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, ~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

/pdf/a-human-gut-microbial-gene-catalogue-established-by-3k6ouy2d6d.pdf

A human gut microbial gene catalogue established by metagenomic sequencing

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

/pdf/string-v10-protein-protein-interaction-networks-integrated-2gg77rxec0.pdf

STRING v10: protein–protein interaction networks, integrated over the tree of life

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

/pdf/the-string-database-in-2017-quality-controlled-protein-1u6rx6raii.pdf

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

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.

/pdf/enterotypes-of-the-human-gut-microbiome-55a88sj7we.pdf

Enterotypes of the human gut microbiome

A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.

/pdf/the-human-microbiome-project-1armeyuv2c.pdf

The human microbiome project.

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a metadatabase that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.

/pdf/string-8-a-global-view-on-proteins-and-their-functional-56hd0uvpxl.pdf

STRING 8—a global view on proteins and their functional interactions in 630 organisms

The taxonomic composition of environmental communities is an important indicator of their ecology and function. We used a set of protein-coding marker genes, extracted from large-scale environmental shotgun sequencing data, to provide a more direct, quantitative, and accurate picture of community composition than that provided by traditional ribosomal RNA-based approaches depending on the polymerase chain reaction. Mapping marker genes from four diverse environmental data sets onto a reference species phylogeny shows that certain communities evolve faster than others. The method also enables determination of preferred habitats for entire microbial clades and provides evidence that such habitat preferences are often remarkably stable over time.

/pdf/quantitative-phylogenetic-assessment-of-microbial-4g56xsysq2.pdf

Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments

Quantitative phylogenetic assessment of microbial communities in diverse environments von Mering C 1,4 , Hugenholtz P 2 , Raes J 1 , Tringe SG 2 , Doerks T 1 , Jensen LJ 1 , Ward N 3 , Bork P 1 . European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA The Institute for Genomic Research, Rockville, MD 20850, USA present address: University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland The taxonomic composition of environmental communities is an important indicator of their ecology and function. Here, we use a set of protein-coding marker genes, extracted from large- scale environmental shotgun sequencing data, to provide a more direct, quantitative and accurate picture of community composition than traditional rRNA-based approaches using polymerase chain reaction (PCR). By mapping marker genes from four diverse environmental data sets onto a reference species phylogeny, we show that certain communities evolve faster than others, determine preferred habitats for entire microbial clades, and provide evidence that such habitat preferences are often remarkably stable over time.

T. Doerks

Papers

STRING 8—a global view on proteins and their functional interactions in 630 organisms

Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments

Quantitative phylogenetic assessment of microbial communities in diverse environments