Comparative transcriptomics of pathogenic and non-pathogenic Listeria species

Omri Wurtzel, Nina Sesto, J. R. Mellin, Iris Karunker, Sarit Edelheit, Christophe Bécavin, Cristel Archambaud, Pascale Cossart, Rotem Sorek

Research output: Contribution to journalArticlepeer-review

Abstract

Listeria monocytogenes is a human, food-borne pathogen. Genomic comparisons between L. monocytogenes and Listeria innocua, a closely related non-pathogenic species, were pivotal in the identification of protein-coding genes essential for virulence. However, no comprehensive comparison has focused on the non-coding genome. We used strand-specific cDNA sequencing to produce genome-wide transcription start site maps for both organisms, and developed a publicly available integrative browser to visualize and analyze both transcriptomes in different growth conditions and genetic backgrounds. Our data revealed conservation across most transcripts, but significant divergence between the species in a subset of non-coding RNAs. In L. monocytogenes, we identified 113 small RNAs (33 novel) and 70 antisense RNAs (53 novel), significantly increasing the repertoire of ncRNAs in this species. Remarkably, we identified a class of long antisense transcripts (lasRNAs) that overlap one gene while also serving as the 5 UTR of the adjacent divergent gene. Experimental evidence suggests that lasRNAs transcription inhibits expression of one operon while activating the expression of another. Such a lasRNA/operon structure, that we named 'excludon', might represent a novel form of regulation in bacteria.

Original languageEnglish
Article number583
JournalMolecular Systems Biology
Volume8
Issue number1
DOIs
StatePublished - 2012

Keywords

  • Listeria monocytogenes
  • RNA-seq
  • TSS map
  • comparative genomics
  • transcriptome

All Science Journal Classification (ASJC) codes

  • Information Systems
  • General Immunology and Microbiology
  • Applied Mathematics
  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • Computational Theory and Mathematics

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