The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognosis and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to combine clinical needs with technological advancements. Omics technologies allow for advanced investigations into biological processes from the genes to transcription and regulation, to circulating protein, metabolite and lipid levels, as well as the microbiome including bacteria, viruses and fungi. We consequently find ourselves in a period of rapid progress in technology and bioinformatics that may allow for development of precision biomarkers for personalised medicine. However, there are important barriers to consider in omics biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate across diverse populations, presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression in different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated through omics-technologies holds the power of hypothesis-free discovery of a plethora of candidate biomarkers to be further validated. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Alcohol is the most common cause of liver-related mortality and morbidity. We therefore aimed to assess and compare the prognostic performance of elastography and blood-based markers to predict time to the first liver-related event, severe infection, and all-cause mortality in patients with a history of excess drinking.

Liver fibrosis accumulation is considered a turnover disease, with formation exceeding degradation, although this hypothesis has never been tested in humans.

The human gut microbiome has emerged as a major player in human health and disease. The liver, as the first organ to encounter microbial products that cross the gut epithelial barrier, is affected by the gut microbiome in many ways. Thus, the gut microbiome might play a major part in the development of liver diseases. The common end stage of liver disease is decompensated cirrhosis and the further development towards acute-on-chronic liver failure (ACLF). These conditions have high short-term mortality. There is evidence that translocation of components of the gut microbiota, facilitated by different pathogenic mechanisms such as increased gut epithelial permeability and portal hypertension, is an important driver of decompensation by induction of systemic inflammation, and thereby also ACLF. Elucidating the role of the gut microbiome in the aetiology of decompensated cirrhosis and ACLF deserves further investigation and improvement; and might be the basis for development of diagnostic and therapeutic strategies. In this Review, we focus on the possible pathogenic, diagnostic and therapeutic role of the gut microbiome in decompensation of cirrhosis and progression to ACLF.

Gut viruses are important, yet often neglected, players in the complex human gut microbial ecosystem. Recently, the number of human gut virome studies has been increasing; however, we are still only scratching the surface of the immense viral diversity. In this study, 254 virus-enriched fecal metagenomes from 204 Danish subjects were used to generate the anish nteric irme atalog (DEVoC) containing 12,986 nonredundant viral scaffolds, of which the majority was previously undescribed, encoding 190,029 viral genes. The DEVoC was used to compare 91 healthy DEVoC gut viromes from children, adolescents, and adults that were used to create the DEVoC. Gut viromes of healthy Danish subjects were dominated by phages. While most phage genomes (PGs) only occurred in a single subject, indicating large virome individuality, 39 PGs were present in more than 10 healthy subjects. Among these 39 PGs, the prevalences of three PGs were associated with age. To further study the prevalence of these 39 prevalent PGs, 1,880 gut virome data sets of 27 studies from across the world were screened, revealing several age-, geography-, and disease-related prevalence patterns. Two PGs also showed a remarkably high prevalence worldwide-a crAss-like phage (20.6% prevalence), belonging to the tentative subfamily, and a previously undescribed circular temperate phage infecting Bacteroides dorei (14.4% prevalence), called LoVEphage because it encodes ots f iral lements. Due to the LoVEphage's high prevalence and novelty, public data sets in which the LoVEphage was detected were assembled, resulting in an additional 18 circular LoVEphage-like genomes (67.9 to 72.4 kb). Through generation of the DEVoC, we added numerous previously uncharacterized viral genomes and genes to the ever-increasing worldwide pool of human gut viromes. The DEVoC, the largest human gut virome catalog generated from consistently processed fecal samples, facilitated the analysis of the 91 healthy Danish gut viromes. Characterizing the biggest cohort of healthy gut viromes from children, adolescents, and adults to date confirmed the previously established high interindividual variation in human gut viromes and demonstrated that the effect of age on the gut virome composition was limited to the prevalence of specific phage (groups). The identification of a previously undescribed prevalent phage illustrates the usefulness of developing virome catalogs, and we foresee that the DEVoC will benefit future analysis of the roles of gut viruses in human health and disease.

Alcohol-related liver disease is often undetected until irreversible late-stage decompensated disease manifests. Consequently, there is an unmet need for effective and economically reasonable pathways to screen for advanced alcohol-related fibrosis. We used real-world data from a large biopsy-controlled study of excessive drinkers recruited from primary and secondary care, to evaluate the cost-effectiveness of four primary care initiated strategies: 1) routine liver function tests with follow-up ultrasonography for test-positives, 2) the enhanced liver fibrosis (ELF) test, with hospital liver stiffness measurement (LSM) for positives, 3) three-tier strategy using Forns Index to control before strategy two, 4) direct referral of all to LSM. We used linked decision trees and Markov models to evaluate outcomes short-term (cost-per-accurate-diagnosis) and long-term (quality-adjusted life-years, QALYs). For low-prevalence populations, ELF with LSM follow-up was most cost-effective, both short-term (accuracy 96%, 196$ per patient), and long-term incremental cost-effectiveness ratio (ICER) $5,387-8,430/QALY, depending on whether diagnostic testing had lasting or temporary effects on abstinence rates. Adding Forns Index decreased costs to $72 per patient and accuracy to 95%. The strategy resulted in less QALY's due to more false negatives, but an ICER of $3,012, making this strategy suited for areas with restricted access to ELF and TE, or lower willingness-to-pay. For high-prevalence populations, direct referral to LSM was highly cost-effective (accuracy 93%, $297 per patient), with ICERs between $490 and $1,037/QALY CONCLUSION: Non-invasive screening for advanced, alcohol-related fibrosis is a cost-effective intervention when different referral pathways are used according to the prevalence of advanced fibrosis. Patients in the primary healthcare sector should be tested with the ELF test followed by LSM if test-positive, whereas direct referral to LSM is highly cost-effective in high-prevalence cohorts.

Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.

Population stratification is a useful approach for a better understanding of complex biological problems in human health and wellbeing. The proposal that such stratification applies to the human gut microbiome, in the form of distinct community composition types termed enterotypes, has been met with both excitement and controversy. In view of accumulated data and re-analyses since the original work, we revisit the concept of enterotypes, discuss different methods of dividing up the landscape of possible microbiome configurations, and put these concepts into functional, ecological and medical contexts. As enterotypes are of use in describing the gut microbial community landscape and may become relevant in clinical practice, we aim to reconcile differing views and encourage a balanced application of the concept.

In the version of this Perspective originally published, the first and last name of co-author Manimozhiyan Arumugam were switched. This has now been corrected in all versions of the Perspective.

To minimize the impact of antibiotics, gut microorganisms harbour and exchange antibiotics resistance genes, collectively called their resistome. Using shotgun sequencing-based metagenomics, we analysed the partial eradication and subsequent regrowth of the gut microbiota in 12 healthy men over a 6-month period following a 4-day intervention with a cocktail of 3 last-resort antibiotics: meropenem, gentamicin and vancomycin. Initial changes included blooms of enterobacteria and other pathobionts, such as Enterococcus faecalis and Fusobacterium nucleatum, and the depletion of Bifidobacterium species and butyrate producers. The gut microbiota of the subjects recovered to near-baseline composition within 1.5 months, although 9 common species, which were present in all subjects before the treatment, remained undetectable in most of the subjects after 180 days. Species that harbour β-lactam resistance genes were positively selected for during and after the intervention. Harbouring glycopeptide or aminoglycoside resistance genes increased the odds of de novo colonization, however, the former also decreased the odds of survival. Compositional changes under antibiotic intervention in vivo matched results from in vitro susceptibility tests. Despite a mild yet long-lasting imprint following antibiotics exposure, the gut microbiota of healthy young adults are resilient to a short-term broad-spectrum antibiotics intervention and their antibiotics resistance gene carriage modulates their recovery processes.

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

It has been drawn to our attention that the methods described in the main text and the Supplementary Information of this Article have been considered by some researchers to be insufficient to enable them to identify enterotypes in their own data sets. Enterotypes were originally defined in this Article (page 177) as “densely populated areas in a multi-dimensional space of community composition� and should not be seen as discrete clusters, but as a way of stratifying samples to reduce complexity. Additionally, the Fig. 2 legend should not imply that between-class analysis is simply a method of visualizing principal component analysis (PCA); rather, it is a supervised rather than an unsupervised analysis of data because it incorporates the outcome of clustering of data. To simplify enterotype identification in the original and other data sets, we have developed a comprehensive tutorial at http://enterotype.embl.de—which is a website on enterotypes that will be updated as methods improve. We thank Ivica Letunic and Paul Costea from EMBL for setting up the tutorial.

Many analyses of the human gut microbiome depend on a catalog of reference genes. Existing catalogs for the human gut microbiome are based on samples from single cohorts or on reference genomes or protein sequences, which limits coverage of global microbiome diversity. Here we combined 249 newly sequenced samples of the Metagenomics of the Human Intestinal Tract (MetaHit) project with 1,018 previously sequenced samples to create a cohort from three continents that is at least threefold larger than cohorts used for previous gene catalogs. From this we established the integrated gene catalog (IGC) comprising 9,879,896 genes. The catalog includes close-to-complete sets of genes for most gut microbes, which are also of considerably higher quality than in previous catalogs. Analyses of a group of samples from Chinese and Danish individuals using the catalog revealed country-specific gut microbial signatures. This expanded catalog should facilitate quantitative characterization of metagenomic, metatranscriptomic and metaproteomic data from the gut microbiome to understand its variation across populations in human health and disease.

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.

Despite increasing concerns over inappropriate use of antibiotics in medicine and food production, population-level resistance transfer into the human gut microbiota has not been demonstrated beyond individual case studies. To determine the "antibiotic resistance potential" for entire microbial communities, we employ metagenomic data and quantify the totality of known resistance genes in each community (its resistome) for 68 classes and subclasses of antibiotics. In 252 fecal metagenomes from three countries, we show that the most abundant resistance determinants are those for antibiotics also used in animals, and for antibiotics that have been available longer. Resistance genes are also more abundant in samples from Spain, Italy and France than from Denmark, the US, or Japan. Where comparable country-level data on antibiotic use in both humans and animals are available, differences in these statistics match the observed resistance potential differences. The results are robust over time as the antibiotic resistance determinants of individuals persist in the human gut flora for at least a year.

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.

Whereas large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the practical impact of variation is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 faecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short insertions/deletions, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This indicates that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake.

To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome-based methods. An implementation of the method is available at http://www.bork.embl.de/software/mOTU/.

The breakthrough of next generation sequencing-technologies has enabled large-scale studies of natural microbial communities and the 16S rRNA genes have been widely used as a phylogenetic marker to study community structure. However, major limitations of this approach are that neither strain-level resolution nor genomic context of microorganisms can be provided. This information, however, is crucial to answer fundamental questions about the temporal stability and distinctiveness of natural microbial communities.

ABSTRACT: BACKGROUND: Proteomes of thermophilic prokaryotes have been instrumental in structural biology and successfully exploited in biotechnology, however many proteins required for eukaryotic cell function are absent from bacteria or archaea. With Chaetomium thermophilum, Thielavia terrestris and Thielavia heterothallica three genome sequences of thermophilic eukaryotes have been published. RESULTS: Studying the genomes and proteomes of these thermophilic fungi, we found common strategies of thermal adaptation across the different kingdoms of Life, including amino acid biases and a reduced genome size. A phylogenetics-guided comparison of thermophilic proteomes with those of other, mesophilic Sordariomycetes revealed consistent amino acid substitutions associated to thermophily that were also present in an independent lineage of thermophilic fungi. The most consistent pattern is the substitution of lysine by arginine, which we could find in almost all lineages but has not been extensively used in protein stability engineering. By exploiting mutational paths towards the thermophiles, we could predict particular amino acid residues in individual proteins that contribute to thermostability and validated some of them experimentally. By determining the three-dimensional structure of an exemplar protein from C. Thermophilum (Arx1), we could also characterise the molecular consequences of some of these mutations. CONCLUSIONS: The comparative analysis of these three genomes not only enhances our understanding of the evolution of thermophily, but also provides new ways to engineer protein stability.

MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.

The human gastrointestinal tract (GI tract) harbors a complex community of microbes. The microbiota composition varies between different locations in the GI tract, but most studies focus on the fecal microbiota, and that inhabiting the colonic mucosa. Consequently, little is known about the microbiota at other parts of the GI tract, which is especially true for the small intestine because of its limited accessibility. Here we deduce an ecological model of the microbiota composition and function in the small intestine, using complementing culture-independent approaches. Phylogenetic microarray analyses demonstrated that microbiota compositions that are typically found in effluent samples from ileostomists (subjects without a colon) can also be encountered in the small intestine of healthy individuals. Phylogenetic mapping of small intestinal metagenome of three different ileostomy effluent samples from a single individual indicated that Streptococcus sp., Escherichia coli, Clostridium sp. and high G+C organisms are most abundant in the small intestine. The compositions of these populations fluctuated in time and correlated to the short-chain fatty acids profiles that were determined in parallel. Comparative functional analysis with fecal metagenomes identified functions that are overrepresented in the small intestine, including simple carbohydrate transport phosphotransferase systems (PTS), central metabolism and biotin production. Moreover, metatranscriptome analysis supported high level in-situ expression of PTS and carbohydrate metabolic genes, especially those belonging to Streptococcus sp. Overall, our findings suggest that rapid uptake and fermentation of available carbohydrates contribute to maintaining the microbiota in the human small intestine.

Despite decades of research, the structure and assembly of the nuclear pore complex (NPC), which is composed of ∼30 nucleoporins (Nups), remain elusive. Here, we report the genome of the thermophilic fungus Chaetomium thermophilum (ct) and identify the complete repertoire of Nups therein. The thermophilic proteins show improved properties for structural and biochemical studies compared to their mesophilic counterparts, and purified ctNups enabled the reconstitution of the inner pore ring module that spans the width of the NPC from the anchoring membrane to the central transport channel. This module is composed of two large Nups, Nup192 and Nup170, which are flexibly bridged by short linear motifs made up of linker Nups, Nic96 and Nup53. This assembly illustrates how Nup interactions can generate structural plasticity within the NPC scaffold. Our findings therefore demonstrate the utility of the genome of a thermophilic eukaryote for studying complex molecular machines.

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.

Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences-the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The 'environmental packages' apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

SUMMARY: SmashCommunity is a stand-alone metagenomic annotation and analysis pipeline suitable for data from Sanger and 454 sequencing technologies. It supports state-of-the-art software for essential metagenomic tasks such as assembly and gene prediction. It provides tools to estimate the quantitative phylogenetic and functional compositions of metagenomes, to compare compositions of multiple metagenomes and to produce intuitive visual representations of such analyses. AVAILABILITY: SmashCommunity is freely available at http://www.bork.embl.de/software/smash CONTACT: bork@embl.de.

A major challenge in the human metagenomics field is to identify associations of the bacterial genes and human phenotypes and act to modulate microbial populations in order to improve human health and wellbeing. MetaHIT project addresses this ambitious challenge by developing and integrating a number of necessary approaches within the context of the gut microbiome. Among the first results is the establishment of a broad catalog of the human gut microbial genes, which was achieved by an original application of the new generation sequencing technology. The catalog contains 3.3 million non-redundant genes, 150-fold more than the human genome equivalent and includes a large majority of the gut metagenomic sequences determined across three continents, Europe, America and Asia. Its content corresponds to some 1000 bacterial species, which likely represent a large fraction of species associated with humankind intestinal tract. The catalog enables development of the gene profiling approaches aiming to detect associations of bacterial genes and phenotypes. These should lead to the speedy development of diagnostic and prognostic tools and open avenues to reasoned approaches to the modulation of the individual's microbiota in order to optimize health and well-being.

SUMMARY: Recent advances in single-cell manipulation technology, whole genome amplification and high-throughput sequencing have now made it possible to sequence the genome of an individual cell. The bioinformatic analysis of these genomes however is far more complicated than the analysis of those generated using traditional, culture-based methods. In order to simplify this analysis we have developed SmashCell (Simple Metagenomics Analysis SHell-for sequences from single Cells). It is designed to automate the main steps in microbial genome analysis - assembly, gene prediction, functional annotation - in a way that allows parameter and algorithm exploration at each step in the process. It alsomanages the data created by these analyses and provides visualisation methods to allow rapid analysis of the results. AVAILABILITY: The SmashCell source code and a comprehensive manual are available at http://asiago.stanford.edu/SmashCell CONTACT: eoghanh@stanford.edu.

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.

To study basic principles of transcriptome organization in bacteria, we analyzed one of the smallest self-replicating organisms, Mycoplasma pneumoniae. We combined strand-specific tiling arrays, complemented by transcriptome sequencing, with more than 252 spotted arrays. We detected 117 previously undescribed, mostly noncoding transcripts, 89 of them in antisense configuration to known genes. We identified 341 operons, of which 139 are polycistronic; almost half of the latter show decaying expression in a staircase-like manner. Under various conditions, operons could be divided into 447 smaller transcriptional units, resulting in many alternative transcripts. Frequent antisense transcripts, alternative transcripts, and multiple regulators per gene imply a highly dynamic transcriptome, more similar to that of eukaryotes than previously thought.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.