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Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs.

Oliver HF, Orsi RH, Ponnala L, Keich U, Wang W, Sun Q, Cartinhour SW, Filiatrault MJ, Wiedmann M, Boor KJ - BMC Genomics (2009)

Bottom Line: Specifically, bacterial transcriptomes were compared between stationary phase cells of L. monocytogenes 10403S and an otherwise isogenic DeltasigB mutant, which does not express the alternative sigma factor sigmaB, a major regulator of genes contributing to stress response, including stresses encountered upon entry into stationary phase.A total of 96 genes had significantly higher transcript levels in 10403S than in DeltasigB, indicating sigmaB-dependent transcription of these genes.The RNA-Seq data also enabled annotation of putative operons as well as visualization of 5'- and 3'-UTR regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Food Science, Cornell University, Ithaca, NY, USA. haf9@cornell.edu

ABSTRACT

Background: Identification of specific genes and gene expression patterns important for bacterial survival, transmission and pathogenesis is critically needed to enable development of more effective pathogen control strategies. The stationary phase stress response transcriptome, including many sigmaB-dependent genes, was defined for the human bacterial pathogen Listeria monocytogenes 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 DeltasigB mutant, which does not express the alternative sigma factor sigmaB, a major regulator of genes contributing to stress response, including stresses encountered upon entry into stationary phase.

Results: Overall, 83% of all L. monocytogenes genes were transcribed in stationary phase cells; 42% of currently annotated L. monocytogenes genes showed medium to high transcript levels under these conditions. A total of 96 genes had significantly higher transcript levels in 10403S than in DeltasigB, indicating sigmaB-dependent transcription of these genes. RNA-Seq analyses indicate that a total of 67 noncoding RNA molecules (ncRNAs) are transcribed in stationary phase L. monocytogenes, including 7 previously unrecognized putative ncRNAs. Application of a dynamically trained Hidden Markov Model, in combination with RNA-Seq data, identified 65 putative sigmaB promoters upstream of 82 of the 96 sigmaB-dependent genes and upstream of the one sigmaB-dependent ncRNA. The RNA-Seq data also enabled annotation of putative operons as well as visualization of 5'- and 3'-UTR regions.

Conclusions: The results from these studies provide powerful evidence that RNA-Seq data combined with appropriate bioinformatics tools allow quantitative characterization of prokaryotic transcriptomes, thus providing exciting new strategies for exploring transcriptional regulatory networks in bacteria.

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Examples of σB-dependent transcripts identified by RNA-Seq. In each panel (A, B, and C), red and blue lines representing normalized RNA-Seq coverage (i.e. the number of reads that match an annotated gene after normalization across runs) in the two 10403S replicates and green and black lines represent normalized RNA-Seq coverage in the ΔsigB strain replicates; the numbers at the top right in each panel indicates the normalized RNA-Seq coverage represented by the horizontal line shown. Panel (A) depicts LMRG_02382 and LMRG_02383 (shown as blue bars), which form an operon (indicated by a long white bar) with a defined Rho-independent terminator (purple bar) downstream of LMRG_02383; the three positive frames of translation with the coding regions in blue and stop codons shown as vertical black bars are also shown. A σB-dependent promoter (red bar) was identified upstream of the operon and the RNA-Seq coverage data clearly shows that the transcription of this operon is positively regulated by σB (i.e. almost no coverage was obtained from the ΔsigB strain). Panel (B) depicts SbrE (Rli47), a σB-dependent noncoding RNA (ncRNA) with Rho-independent terminator and a σB-dependent promoter identified; annotated features as well as positive and negative frames of translation are shown at the bottom with stop codons shown as vertical black bars. Panel (C) shows the 5' end of LMRG_01602 illustrating the position of a σB-dependent promoter in relation to the start codon of the gene and the transcriptional start site determined by RNA-Seq. The black triangle indicates the transcriptional start site determined by RACE-PCR as previously described by Kazmierczak et al. [23].
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Figure 4: Examples of σB-dependent transcripts identified by RNA-Seq. In each panel (A, B, and C), red and blue lines representing normalized RNA-Seq coverage (i.e. the number of reads that match an annotated gene after normalization across runs) in the two 10403S replicates and green and black lines represent normalized RNA-Seq coverage in the ΔsigB strain replicates; the numbers at the top right in each panel indicates the normalized RNA-Seq coverage represented by the horizontal line shown. Panel (A) depicts LMRG_02382 and LMRG_02383 (shown as blue bars), which form an operon (indicated by a long white bar) with a defined Rho-independent terminator (purple bar) downstream of LMRG_02383; the three positive frames of translation with the coding regions in blue and stop codons shown as vertical black bars are also shown. A σB-dependent promoter (red bar) was identified upstream of the operon and the RNA-Seq coverage data clearly shows that the transcription of this operon is positively regulated by σB (i.e. almost no coverage was obtained from the ΔsigB strain). Panel (B) depicts SbrE (Rli47), a σB-dependent noncoding RNA (ncRNA) with Rho-independent terminator and a σB-dependent promoter identified; annotated features as well as positive and negative frames of translation are shown at the bottom with stop codons shown as vertical black bars. Panel (C) shows the 5' end of LMRG_01602 illustrating the position of a σB-dependent promoter in relation to the start codon of the gene and the transcriptional start site determined by RNA-Seq. The black triangle indicates the transcriptional start site determined by RACE-PCR as previously described by Kazmierczak et al. [23].

Mentions: As illustrated in Figure 4A, RNA-Seq data are useful for predicting multi-gene operons controlled by a given regulator such as σB. Thirty-eight of the 96 genes up-regulated by σB are organized into a total of 20 operons, including (i) opuCABCD, which encodes the subunits of a glycine betaine/carnitine/choline ABC transporter, (ii) lmo0781-lmo0784, which encode the four subunits of a putative mannose-specific phosphotransferase system, (iii) lmo2484-lmo2485, which encode a putative membrane-associated protein and a putative transcriptional regulator similar to PspC, respectively, and (iv) lmo0133 and lmo0134 (Figure 4A), which encode proteins similar to E. coli YjdI and YjdJ, respectively.


Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs.

Oliver HF, Orsi RH, Ponnala L, Keich U, Wang W, Sun Q, Cartinhour SW, Filiatrault MJ, Wiedmann M, Boor KJ - BMC Genomics (2009)

Examples of σB-dependent transcripts identified by RNA-Seq. In each panel (A, B, and C), red and blue lines representing normalized RNA-Seq coverage (i.e. the number of reads that match an annotated gene after normalization across runs) in the two 10403S replicates and green and black lines represent normalized RNA-Seq coverage in the ΔsigB strain replicates; the numbers at the top right in each panel indicates the normalized RNA-Seq coverage represented by the horizontal line shown. Panel (A) depicts LMRG_02382 and LMRG_02383 (shown as blue bars), which form an operon (indicated by a long white bar) with a defined Rho-independent terminator (purple bar) downstream of LMRG_02383; the three positive frames of translation with the coding regions in blue and stop codons shown as vertical black bars are also shown. A σB-dependent promoter (red bar) was identified upstream of the operon and the RNA-Seq coverage data clearly shows that the transcription of this operon is positively regulated by σB (i.e. almost no coverage was obtained from the ΔsigB strain). Panel (B) depicts SbrE (Rli47), a σB-dependent noncoding RNA (ncRNA) with Rho-independent terminator and a σB-dependent promoter identified; annotated features as well as positive and negative frames of translation are shown at the bottom with stop codons shown as vertical black bars. Panel (C) shows the 5' end of LMRG_01602 illustrating the position of a σB-dependent promoter in relation to the start codon of the gene and the transcriptional start site determined by RNA-Seq. The black triangle indicates the transcriptional start site determined by RACE-PCR as previously described by Kazmierczak et al. [23].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2813243&req=5

Figure 4: Examples of σB-dependent transcripts identified by RNA-Seq. In each panel (A, B, and C), red and blue lines representing normalized RNA-Seq coverage (i.e. the number of reads that match an annotated gene after normalization across runs) in the two 10403S replicates and green and black lines represent normalized RNA-Seq coverage in the ΔsigB strain replicates; the numbers at the top right in each panel indicates the normalized RNA-Seq coverage represented by the horizontal line shown. Panel (A) depicts LMRG_02382 and LMRG_02383 (shown as blue bars), which form an operon (indicated by a long white bar) with a defined Rho-independent terminator (purple bar) downstream of LMRG_02383; the three positive frames of translation with the coding regions in blue and stop codons shown as vertical black bars are also shown. A σB-dependent promoter (red bar) was identified upstream of the operon and the RNA-Seq coverage data clearly shows that the transcription of this operon is positively regulated by σB (i.e. almost no coverage was obtained from the ΔsigB strain). Panel (B) depicts SbrE (Rli47), a σB-dependent noncoding RNA (ncRNA) with Rho-independent terminator and a σB-dependent promoter identified; annotated features as well as positive and negative frames of translation are shown at the bottom with stop codons shown as vertical black bars. Panel (C) shows the 5' end of LMRG_01602 illustrating the position of a σB-dependent promoter in relation to the start codon of the gene and the transcriptional start site determined by RNA-Seq. The black triangle indicates the transcriptional start site determined by RACE-PCR as previously described by Kazmierczak et al. [23].
Mentions: As illustrated in Figure 4A, RNA-Seq data are useful for predicting multi-gene operons controlled by a given regulator such as σB. Thirty-eight of the 96 genes up-regulated by σB are organized into a total of 20 operons, including (i) opuCABCD, which encodes the subunits of a glycine betaine/carnitine/choline ABC transporter, (ii) lmo0781-lmo0784, which encode the four subunits of a putative mannose-specific phosphotransferase system, (iii) lmo2484-lmo2485, which encode a putative membrane-associated protein and a putative transcriptional regulator similar to PspC, respectively, and (iv) lmo0133 and lmo0134 (Figure 4A), which encode proteins similar to E. coli YjdI and YjdJ, respectively.

Bottom Line: Specifically, bacterial transcriptomes were compared between stationary phase cells of L. monocytogenes 10403S and an otherwise isogenic DeltasigB mutant, which does not express the alternative sigma factor sigmaB, a major regulator of genes contributing to stress response, including stresses encountered upon entry into stationary phase.A total of 96 genes had significantly higher transcript levels in 10403S than in DeltasigB, indicating sigmaB-dependent transcription of these genes.The RNA-Seq data also enabled annotation of putative operons as well as visualization of 5'- and 3'-UTR regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Food Science, Cornell University, Ithaca, NY, USA. haf9@cornell.edu

ABSTRACT

Background: Identification of specific genes and gene expression patterns important for bacterial survival, transmission and pathogenesis is critically needed to enable development of more effective pathogen control strategies. The stationary phase stress response transcriptome, including many sigmaB-dependent genes, was defined for the human bacterial pathogen Listeria monocytogenes 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 DeltasigB mutant, which does not express the alternative sigma factor sigmaB, a major regulator of genes contributing to stress response, including stresses encountered upon entry into stationary phase.

Results: Overall, 83% of all L. monocytogenes genes were transcribed in stationary phase cells; 42% of currently annotated L. monocytogenes genes showed medium to high transcript levels under these conditions. A total of 96 genes had significantly higher transcript levels in 10403S than in DeltasigB, indicating sigmaB-dependent transcription of these genes. RNA-Seq analyses indicate that a total of 67 noncoding RNA molecules (ncRNAs) are transcribed in stationary phase L. monocytogenes, including 7 previously unrecognized putative ncRNAs. Application of a dynamically trained Hidden Markov Model, in combination with RNA-Seq data, identified 65 putative sigmaB promoters upstream of 82 of the 96 sigmaB-dependent genes and upstream of the one sigmaB-dependent ncRNA. The RNA-Seq data also enabled annotation of putative operons as well as visualization of 5'- and 3'-UTR regions.

Conclusions: The results from these studies provide powerful evidence that RNA-Seq data combined with appropriate bioinformatics tools allow quantitative characterization of prokaryotic transcriptomes, thus providing exciting new strategies for exploring transcriptional regulatory networks in bacteria.

Show MeSH
Related in: MedlinePlus