Dataset Information

ConStrains identifies microbial strains in metagenomic datasets.

ABSTRACT: 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.

SUBMITTER: Luo C

PROVIDER: S-EPMC4676274 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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ConStrains identifies microbial strains in metagenomic datasets.

Luo Chengwei C Knight Rob R Siljander Heli H Knip Mikael M Xavier Ramnik J RJ Gevers Dirk D

Nature biotechnology 20150907 10

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 ...[more]

PMID: 26344404

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Project description:BackgroundThe 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.ResultsWe 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.ConclusionWe 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.ReviewersThis article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.

Project description:The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

Project description:BackgroundThe overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak--a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.ResultsValidation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.ConclusionsWith novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.Availability and implementationA web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).

Project description:The paucity of microbiome studies at intestinal tissues has contributed to a yet limited understanding of potential viral and bacterial cofactors of colorectal cancer (CRC) carcinogenesis or progression. We analysed whole-genome sequences of CRC primary tumours, their corresponding metastases and matched normal tissue for sequences of viral, phage and bacterial species. Bacteriome analysis showed Fusobacterium nucleatum, Streptococcus sanguinis, F. Hwasookii, Anaerococcus mediterraneensis and further species enriched in primary CRCs. The primary CRC of one patient was enriched for F. alocis, S. anginosus, Parvimonas micra and Gemella sp. 948. Enrichment of Escherichia coli strains IAI1, SE11, K-12 and M8 was observed in metastases together with coliphages enterobacteria phage φ80 and Escherichia phage VT2φ_272. Virome analysis showed that phages were the most preponderant viral species (46%), the main families being Myoviridae, Siphoviridae and Podoviridae. Primary CRCs were enriched for bacteriophages, showing five phages (Enterobacteria, Bacillus, Proteus, Streptococcus phages) together with their pathogenic hosts in contrast to normal tissues. The most frequently detected, and Blast-confirmed, viruses included human endogenous retrovirus K113, human herpesviruses 7 and 6B, Megavirus chilensis, cytomegalovirus (CMV) and Epstein-Barr virus (EBV), with one patient showing EBV enrichment in primary tumour and metastases. EBV was PCR-validated in 80 pairs of CRC primary tumour and their corresponding normal tissues; in 21 of these pairs (26.3%), it was detectable in primary tumours only. The number of viral species was increased and bacterial species decreased in CRCs compared with normal tissues, and we could discriminate primary CRCs from metastases and normal tissues by applying the Hutcheson t-test on the Shannon indices based on viral and bacterial species. Taken together, our results descriptively support hypotheses on microorganisms as potential (co)risk factors of CRC and extend putative suggestions on critical microbiome species in CRC metastasis.

Dataset Information

ConStrains identifies microbial strains in metagenomic datasets.

Publications

ConStrains identifies microbial strains in metagenomic datasets.

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