NCI Cancer Research Data Commons: Core Standards and Services.

The NCI Cancer Research Data Commons (CRDC) is a collection of data commons, analysis platforms, and tools that make existing cancer data more findable and accessible by the cancer research community. In practice, the two biggest hurdles to finding and using data for discovery are the wide variety of models and ontologies used to describe data, and the dispersed storage of that data. Here, we outline core CRDC services to aggregate descriptive information from multiple studies for findability via a single interface and to provide a single access method that spans multiple data commons. See related articles by Wang et al., p. 1388, Pot et al., p. 1396, and Kim et al., p. 1404.

Making Common Fund data more findable: catalyzing a data ecosystem.

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

Compositional and Functional Differences in the Human Gut Microbiome Correlate with Clinical Outcome following Infection with Wild-Type Salmonella enterica Serovar Typhi.

Insights into disease susceptibility as well as the efficacy of vaccines against typhoid and other enteric pathogens may be informed by better understanding the relationship between the effector immune response and the gut microbiota. In the present study, we characterized the composition (16S rRNA gene profiling) and function (RNA sequencing [RNA-seq]) of the gut microbiota following immunization and subsequent exposure to wild-type Salmonella enterica serovar Typhi in a human challenge model to further investigate the central hypothesis that clinical outcomes may be linked to the gut microbiota. Metatranscriptome analysis of longitudinal stool samples collected from study subjects revealed two stable patterns of gene expression for the human gut microbiota, dominated by transcripts from either Methanobrevibacter or a diverse representation of genera in the Firmicutes phylum. Immunization with one of two live oral attenuated vaccines against S. Typhi had minimal effects on the composition or function of the gut microbiota. It was observed that subjects harboring the methanogen-dominated transcriptome community at baseline displayed a lower risk of developing symptoms of typhoid following challenge with wild-type S. Typhi. Furthermore, genes encoding antioxidant proteins, metal homeostasis and transport proteins, and heat shock proteins were expressed at a higher level at baseline or after challenge with S. Typhi in subjects who did not develop symptoms of typhoid. These data suggest that functional differences relating to redox potential and ion homeostasis in the gut microbiota may impact clinical outcomes following exposure to wild-type S. Typhi.IMPORTANCES. Typhi is a significant cause of systemic febrile morbidity in settings with poor sanitation and limited access to clean water. It has been demonstrated that the human gut microbiota can influence mucosal immune responses, but there is little information available on the impact of the human gut microbiota on clinical outcomes following exposure to enteric pathogens. Here, we describe differences in the composition and function of the gut microbiota in healthy adult volunteers enrolled in a typhoid vaccine trial and report that these differences are associated with host susceptibility to or protection from typhoid after challenge with wild-type S Typhi. Our observations have important implications in interpreting the efficacy of oral attenuated vaccines against enteric pathogens in diverse populations.

Erratum: Strains, functions and dynamics in the expanded Human Microbiome Project.

This corrects the article DOI: 10.1038/nature23889.

Strains, functions and dynamics in the expanded Human Microbiome Project.

The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, diversity, biogeography, and molecular function. The National Institutes of Health Human Microbiome Project has provided one of the broadest such characterizations so far. Here we introduce a second wave of data from the study, comprising 1,631 new metagenomes (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to provide new characterizations of microbiome personalization. Strain identification revealed subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity and enables an understanding of personalized microbiome function and dynamics.

Functional dynamics of the gut microbiome in elderly people during probiotic consumption.

UNLABELLED: A mechanistic understanding of the purported health benefits conferred by consumption of probiotic bacteria has been limited by our knowledge of the resident gut microbiota and its interaction with the host. Here, we detail the impact of a single-organism probiotic, Lactobacillus rhamnosus GG ATCC 53103 (LGG), on the structure and functional dynamics (gene expression) of the gut microbiota in a study of 12 healthy individuals, 65 to 80 years old. The analysis revealed that while the overall community composition was stable as assessed by 16S rRNA profiling, the transcriptional response of the gut microbiota was modulated by probiotic treatment. Comparison of transcriptional profiles based on taxonomic composition yielded three distinct transcriptome groups that displayed considerable differences in functional dynamics. The transcriptional profile of LGG in vivo was remarkably concordant across study subjects despite the considerable interindividual nature of the gut microbiota. However, we identified genes involved in flagellar motility, chemotaxis, and adhesion from Bifidobacterium and the dominant butyrate producers Roseburia and Eubacterium whose expression was increased during probiotic consumption, suggesting that LGG may promote interactions between key constituents of the microbiota and the host epithelium. These results provide evidence for the discrete functional effects imparted by a specific single-organism probiotic and challenge the prevailing notion that probiotics substantially modify the resident microbiota within nondiseased individuals in an appreciable fashion. IMPORTANCE: Probiotic bacteria have been used for over a century to promote digestive health. Many individuals report that probiotics alleviate a number of digestive issues, yet little evidence links how probiotic microbes influence human health. Here, we show how the resident microbes that inhabit the healthy human gut respond to a probiotic. The well-studied probiotic Lactobacillus rhamnosus GG ATCC 53103 (LGG) was administered in a clinical trial, and a suite of measurements of the resident microbes were taken to evaluate potential changes over the course of probiotic consumption. We found that LGG transiently enriches for functions to potentially promote anti-inflammatory pathways in the resident microbes.

MetaRef: a pan-genomic database for comparative and community microbial genomics.

Microbial genome sequencing is one of the longest-standing areas of biological database development, but high-throughput, low-cost technologies have increased its throughput to an unprecedented number of new genomes per year. Several thousand microbial genomes are now available, necessitating new approaches to organizing information on gene function, phylogeny and microbial taxonomy to facilitate downstream biological interpretation. MetaRef, available at http://metaref.org, is a novel online resource systematically cataloguing a comprehensive pan-genome of all microbial clades with sequenced isolates. It organizes currently available draft and finished bacterial and archaeal genomes into quality-controlled clades, reports all core and pan gene families at multiple levels in the resulting taxonomy, and it annotates families' conservation, phylogeny and consensus functional information. MetaRef also provides a comprehensive non-redundant reference gene catalogue for metagenomic studies, including the abundance and prevalence of all gene families in the >700 shotgun metagenomic samples of the Human Microbiome Project. This constitutes a systematic mapping of clade-specific microbial functions within the healthy human microbiome across multiple body sites and can be used as reference for identifying potential functional biomarkers in disease-associate microbiomes. MetaRef provides all information both as an online browsable resource and as downloadable sequences and tabular data files that can be used for subsequent offline studies.

Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models.

Metagenomics projects collect DNA from uncharacterized environments that may contain thousands of species per sample. One main challenge facing metagenomic analysis is phylogenetic classification of raw sequence reads into groups representing the same or similar taxa, a prerequisite for genome assembly and for analyzing the biological diversity of a sample. New sequencing technologies have made metagenomics easier, by making sequencing faster, and more difficult, by producing shorter reads than previous technologies. Classifying sequences from reads as short as 100 base pairs has until now been relatively inaccurate, requiring researchers to use older, long-read technologies. We present Phymm, a classifier for metagenomic data, that has been trained on 539 complete, curated genomes and can accurately classify reads as short as 100 base pairs, a substantial improvement over previous composition-based classification methods. We also describe how combining Phymm with sequence alignment algorithms improves accuracy.

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.