A tissue-specific landscape of sense/antisense transcription in the mouse intestine.

Ulrich C Klostermeier, Matthias Barann, Michael Wittig, Robert Häsler, Andre Franke, Olga Gavrilova, Benjamin Kreck, Christian Sina, Markus B Schilhabel, Stefan Schreiber, Philip Rosenstiel
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BMC genomics
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The current study demonstrates the complexity of an archetypal mammalian intestinal mRNA transcriptome in high resolution and identifies novel transcriptionally active regions at strand-specific, single base resolution. Our analysis for the first time shows a strand-specific comparative picture of nTARs in two tissues and represents a resource for further investigating the transcriptional processes that contribute to tissue identity.We report the ultra-deep characterization of the polyadenylated transcriptome in two closely related, yet distinct regions of the mouse intestinal tract (small intestine and colon). We assessed tissue-specific transcriptomal architecture and the presence of novel transcriptionally active regions (nTARs). In the first step, signatures of 20,541 NCBI RefSeq transcripts could be identified in the intestine (74.1% of annotated genes), thereof 16,742 are common in both tissues. Although the majority of reads could be linked to annotated genes, 27,543 nTARs not consistent with current gene annotations in RefSeq or ENSEMBL were identified. By use of a second independent strand-specific RNA-Seq protocol, 20,966 of these nTARs were confirmed, most of them in vicinity of known genes. We further categorized our findings by their relative adjacency to described exonic elements and investigated regional differences of novel transcribed elements in small intestine and colon.The intestinal mucosa is characterized by complex metabolic and immunological processes driven highly dynamic gene expression programs. With the advent of next generation sequencing and its utilization for the analysis of the RNA sequence space, the level of detail on the global architecture of the transcriptome reached a new order of magnitude compared to microarrays.