Alterations of the human gut microbiome in liver cirrhosis.

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.

Dietary intervention impact on gut microbial gene richness.

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.

Richness of human gut microbiome correlates with metabolic markers.

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.

A metagenome-wide association study of gut microbiota in type 2 diabetes.

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

Enterotypes of the human gut microbiome.

Our knowledge of species and functional composition of the human gut microbiome is rapidly increasing, but it is still based on very few cohorts and little is known about variation across the world. By combining 22 newly sequenced faecal metagenomes of individuals from four countries with previously published data sets, here we identify three robust clusters (referred to as enterotypes hereafter) that are not nation or continent specific. We also confirmed the enterotypes in two published, larger cohorts, indicating that intestinal microbiota variation is generally stratified, not continuous. This indicates further the existence of a limited number of well-balanced host-microbial symbiotic states that might respond differently to diet and drug intake. The enterotypes are mostly driven by species composition, but abundant molecular functions are not necessarily provided by abundant species, highlighting the importance of a functional analysis to understand microbial communities. Although individual host properties such as body mass index, age, or gender cannot explain the observed enterotypes, data-driven marker genes or functional modules can be identified for each of these host properties. For example, twelve genes significantly correlate with age and three functional modules with the body mass index, hinting at a diagnostic potential of microbial markers.

A human gut microbial gene catalogue established by metagenomic sequencing.

To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, approximately 150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively.

Quality control of microbiota metagenomics by k-mer analysis.

BACKGROUND: The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case-control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue. RESULTS: We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from "empty" ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets. CONCLUSIONS: We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.

Lactobacillus delbrueckii ssp. lactis and ssp. bulgaricus: a chronicle of evolution in action.

BACKGROUND: Lactobacillus delbrueckii ssp. lactis and ssp. bulgaricus are lactic acid producing bacteria that are largely used in dairy industries, notably in cheese-making and yogurt production. An earlier in-depth study of the first completely sequenced ssp. bulgaricus genome revealed the characteristics of a genome in an active phase of rapid evolution, in what appears to be an adaptation to the milk environment. Here we examine for the first time if the same conclusions apply to the ssp. lactis, and discuss intra- and inter-subspecies genomic diversity in the context of evolutionary adaptation. RESULTS: Both L. delbrueckii ssp. show the signs of reductive evolution through the elimination of superfluous genes, thereby limiting their carbohydrate metabolic capacities and amino acid biosynthesis potential. In the ssp. lactis this reductive evolution has gone less far than in the ssp. bulgaricus. Consequently, the ssp. lactis retained more extended carbohydrate metabolizing capabilities than the ssp. bulgaricus but, due to high intra-subspecies diversity, very few carbohydrate substrates, if any, allow a reliable distinction of the two ssp. We further show that one of the most important traits, lactose fermentation, of one of the economically most important dairy bacteria, L. delbruecki ssp. bulgaricus, relies on horizontally acquired rather than deep ancestral genes. In this sense this bacterium may thus be regarded as a natural GMO avant la lettre. CONCLUSIONS: The dairy lactic acid producing bacteria L. delbrueckii ssp. lactis and ssp. bulgaricus appear to represent different points on the same evolutionary track of adaptation to the milk environment through the loss of superfluous functions and the acquisition of functions that allow an optimized utilization of milk resources, where the ssp. bulgaricus has progressed further away from the common ancestor.

Construction of a dairy microbial genome catalog opens new perspectives for the metagenomic analysis of dairy fermented products.

BACKGROUND: Microbial communities of traditional cheeses are complex and insufficiently characterized. The origin, safety and functional role in cheese making of these microbial communities are still not well understood. Metagenomic analysis of these communities by high throughput shotgun sequencing is a promising approach to characterize their genomic and functional profiles. Such analyses, however, critically depend on the availability of appropriate reference genome databases against which the sequencing reads can be aligned. RESULTS: We built a reference genome catalog suitable for short read metagenomic analysis using a low-cost sequencing strategy. We selected 142 bacteria isolated from dairy products belonging to 137 different species and 67 genera, and succeeded to reconstruct the draft genome of 117 of them at a standard or high quality level, including isolates from the genera Kluyvera, Luteococcus and Marinilactibacillus, still missing from public database. To demonstrate the potential of this catalog, we analysed the microbial composition of the surface of two smear cheeses and one blue-veined cheese, and showed that a significant part of the microbiota of these traditional cheeses was composed of microorganisms newly sequenced in our study. CONCLUSIONS: Our study provides data, which combined with publicly available genome references, represents the most expansive catalog to date of cheese-associated bacteria. Using this extended dairy catalog, we revealed the presence in traditional cheese of dominant microorganisms not deliberately inoculated, mainly Gram-negative genera such as Pseudoalteromonas haloplanktis or Psychrobacter immobilis, that may contribute to the characteristics of cheese produced through traditional methods.

Genome sequence of Taylorella equigenitalis MCE9, the causative agent of contagious equine metritis.

Taylorella equigenitalis is the causative agent of contagious equine metritis (CEM), a sexually transmitted infection of horses. We herein report the genome sequence of T. equigenitalis strain MCE9, isolated in 2005 from the urethral fossa of a 4-year-old stallion in France.

Prediction of the intestinal resistome by a three-dimensional structure-based method.

The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens via mobile genetic elements. Yet, this assumption is poorly supported by empirical evidence due to the distant homologies between known ARDs (mostly from culturable bacteria) and ARDs from the intestinal microbiota. Consequently, an accurate census of intestinal ARDs (that is, the intestinal resistome) has not yet been fully determined. For this purpose, we developed and validated an annotation method (called pairwise comparative modelling) on the basis of a three-dimensional structure (homology comparative modelling), leading to the prediction of 6,095 ARDs in a catalogue of 3.9 million proteins from the human intestinal microbiota. We found that the majority of predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino acid identity 29.8%) and found little evidence supporting their transfer between species. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n = 663) into six resistotypes that were connected to the previously described enterotypes. Finally, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results indicate that the majority of intestinal microbiota ARDs can be considered intrinsic to the dominant commensal microbiota and that these genes are rarely shared with bacterial pathogens.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Genomic characterization of the Taylorella genus.

The Taylorella genus comprises two species: Taylorella equigenitalis, which causes contagious equine metritis, and Taylorella asinigenitalis, a closely-related species mainly found in donkeys. We herein report on the first genome sequence of T. asinigenitalis, analyzing and comparing it with the recently-sequenced T. equigenitalis genome. The T. asinigenitalis genome contains a single circular chromosome of 1,638,559 bp with a 38.3% GC content and 1,534 coding sequences (CDS). While 212 CDSs were T. asinigenitalis-specific, 1,322 had orthologs in T. equigenitalis. Two hundred and thirty-four T. equigenitalis CDSs had no orthologs in T. asinigenitalis. Analysis of the basic nutrition metabolism of both Taylorella species showed that malate, glutamate and alpha-ketoglutarate may be their main carbon and energy sources. For both species, we identified four different secretion systems and several proteins potentially involved in binding and colonization of host cells, suggesting a strong potential for interaction with their host. T. equigenitalis seems better-equipped than T. asinigenitalis in terms of virulence since we identified numerous proteins potentially involved in pathogenicity, including hemagluttinin-related proteins, a type IV secretion system, TonB-dependent lactoferrin and transferrin receptors, and YadA and Hep_Hag domains containing proteins. This is the first molecular characterization of Taylorella genus members, and the first molecular identification of factors potentially involved in T. asinigenitalis and T. equigenitalis pathogenicity and host colonization. This study facilitates a genetic understanding of growth phenotypes, animal host preference and pathogenic capacity, paving the way for future functional investigations into this largely unknown genus.

Development of software facilities to characterize regulatory binding motifs and application to streptococcaceae.

Gene expression regulation often involves the recognition of particular DNA or RNA sequences, called motifs. Detection and characterization of such motifs together with biological expertise allow to build gene expression regulatory maps that facilitate the comprehension of complex cellular processes. In this frame, we developed a software integrating relevant information for the detection and characterization of conserved motifs in genomic sequences. A relational database was built up to host data related to genomic information and transcriptional experiments. A user-friendly interface was designed to allow a convenient representation of these data and to run the detection motif program. A set of complementary utilities was also developed to improve the determination of motif consensus sequences and the detection of additional potential regulator targets in the genome.