Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

Xiong, Xuejian; Frank, Daniel N.; Robertson, Charles E.; Hung, Stacy S.; Markle, Janet; Canty, Angelo J.; McCoy, Kathy D.; Macpherson, Andrew J.; Poussier, Philippe; Danska, Jayne S.; Parkinson, John (2012). Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing. PLoS ONE, 7(4), e36009. Lawrence, Kans.: Public Library of Science 10.1371/journal.pone.0036009

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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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