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A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data.

ABSTRACT: Deep sequencing of the 16S rRNA gene provides a comprehensive view of bacterial communities in a particular environment and has expanded our ability to study the impact of the microflora on human health and disease. Current analysis methods rely on comparisons of the sequences generated with an expanding but limited set of annotated 16S rRNA sequences or phylogenic clustering of sequences based on arbitrary similarity cutoffs. We describe a novel approach to characterize bacterial composition using deep sequencing of 16S rRNA gene. Our method defines operational taxonomic units based on phylogenetic tree reconstruction and dynamic clustering of sequences using solely sequencing data. These OTUs can be used to identify differences in bacteria abundance between environments. This approach can perform better than previous phylogenetic methods and will significantly improve our understanding of the microfloral role on human diseases by providing a comprehensive analysis of the microbial composition from various bacterial communities.

SUBMITTER: Chan ER

PROVIDER: S-EPMC3168955 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data.

Chan Ernest R ER Hester James J Kalady Matthew M Xiao Hui H Li Xiaoxia X Serre David D

Genomics 20110415 4

Deep sequencing of the 16S rRNA gene provides a comprehensive view of bacterial communities in a particular environment and has expanded our ability to study the impact of the microflora on human health and disease. Current analysis methods rely on comparisons of the sequences generated with an expanding but limited set of annotated 16S rRNA sequences or phylogenic clustering of sequences based on arbitrary similarity cutoffs. We describe a novel approach to characterize bacterial composition us ...[more]

PMID: 21515358

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Project description:Associations between microbes and animals are ubiquitous and hosts may benefit from harbouring microbial communities through improved resource exploitation or resistance to environmental stress. The pea aphid, Acyrthosiphon pisum, is the host of heritable bacterial symbionts, including the obligate endosymbiont Buchnera aphidicola and several facultative symbionts. While obligate symbionts supply aphids with key nutrients, facultative symbionts influence their hosts in many ways such as protection against natural enemies, heat tolerance, color change and reproduction alteration. The pea aphid also encompasses multiple plant-specialized biotypes, each adapted to one or a few legume species. Facultative symbiont communities differ strongly between biotypes, although bacterial involvement in plant specialization is uncertain. Here, we analyse the diversity of bacterial communities associated with nine biotypes of the pea aphid complex using amplicon pyrosequencing of 16S rRNA genes. Combined clustering and phylogenetic analyses of 16S sequences allowed identifying 21 bacterial OTUs (Operational Taxonomic Unit). More than 98% of the sequencing reads were assigned to known pea aphid symbionts. The presence of Wolbachia was confirmed in A. pisum while Erwinia and Pantoea, two gut associates, were detected in multiple samples. The diversity of bacterial communities harboured by pea aphid biotypes was very low, ranging from 3 to 11 OTUs across samples. Bacterial communities differed more between than within biotypes but this difference did not correlate with the genetic divergence between biotypes. Altogether, these results confirm that the aphid microbiota is dominated by a few heritable symbionts and that plant specialization is an important structuring factor of bacterial communities associated with the pea aphid complex. However, since we examined the microbiota of aphid samples kept a few generations in controlled conditions, it may be that bacterial diversity was underestimated due to the possible loss of environmental or transient taxa.

Project description:BACKGROUND:The 16S rRNA gene-based amplicon sequencing analysis is widely used to determine the taxonomic composition of microbial communities. Once the taxonomic composition of each community is obtained, evolutionary relationships among taxa are inferred by a phylogenetic tree. Thus, the combined representation of taxonomic composition and phylogenetic relationships among taxa is a powerful method for understanding microbial community structure; however, applying phylogenetic tree-based representation with information on the abundance of thousands or more taxa in each community is a difficult task. For this purpose, we previously developed the tool VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community), which is based on the genome-sequenced microbes' phylogenetic information. Here, we introduce VITCOMIC2, which incorporates substantive improvements over VITCOMIC that were necessary to address several issues associated with 16S rRNA gene-based analysis of microbial communities. RESULTS:We developed VITCOMIC2 to provide (i) sequence identity searches against broad reference taxa including uncultured taxa; (ii) normalization of 16S rRNA gene copy number differences among taxa; (iii) rapid sequence identity searches by applying the graphics processing unit-based sequence identity search tool CLAST; (iv) accurate taxonomic composition inference and nearly full-length 16S rRNA gene sequence reconstructions for metagenomic shotgun sequencing; and (v) an interactive user interface for simultaneous representation of the taxonomic composition of microbial communities and phylogenetic relationships among taxa. We validated the accuracy of processes (ii) and (iv) by using metagenomic shotgun sequencing data from a mock microbial community. CONCLUSIONS:The improvements incorporated into VITCOMIC2 enable users to acquire an intuitive understanding of microbial community composition based on the 16S rRNA gene sequence data obtained from both metagenomic shotgun and amplicon sequencing.

Dataset Information

A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data.

Publications

A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data.

Similar Datasets

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