Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.

A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

Environmental DNA sequencing has revealed the expansive biodiversity of microorganisms and clarified the relationship between host-associated microbial communities and host phenotype. Shotgun metagenomic DNA sequencing is a relatively new and powerful environmental sequencing approach that provides insight into community biodiversity and function. But, the analysis of metagenomic sequences is complicated due to the complex structure of the data. Fortunately, new tools and data resources have been developed to circumvent these complexities and allow researchers to determine which microbes are present in the community and what they might be doing. This review describes the analytical strategies and specific tools that can be applied to metagenomic data and the considerations and caveats associated with their use. Specifically, it documents how metagenomes can be analyzed to quantify community structure and diversity, assemble novel genomes, identify new taxa and genes, and determine which metabolic pathways are encoded in the community. It also discusses several methods that can be used compare metagenomes to identify taxa and functions that differentiate communities.

While most Ascomycetes tend to associate principally with plants, the dimorphic fungi Coccidioides immitis and Coccidioides posadasii are primary pathogens of immunocompetent mammals, including humans. Infection results from environmental exposure to Coccidiodies, which is believed to grow as a soil saprophyte in arid deserts. To investigate hypotheses about the life history and evolution of Coccidioides, the genomes of several Onygenales, including C. immitis and C. posadasii; a close, nonpathogenic relative, Uncinocarpus reesii; and a more diverged pathogenic fungus, Histoplasma capsulatum, were sequenced and compared with those of 13 more distantly related Ascomycetes. This analysis identified increases and decreases in gene family size associated with a host/substrate shift from plants to animals in the Onygenales. In addition, comparison among Onygenales genomes revealed evolutionary changes in Coccidioides that may underlie its infectious phenotype, the identification of which may facilitate improved treatment and prevention of coccidioidomycosis. Overall, the results suggest that Coccidioides species are not soil saprophytes, but that they have evolved to remain associated with their dead animal hosts in soil, and that Coccidioides metabolism genes, membrane-related proteins, and putatively antigenic compounds have evolved in response to interaction with an animal host.

Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

Obesity is an important and intractable public health problem. In addition to the well-known risk factors of behavior, diet, and genetics, gut microbial communities were recently identified as another possible source of risk and a potential therapeutic target. However, human and animal-model studies have yielded conflicting results about the precise nature of associations between microbiome composition and obesity. In this paper, we use publicly available data from the Human Microbiome Project (HMP) and MetaHIT, both surveys of healthy adults that include obese individuals, plus two smaller studies that specifically examined lean versus obese adults. We find that inter-study variability in the taxonomic composition of stool microbiomes far exceeds differences between lean and obese individuals within studies. Our analyses further reveal a high degree of variability in stool microbiome composition and diversity across individuals. While we confirm the previously published small, but statistically significant, differences in phylum-level taxonomic composition between lean and obese individuals in several cohorts, we find no association between BMI and taxonomic composition of stool microbiomes in the larger HMP and MetaHIT datasets. We explore a range of different statistical techniques and show that this result is robust to the choice of methodology. Differences between studies are likely due to a combination of technical and clinical factors. We conclude that there is no simple taxonomic signature of obesity in the microbiota of the human gut.

We have sequenced the genomes of 18 isolates of the closely related human pathogenic fungi Coccidioides immitis and Coccidioides posadasii to more clearly elucidate population genomic structure, bringing the total number of sequenced genomes for each species to 10. Our data confirm earlier microsatellite-based findings that these species are genetically differentiated, but our population genomics approach reveals that hybridization and genetic introgression have recently occurred between the two species. The directionality of introgression is primarily from C. posadasii to C. immitis, and we find more than 800 genes exhibiting strong evidence of introgression in one or more sequenced isolates. We performed PCRbased sequencing of one region exhibiting introgression in 40 C. immitis isolates to confirm and better define the extent of gene flow between the species. We find more coding sequence than expected by chance in the introgressed regions, suggesting that natural selection may play a role in the observed genetic exchange. We find notable heterogeneity in repetitive sequence composition among the sequenced genomes and present the first detailed genome-wide profile of a repeat-induced point mutation (RIP) process distinctly different from what has been observed in Neurospora. We identify promiscuous HLA-I and HLA-II epitopes in both proteomes and discuss the possible implications of introgression and population genomic data for public health and vaccine candidate prioritization. This study highlights the importance of population genomic data for detecting subtle but potentially important phenomena such as introgression.

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