies concluded that cheeses produced with a reduced salt content had a sensory acceptance that was similar to that of the control cheeses. Similar studies conducted on Prato and Coalho cheeses produced with reduced salt content on the order of 25-30% led to the same conclusion [START_REF] Baptista | Reduction of 25% salt in Prato cheese does not affect proteolysis and sensory acceptance[END_REF][START_REF] Costa | Manufacture of reduced-sodium Coalho cheese by partial replacement of NaCl with KCl[END_REF].

The ripening of soft cheeses with a bloomy rind, such as Camembert or Brie, is mainly due to the activity of G. candidum and P. camemberti, and these two fungal species are major contributors to the texture and flavor of this type of cheese [START_REF] Lessard | The Composition of Camembert Cheese-Ripening Cultures Modulates both Mycelial Growth and Appearance[END_REF][START_REF] Marcellino | Diversity of Geotrichum candidum Strains Isolated from Traditional Cheesemaking Fabrications in France[END_REF]. It is generally accepted that G. candidum and P. camemberti play a role in the white and bloomy aspect of the rind, the firmness and the thickness, and have typical sensory characteristics due to the lipolysis and proteolysis that occurred during ripening [START_REF] Boutrou | Interests in Geotrichum candidum for cheese technology[END_REF][START_REF] Lessard | Metatranscriptome analysis of fungal strains Penicillium camemberti and Geotrichum candidum reveal cheese matrix breakdown and potential development of sensory properties of ripened Camembert-type cheese[END_REF]. Nevertheless, before this study, no data was available on the consequence of salt reduction on the quality of soft cheeses with a bloomy rind and on the behavior of these species in this context. We did not observe higher counts of G. candidum in both R and S cheeses than in controls during ripening. A previous study on Torta del Casar, a Spanish soft ewe cheese reported that yeast-molds growth was not affected by NaCl content at levels comprised between 0.48 and 2.79% [START_REF] Tabla | Salt influence on surface microorganisms and ripening of soft ewe cheese[END_REF]. However, the relative activity of G. candidum in our study-as determined by RNA-seq profiles -increased. Reducing salt content did not significantly affect the counts of P. camemberti but led to a decrease of its relative activity. In this type of cheese, reducing NaCl content may thus modify the balance between G. candidum and P. camemberti. Although not observed in our study, where the NaCl reduction was only about 20%, it has already been reported that G. candidum may outcompete P. camemberti in cheese produced with low NaCl content and form a surface defect known as 'toad skin' [START_REF] Kindstedt | The Basics of Cheesemaking[END_REF].

Modification of several sensory attributes, as observed in our results on soft cheese, could be attributed to the change of the balance between the activity of G. candidum and P. camemberti.

Bitterness and ammoniacal flavors are common to cheeses matured by molds and are generally associated with an intense proteolytic activity [START_REF] Galli | Sensory quality of Camembert-type cheese: Relationship between starter cultures and ripening molds[END_REF].

For example, the production of small peptides from the breakdown of caseins contributes to the bitterness of cheeses [START_REF] Engel | Determination of taste-active compounds of a bitter Camembert cheese by omission tests[END_REF][START_REF] Galli | Sensory quality of Camembert-type cheese: Relationship between starter cultures and ripening molds[END_REF].

Bitterness has already been associated with higher proteolysis in Cheddar cheese produced with reduced salt content [START_REF] Rulikowska | The impact of reduced sodium chloride content on Cheddar cheese quality[END_REF]. However, the bitterness rating for Cheddar cheese produced with KCl was not significantly different from the full-sodium control (Grummer, Bobowski, Karalus, Vickers, & Schoenfuss, 2013). Because G. candidum is strongly proteolytic [START_REF] Boutrou | Contribution of Geotrichum candidum to the proteolysis of soft cheese[END_REF], its greater relative activity, as observed with RNA-sequencing, in soft cheese produced with reduced NaCl content or partial replacement of NaCl with KCl could thus have resulted in stronger proteolysis and, consequently, higher bitterness perception.

Sensory analysis also revealed that R soft cheeses and, to a lesser extent, S cheeses, exhibited a higher odor and taste intensity than control soft cheeses. For R cheeses, this stronger perception might be associated with the higher area of total aroma compounds detected by GC-MS and, more precisely, the higher levels of some alcohols and esters. G. candidum can produce such compounds, e.g., 2-methylpropanol, 2-and 3-methylbutanol and ethylhexanoate [START_REF] Boutrou | Interests in Geotrichum candidum for cheese technology[END_REF][START_REF] Mdaini | Production of natural fruity aroma by Geotrichum candidum[END_REF]. The production of dimethyl disulfide and other volatile sulfur compounds as well by G. candidum again could generate the "animal/cowshed" attribute [START_REF] Berger | Production of Sulfur Flavors by Ten Strains ofGeotrichum candidum[END_REF], which was also more perceived by panelists in both R and S cheeses.

In our study, a 20% reduction of NaCl content in soft cheeses had no impact on the development of the inoculated spoilage yeast Y. lipolytica. However, the relative abundance of the noninoculated Enterobacter group aerogenes was higher. Enterobacter sp. has already been detected in cheeses and is generally considered as a contaminant [START_REF] Coton | Diversity and assessment of potential risk factors of Gram-negative isolates associated with French cheeses[END_REF][START_REF] Tabla | Enterobacteriaceae species during manufacturing and ripening of semi-hard and soft raw ewe's milk cheese: Gas production capacity[END_REF]. Controlling the microbial quality of both milk and the dairy environment would thus be crucial when producing soft cheeses with reduced levels of salt.

Semi-hard cheeses with washed rinds such as Reblochon are usually made using lactic acid bacteria and aerobic ripening bacteria like B. aurantiacum and yeasts, principally G. candidum and D. hansenii [START_REF] Cogan | Biodiversity of the Surface Microbial Consortia from Limburger, Reblochon, Livarot, Tilsit, and Gubbeen Cheeses[END_REF]. Our results revealed that salt reduction or partial NaCl substitution with KCl did not affect the physicochemical, biochemical, microbiological and sensory characteristics of this type of cheese, which is in agreement with several other studies performed on hard or semi-hard cheeses [START_REF] Baptista | Reduction of 25% salt in Prato cheese does not affect proteolysis and sensory acceptance[END_REF][START_REF] Costa | Manufacture of reduced-sodium Coalho cheese by partial replacement of NaCl with KCl[END_REF][START_REF] Costa | Effect of partial replacement of sodium chloride with potassium chloride on the characteristics of Minas Padrão cheese[END_REF][START_REF] Sihufe | Effect of sodium chloride reduction on physicochemical, biochemical, rheological, structural and sensory characteristics of Tybo cheese[END_REF][START_REF] Soares | The effect of lowering salt on the physicochemical, microbiological and sensory properties of São João cheese of Pico Island[END_REF]. Moreover, studies showed that overall acceptance was the same for semi-hard Coalho and Minas Padrão cheeses with 25% KCl and control cheeses [START_REF] Costa | Manufacture of reduced-sodium Coalho cheese by partial replacement of NaCl with KCl[END_REF][START_REF] Costa | Effect of partial replacement of sodium chloride with potassium chloride on the characteristics of Minas Padrão cheese[END_REF]. However, studies performed on Cheddar, a hard cheese, indicated that decreasing salt content can result in enhanced growth of non-starter lactic acid bacteria at the beginning of ripening, in higher proteolysis, in lower hardness and in higher off-flavor intensity and, more particularly, bitterness and sulfur attributes [START_REF] Murtaza | Texture, flavor, and sensory quality of buffalo milk Cheddar cheese as influenced by reducing sodium salt content[END_REF][START_REF] Rulikowska | The impact of reduced sodium chloride content on Cheddar cheese quality[END_REF]. Other authors showed that salt content did not significantly influence the overall bacterial community structure, except that lower salt levels resulted in enhanced starter survival [START_REF] Porcellato | Application of ARISA to assess the influence of salt content and cation type on microbiological diversity of Cheddar cheese[END_REF].

In this study, we observed a greater development of Pseudomonas spp. in both R and S cheeses when the spoiler was deliberately inoculated, which confirms the results obtained in previous studies (Dugat-Bony et al., 2016;[START_REF] Tabla | Salt influence on surface microorganisms and ripening of soft ewe cheese[END_REF]. In the presence of Pseudomonas spp., primary and secondary proteolysis as well as lipolysis were higher than in non-spoiled cheeses.

Moreover, the expression of several genes encoding for proteases and peptidases in Pseudomonas spp. were overexpressed in low NaCl conditions. It is known that these bacteria are able to produce extracellular, heat-resistant proteolytic and lipolytic enzymes [START_REF] Baur | Quantification of the proteolytic and lipolytic activity of microorganisms isolated from raw milk[END_REF][START_REF] Meng | Characterization of Pseudomonas spp. and Associated Proteolytic Properties in Raw Milk Stored at Low Temperatures[END_REF] that could generate off-flavors, but genes encoding those enzymes remain to be identified. The expression data presented here could be used in future studies to select a reduced set of candidate genes potentially involved in these functions.

In our study, sensory analysis was not performed on semi-hard cheeses spoiled with Pseudomonas spp., but other authors demonstrated that volatile profiles of cheeses made from milk inoculated with Pseudomonas strains exhibit high levels of alcohols, esters and sulfur compounds [START_REF] Morales | Production of volatile compounds in cheese by Pseudomonas fragi strains of dairy origin[END_REF]. For these reasons, once again, the microbial quality of milk and the dairy environment need to be carefully controlled by cheese makers when producing semi-hard cheeses with reduced salt content or the partial substitution of NaCl with KCl.

Conclusions

In conclusion, impacts on the physicochemical criteria of cheeses have been observed more intensely in soft cheeses than in semi-hard cheeses. For soft cheeses, sensory analysis detected many more changes related to changes in salting conditions than for semi-hard cheeses.

Little impact on the architecture and the quantitative and qualitative composition of the microbial species was observed for either technology when the salting conditions were modified. In addition, NaCl appears to represent a barrier against Pseudomonas growth in semihard cheeses but not against Yarrowia in soft cheeses, which appears to be weakly affected by salt levels. The impact of a reduction in the NaCl level or its substitution with KCl is then dependent on the cheese technology studied. The physicochemical and microbiological modifications related to the salting methods highlighted in this study must be taken into account in order to adapt cheese production practices to the desired organoleptic qualities of the finished products. In addition, the elimination, reduction or substitution of salt should be the subject of specific studies for each type of product while remaining vigilant about the quality of raw material, the microbiological stability of the products and their organoleptic characteristics.

  15 or 14 g of NaCl/kg of cheese Reduced salt cheese: 80% of [NaCl] Substituted salt cheeses: 80% NaCl +

Table 1 :

 1 Biochemical composition of soft cheeses at day 21 of ripening.

				spoilage						spoilage
		cheese			cheese*spoilage		cheese
				(Yarrowia lipolytica)						(Yarrowia lipolytica)
		F	p-value	F	p-value	F	p-value	R cheese	S cheese	C cheese not spoiled	spoiled
	pH core	1.968	0.182	20.776	0.001	2.258	0.147	5.32 a	5.38 a	5.30 a	5.421 a	5.259 b
	pH rind	0.970	0.407	0.282	0.605	1.279	0.314	6.05 a	6.18 a	6.10 a	6.133 a	6.092 a
	aw core	3.793	0.053	1.656	0.222	0.003	0.997	0.977 a	0.971 a	0.973 a	0.975 a	0.973 a
	aw rind	0.809	0.468	7.048	0.021	0.085	0.919	0.969 a	0.965 a	0.966 a	0.970 a	0.964 a
	Lactate (mg/100g)	1.046	0.381	0.049	0.829	0.355	0.708	576 a	672 a	650 a	639 b	627 a
	Proteolysis (mg/100g)
	SN:TN ratio	8.616	0.005	0.006	0.937	1.221	0.329	54.1 a	49.7 ab	45.5 b	49.8 a	49.7 b
	Proteolysis (mg/100g)
	NPN:TN ratio	4.283	0.039	0.554	0.471	1.212	0.332	26.9 a	23.3 ab	20.9 b	23.1 a	24.3 b
	Lipolysis index	5.119	0.025	24.819	0.000	1.630	0.236	10.0 a	9.2 ab	8.4 b	10.2 a	8.2 b
	C: control cheese; R: reduced salt cheese; S: substituted salt cheese.
	All data were subjected to two-way ANOVA (p-value threshold = 0.05).

Table 2 :

 2 Biochemical composition of semi-hard cheeses at day 21 of ripening.Values are the means of triplicate trials ± standard deviation. Means with columns without a common superscript letter differ significantly (ANOVA, p<0.05).

					spoilage						spoilage
		cheese			cheese*spoilage		cheese
				(Pseudomonas fluorescens)						(Pseudomonas fluorescens)
		F	p-value	F	p-value	F	p-value	R cheese	S cheese	C cheese	not spoiled	spoiled
		0.466	0.638	6.424	0.026	0.122	0.887	5.50 a	5.55 a	5.49 a	5.45 b	5.58 a
	pH core
		1.265	0.317	0.874	0.368	0.330	0.725	5.83 a	5.86 a	5.67 a	5.74 a	5.84 a
	pH rind
		0.642	0.544	0.853	0.374	0.818	0.465	0.969 a	0.977 a	0.97 a	0.978 a	0.972 a
	aw core
		1.387	0.287	0.010	0.921	0.235	0.794	0.981 a	0.978 a	0.979 a	0.979 a	0.979 a
	aw rind
		0.482	0.629	6.919	0.022	0.084	0.920	285 a	267 a	270 a	296 a	252 b
	Lactate (mg/100g)
	Proteolysis
	(mg/100g) SN:TN	2.342	0.138	24.843	0.000	3.555	0.061	23.1 a	21.4 a	21.0 a	19.7 b	24.0 a
	ratio
	Proteolysis
	(mg/100g) NPN:TN	1.902	0.192	9.401	0.010	1.656	0.232	10.6 a	9.9 a	9.7 a	9.5 b	10.7 a
	ratio
		8.807	0.004	5.944	0.031	5.878	0.017	5.9 a	4.2 b	4.5 b	4.4 b	5.3 a
	Lipolysis index
	C: control cheese; R: reduced salt cheese; S: substituted salt cheese.
	All data were subjected to two-way ANOVA (p-value threshold = 0.05).

C: control cheese; R: reduced salt cheese; S: substituted salt cheese; PC: Penicillium camemberti; GC: Geotrichum candidum.

Table 3B :

 3B Microbiological concentrations (log CFU/g) of semi-hard cheeses during ripening. C: control cheese; R: reduced salt cheese; S: substituted salt cheese; Brevi: Brevibacterium sp.; DH: Debaryomyces hansenii.Values are the means of triplicate trials ± standard deviation. Means with columns without a common superscript letter differ significantly (ANOVA, p<0.05).

	Cheese samples Semi-hard C Semi-hard R Semi-hard S	Lactococci 7.44 ± 0.06 a 7.63 ± 0.06 a 7.57 ± 0.18 a	Streptocci 8.87 ± 0.16 a 8.87 ± 0.12 a 8.84 ± 0.12 a	Day 1 Coliforms 2.63 ± 0.39 a 2.49 ± 0.89 a 2.44 ± 0.85 a	Brevi 3.56 ± 0.07 a 3.60 ± 0.25 a 3.63 ± 0.14 a	DH 4.78 ± 0.18 a 4.93 ± 0.19 a 5.34 ± 0.45 a	Molds 2.49 ± 0.50 a 2.49 ± 0.20 a 2.46 ± 0.15 a	Lactococci 7.88 ± 0.14 a 7.92 ± 0.21 a 7.76 ± 0.08 a	Day 7 Coliforms 9.29 ± 0.33 a 2.35 ± 0.58 Streptococci a 9.17 ± 0.13 a 2.45 ± 1.31 a 9.33 ± 0.22 a 2.24 ± 0.65 a	Brevi 3.97 ± 0.10 a 4.04 ± 0.26 a 3.99 ± 0.15 a	DH 7.92 ± 0.66 a 8.47 ± 0.1 a 8.55 ± 0.04 a	798 799 800 801 Molds 802 803 804 6.39 ± 805 0.61 a 806 6.75 ± 807 0.36 a 808 6.94 ± 809 810 0.27 a
	Cheese
	samples			Day 14						Day 21
						DH						DH
		Lactococci Streptococci	Coliforms	Brevi		Molds Lactococci	Streptococci	Coliforms	Brevi		Molds
	Semi-hard C 7.61 ± 0.22 a	9.19± 0.63 a	2.79 ± 0.84 a	4.84 ± 0.46 a	8.65 ± 0.04 a	8.01 ± 0.15 a	6.16 ± 0.89 a	9.19 ± 0.33 a 2.80 ± 0.70 a	5.65 ± 1.29 a	8.67 ± 0.08 a	8.23 ± 0.18 a
	Semi-hard R 7.09 ± 0.13 a	9.04± 0.10 a	2.20 ± 0.17 a	4.76 ± 0.85 a	8.62 ± 0.10 a	8.09 ± 0.18 a	6.85 ± 0.53 a	9.48 ± 0.04 a 2.04 ± 0.90 a	6.05 ± 1.05 a	8.83 ± 0.04 a	8.40 ± 0.08 a
	Semi-hard S	6.70 ± 0.57 a	8.97± 0.06 a	3.1 1± 0.84 a	4.92 ± 0.67 a	8.75 ± 0.09 a	7.99 ± 0.15 a	6.53 ± 0.73 a	9.49 ± 0.04 a 3.13 ± 0.88 a	5.59 ± 1.24 a	8.74 ± 0.08 a	8.15 ± 0.27 a

Table 4 :

 4 Average intensity of sensory attributes determined for soft cheese at day 21 of ripening using a scale ranging from 0 to 10. Means in the same line without a common superscript letter are significantly different (ANOVA, p<0.05).

	Attributes	Control cheese (C) Reduced salt cheese (R) Substituted salt cheese (S)
	Color of cheese rind	3.72 a	4.59 b	4.73 b
	Thickness of rind	3.44 a	3.50 a	3.36 a
	Color of cheese core	5.31 a	5.31 a	5.06 a
	Flowing aspect	5.10 a	6.2 b	6.64 b
	Global intensity of odor	6.25 a	7.25 ab	6.71 b
	Mushroom odor	4.59 a	3.05 b	3.63 b
	Ammonia odor	2.30 a	3.44 b	3.21 b
	Tactile perception of soft texture	4.65 a	4.49 a	4.63 a
	Firm texture (in mouth)	2.59 a	2.15 a	1.18 b
	Stickiness of core	5.25 a	5.51 a	5.11 a
	Granular texture of core	0.66 a	0.66 a	0.43 a
	Creaminess of core	6.52 a	6.95 a	6.94 a
	Global intensity of taste	6.83 a	7.44 b	7.36 b
	Salty taste	5.52 a	5.28 a	5.57 a
	Acidic taste	2.49 a	2.97 b	3.07 b
	Bitter taste	2.99 a	3.57 ab	3.84 b
	Rancid taste	0.71 a	0.95 a	0.78 a
	Oxidized/metallic taste	1.27 a	1.81 b	1.67 ab
	Aromatic richness	5.33 a	5.00 a	5.31 a
	Mushroom aroma of rind	3.52 a	2.76 b	3.56 a

Acknowledgements

This work was supported by FranceAgriMer (RedSel project, grant number 320143436) and received the financial support of the CNIEL (Centre National Interprofessionnel de l'Economie Laitière). We thank Armelle Delille (UMR GMPA, INRA) for GC-MS analysis and the INRA GeT-PlaGE Platform (http://get.genotoul.fr) for assistance in 16S rRNA amplicon sequencing.

For RNA sequencing, this work has benefitted from the facilities and expertise of the highthroughput sequencing platform of I2BC (Centre de Recherche de Gif -www.i2bc-saclay.fr).

We are grateful to the INRA MIGALE bioinformatics platform (http://migale.jouy.inra.fr) for providing computational resources and support, and to our undergraduate students Maxime Guilleton and Marie Bogusz for bioinformatics analysis.