Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing

Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Canada.
PLoS ONE (Impact Factor: 3.23). 04/2012; 7(4):e36009. DOI: 10.1371/journal.pone.0036009
Source: PubMed

ABSTRACT With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

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Available from: John Parkinson, Aug 27, 2015
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    • "Metatranscriptomic data analysis can be considerably facilitated when performed in tandem with metagenomics. Xiong et al. (2012) developed an experimental and analytical pipeline for the analysis of metatranscriptomes in the absence of extended sets of reference genomes [50]. Their workflow employs a peptide-centric search strategy by performing in silico translation of detected transcripts. "
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