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. 2011 Jan;77(1):187-200.
doi: 10.1128/AEM.00952-10. Epub 2010 Oct 29.

Transcriptomic and phenotypic analyses identify coregulated, overlapping regulons among PrfA, CtsR, HrcA, and the alternative sigma factors sigmaB, sigmaC, sigmaH, and sigmaL in Listeria monocytogenes

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Transcriptomic and phenotypic analyses identify coregulated, overlapping regulons among PrfA, CtsR, HrcA, and the alternative sigma factors sigmaB, sigmaC, sigmaH, and sigmaL in Listeria monocytogenes

Soraya Chaturongakul et al. Appl Environ Microbiol. 2011 Jan.

Abstract

A set of seven Listeria monocytogenes 10403S mutant strains, each bearing an in-frame null mutation in a gene encoding a key regulatory protein, was used to characterize transcriptional networks in L. monocytogenes; the seven regulatory proteins addressed include all four L. monocytogenes alternative sigma factors (σ(B), σ(C), σ(H), and σ(L)), the virulence gene regulator PrfA, and the heat shock-related negative regulators CtsR and HrcA. Whole-genome microarray analyses, used to identify regulons for each of these 7 transcriptional regulators, showed considerable overlap among regulons. Among 188 genes controlled by more than one regulator, 176 were coregulated by σ(B), including 92 genes regulated by both σ(B) and σ(H) (with 18 of these genes coregulated by σ(B), σ(H), and at least one additional regulator) and 31 genes regulated by both σ(B) and σ(L) (with 10 of these genes coregulated by σ(B), σ(L), and at least one additional regulator). Comparative phenotypic characterization measuring acid resistance, heat resistance, intracellular growth in J774 cells, invasion into Caco-2 epithelial cells, and virulence in the guinea pig model indicated contributions of (i) σ(B) to acid resistance, (ii) CtsR to heat resistance, and (iii) PrfA, σ(B), and CtsR to virulence-associated characteristics. Loss of the remaining transcriptional regulators (i.e., sigH, sigL, or sigC) resulted in limited phenotypic consequences associated with stress survival and virulence. Identification of overlaps among the regulons provides strong evidence supporting the existence of complex regulatory networks that appear to provide the cell with regulatory redundancies, along with the ability to fine-tune gene expression in response to rapidly changing environmental conditions.

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Figures

FIG. 1.
FIG. 1.
Self-organizing tree algorithm (SOTA) clustering for genes with similar expression patterns. (A) Heat map of SOTA clusters. The average log2 expression ratio (parent/mutant) for genes in each SOTA cluster is represented as a heat map. Each row represents a SOTA cluster (labeled 1 to 13), and each column represents data from a parent-versus-mutant microarray comparison. Red clusters have a positive log2 expression ratio, which represents higher expression of genes in these clusters in the parent strain than in the mutant strain. Clusters that appear green have a negative log2 expression ratio, which represents higher expression of genes in these clusters in the mutant strain than in the parent strain, indicating that these genes are negatively regulated by that particular regulator. “i” indicates comparisons conducted with either ctsR or sigC expressed under the control of an IPTG-inducible promoter. (B) Number of genes in each SOTA cluster belonging to selected role category (total number of genes in a given role category is shown in parenthesis); genes classified in other role categories (e.g., hypothetical proteins, unknown function, unclassified, and unassigned) are not included here. Genes in each cluster are listed in Table S9 in the supplemental material.
FIG. 2.
FIG. 2.
σB-dependent GUS activities in 10403S PmcsA-gus (black), the ΔsigB PmcsA-gus strain (gray), 10403S PhrcA-gus (white), and the ΔsigB PhrcA-gus strain (hatched), grown either to late stationary phase (18 h) in BHI or to log phase (OD600 = 0.4) and exposed to 0.3 M NaCl for 2 h. Data shown represent the averages for three independent experiments; error bars indicate 1 standard deviation from mean values. P values are indicated where the differences are statistically significant.
FIG. 3.
FIG. 3.
Partial transcriptional interaction network based on microarray data and TaqMan and RACE results for the alternative sigma factors σB, σC, σH, and σL (blue ellipses), the virulence gene regulator PrfA (orange ellipse), and the negative regulators CtsR and HrcA (green ellipses). Color-coded shapes were used to identify transcriptional regulators (yellow ellipse), regulators of transcriptional regulators (purple rounded rectangles), virulence proteins (red rectangles), stress response proteins (gray hexagons), and σC-dependent genes (green hexagons). Genes arranged in vertical columns represent operons. Black target arrows (↓) originating from a given regulator indicate positive regulation; black target stops (⊥) indicate negative regulation by a given regulator. Blue arrows or target stops target groups of genes coregulated by more than one regulator (blue font); in addition, a blue arrow indicates that Hfq regulates small RNAs (“sRNA regulation”). Green loops indicate autoregulation. The red target stop directed at HrcA indicates posttranscriptional regulation of HrcA by GroES and GroEL, as shown in B. subtilis (51); the red target stop directed at CtsR indicates posttranscriptional regulation of CtsR by McsA, McsB, and ClpC, as shown in B. subtilis (39-41).

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