Joint Analysis of Multiple Metagenomic Samples

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed “binning”) algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough

Complementary Metagenomic Approaches Improve Reconstruction of Microbial Diversity in a Forest Soil.

Soil ecosystems harbor diverse microorganisms and yet remain only partially characterized as neither single-cell sequencing nor whole-community sequencing offers a complete picture of these complex communities. Thus, the genetic and metabolic potential of this "uncultivated majority" remains underexplored. To address these challenges, we applied a pooled-cell-sorting-based mini-metagenomics approach and compared the results to bulk metagenomics. Informatic binning of these data produced 200 mini-metagenome assembled genomes (sorted-MAGs) and 29 bulk metagenome assembled genomes (MAGs). The sorted and bulk MAGs increased the known phylogenetic diversity of soil taxa by 7.2% with respect to the Joint Genome Institute IMG/M database and showed clade-specific sequence recruitment patterns across diverse terrestrial soil metagenomes. Additionally, sorted-MAGs expanded the rare biosphere not captured through MAGs from bulk sequences, exemplified through phylogenetic and functional analyses of members of the phylum Bacteroidetes Analysis of 67 Bacteroidetes sorted-MAGs showed conserved patterns of carbon metabolism across four clades. These results indicate that mini-metagenomics enables genome-resolved investigation of predicted metabolism and demonstrates the utility of combining metagenomics methods to tap into the diversity of heterogeneous microbial assemblages.IMPORTANCE Microbial ecologists have historically used cultivation-based approaches as well as amplicon sequencing and shotgun metagenomics to characterize microbial diversity in soil. However, challenges persist in the study of microbial diversity, including the recalcitrance of the majority of microorganisms to laboratory cultivation and limited sequence assembly from highly complex samples. The uncultivated majority thus remains a reservoir of untapped genetic diversity. To address some of the challenges associated with bulk metagenomics as well as low throughput of single-cell genomics, we applied flow cytometry-enabled mini-metagenomics to capture expanded microbial diversity from forest soil and compare it to soil bulk metagenomics. Our resulting data from this pooled-cell sorting approach combined with bulk metagenomics revealed increased phylogenetic diversity through novel soil taxa and rare biosphere members. In-depth analysis of genomes within the highly represented Bacteroidetes phylum provided insights into conserved and clade-specific patterns of carbon metabolism

Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health

Identification of Carbohydrate Metabolism Genes in the Metagenome of a Marine Biofilm Community Shown to Be Dominated by Gammaproteobacteria and Bacteroidetes

Polysaccharides are an important source of organic carbon in the marine environment and degradation of the insoluble and globally abundant cellulose is a major component of the marine carbon cycle. Although a number of species of cultured bacteria are known to degrade crystalline cellulose, little is known of the polysaccharide hydrolases expressed by cellulose-degrading microbial communities, particularly in the marine environment. Next generation 454 Pyrosequencing was applied to analyze the microbial community that colonizes and degrades insoluble polysaccharides in situ in the Irish Sea. The bioinformatics tool MG-RAST was used to examine the randomly sampled data for taxonomic markers and functional genes, and showed that the community was dominated by members of the Gammaproteobacteria and Bacteroidetes. Furthermore, the identification of 211 gene sequences matched to a custom-made database comprising the members of nine glycoside hydrolase families revealed an extensive repertoire of functional genes predicted to be involved in cellulose utilization. This demonstrates that the use of an in situ cellulose baiting method yielded a marine microbial metagenome considerably enriched in functional genes involved in polysaccharide degradation. The research reported here is the first designed to specifically address the bacterial communities that colonize and degrade cellulose in the marine environment and to evaluate the glycoside hydrolase (cellulase and chitinase) gene repertoire of that community, in the absence of the biases associated with PCR-based molecular techniques

Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

Shotgun metagenomic analysis of microbial communities from the Loxahatchee nature preserve in the Florida Everglades.

BackgroundCurrently, much is unknown about the taxonomic diversity and the mechanisms of methane metabolism in the Florida Everglades ecosystem. The Loxahatchee National Wildlife Refuge is a section of the Florida Everglades that is almost entirely unstudied in regard to taxonomic profiling. This short report analyzes the metagenome of soil samples from this Refuge to investigate the predominant taxa, as well as the abundance of genes involved in environmentally significant metabolic pathways related to methane production (nitrogen fixation and dissimilatory sulfite reduction).MethodsShotgun metagenomic sequencing using the Illumina platform was performed on 17 soil samples from four different sites within the Loxahatchee National Wildlife Refuge, and underwent quality control, assembly, and annotation. The soil from each sample was tested for water content and concentrations of organic carbon and nitrogen.ResultsThe three most common phyla of bacteria for every site were Actinobacteria, Acidobacteria, and Proteobacteria; however, there was variation in relative phylum composition. The most common phylum of Archaea was Euryarchaeota for all sites. Alpha and beta diversity analyses indicated significant congruity in taxonomic diversity in most samples from Sites 1, 3, and 4 and negligible congruity between Site 2 and the other sites. Shotgun metagenomic sequencing revealed the presence of biogeochemical biomarkers of particular interest (e.g., mrcA, nifH, and dsrB) within the samples. The normalized abundances of mcrA, nifH, and dsrB exhibited a positive correlation with nitrogen concentration and water content, and a negative correlation with organic carbon concentration.ConclusionThis Everglades soil metagenomic study allowed examination of wetlands biological processes and showed expected correlations between measured organic constituents and prokaryotic gene frequency. Additionally, the taxonomic profile generated gives a basis for the diversity of prokaryotic microbial life throughout the Everglades