Thomas J. Sharpton

Comparative genomic analyses of the human fungal pathogens Coccidioides and their relatives.

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.

An introduction to the analysis of shotgun metagenomic data

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.

A Taxonomic Signature of Obesity in the Microbiome? Getting to the Guts of the Matter

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.

Population genomic sequencing of Coccidioides fungi reveals recent hybridization and transposon control

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 PCR-based 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.

Global marine bacterial diversity peaks at high latitudes in winter

Genomic approaches to characterizing bacterial communities are revealing significant differences in diversity and composition between environments. But bacterial distributions have not been mapped at a global scale. Although current community surveys are way too sparse to map global diversity patterns directly, there is now sufficient data to fit accurate models of how bacterial distributions vary across different environments and to make global scale maps from these models. We apply this approach to map the global distributions of bacteria in marine surface waters. Our spatially and temporally explicit predictions suggest that bacterial diversity peaks in temperate latitudes across the world’s oceans. These global peaks are seasonal, occurring 6 months apart in the two hemispheres, in the boreal and austral winters. This pattern is quite different from the tropical, seasonally consistent diversity patterns observed for most macroorganisms. However, like other marine organisms, surface water bacteria are particularly diverse in regions of high human environmental impacts on the oceans. Our maps provide the first picture of bacterial distributions at a global scale and suggest important differences between the diversity patterns of bacteria compared with other organisms.

Mechanisms of Intron Gain and Loss in Cryptococcus

BackgroundGenome comparisons across deep phylogenetic divergences have revealed that spliceosomal intron gain and loss are common evolutionary events. However, because of the deep divergences involved in these comparisons, little is understood about how these changes occur, particularly in the case of intron gain. To ascertain mechanisms of intron gain and loss, we compared five relatively closely related genomes from the yeast Cryptococcus.ResultsWe observe a predominance of intron loss over gain and identify a relatively slow intron loss rate in Cryptococcus. Some genes preferentially lose introns and a large proportion of intron losses occur in the middle of genes (so called internal intron loss). Finally, we identify a gene that displays a differential number of introns in a repetitive DNA region.ConclusionBased the observed patterns of intron loss and gain, population resequencing and population genetic analysis, it appears that recombination causes the widely observed but poorly understood phenomenon of internal intron loss and that DNA repeat expansion can create new introns in a population.

Novel Bacterial Taxa in the Human Microbiome

The human gut harbors thousands of bacterial taxa. A profusion of metagenomic sequence data has been generated from human stool samples in the last few years, raising the question of whether more taxa remain to be identified. We assessed metagenomic data generated by the Human Microbiome Project Consortium to determine if novel taxa remain to be discovered in stool samples from healthy individuals. To do this, we established a rigorous bioinformatics pipeline that uses sequence data from multiple platforms (Illumina GAIIX and Roche 454 FLX Titanium) and approaches (whole-genome shotgun and 16S rDNA amplicons) to validate novel taxa. We applied this approach to stool samples from 11 healthy subjects collected as part of the Human Microbiome Project. We discovered several low-abundance, novel bacterial taxa, which span three major phyla in the bacterial tree of life. We determined that these taxa are present in a larger set of Human Microbiome Project subjects and are found in two sampling sites (Houston and St. Louis). We show that the number of false-positive novel sequences (primarily chimeric sequences) would have been two orders of magnitude higher than the true number of novel taxa without validation using multiple datasets, highlighting the importance of establishing rigorous standards for the identification of novel taxa in metagenomic data. The majority of novel sequences are related to the recently discovered genus Barnesiella, further encouraging efforts to characterize the members of this genus and to study their roles in the microbial communities of the gut. A better understanding of the effects of less-abundant bacteria is important as we seek to understand the complex gut microbiome in healthy individuals and link changes in the microbiome to disease.

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity?

Profile Hidden Markov Models for the Detection of Viruses within Metagenomic Sequence Data

Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs (“vFams”) to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam).

Comparative transcriptomics of the saprobic and parasitic growth phases in Coccidioides spp

Coccidioides immitis and C. posadasii, the causative agents of coccidioidomycosis, are dimorphic fungal pathogens, which grow as hyphae in the saprobic phase in the environment and as spherules in the parasitic phase in the mammalian host. In this study, we use comparative transcriptomics to identify gene expression differences between the saprobic and parasitic growth phases. We prepared Illumina mRNA sequencing libraries for saprobic-phase hyphae and parasitic-phase spherules in vitro for C. immitis isolate RS and C. posadasii isolate C735 in biological triplicate. Of 9,910 total predicted genes in Coccidioides, we observed 1,298 genes up-regulated in the saprobic phase of both C. immitis and C. posadasii and 1,880 genes up-regulated in the parasitic phase of both species. Comparing the saprobic and parasitic growth phases, we observed considerable differential expression of cell surface-associated genes, particularly chitin-related genes. We also observed differential expression of several virulence factors previously identified in Coccidioides and other dimorphic fungal pathogens. These included alpha (1,3) glucan synthase, SOWgp, and several genes in the urease pathway. Furthermore, we observed differential expression in many genes predicted to be under positive selection in two recent Coccidioides comparative genomics studies. These results highlight a number of genes that may be crucial to dimorphic phase-switching and virulence in Coccidioides. These observations will impact priorities for future genetics-based studies in Coccidioides and provide context for studies in other fungal pathogens.