Extensive strain-level copy-number variation across human gut microbiome species

Gut microbiota diversity predicts immune status in HIV-1 infection

OBJECTIVE

HIV-1 infection is characterized by altered intestinal barrier, gut microbiota dysbiosis, and systemic inflammation. We hypothesized that changes of the gut microbiota predict immune dysfunction and HIV-1 progression, and that antiretroviral therapy (ART) partially restores the microbiota composition.

DESIGN

An observational study including 28 viremic patients, three elite controllers, and nine uninfected controls. Blood and stool samples were collected at baseline and for 19 individuals at follow-up (median 10 months) during ART.

METHODS

Microbiota composition was determined by 16S rRNA sequencing (Illumina MiSeq). Soluble markers of microbial translocation and monocyte activation were analyzed by Limulus Amebocyte Lysate assay or ELISA.

RESULTS

Several alpha-diversity measures, including number of observed bacterial species and Shannon index, were significantly lower in viremic patients compared to controls. The alpha diversity correlated with CD4 T-cell counts and inversely with markers of microbial translocation and monocyte activation. In multivariate linear regression, for every age and sex-adjusted increase in the number of bacterial species, the CD4 T-cell count increased with 0.88 (95% confidence interval 0.35-1.41) cells/μl (P = 0.002). After introduction of ART, microbiota alterations persisted with further reduction in alpha diversity. The microbiota composition at the genus level was profoundly altered in viremic patients, both at baseline and after ART, with Prevotella reduced during ART (P < 0.007).

CONCLUSIONS

Gut microbiota alterations are closely associated with immune dysfunction in HIV-1 patients, and these changes persist during short-term ART. Our data implicate that re-shaping the microbiota may be an adjuvant therapy in patients commencing successful ART.

Mechanisms of Microbe-Host Interaction in Crohn's Disease: Dysbiosis vs. Pathobiont Selection

Crohn’s disease (CD) is a systemic chronic inflammatory condition mainly characterized by discontinuous transmural pathology of the gastrointestinal tract and frequent extraintestinal manifestations with intermittent episodes of remission and relapse. Genome-wide association studies identified a number of risk loci that, catalyzed by environmental triggers, result in the loss of tolerance toward commensal bacteria based on dysregulated innate effector functions and antimicrobial defense, leading to exacerbated adaptive immune responses responsible for chronic immune-mediated tissue damage. In this review, we discuss the inter-related role of changes in the intestinal microbiota, epithelial barrier integrity, and immune cell functions on the pathogenesis of CD, describing the current approaches available to investigate the molecular mechanisms underlying the disease. Substantial effort has been dedicated to define disease-associated changes in the intestinal microbiota (dysbiosis) and to link pathobionts to the etiology of inflammatory bowel diseases. A cogent definition of dysbiosis is lacking, as well as an agreement of whether pathobionts or complex shifts in the microbiota trigger inflammation in the host. Among the rarely available animal models, SAMP/Yit and TNF^deltaARE mice are the best known displaying a transmural CD-like phenotype. New hypothesis-driven mouse models, e.g., epithelial-specific Caspase8^−/−, ATG16L1^−/−, and XBP1^−/− mice, validate pathway-focused function of specific CD-associated risk genes highlighting the role of Paneth cells in antimicrobial defense. To study the causal role of bacteria in initiating inflammation in the host, the use of germ-free mouse models is indispensable. Unraveling the interactions of genes, immune cells and microbes constitute a criterion for the development of safe, reliable, and effective treatment options for CD.

Genus-Wide Comparative Genomics of Malassezia Delineates Its Phylogeny, Physiology, and Niche Adaptation on Human Skin

Malassezia is a unique lipophilic genus in class Malasseziomycetes in Ustilaginomycotina, (Basidiomycota, fungi) that otherwise consists almost exclusively of plant pathogens. Malassezia are typically isolated from warm-blooded animals, are dominant members of the human skin mycobiome and are associated with common skin disorders. To characterize the genetic basis of the unique phenotypes of Malassezia spp., we sequenced the genomes of all 14 accepted species and used comparative genomics against a broad panel of fungal genomes to comprehensively identify distinct features that define the Malassezia gene repertoire: gene gain and loss; selection signatures; and lineage-specific gene family expansions. Our analysis revealed key gene gain events (64) with a single gene conserved across all Malassezia but absent in all other sequenced Basidiomycota. These likely horizontally transferred genes provide intriguing gain-of-function events and prime candidates to explain the emergence of Malassezia. A larger set of genes (741) were lost, with enrichment for glycosyl hydrolases and carbohydrate metabolism, concordant with adaptation to skin’s carbohydrate-deficient environment. Gene family analysis revealed extensive turnover and underlined the importance of secretory lipases, phospholipases, aspartyl proteases, and other peptidases. Combining genomic analysis with a re-evaluation of culture characteristics, we establish the likely lipid-dependence of all Malassezia. Our phylogenetic analysis sheds new light on the relationship between Malassezia and other members of Ustilaginomycotina, as well as phylogenetic lineages within the genus. Overall, our study provides a unique genomic resource for understanding Malassezia niche-specificity and potential virulence, as well as their abundance and distribution in the environment and on human skin.

Malassezia are the dominant eukaryotic residents of human skin and are associated with the most common skin disorders, including dandruff, atopic dermatitis, eczema, and others. Despite significant effort, the role of Malassezia in skin disease and homeostasis remains unclear. Malassezia are also unique among fungi by requiring lipids for growth, but the breadth and genetic basis of their lipophilic lifestyle has not been comprehensively studied. Here we report the complete genomes of all 14 Malassezia species (including multiple strains of the most common species found on humans) and systematically identify features that define the genus and its sub-lineages, including horizontally transferred genes likely to represent key gain-of-function events and which may have enabled evolution of the genus from plant to animal inhabitants. Genus wide expansion of lipid hydrolases and loss of carbohydrate metabolism genes underscore the entire genus’ gradual evolution to lipid-dependency, which was confirmed even in the previously thought to be lipophilic M. pachydermatis, via genomics with experimental confirmation. Finally, these reference genomes will serve as a valuable resource for future metagenomic investigations into the role of Malassezia species in normal healthy skin and diseases.

Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data

Background

Identification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.

Results

We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100 % identification accuracy at supra-species level and 78 % accuracy at the species level.

Conclusion

CNIDARIA allows for fast, resource-efficient comparison and identification of both raw and assembled genome and transcriptome data. This can help answer both fundamental (e.g. in phylogeny, ecological diversity analysis) and practical questions (e.g. sequencing quality control, primer design).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0806-7) contains supplementary material, which is available to authorized users.

16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies

The 16S rRNA gene (16S) is an accepted marker of bacterial taxonomic diversity, even though differences in copy number obscure the relationship between amplicon and organismal abundances. Ancestral state reconstruction methods can predict 16S copy numbers through comparisons with closely related reference genomes; however, the database of closed genomes is limited. Here, we extend the reference database of 16S copy numbers to de novo assembled draft genomes by developing 16Stimator, a method to estimate 16S copy numbers when these repetitive regions collapse during assembly. Using a read depth approach, we estimate 16S copy numbers for 12 endophytic isolates from Arabidopsis thaliana and confirm estimates by qPCR. We further apply this approach to draft genomes deposited in NCBI and demonstrate accurate copy number estimation regardless of sequencing platform, with an overall median deviation of 14%. The expanded database of isolates with 16S copy number estimates increases the power of phylogenetic correction methods for determining organismal abundances from 16S amplicon surveys.

ConStrains identifies microbial strains in metagenomic datasets

An important fraction of microbial diversity is harbored in strain individuality, so identification of conspecific bacterial strains is imperative for improved understanding of microbial community functions. Limitations in bioinformatics and sequencing technologies have to date precluded strain identification owing to difficulties in phasing short reads to faithfully recover the original strain-level genotypes, which have highly similar sequences. We present ConStrains, an open-source algorithm that identifies conspecific strains from metagenomic sequence data and reconstructs the phylogeny of these strains in microbial communities. The algorithm uses single-nucleotide polymorphism (SNP) patterns in a set of universal genes to infer within-species structures that represent strains. Applying ConStrains to simulated and host-derived datasets provides insights into microbial community dynamics.

In Vitro Method To Assess Soil Arsenic Metabolism by Human Gut Microbiota: Arsenic Speciation and Distribution

Arsenic (As) speciation and distribution are two important factors in assessing human health risk from As-contaminated soil. In this study, we used the combination of physiologically based extraction test (PBET) and Simulator of Human Intestinal Microbial Ecosystem (SHIME) to determine soil As metabolism by human gut microbiota. The results showed that the percentage of soil arsenate [As(V)] transformation reached 22.1-38.2%, while that of arsenite [As(III)] attained 66.5-92.0%; 30.1-56.4% of As(V) transformed was attached to the soil solid phase. In comparison to sequential extraction results, almost all amorphous Fe/Al-oxide-bound As was liberated in the colon phase. An X-ray absorption near-edge structure (XANES) showed that the As(III) percentage in the soil solid phase reached 16.6-26.9% and reached 73.4% (soil 1) in the colon phase. Additionally, plenty of As(III) and different extents of methylation were also observed in colon extraction solution. As bioaccessibility in the colon phase was 1.8-2.8 times that in the small intestinal phase. Our results indicated that human gut microbiota increased As bioaccessibility, and large amounts of As(III) were adsorbed onto the soil solid phase as a result of microbial reduction. Determining As speciation and distribution in extraction solution and soil solid phases will allow for an accurate assessment of the risk to human health upon soil As exposure.

From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems

Microorganisms and the viruses that infect them are the most numerous biological entities on Earth and enclose its greatest biodiversity and genetic reservoir. With strength in their numbers, these microscopic organisms are major players in the cycles of energy and matter that sustain all life. Scientists have only scratched the surface of this vast microbial world through culture-dependent methods. Recent developments in generating metagenomes, large random samples of nucleic acid sequences isolated directly from the environment, are providing comprehensive portraits of the composition, structure, and functioning of microbial communities. Moreover, advances in metagenomic analysis have created the possibility of obtaining complete or nearly complete genome sequences from uncultured microorganisms, providing important means to study their biology, ecology, and evolution. Here we review some of the recent developments in the field of metagenomics, focusing on the discovery of genetic novelty and on methods for obtaining uncultured genome sequences, including through the recycling of previously published datasets. Moreover we discuss how metagenomics has become a core scientific tool to characterize eco-evolutionary patterns of microbial ecosystems, thus allowing us to simultaneously discover new microbes and study their natural communities. We conclude by discussing general guidelines and challenges for modeling the interactions between uncultured microorganisms and viruses based on the information contained in their genome sequences. These models will significantly advance our understanding of the functioning of microbial ecosystems and the roles of microbes in the environment.

Functional Metagenomics of the Bronchial Microbiome in COPD

The course of chronic obstructive pulmonary disease (COPD) is frequently aggravated by exacerbations, and changes in the composition and activity of the microbiome may be implicated in their appearance. The aim of this study was to analyse the composition and the gene content of the microbial community in bronchial secretions of COPD patients in both stability and exacerbation. Taxonomic data were obtained by 16S rRNA gene amplification and pyrosequencing, and metabolic information through shotgun metagenomics, using the Metagenomics RAST server (MG-RAST), and the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) programme, which predict metagenomes from 16S data. Eight severe COPD patients provided good quality sputum samples, and no significant differences in the relative abundance of any phyla and genera were found between stability and exacerbation. Bacterial biodiversity (Chao1 and Shannon indexes) did not show statistical differences and beta-diversity analysis (Bray-Curtis dissimilarity index) showed a similar microbial composition in the two clinical situations. Four functional categories showed statistically significant differences with MG-RAST at KEGG level 2: in exacerbation, Cell growth and Death and Transport and Catabolism decreased in abundance [1.6 (0.2-2.3) vs 3.6 (3.3-6.9), p = 0.012; and 1.8 (0-3.3) vs 3.6 (1.8-5.1), p = 0.025 respectively], while Cancer and Carbohydrate Metabolism increased [0.8 (0-1.5) vs 0 (0-0.5), p = 0.043; and 7 (6.4-9) vs 5.9 (6.3-6.1), p = 0.012 respectively]. In conclusion, the bronchial microbiome as a whole is not significantly modified when exacerbation symptoms appear in severe COPD patients, but its functional metabolic capabilities show significant changes in several pathways.