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En bioremédiation, la bioaugmentation consiste en l’introduction de microorganismes dans un milieu contaminé afin d’augmenter et/ou de suppléer la population bactérienne en place, dans l’objectif de le dépolluer. Cette approche est cependant limitée par la difficulté de choisir des microorganismes efficients - communauté ou souche spécifique – à inoculer, en lien avec les conditions abiotiques et les microorganismes indigènes du milieu pollué.Dans ce travail nous avons cherché à prédire le potentiel microbien de dégradation de deux pesticides, le glyphosate et l’isoproturon, de communautés microbiennes telluriques en utilisant des méthodes statistiques issues de la prédiction génomique. Pour cela nous avons obtenu des variants compositionnels de communautés dégradant ces deux herbicides (une communauté par herbicide) via une approche couplée de dilutions et de traitements biocides.Ensuite, dans le but de relier la composition en OTU de ces variants avec leur capacité de dégradation, nous avons appliqué 3 méthodes de prédiction : Ridge Regression, Lasso, et Random Forest. Les résultats indiquent une corrélation de plus de 80 % entre capacité de dégradation prédites et mesurées.Nous avons ensuite construit des communautés multidégradantes par une approche de coalescence en basant nos choix sur des propriétés intrinsèques des communautés dégradant fortement l’isoproturon pour certaines et le glyphosate pour d’autres. Finalement, nous avons évalué l’efficacité de ces nouvelles communautés multidégradantes dans un sol pollué non dégradant. Les résultats montrent le transfert des capacités de dégradation des herbicides, et ceci même à faible dose d’inoculation dans le cas de l’isoproturon.

International audience

Le CBL (Club des Bactéries Lactiques) est une manifestation scientifique qui réunit chercheurs, enseignants-chercheurs et industriels R&D, pour échanger sur les avancées scientifiques et techniques réalisées dans le domaine des bactéries lactiques. Les thèmes abordés sont le reflet des larges potentiels de ces bactéries et de leurs applications : fermentation alimentaire, santé humaine et animale, probiotique, environnement…; International audience; En fabrication fromagère, les bactéries lactiques (BL) et propioniques sont des actrices clés de la production de métabolites conférant les qualités nutritionnelles et organoleptiques aux fromages. Pourtant, la contribution de chaque espèce à la qualité finale du fromage n’est pas totalement élucidée. L’objectif était de déterminer quelles espèces contribuent à acidifier et produire des composés d’arôme, par quelles voies métaboliques, selon quelle temporalité et aussi d’investiguer les interactions métaboliques bactériennes contribuant au fonctionnement de l’écosystème fromager.Nous avons séquencé et annoté : Lactococcus lactis subsp. lactis biovar diacetylactis CIRM-BIA1206 (LL), Lactiplantibacillus plantarum CIRM-BIA465 (LP), Propionibacterium freudenreichii CIRM-BIA122 (PF). Nous avons reconstruit les voies métaboliques et élaboré un modèle métabolique de communauté. Un fromage à pâte pressée non cuite sans croûte a été réalisé avec ces bactéries et analysé pendant la fabrication et l’affinage (4 réplicats). Des dénombrements bactériens, des dosages de sucres, d’acides organiques et de composés d’arôme [1] ont été réalisés ainsi qu’un séquençage des ARN bactériens. L’analyse des gènes différentiellement exprimés a permis de montrer à quel moment de la fabrication étaient induits les gènes impliqués dans le catabolisme du lactose et du citrate et dans les voies de synthèse de différents composés d’arômes : acides lactique, acétique, propionique, isovalérique (arôme ‘vieux fromage’), diacétyle (arôme beurré). Le catabolisme du lactose était opéré chez LL via la voie du tagatose pendant l’acidification puis via la voie de Leloir et uniquement par cette dernière voie chez LP. La production de diacétyle était effectuée par LL dès le début de la fabrication puis plus modérément par LP. Les synthèses d’acides propionique et isovalérique ont été attribuées à PF et les métabolismes correspondants induits pendant la phase d’affinage. Enfin, les voies de fermentation mixtes ayant été induites pendant l’affinage, l’acide acétique a été vraisemblablement produit par les trois espèces à cette étape. L’implémentation du modèle métabolique communautaire dans l’outil Smetana [2] a révélé les bases moléculaires du commensalisme précédemment évoqué entre BL et PF [3–5]. Les BL produisent de l’acide lactique et potentiellement du glycérol, de la sérine et de la phénylalanine au profit de PF. Ces interactions identifiées in silico restent à valider in vitro.L’ensemble de ces résultats font de la métatranscriptomique associée aux modèles métaboliques, des outils de choix pour mieux comprendre et maîtriser les métabolismes et les interactions régissant le fonctionnement des écosystèmes fromagers.

International audience; The formation of cheese flavor mainly results from the production of volatile compounds by microorganisms. We investigated how fine-tuning cheese-making process parameters changed the cheese volatilome in a semi-hard cheese inoculated with Lactococcus (L.) lactis, Lactiplantibacillus (L.) plantarum, and Propionibacterium (P.) freudenreichii. A standard (Std) cheese was compared with three variants of technological itineraries: a shorter salting time (7 h vs 10 h, Salt7h), a shorter stirring time (15 min vs 30 min, Stir15min), or a higher ripening temperature (16°C vs 13°C, Rip16°C). Bacterial counts were similar in the four cheese types, except for a 1.4 log 10 reduction of L. lactis counts in Rip16°C cheeses after 7 weeks of ripening. Compared to Std, Stir15min and Rip16°C increased propionibacterial activity, causing higher concentrations of acetic, succinic, and propanoic acids and lower levels of lactic acid. Rip16°C accelerated secondary proteolysis and volatile production. We thus demonstrated that fine-tuning process parameters could modulate the cheese volatilome by influencing specific bacterial metabolisms.

National audience; Microbial communities are at the heart of all ecosystems and yet, a sound understanding of the ecological processes governing the assembly of these communities in the environment is missing. To address the role of biotic interactions in assembly and functioning of the soil microbiota, we used a top down manipulation approach based on the removal of various populations in a natural microbial community. Suspensions of the soil microbiota were subjected to various biocidal and filtration treatments before being inoculated into the same sterilized soil. We hypothesized that if biotic interactions are an important shaping force of the microbiota assembly, removal of microbial groups should largely affect the fitness of the remaining ones during soil recolonization. We show that nearly half of the dominant bacterial taxa were subjected to competitive interactions, underlining the importance of biotic interactions in the assembly of microbial community in soil. Moreover, evidence for competitive exclusion between members of Bacillales and Proteobacteriales suggests that potential general rules of microbial community assembly can be identified. Our findings also indicate that effects on biotic interactions results in more prominent changes in activities related to N- than to C-cycling. Such removal approach can provide a new avenue to study microbial interactions in complex ecosystems

International audience; Early embryogenesis relies on the translational regulation of maternally stored mRNAs. In sea urchin, fertilization triggers a dramatic rise in translation activity, necessary for the onset of cell division. Here, the full spectrum of the mRNAs translated upon fertilization was investigated by polysome profiling and sequencing. The translatome of the early sea urchin embryo gave a complete picture of the polysomal recruitment dynamics following fertilization. Our results indicate that only a subset of maternal mRNAs were selectively recruited onto polysomes, with over-represented functional categories in the translated set. The increase in translation upon fertilization depends on the formation of translation initiation complexes following mTOR pathway activation. Surprisingly, mTOR pathway inhibition differentially affected polysomal recruitment of the newly translated mRNAs, which thus appeared either mTOR-dependent or mTOR-independent. Therefore, our data argue for an alternative to the classical cap-dependent model of translation in early development. The identification of the mRNAs translated following fertilization helped assign translational activation events to specific mRNAs. This translatome is the first step to a comprehensive analysis of the molecular mechanisms governing translation upon fertilization and the translational regulatory networks that control the egg-to-embryo transition as well as the early steps of embryogenesis.

International audience; In the context of highly pressured agricultural production using low levels of inputs, cropping strategies could take advantage of existing plant-microbiome interactions that can improve both plant growth and health. We believe that plant genetics can provide some clues to better understand the mechanisms underlying microbiome recruitment by the plant.The aims of this work were (i) to assess plant genotype effect on the rhizosphere bacterial communities in relation to the plant nutritional strategies for a core collection of 155 genotypes of Medicago truncatula and (ii) to highlight the genetic determinisms potentially associated in these interactions. To achieve these aims, we developed a multidisciplinary approach where the plant growth and nutritional strategies were characterized using an ecophysiological framework, the rhizosphere bacterial communities were described using massive parallel sequencing, and the genetic determinisms were analysed using Genome-Wide-Association Studies. We observed a significant effect of the plant genotype and identified different groups of plant genotypes according to their nutritional strategies and to the structure and composition of their associated rhizosphere bacterial communities. To go further, we also highlighted loci linked to microbiome recruitment by the plant.First, we showed that plant genetics may provide the levers to manipulate soil microflora through plant microbiome recruitment. Then, this work demonstrates the importance of combining approaches from different disciplines to better unravel plant-microbiome interactions.

Early embryogenesis relies on the translational regulation of maternally stored mRNAs. In sea urchin, fertilization triggers a dramatic rise in translation activity, necessary for the onset of cell division. Here, the full spectrum of the mRNAs translated upon fertilization was investigated by polysome profiling and sequencing. The translatome of the early sea urchin embryo gave a complete picture of the polysomal recruitment dynamics following fertilization. Our results indicate that only a subset of maternal mRNAs were selectively recruited onto polysomes, with over-represented functional categories in the translated set. The increase in translation upon fertilization depends on the formation of translation initiation complexes following mTOR pathway activation. Surprisingly, mTOR pathway inhibition differentially affected polysomal recruitment of the newly translated mRNAs, which thus appeared either mTOR-dependent or mTOR-independent. Therefore, our data argue for an alternative to the classical cap-dependent model of translation in early development. The identification of the mRNAs translated following fertilization helped assign translational activation events to specific mRNAs. This translatome is the first step to a comprehensive analysis of the molecular mechanisms governing translation upon fertilization and the translational regulatory networks that control the egg-to-embryo transition as well as the early steps of embryogenesis.

CHMM : an R package for coupled Hidden Markov Models. R User Conference 2017

The technological progress of the last twenty years allowed the emergence of an high-throuput biology basing on large-scale data obtained in a automatic way. The statisticians have an important role to be played in the modelling and the analysis of these numerous, noisy, sometimes heterogeneous and collected at various scales. This role can be from several nature. The statistician can propose new concepts, or new methods inspired by questions asked by this biology. He can propose a fine modelling of the phenomena observed by means of these technologies. And when methods exist and require only an adaptation, the role of the statistician can be the one of an expert, who knows the methods, their limits and the advantages.In a first part, I introduce different methods developed with my co-authors for the analysis of high-throughput biological data, based on latent variables models. These models make it possible to explain a observed phenomenon using hidden or latent variables. The simplest latent variable model is the mixture model. The first two presented methods constitutes two examples: the first in a context of multiple tests and the second in the framework of the definition of a hybridization threshold for data derived from microarrays. I also present a model of coupled hidden Markov chains for the detection of variations in the number of copies in genomics taking into account the dependence between individuals, due for example to a genetic proximity. For this model we propose an approximate inference based on a variational approximation, the exact inference not being able to be considered as the number of individuals increases. We also define a latent-block model modeling an underlying structure per block of rows and columns adapted to count data from microbial ecology. Metabarcoding and metagenomic data correspond to the abundance of each microorganism in a microbial community within the environment (plant rhizosphere, human digestive tract, ocean, for example). These data have the particularity of presenting a dispersion stronger than expected under the most conventional models (we speak of over-dispersion). Biclustering is a way to study the interactions between the structure of microbial communities and the biological samples from which they are derived. We proposed to model this phenomenon using a Poisson-Gamma distribution and developed another variational approximation for this particular latent block model as well as a model selection criterion. The model's flexibility and performance are illustrated on three real datasets.A second part is devoted to work dedicated to the analysis of transcriptomic data derived from DNA microarrays and RNA sequencing. The first section is devoted to the normalization of data (detection and correction of technical biases) and presents two new methods that I proposed with my co-authors and a comparison of methods to which I contributed. The second section devoted to experimental design presents a method for analyzing so-called dye-switch design.In the last part, I present two examples of collaboration, derived respectively from an analysis of genes differentially expressed from microrrays data, and an analysis of translatome in sea urchins from RNA-sequencing data, how statistical skills are mobilized, and the added value that statistics bring to genomics projects.; Les progrès technologiques des vingt dernières années ont permis l’avènement d'une biologie à haut-débit reposant sur l'obtention de données à grande échelle de façon automatique. Les statisticiens ont un rôle important à jouer dans la modélisation et l'analyse de ces données nombreuses, bruitées, parfois hétérogènes et recueillies à différentes échelles. Ce rôle peut être de plusieurs natures. Le statisticien peut proposer de nouveaux concepts ou méthodes inspirées par les questions posées par cette biologie. Il peut proposer une modélisation fine des phénomènes observés à l'aide de ces technologies. Et lorsque des méthodes existent et nécessitent seulement une adaptation, le rôle du statisticien peut être celui d'un expert, qui connaît les méthodes, leurs limites et avantages. Le travail présenté dans cette thèse se situe à l'interface entre mathématiques appliquées et biologie, et relève plutôt des deuxième et troisième type de rôles mentionnés.Dans une première partie, j’introduis différentes méthodes développées pour l'analyse de données biologiques à haut débit, basées sur des modèles à variables latentes. Ces modèles permettent d'expliquer un phénomène observé à l'aide de variables cachées. Le modèle à variables latentes le plus simple est le modèle de mélange. Les deux premières méthodes présentées en sont des exemples: la première dans un contexte de tests multiples et la deuxième dans le cadre de la définition d'un seuil d'hybridation pour des données issues de puces à ADN. Je présente également un modèle de chaînes de Markov cachées couplées pour la détection de variations du nombre de copies en génomique prenant en compte de la dépendance entre les individus, due par exemple à une proximité génétique. Pour ce modèle, nous proposons une inférence approchée fondée sur une approximation variationnelle, l'inférence exacte ne pouvant pas être envisagée dès lors que le nombre d'individus augmente. Nous définissons également un modèle à blocs latents modélisant une structure sous-jacente par bloc de lignes et colonnes adaptées à des données de comptage issue de l'écologie microbienne. Les données issues de méta-codebarres ou de métagénomique correspondent à l'abondance de chaque unité d'intérêt (par exemple micro-organisme) d'une communauté microbienne au sein d'environnement (rhizosphère de plante, tube digestif humain, océan par exemple). Ces données ont la particularité de présenter une dispersion plus forte qu'attendue sous les modèles les plus classiques (on parle de sur-dispersion). La classification croisée est une façon d'étudier les interactions entre la structure des communautés microbiennes et les échantillons biologiques dont elles sont issues. Nous avons proposé de modéliser ce phénomène à l'aide d'une distribution Poisson-Gamma et développé une autre approximation variationnelle pour ce modèle particulier ainsi qu'un critère de sélection de modèle. La flexibilité et la performance du modèle sont illustrées sur trois jeux de données réelles.Une deuxième partie est consacrée à des travaux dédiés à l'analyse de données de transcriptomique issues des technologies de puce à ADN et de séquençage de l’ARN. La première section concerne la normalisation des données (détection et correction de biais techniques) et présente deux nouvelles méthodes que j’ai proposées avec mes co-auteurs et une comparaison de méthodes à laquelle j’ai contribuée. La deuxième section dédiée à la planification expérimentale présente une méthode pour analyser les dispositifs dit en dye-switch.Dans une dernière partie, je montre à travers deux exemples de collaboration, issues respectivement d'une analyse de gènes différentiellement exprimés à partir de données issues de puces à ADN, et d'une analyse du traductome chez l'oursin à partir de données de séquençage de l'ARN, la façon dont les compétences statistiques sont mobilisées et la plus-value apportée par les statistiques aux projets de génomique.

CHMM : an R package for coupled Hidden Markov Models. R User Conference 2017

International audience; Interactions between plants and phytophagous insects play an important part in shaping the biochemical composition of plants. Reciprocally plant metabolites can influence major life history traits in these insects and largely contribute to their fitness. Plant rhizospheric microorganisms are an important biotic factor modulating plant metabolites and adaptation to stress. While plant-insects or plant-microorganisms interactions and their consequences on the plant metabolite signature are well-documented, the impact of soil microbial communities on plant defenses against phytophagous insects remains poorly known. In this study, we used oilseed rape (Brassica napus) and the cabbage root fly (Delia radicum) as biological models to tackle this question. Even though D. radicum is a belowground herbivore as a larva, its adult life history traits depend on aboveground signals. We therefore tested whether soil microbial diversity influenced emergence rate and fitness but also fly oviposition behavior, and tried to link possible effects to modifications in leaf and root metabolites. Through a removal-recolonization experiment, 3 soil microbial modalities ("high," "medium," "low") were established and assessed through amplicon sequencing of 16S and 18S ribosomal RNA genes. The "medium" modality in the rhizosphere significantly improved insect development traits. Plant-microorganism interactions were marginally associated to modulations of root metabolites profiles, which could partly explain these results. We highlighted the potential role of plant-microbial interaction in plant defenses against Delia radicum. Rhizospheric microbial communities must be taken into account when analyzing plant defenses against herbivores, being either below or aboveground.

Latent Block Model for metagenomic data. 15. European Conference on Computational Biology (ECCB 2016), Workshop "Recent Computational Advances in Metagenomics (RCAM)"

International audience

Latent Block Model for ecological abundance data. 30. European Meeting of Statisticians

Latent Block Model for ecological abundance data. ISB Channel Network

How to Design a good RNA-Seq experiment in an interdisciplinary context?. European conference on Computational Biology

Présentation orale faite par Julie Aubert dans le minisymposium organisé par Béatrice Laroche et Sophie Schbath, intitulé : "Modeling Microbial ecosystem using Meta-Omic data"; Metagenomics is the study of microbial communities directly from environmental samples. We will propose a model and the tools of inference associated for a simultaneous clustering: the one on the populations of bacteria constituting the metagenome, and the other one on the samples. We will apply this model on the plant-microbial communities interactions in the rhizosphere.

Les modèles à blocs latents fournissent un cadre probabiliste pour la double classification de lignes et colonnes d’une matrice de données. Dans cet article nous considérons un modèle à blocs latents pour des données de comptage surdispersées. Les variables latentes ne sont pas indépendantes conditionnellement aux variables observées ce qui rend l’inférence classique par maximum de vraisemblance impossible. Nous présenterons un algorithme d’inférence basé sur une approche variationnelle. Nous appliquerons ce modèle sur des données de métagénomique pour étudier les interactions entre les bactéries présentes dans la rhizosphère et les plantes.

SPEEAEcolDurGenoSolGEAPSI; The importance of telluric microorganisms linked together by trophic exchanges with plants, that sustain all ecosystems through primary production, is known. However, the role of soil microbial diversity for primary production remains controversial. A diversity decreasing was achieved, by inoculating a sterilized soil with serial dilutions of a suspension from the same non-sterilized soil, to determine the consequences of microbial diversity erosion on the growth and fitness of three plant species more or less dependent on symbionts, Medicago truncatula, Brachypodium distachyon and Arabidopsis thaliana. The results showed that the impact of microbial diversity decreasing on plants depends on their reliance on symbionts. On the whole, M. truncatula was negatively affected by erosion, B. distachyon was positively affected and there was no significant effect on A. thaliana. These results are of interest to predict the consequences of soil microbial diversity erosion, on crop plants productivity under low nutrient inputs, according to microbial communities and symbiotic interactions.

Resilience in a cheese ecosystem. 14. Journées Ouvertes en Biologie, Informatique et Mathématiques - JOBIM 2013

International audience

An evaluation of normalization methods for the sequencing depth bias in comparative metagenomics studies. 5. Workshop Statistical Methods for Post-Genomic Data (SMPGD)

absent

Propionibacterium freudenreichii is used as a ripening culture in Swiss cheese manufacture. Swiss cheeses are placedin a warm room (about 24°C) during the ripening to induce propionibacteria growth. Cheeses with a suitable levelof opening are then transferred to a cold room (about 4°C). In the cold, propionibacteria slowdown propionicfermentation, but remain active. To investigate the strategies of adaptation and survival of P. freudenreichii in the cold,we performed the first global gene expression profile for this species. The time-course transcriptomic responses of thetype strain CIRM-BIA1T and of 6 other P. freudenreichii strains were analyzed during growth at 30°C then for 9 daysat 4°C, under conditions preventing nutrient starvation, as in cheese. The main metabolites were quantified in culturesupernatants. The changes in metabolism of P. freudenreichii CIRM-BIA1T were also investigated by RT-qPCR for28 genes and by proteomics. Microarray analysis revealed that a large number of genes (565, i.e. 25% of the proteincodingsequences of P. freudenreichii CIRM-BIA1T genome) were differentially expressed during transition from 30°Cto 4°C (P < 0.05 and |fold change| > 1). At 4°C, a general slowing down was observed for genes implicated in the cellmachinery. On the contrary, P. freudenreichii CIRM-BIA1T strain over-expressed genes involved in lactate, alanine andserine conversion to pyruvate, in gluconeogenesis, and in glycogen synthesis. Interestingly, the expression of differentgenes encoding esterases and amino acid-converting enzymes, involved in the formation of cheese flavor compounds,remained unchanged at 4°C. This could explain the contribution of P. freudenreichii to the formation of cheese flavoreven in the cold. The six other strains tested exhibited similar adaptive behaviours. In conclusion, P. freudenreichiiremains metabolically active at 4°C and induces pathways to maintain its long-term survival.

On the use of the negative binomial regression model for comparing differential expression or abundance with ngs data. 12.Journées Ouvertes en Biologie, Informatique et Mathématiques - JOBIM 2011

Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are Cy3 and Cy5. Recent improvements in the technology (dye-labelling, scanner and image analysis) allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data. We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalize p differently labelled targets co-hybridized on a same array, for any value of p greater than 2. The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding twocolor experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples.

Poster session 7: Statistical analyses of gene expression data; absent

absent

Background: Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile. Results: Using process intensity estimation methods, we define and give estimates of the local FDR, which may be considered as the probability for a gene to be a false positive. After a global assessment rule controlling the false positive error, the local FDR is a valuable guideline for deciding wether a gene is differentially expressed. The interest of the method is illustrated on three well known data sets. A R routine for computing local FDR estimates from p-values is available at http:/ /www.inapg.fr/ens_rech/mathinfo/recherche/mathematique/outil.html. Conclusions: The local FDR associated with each gene measures the probability that it is a false positive. It gives the opportunity to compute the FDR of any given group of clones (of the same gene) or genes pertaining to the same regulation network or the same chromosomic region.

Background: Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile. Results: Using process intensity estimation methods, we define and give estimates of the local FDR, which may be considered as the probability for a gene to be a false positive. After a global assessment rule controlling the false positive error, the local FDR is a valuable guideline for deciding wether a gene is differentially expressed. The interest of the method is illustrated on three well known data sets. A R routine for computing local FDR estimates from p-values is available at http:/ /www.inapg.fr/ens_rech/mathinfo/recherche/mathematique/outil.html. Conclusions: The local FDR associated with each gene measures the probability that it is a false positive. It gives the opportunity to compute the FDR of any given group of clones (of the same gene) or genes pertaining to the same regulation network or the same chromosomic region.

Expresses gratitude for the sending.

International audience; In cheese production, lactic acid bacteria (LAB) and propionic bacteria are key players in the production of metabolites that confer nutritional and organoleptic qualities. However, the contribution of each species to the final quality of cheeses is not fully elucidated. The objective was to determine which species contribute to acidify and produce flavour compounds, by which metabolic pathways, according to which temporality and also to investigate the bacterial metabolic interactions contributing to the functioning of the cheese ecosystem.We sequenced and annotated: Lactococcus lactis subsp. lactis biovar diacetylactis CIRM-BIA1206 (LL), Lactiplantibacillus plantarum CIRM-BIA465 (LP), Propionibacterium freudenreichii CIRM-BIA122 (PF). We reconstructed the metabolic pathways and developed a community metabolic model. Four semi-hard cheeses were made with LL, LP and PF and were analyzed throughout manufacturing. Bacteria, sugars, organic acids and flavour compounds were quantified and RNA sequenced.The analysis of differentially expressed genes showed at which moment of the manufacturing process the genes involved in the catabolism of lactose and citrate and in the synthesis pathways of different flavour compounds: lactic, acetic, propionic, isovaleric (old cheese flavour), acetoin (cheesy flavour) acids were induced. Lactose catabolism was induced in LP and PF (Leloir pathway) during acidification and then in LL (tagatose pathway) during ripening. Acetoin production was induced in LL, LP and PF from the beginning of the manufacturing process to the beginning of ripening. The synthesis of propionic and isovaleric acids were attributed to PF and the corresponding metabolisms were induced from the beginning of the manufacturing then stably expressed during the first month of the ripening. Since the mixed fermentation pathways were induced during ripening, acetic acid was likely produced by LL, LP and PF at this stage. Implementation of the metabolic community model in the Smetana tool revealed the molecular basis of the previously discussed commensalism between LAB and PF. LAB produce lactic acid and potentially ribose, succinate, glycerol, serine and phenylalanine for the benefit of PF. These interactions, identified in silico, remain to be validated in vitro.All these results make metatranscriptomics associated with metabolic models, tools of choice to better understand and control the metabolisms and interactions governing the functioning of cheese ecosystems.