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

作者：Xuejian Xiong, Daniel N. Frank, Charles E. Robertson, Stacy Hung, Janet Markle, Angelo J. Canty, Kathy D. McCoy, Andrew J. Macpherson, Philippe Poussier, Jayne S. Danska, John Parkinson · 发表于：PLoS ONE · 年份：2012 · DOI：10.1371/journal.pone.0036009 · 被引用次数：75 · 研究领域：Gut microbiota and health、Genomics and Phylogenetic Studies、Metabolomics and Mass Spectrometry Studies

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...