|Oliver, Haley - Cornell University - New York|
|Orsi, Renato - Cornell University - New York|
|Ponnala, Lalit - Cornell University - New York|
|Keich, Uri - Cornell University - New York|
|Wang, Wei - Cornell University - New York|
|Sun, Qi - Cornell University - New York|
|Wiedmann, Martin - Cornell University - New York|
|Boor, Kathryn - Cornell University - New York|
Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/30/2009
Publication Date: 12/30/2009
Citation: Oliver, H.F., Orsi, R.H., Ponnala, L., Keich, U., Wang, W., Sun, Q., Cartinhour, S.W., Filiatrault, M.J., Wiedmann, M., Boor, K.J. 2009. Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. Biomed Central (BMC) Genomics. 10:641.
Interpretive Summary: Identifying the genes expressed by bacteria and how bacteria turn genes on and off is a difficult task but very important to understanding how bacteria respond to the environment and cause infection. A method to sequence RNA, termed RNA-Seq, was used to identify genes that are important in stationary phase growth of Listeria monocytogenes. We found that approximately 83% of L. monocytogenes genes are expressed when the bacterium is grown in stationary phase. The RNA profile of the wild-type strain was compared to a mutant strain unable to express the alternative sigma factor sigma B and we found 96 genes that showed differential expression between the two strains. RNA-Seq also identified 65 noncoding RNAs, 53 of which had not been previously identified in L. monocytogenes. The expression of one of these noncoding RNAs was found to be dependent on sigma B. By combining the RNA-Seq data with a mathematical model, putative binding sites for sigma B were identified. Also, the RNA-Seq data allowed us to identify putative operons, putative transcriptional start sites, and terminator regions. Overall, by combining RNA-Seq with bioinformatics predictions, a more thorough analysis of transcriptional activity in a bacterial cell can be obtained. The methods can be used to explore transcriptional activity and regulatory networks in other bacteria as well.
Technical Abstract: Comprehensive, quantitative measurements of the transcriptional responses of bacterial pathogens under a variety of environmental conditions will identify specific genes and gene expression patterns important for bacterial survival, transmission and pathogenesis. The stationary phase stress response transcriptome of the human bacterial pathogen Listeria monocytogenes was defined using RNA sequencing (RNA-Seq) with the Illumina Genome Analyzer. Specifically, bacterial transcriptomes were compared between stationary phase cells of L. monocytogenes 10403S and an otherwise isogenic sigma B mutant, which does not express the alternative sigma factor sigma B, a major regulator of genes contributing to stress response. Overall, 42% of currently annotated L. monocytogenes genes showed medium to high transcript levels in stationary phase cells; 83% of all genes were transcribed under these conditions. A total of 96 genes had significantly higher transcript levels in 10403S than in the sigma B mutant, indicating sigma B-dependent transcription of these genes. RNA-Seq analyses suggest that a total of 65 noncoding RNA molecules (ncRNAs) are transcribed in stationary phase L. monocytogenes, including (i) 15 previously unrecognized putative ncRNAs; one of which was identified as sigma B-dependent, (ii) 38 ncRNAs resembling ncRNAs described in other bacteria, but not previously experimentally validated in L. monocytogenes, and (iii) 12 ncRNAs previously reported in L. monocytogenes. Application of a dynamically trained Hidden Markov Model, in combination with RNA-Seq data, identified 65 putative sigma B promoters upstream of 82 of the 96 sigma B-dependent genes and upstream of the one sigma B-dependent ncRNA. The RNA-Seq data also enabled annotation of putative operons as well as visualization of 5’- and 3’-UTR regions. The results from these studies provide compelling evidence that, in combination with bioinformatics tools, RNA-Seq allows quantitative characterization of prokaryotic transcriptomes, thus providing exciting new strategies for exploring transcriptional regulatory networks in bacteria.