Dataset Information

Strain profiling and epidemiology of bacterial species from metagenomic sequencing.

ABSTRACT: Microbial communities are often composed by complex mixtures of multiple strains of the same species, characterized by a wide genomic and phenotypic variability. Computational methods able to identify, quantify and classify the different strains present in a sample are essential to fully exploit the potential of metagenomic sequencing in microbial ecology, with applications that range from the epidemiology of infectious diseases to the characterization of the dynamics of microbial colonization. Here we present a computational approach that uses the available genomic data to reconstruct complex strain profiles from metagenomic sequencing, quantifying the abundances of the different strains and cataloging them according to the population structure of the species. We validate the method on synthetic data sets and apply it to the characterization of the strain distribution of several important bacterial species in real samples, showing how its application provides novel insights on the structure and complexity of the microbiota.

SUBMITTER: Albanese D

PROVIDER: S-EPMC5741664 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Strain profiling and epidemiology of bacterial species from metagenomic sequencing.

Albanese Davide D Donati Claudio C

Nature communications 20171222 1

Microbial communities are often composed by complex mixtures of multiple strains of the same species, characterized by a wide genomic and phenotypic variability. Computational methods able to identify, quantify and classify the different strains present in a sample are essential to fully exploit the potential of metagenomic sequencing in microbial ecology, with applications that range from the epidemiology of infectious diseases to the characterization of the dynamics of microbial colonization. ...[more]

PMID: 29273717

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Project description:BACKGROUND:The microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Microbes thriving in urban environments may be crucially important for human health, but have received less attention than those of other environments. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context. It is thus currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are also effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Here, we aimed at a) testing the currently available metagenomic profiling tools on urban metagenomics; b) characterizing the organisms in urban environment at the resolution of single strain and c) discussing the biological insights that can be inferred from such methods. RESULTS:We applied three complementary methods on the 1614 metagenomes of the CAMDA 2017 challenge. With MetaMLST we identified 121 known sequence-types from 15 species of clinical relevance. For instance, we identified several Acinetobacter strains that were close to the nosocomial opportunistic pathogen A. nosocomialis. With StrainPhlAn, a generalized version of the MetaMLST approach, we inferred the phylogenetic structure of Pseudomonas stutzeri strains and suggested that the strain-level heterogeneity in environmental samples is higher than in the human microbiome. Finally, we also probed the functional potential of the different strains with PanPhlAn. We further showed that SNV-based and pangenome-based profiling provide complementary information that can be combined to investigate the evolutionary trajectories of microbes and to identify specific genetic determinants of virulence and antibiotic resistances within closely related strains. CONCLUSION:We show that strain-level methods developed primarily for the analysis of human microbiomes can be effective for city-associated microbiomes. In fact, (opportunistic) pathogens can be tracked and monitored across many hundreds of urban metagenomes. However, while more effort is needed to profile strains of currently uncharacterized species, this work poses the basis for high-resolution analyses of microbiomes sampled in city and mass transportation environments. REVIEWERS:This article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.

Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:The gut microbiome becomes shaped in the first days of life and continues to increase its diversity during the first months. Links between the configuration of the infant gut microbiome and infant health are being shown, but a comprehensive strain-level assessment of microbes vertically transmitted from mother to infant is still missing. We collected fecal and breast milk samples from multiple mother-infant pairs during the first year of life and applied shotgun metagenomic sequencing followed by computational strain-level profiling. We observed that several specific strains, including those of Bifidobacterium bifidum, Coprococcus comes, and Ruminococcus bromii, were present in samples from the same mother-infant pair, while being clearly distinct from those carried by other pairs, which is indicative of vertical transmission. We further applied metatranscriptomics to study the in vivo gene expression of vertically transmitted microbes and found that transmitted strains of Bacteroides and Bifidobacterium species were transcriptionally active in the guts of both adult and infant. By combining longitudinal microbiome sampling and newly developed computational tools for strain-level microbiome analysis, we demonstrated that it is possible to track the vertical transmission of microbial strains from mother to infants and to characterize their transcriptional activity. Our work provides the foundation for larger-scale surveys to identify the routes of vertical microbial transmission and its influence on postinfancy microbiome development. IMPORTANCE Early infant exposure is important in the acquisition and ultimate development of a healthy infant microbiome. There is increasing support for the idea that the maternal microbial reservoir is a key route of microbial transmission, and yet much is inferred from the observation of shared species in mother and infant. The presence of common species, per se, does not necessarily equate to vertical transmission, as species exhibit considerable strain heterogeneity. It is therefore imperative to assess whether shared microbes belong to the same genetic variant (i.e., strain) to support the hypothesis of vertical transmission. Here we demonstrate the potential of shotgun metagenomics and strain-level profiling to identify vertical transmission events. Combining these data with metatranscriptomics, we show that it is possible not only to identify and track the fate of microbes in the early infant microbiome but also to investigate the actively transcribing members of the community. These approaches will ultimately provide important insights into the acquisition, development, and community dynamics of the infant microbiome.

Dataset Information

Strain profiling and epidemiology of bacterial species from metagenomic sequencing.

Publications

Strain profiling and epidemiology of bacterial species from metagenomic sequencing.

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