We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities

Complex gene-environment interactions are considered important in the development of obesity. The composition of the gut microbiota can determine the efficacy of energy harvest from food and changes in dietary composition have been associated with changes in the composition of gut microbial populations. The capacity to explore microbiota composition was markedly improved by the development of metagenomic approaches, which have already allowed production of the first human gut microbial gene catalogue and stratifying individuals by their gut genomic profile into different enterotypes, but the analyses were carried out mainly in non-intervention settings. To investigate the temporal relationships between food intake, gut microbiota and metabolic and inflammatory phenotypes, we conducted diet-induced weight-loss and weight-stabilization interventions in a study sample of 38 obese and 11 overweight individuals. Here we report that individuals with reduced microbial gene richness (40%) present more pronounced dys-metabolism and low-grade inflammation, as observed concomitantly in the accompanying paper. Dietary intervention improves low gene richness and clinical phenotypes, but seems to be less efficient for inflammation variables in individuals with lower gene richness. Low gene richness may therefore have predictive potential for the efficacy of intervention.

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Paradoxically, loss of immunoglobulin A (IgA), one of the most abundant antibodies, does not irrevocably lead to severe infections in humans but rather is associated with relatively mild respiratory infections, atopy, and auto immunity. IgA might therefore also play covert roles, not uniquely associated with control of pathogens. We show that human IgA deficiency is not associated with massive quantitative perturbations of gut microbial ecology. Metagenomic analysis highlights an expected pathobiont expansion but a less expected depletion in some typi cally beneficial symbionts. Gut colonization by species usually present in the oropharynx is also reminiscent of spatial microbiota disorganization. IgM only partially rescues IgA deficiency because not all typical IgA targets are efficiently bound by IgM in the intestinal lumen. Together, IgA appears to play a nonredundant role at the fore front of the immune/microbial interface, away from the intestinal barrier, ranging from pathobiont control and regulation of systemic inflammation to preservation of commensal diversity and community networks.

Segmented filamentous bacterium (SFB) is a symbiont that drives postnatal maturation of gut adaptive immune responses. In contrast to nonpathogenic E. coli, SFB stimulated vigorous development of Peyer's patches germinal centers but paradoxically induced only a low frequency of specific immunoglobulin A (IgA)-secreting cells with delayed accumulation of somatic mutations. Moreover, blocking Peyer's patch development abolished IgA responses to E. coli, but not to SFB. Indeed, SFB stimulated the postnatal development of isolated lymphoid follicles and tertiary lymphoid tissue, which substituted for Peyer's patches as inductive sites for intestinal IgA and SFB-specific T helper 17 (Th17) cell responses. Strikingly, in mice depleted of gut organized lymphoid tissue, SFB still induced a substantial but nonspecific intestinal Th17 cell response. These results demonstrate that SFB has the remarkable capacity to induce and stimulate multiple types of intestinal lymphoid tissues that cooperate to generate potent IgA and Th17 cell responses displaying only limited target specificity.

BackgroundPeptide spectrum matching (PSM) is the standard method in shotgun proteomics data analysis. It relies on the availability of an accurate and complete sample proteome that is used to make interpretation of the spectra feasible. Although this procedure has proven to be effective in many proteomics studies, the approach has limitations when applied on complex samples of microbial communities, such as those found in the human intestinal tract. Metagenome studies have indicated that the human intestinal microbiome contains over 100 times more genes than the human genome and it has been estimated that this ecosystem contains over 5000 bacterial species. The genomes of the vast majority of these species have not yet been sequenced and hence their proteomes remain unknown. To enable data analysis of shotgun proteomics data using PSM, and circumvent the lack of a defined matched metaproteome, an iterative workflow was developed that is based on a synthetic metaproteome and the developing metagenomic databases that are both representative for but not necessarily originating from the sample of interest.ResultsTwo human fecal samples for which metagenomic data had been collected, were analyzed for their metaproteome using liquid chromatography-mass spectrometry and used to benchmark the developed iterative workflow to other methods. The results show that the developed method is able to detect over 3,000 peptides per fecal sample from the spectral data by circumventing the lack of a defined proteome without naive translation of matched metagenomes and cross-species peptide identification.ConclusionsThe developed iterative workflow achieved an approximate two-fold increase in the amount of identified spectra at a false discovery rate of 1% and can be applied in metaproteomic studies of the human intestinal tract or other complex ecosystems.

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Background: Besides intestinal barrier function, the host tolerates gut commensals through both innate and adaptive immune mechanisms. It is now clear that gut commensals induce local immunoglobulin A (IgA) responses, but it remains unclear whether anti-microbiota responses remain confined to the gut. Objective: The aim of this study was to investigate systemic and intestinal responses against the whole microbiota under homeostatic conditions, and in the absence of IgA. Methods: We analyzed blood and feces from healthy donors, patients with selective IgA deficiency (SIgAd) and common variable immunodeficiency (CVID). Immunoglobulincoated bacterial repertoires were analyzed by combined bacterial fluorescence-activated cell sorting and 16S rRNA sequencing, and bacterial lysates were probed by western blot analysis with healthy donors serums. Results: Although absent from the healthy gut, serum anti-microbiota IgG are present in healthy individuals, and increased in SIgAd patients. IgG converge with non-overlapping secretory IgA repertoires to target the same bacteria. Each individual targets a diverse, microbiota repertoire whose proportion inversely correlates with systemic inflammation. Finally, Intravenous Immunoglobulin preparations (IVIG) target much less efficiently CVID gut microbiota than healthy microbiota. Conclusion: Secretory IgA is pivotal for induction of tolerance to gut microbiota. SIgAdassociated inflammation is inversely correlated with systemic anti-commensal IgG responses, which may thus serve as a second line of defense. We speculate that SIgAd patients could benefit from oral IgA supplementation. Our data also suggest that IVIG preparations might be supplemented with IgG from IgA deficient patients pools in order to offer a better protection against gut bacterial translocations in CVID. Key Messages:-Systemic IgG and secretory IgA bind a common spectrum of commensals.-Increased proportions of IgG+ microbiota and inflammatory markers in SIgAd.-IVIG poorly target CVID and SIgAd gut microbiota.