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Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/bmjresp-2018-000374).; International audience; Introduction : Pseudomonas aeruginosa pulmonary infections are the primary cause of morbi-mortality in patients with cystic fibrosis (CF). In this cohort study, the objective was to identify candidate biomarkers of P. aeruginosa infection within the airway microbiota. Methods : A 3-year prospective multicentre study (PYOMUCO study) was conducted in Western France and included patients initially P. aeruginosa free for at least 1year. A 16S-targeted metagenomics approach was applied on iterative sputum samples of a first set of patients (n=33). The composition of airway microbiota was compared according to their P. aeruginosa status at the end of the follow-up (colonised vs non-colonised), and biomarkers associated with P. aeruginosa were screened. In a second step, the distribution of a candidate biomarker according to the two groups of patients was verified by qPCR on a second set of patients (n=52) coming from the same cohort and its load quantified throughout the follow-up. Results : Porphyromonas (mainly P. catoniae) was found to be an enriched phylotype in patients uninfected by P. aeruginosa (pDiscussion : Further studies on replication cohorts are needed to validate this potential predictive biomarker, which may be relevant for the follow-up in the early years of patients with CF. The identification of infection candidate biomarkers may offer new strategies for CF prevision medicine.

Figure S1. Raw and transformed distributions and violin plots of α-diversity phenotypes. Figure S2. Multidimensional scaling plots of Jaccard and Unifrac distance matrices. Figure S3. Number and overlap of non-genetic variables associated with α-diversity phenotypes. Figure S4. Correlations of age and ALT levels with Simpson’s diversity index. Figure S5. Number and overlap of non-genetic variables associated with ß-diversity matrices. Figure S6. Data plots showing identified correlations of non-genetic variables with three individual taxa. Figure S7. Manhattan plots for α-diversity metrics: richness, Chao1 and ACE. Figure S8. Manhattan plots for ß-diversity matrices: Jaccard and Unifrac. Figure S9. QQ plots and lambda values of GWAS of α-diversity phenotypes. Figure S10. QQ plots and lambda values of GWAS of β-diversity indexes. Figure S11.Manhattan plot of HLA and KIR association results with all phenotypes. Figure S12. PCoA plot of samples obtained from different sequencing batches. Figure S13. PCA plot of the genetic matrix data of MI donors. (DOCX 2702 kb)

International audience

Discipline Epidemiologie. Introduction et but de l’etude Le co-metabolisme hote-microbiote est a l’origine d’un tres grand nombre de molecules integrees au sein d’axes metaboliques complexes. De nombreuses etudes se sont attachees a la caracterisation fonctionnelle specifique de certaines de ces molecules (AGCC, BCAA, TMAO, etc.), mais les etudes envisageant plus globalement les relations metaboliques entre l’hote et son microbiote intestinal restent rares. A ce titre, l’etude globale des metabolites endogenes et exogenes presents dans le plasma par metabolomique non ciblee semble prometteuse. L’objectif de cette etude etait de caracteriser les associations entre profils metabolomiques plasmatiques et composition du microbiote intestinal dans une population d’adultes en bonne sante. Materiel et methodes La composition du microbiote intestinal a ete determinee dans les selles (sequencage du gene ARNr16S, les matrices de Jaccard et Bray-Curtis ont ete determinees) et les profils metabolomiques ont ete generes en utilisant les sequences RMN CPMG et NOESY sur des echantillons de plasma, chez 846 individus de la population Milieu Interieur. La co-structure globale des donnees 16S et RMN a ete evaluee par co-inertie. Les associations entre variables metabolomiques d’une part et matrices de β-diversite ou abondance des taxons d’autre part ont ete calculees par PERMANOVA ou MaAsLin ajustes sur le sexe, l’âge, l’IMC, le statut tabagique et l’activite physique (PERMANOVA egalement ajustees sur la profondeur de sequencage). Une correction de Benjamini-Hochberg (FDR-10 %) a ete appliquee. Resultats et Analyse statistique La co-inertie globale des donnees microbiote et metabolomique etait limitee (RV ≤ 0,05). En revanche, des associations specifiques ont ete detectees. Les matrices de Jaccard et de Bray-Curtis etaient significativement et respectivement associees a 51 et 3 variables CPMG, dont certaines ont d’ores et deja ete identifiees (pyruvate, tyrosine, choline, glucose, etc.). Des associations entre le pyruvate et 3 genres bacteriens (positives avec Catabacter et Acholeplasma, negative avec Faecalibacterium) ont ete mises en evidence. L’analyse des associations entre variables metabolomiques NOESY et donnees microbiote 16S, et l’identification des metabolites discriminants sont en cours. Conclusion Ces resultats preliminaires permettent de mettre en evidence des associations entre le profil metabolomique RMN determine sur le plasma de l’hote et la composition du microbiote intestinal. Toutefois, cette etude ne permet pas de conclure sur une potentielle relation causale, et d’autres etudes sont necessaires.

International audience

Figure S2. Impact of experimental feedings and microbial colonization on mouse metabolism. a Body weight development over time. b Regression analysis of fasting blood insulin and leptin concentrations in P- and LHB-fed SPF mice. See the “Methods” section for description of statistical analyses. c Quantification of chromogranin A-positive (ChgA+) cells in colonic tissue sections of SPF mice from the different feeding groups. Description is as Fig. 1e. ***p

National audience

The microorganisms inhabiting the human gut are abundant (1014 cells) and diverse (approximately 500 species per individual). It is nowacknowledged that the microbiota has coevolved with its host to achieve a symbiotic relationship, leading to physiological homeostasis. The gut microbiota ensures vital functions, such as food digestibility, maturation of the host immune system, and protection against pathogens. Over the last few decades, the gut microbiota has also been associated with numerous diseases, such as inflammatory bowel disease, irritable bowel syndrome, obesity, and metabolic diseases. In most of these pathologies, a microbial dysbiosis has been found, indicating shifts in the taxonomic composition of the gut microbiota and changes in its functionality. Our understanding of the influence of the gut microbiota on human health is still growing. Working with microorganisms residing in the gut is challenging since most of them are anaerobic and a vast majority (approximately 75%) are uncultivable to date. Recently, a wide range of new approaches (meta-omics) has been developed to bypass the uncultivability and reveal the intricate mechanisms that sustain gut microbial homeostasis. After a brief description of these approaches (metagenomics, metatranscriptomics, metaproteomics, and metabolomics), this review will discuss the importance of considering the gut microbiome as a structured ecosystem and the use of meta-omics to decipher dysfunctions of the gut microbiome in diseases.

National audience

National audience; By the past, studies have shown that diet modulate intestinal microbiota. Our objective was to compare intestinal microbiota composition of healthy subjects, according to food groups’ consumption. Our study includes 134 individuals from the Milieu Intérieur Healthy Donor Cohort, mean age 40.4y; 57.5% women. Eating habits were assessed using a food frequency questionnaire. Composition of the faecal microbiota was established by 16S rRNA gene sequencing. OTU abundance was log10 transformed and OTUs were grouped by phylum and genus. We compared taxa abundance according to eating habits of subjects (ANOVA tests –age and gender adjusted). Compared to individuals that consume more frequently the food group concerned, those who eat less often fish had more Eubacterium spp., Ruminococcus spp., Streptococcus spp. and Actinobacteria. Those who eat less often cheese had less Coprococcus spp., Faecalibacterium spp., Prevotella spp., and Bacteroidetes, and more of Actinobacteria and Verrucomicrobia. Those who eat less often deep-fried foods had less Coprobacillus spp., Turicibacter spp. and Tenericutes. This preliminary study highlights associations between eating habits and microbiota composition. Investigation of associations between gut microbiota composition and diet patterns rather than specific food compounds could be useful to identify microbiota/nutrition signatures related to health

Advances in sequencing technology and the development of metagenomic and bioinformatics methods have opened up new ways to investigate the 10(14) microorganisms inhabiting the human gut. The gene composition of human gut microbiome in a large and deeply sequenced cohort highlighted an overall non-redundant genome size 150 times larger than the human genome. The in silico predictions based on metagenomic sequencing are now actively followed, compared and challenged using additional 'omics' technologies. Interactions between the microbiota and its host are of key interest in several pathologies and applying meta-omics to describe the human gut microbiome will give a better understanding of this crucial crosstalk at mucosal interfaces. Adding to the growing appreciation of the importance of the microbiome is the discovery that numerous phages, that is, viruses of prokaryotes infecting bacteria (bacteriophages) or archaea with a high host specificity, inhabit the human gut and impact microbial activity. In addition, gene exchanges within the gut microbiota have proved to be more frequent than anticipated. Taken together, these innovative exploratory technologies are expected to unravel new information networks critical for gut homeostasis and human health. Among the challenges faced, the in vivo validation of these networks, together with their integration into the prediction and prognosis of disease, may require further working hypothesis and collaborative efforts.

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International audience; Pollution of the environment by human and animal faecal pollution affects the safety of shellfish, drinking water and recreational beaches. To pinpoint the origin of contaminations, it is essential to define the differences between human microbiota and that of farm animals. A strategy based on real-time quantitative PCR (qPCR) assays was therefore developed and applied to compare the composition of intestinal microbiota of these two groups. Primers were designed to quantify the 16S rRNA gene from dominant and subdominant bacterial groups. TaqMan probes were defined for the qPCR technique used for dominant microbiota. Human faecal microbiota was compared with that of farm animals using faecal samples collected from rabbits, goats, horses, pigs, sheep and cows. Three dominant bacterial groups (Bacteroides/Prevotella, Clostridium coccoides and Bifidobacterium) of the human microbiota showed differential population levels in animal species. The Clostridium leptum group showed the lowest differences among human and farm animal species. Human subdominant bacterial groups were highly variable in animal species. Partial least squares regression indicated that the human microbiota could be distinguished from all farm animals studied. This culture-independent comparative assessment of the faecal microbiota between humans and farm animals will prove useful in identifying biomarkers of human and animal faecal contaminations that can be applied to microbial source tracking methods.

BACKGROUND: The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20-69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition. RESULTS: Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics. CONCLUSION: In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01699893.

Background: Diet is widely recognized as one of the main modifiable drivers of gut microbiota variability, and its influence on microbiota composition is an active area of investigation., Objective: The present work aimed to explore the associations between usual diet and gut microbiota composition in a large sample of healthy French adults., Methods: Gut microbiota composition was established through sequencing of the 16S rRNA gene in stool samples from 862 healthy French adults of the Milieu Interieur study. Usual dietary consumptions were determined through the administration of a food-frequency questionnaire. The associations between dietary variables and alpha- and beta-diversity indexes and relative taxa abundances were tested using Spearman correlations, permutational ANOVAs, and multivariate analyses with linear models, respectively., Results: Foods generally considered as healthy (raw fruits, fish) were positively associated with alpha-diversity, whereas food items for which a limited consumption is generally recommended (fried products, sodas or sugary drinks, fatty sweet products, processed meats, ready-cooked meals, and desserts) were negatively associated with alpha-diversity. Fruits, fried products, ready-cooked meals, and cheese contributed to shifts within microbiota composition (beta-diversity). Our results also highlighted a number of associations between various food group intakes and abundances of specific phyla, genera, and species. For instance, the consumption of cheese was negatively associated with Akkermansia muciniphila abundance., Conclusions: This large-scale population-based study supports that the usual consumption of certain food items is associated with several gut microbial features, and extends the mechanistic arguments linking Western diet to an altered microbiota composition. These results provide new insights into the understanding of complex diet-gut microbiota relations, and their implications for host health deserve further investigation because altered microbiota diversity was consistently linked to increased risk of several health outcomes. This trial was registered at clinicaltrials.gov as NCT01699893.

Microbiome 6(1), 134 (2018). doi:10.1186/s40168-018-0510-8