Salt Stress-Induced Transcription of σ B - and CtsR-Regulated Genes in Persistent and Non-persistent Listeria monocytogenes Strains from Food Processing Plants

Abstract

Listeria monocytogenes is a foodborne pathogen that can persist in food processing environments. Six persistent and six non-persistent strains from fish processing plants and one persistent strain from a meat plant were selected to determine if expression of genes in the regulons of two stress response regulators, σ^B and CtsR, under salt stress conditions is associated with the ability of L. monocytogenes to persist in food processing environments. Subtype data were also used to categorize the strains into genetic lineages I or II. Quantitative reverse transcriptase–polymerase chain reaction (qRT-PCR) was used to measure transcript levels for two σ^B-regulated genes, inlA and gadD3, and two CtsR-regulated genes, lmo1138 and clpB, before and after (t=10 min) salt shock (i.e., exposure of exponential phase cells to BHI+6% NaCl for 10 min at 37°C). Exposure to salt stress induced higher transcript levels relative to levels under non-stress conditions for all four stress and virulence genes across all wildtype strains tested. Analysis of variance (ANOVA) of induction data revealed that transcript levels for one gene (clpB) were induced at significantly higher levels in non-persistent strains compared to persistent strains (p=0.020; two-way ANOVA). Significantly higher transcript levels of gadD3 (p=0.024; two-way ANOVA) and clpB (p=0.053; two-way ANOVA) were observed after salt shock in lineage I strains compared to lineage II strains. No clear association between stress gene transcript levels and persistence was detected. Our data are consistent with an emerging model that proposes that establishment of L. monocytogenes persistence in a specific environment occurs as a random, stochastic event, rather than as a consequence of specific bacterial strain characteristics.

Introduction

A number of studies have shown that Listeria monocytogenes can persist in food processing environments for months to years (Ferreira et al., 2011; Holah et al., 2004; Lundén et al., 2003b). Stress conditions encountered in food processing environments and food products can expose L. monocytogenes cells to conditions known to induce bacterial stress responses, including osmotic stress (Becker et al., 1998; Kazmierczak et al., 2003; Sue et al., 2004), oxidative stress (Chaturongakul and Boor, 2006; Ferreira et al., 2001), and acid stress (Raengpradub et al., 2008; Sue et al., 2004). Transcriptional regulators contributing to the stress response in L. monocytogenes include σ^B, an alternative sigma factor, which regulates >150 genes involved in the general stress response of L. monocytogenes (Kazmierczak et al., 2003; Oliver et al., 2010; Raengpradub et al., 2008) and CtsR, a repressor of class III stress response genes, including genes encoding heat shock proteins (Chastanet et al., 2004; Hu et al., 2007; Nair et al., 2000). Increased piezotolerance of some L. monocytogenes isolates has been attributed to mutations in ctsR (Joerger et al., 2006; Karatzas et al., 2003, 2005). Therefore, transcriptional regulators of stress response genes may contribute to varied stress responses in L. monocytogenes strains.

The goal of this study was to test the hypothesis that persistent and transient L. monocytogenes differ in baseline expression and induction of the σ^B and CtsR regulons; activity of σ^B and CtsR was measured by using quantitative reverse transcriptase–polymerase chain reaction (qRT-PCR) to measure transcript levels of (i) inlA and gadD3, which are directly regulated by σ^B (transcript levels of these genes serve as indirect measures of σ^B activity); and (ii) lmo1138 and clpB, which are directly regulated by CtsR (transcript levels of these genes serve as indirect measures of CtsR activity). We hypothesized that persistent strains obtained from food processing environments would demonstrate higher induction of σ^B- and CtsR-regulated genes after exposure to salt shock compared to non-persistent strains. The target genes (Table 1) used for qRT-PCR were chosen not because we hypothesized that they are directly responsible for persistence, but were chosen as reporters for the activity of these two key transcriptional regulators to provide insight into differences in expression of their regulons based on classification of strains by persistence or lineage. The use of reporter genes (e.g., lacZ promoter or gene fusion constructs) to study the activity of transcriptional regulators is a well-established practice (Boylan et al., 1993). Our study uses qRT-PCR as an appropriate technology to assess transcript levels of key reporter genes known to be directly regulated by σ^B or CtsR.

Table 1.

Genes Chosen in This Study as Reporters for Activity of the Transcriptional Regulators σ^B and CtsR

Gene	Regulon	Reason for use in qRT-PCR assays	References
gadD3	σ^B	Indicator for σ^B activity; gene is solely σ^B dependent	(Cotter et al., 2005; Kazmierczak et al., 2006; Wemekamp-Kamphuis et al., 2004)
inlA	σ^B	Indicator for σ^B activity; inlA is solely σ^B dependent under environmental stress conditions as shown in previous qRT-PCR studies (McGann et al., 2007); this gene is only co-regulated by PrfA if PrfA is active, such as in a PrfA^* strain and inside the host	(Kazmierczak et al., 2003; Lingnau et al., 1995; McGann et al., 2007)
lmo1138	CtsR	Indicator for CtsrR activity; gene is solely CtsR-dependent	(Hu et al., 2007)
clpB	CtsR	Indicator for CtsrR activity; gene is solely CtsR-dependent	(Chastanet et al., 2004; Hu et al., 2007)

qRT-PCR, quantitative reverse transcriptase–polymerase chain reaction.

Materials and Methods

Bacterial strains and growth conditions

Isolates (six persistent and six non-persistent; Table 2) were selected from a 24-month longitudinal study of L. monocytogenes contamination patterns in four smoked fish plants (Lappi et al., 2004). An isolate was considered to represent a persistent strain if multiple isolates belonging to the same ribotype were recovered in the same plant ≥5 times and over a period of >3 months (Lundén et al., 2008). Conversely, isolates representing a ribotype found only once in a plant during the course of the study were deemed representative of non-persistent strains. An additional persistent L. monocytogenes strain (FSL F6-154; previously J2818), which persisted in a food processing plant for >10 years (Orsi et al., 2008), was also included. All isolates were classified to lineage based on ribotyping data (Nadon et al., 2001). L. monocytogenes 10403S and selected isogenic mutant strains (Hu et al., 2007) were included as reference strains because strain 10403S has been used in a number of previous studies of L. monocytogenes stress response systems. Bacteria were grown from frozen stock cultures as previously described (Ollinger et al., 2008), except that cells were grown to mid-exponential phase (OD₆₀₀=0.4) after the final 1:100 dilution into 50 mL of pre-warmed brain heart infusion (BHI).

Table 2.

Listeria monocytogenes Strains Used in This Study

Isolate	Ribotype	Lineage	Characteristics	Reference
10403S	1030A	II	Parent strain, serotype 1/2a	(Bishop and Hinrichs, 1987)
FSL A1-254	1030A	II	10403SΔsigB	(Wiedmann et al., 1998)
FSL H6-190	1030A	II	10403SΔctsR	(Hu et al., 2007)
FSL L4-060	1043A	I	Persistent	(Lappi et al., 2004)
FSL L4-400	1052A	I	Persistent	(Lappi et al., 2004)
FSL L4-408	1044A	I	Persistent	(Lappi et al., 2004)
FSL L4-396	1039C	II	Persistent	(Lappi et al., 2004)
FSL L4-386	1053A	II	Persistent	(Lappi et al., 2004)
FSL L4-249	1039A	II	Persistent	(Lappi et al., 2004)
FSL F6-154	1053A	II	Persistent	(Orsi et al., 2008)
FSL L4-170	1038B	I	Non-persistent	(Lappi et al., 2004)
FSL T1-392	1025A	I	Non-persistent	(Lappi et al., 2004)
FSL L4-025	1042B	I	Non-persistent	(Lappi et al., 2004)
FSL T1-041	1062D	II	Non-persistent	(Lappi et al., 2004)
FSL T1-073	1023C	II	Non-persistent	(Lappi et al., 2004)
FSL L4-151	1062A	II	Non-persistent	(Lappi et al., 2004)

Salt shock experiments

Salt shock experiments were performed as previously described (Sue et al., 2004), with modifications. Briefly, 4.5 mL aliquots of mid-exponential phase cells (OD₆₀₀=0.4) were collected from 50 mL cultures, and 10% phenol in ethanol was added to a final concentration of 1% phenol to stop transcription (Bhagwat et al., 2003). To another 4.5 mL aliquot of the same culture, 4.5 mL of pre-warmed 12% NaCl (w/v)+BHI was added, and the culture was incubated for an additional 10 min at 37°C with shaking, followed by addition of the phenol solution as described above. Phenol-treated samples were placed on ice and centrifuged within 5 min at 1,800×g for 10 min at 4°C. Pellets were kept on ice until RNA extraction, which immediately followed cell collection.

Total RNA isolation

Cell pellets were resuspended in 1 mL TRI Reagent (Applied Biosystems, Foster City, CA), and manufacturer's instructions were followed to obtain purified total RNA after mechanical lysis with acid-washed 0.1 mm zirconium beads (Biospec, Bartlesville, OK) in a beadbeater (Mini-beadbeater-8; Biospec) for 4 min. RNA concentration and integrity were assessed as described elsewhere (Bergholz et al., 2010).

TaqMan qRT-PCR

cDNA was synthesized from total RNA (RIN value ≥ 7.0) treated with DNAse, following manufacturer's instructions (TURBO DNA-free; Applied Biosystems), in a reaction mixture (100 μL) of TaqMan RT-PCR reagents with 500 ng total RNA. qPCR reactions were performed as previously described (Bergholz et al., 2010).

Transcript levels of four target genes (gadD3 [lmo2434], inlA, lmo1138, and clpB) and one housekeeping gene (rpoB) were quantified, using the ABI Prism 7000 Sequence Detection System (Applied Biosystems), to indirectly measure the activity of the transcriptional regulators σ^B and CtsR. TaqMan primers and probes (Table 3) were designed with Primer Express v1.0 software (Applied Biosystems) using target genes sequences obtained for all isolates used here (for primers, see Table 4). Separate clpB primer and probe sets were designed for lineage I and II strains due to high sequence divergence between lineages.

Table 3.

Primers and Probes Used for qRT-PCR in This Study

Gene	Name	Sequence (5′→3′)
lmo1138	DR32 lmo1138 Tqmn F	CATCCTTACCCAAAAATTGATTGAT
	DR33 lmo1138 Tqmn R	CGGCTAACTCCTGATTGATTTCC
	lmo1138 probe DR	CACGCACAGTGTTAAT
gadD3	DR26 gadA Tqmn F	TGAAGACGACAAGCGAAAACA
	DR27 gadA Tqmn R	GCTTTCTTCCTCAGATCCAAAGAG
	gadA Tqmn probe DR	AAAGTTATCGAATCCC
inlA	DR24 inlA Tqmn F	ACAAATGCTCAGGCAGCTACAAT
	DR25 inlA Tqmn R	CGTCTTCATTTTTTCCGCTAGAG
	inlA Tqmn probe DR	ACAAGATACTCCTATTAATC
clpB, lineage I	DR28 clpB lin1 Tqmn F	GGTGTAAACTATGGTCAAGCAATGA
	DR29 clpB lin1 Tqmn R	CGGCATCCCGCATCAA
	clpB lin1 probe DR	CCAAGCACTTTTTC
clpB, lineage II	DR30 clpB lin2 Tqmn F	CACAAAATCTAGCTATTGCATCAGAAC
	DR31 clpB lin2 Tqmn R	CAAAGTCGCTTTCTGTTAATAACACTTT
	clpB lin2 probe DR	TGACGTTGCACACGTT
rpoB	MS16 rpoB-F TqMn	CCGGACGTCACGGTAACAA
	MS18 rpoB-R TqMn	CAGGTGTTCCGTCTGGCATA
	rpoB MGB probe	TTATCTCCCGTATTTTACC

qRT-PCR, quantitative reverse transcriptase–polymerase chain reaction.

Table 4.

Primers Used for Sequencing in This Study

Gene	Name	Sequence (5′→3′)
lmo1138	DR23 lmo1138-5' seq F	CCATTTTCGACCATTRTGATAAGATA
	DR21 lmo1138-5' seq R	TTTCRGACATGGAGTGACGGTTTTCGAGTG
gadD3	DR17 gadA-5' seq F	ATCAAACATAACGAACCTCCTTATAAGTACCATCG
	DR18 gadA-5' seq R	AGACCTCGTTTTTCCGCATKGTTACGCCAGCG
inlA	DR7 inlA-5' seq F	TTCGGATGCAGGAGAAAATC
	DR8 inlA-5' seq R	CCTGAAAGCGCACTAATATCAC
clpB	DR15 clpB-5' seq F	GGTCGTGATTAAGAAATTCAGAAGATCTGCCAACC
	DR16 clpB-5' seq R	GCTTCATAGTTTTCTTCTGCATTTTGAGAAGTCAC
rpoB	DR19 rpoB-5' seq F	AGGAAAMTTTTGATGMACGRTGTTT
	DR20 rpoB-5' seq R	TCTAGTTTTCCATTGAAGTAAACACCTGGAGAACG

Transcript levels of the four target genes were normalized to the housekeeping gene rpoB. Induction, defined as fold change in transcript levels after salt shock (t=10 min) relative to levels before salt shock (t=0), was calculated, using the Pfaffl method (Pfaffl, 2001), for each biological replicate for each isolate. Efficiency values of primer and probe reactions were calculated for each isolate by including duplicate 10-fold dilutions of cDNA (10⁻¹ to 10⁻⁴) for each gene in reactions, and efficiency values were used in subsequent calculations. These values were 91–100%, with an average of 98±8%.

To compare mRNA transcript levels among isolates at both time points [i.e., before (t=0) and after salt-shock (t=10 min)], the ΔΔ-C_t method (Livak and Schmittgen, 2001) was used to calculate transcript levels relative to a calibrator sample, which was total RNA collected from L. monocytogenes 10403S during one biological replicate of salt shock. C_t values obtained from this sample of 10403S cells collected at t=0 were used to calculate fold changes relative to expression levels of 10403S, using rpoB as an internal control. Thus, this relative quantification method represents transcript levels in arbitrary units of “fold change, relative to 10403S at t=0.”

Statistical analysis

Induction and transcript levels relative to 10403S were analyzed using two-way analysis of variance (ANOVA) with JMP (JMP 9.0; SAS, Inc., Cary, NC). Data from two biological replicates of salt stress experiments for the 13 strains investigated were coded by persistence (N [Non-persistent] vs. P [Persistent]) or lineage (I vs. II) for each gene. The following formula was used to determine effects of lineage and persistence and an interaction effect: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland,xspace}\usepackage{amsmath,amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6} \begin{document} \begin{align*} \hbox{\rm fold change}_{ \rm gene} = \mu + \beta_{ \rm lineage} + \beta_{ \rm persistence} + \beta_{ \rm persistence*lineage} + \beta_0 \end{align*} \end{document}

Two biological replicates per strain were considered sufficient, because we did not perform statistical analyses to compare transcript levels for individual strains. Two-way ANOVA was performed on induction data and relative transcript level data separately. Data for transcript levels relative to 10403S were log transformed to fulfill ANOVA assumptions of normality.

Results

Comparison of relative transcript levels of σ^B- and CtsR-regulated genes by persistence and lineage classifications of L. monocytogenes strains from food processing plants

Before salt shock, mean relative transcript levels, in exponential growth phase cells, of σ^B-regulated genes gadD3 and inlA (in fold changes relative to 10403S at t=0) were 1.3–46.3 and 1.1–12.4, respectively, compared to 1.3 and 1.0 for 10403S (Fig. 1). Mean relative transcript levels of CtsR-regulated genes lmo1138 and clpB were 2.5–7.4 and 2.7–20.4, respectively, compared to 1.6 and 1.4 for 10403S. Statistical analysis of relative transcript levels before salt shock (t=0) revealed no significant differences in transcript levels between persistent and non-persistent isolates for any of the four genes (p>0.05; two-way ANOVA) (Fig. 2). However, significantly higher clpB transcript levels were observed in lineage I strains before salt stress (p=0.004; two-way ANOVA). A significant interaction of persistence and lineage for relative transcript levels of inlA (p=0.028; two-way ANOVA) before salt shock was also detected, but a contrast of the means showed no significant separation of the lineage/persistence pairings (p>0.05; post-hoc Tukey HSD).

FIG. 1.

Transcript levels (relative to transcript levels of 10403S at t=0) of σ^B-regulated genes gadD3 (A) and inlA (B), and CtsR-regulated genes lmo1138 (C) and clpB (D) before (t=0, black bars) and after exposure (t=10 min, gray bars) to brain heart infusion (BHI)+6% NaCl (w/v) at 37°C. Bars represent mean fold changes of transcript levels relative to transcripts obtained prior to salt shock from 10403S during one replicate of salt shock experiments, and error bars show standard deviations of two biological replicates per isolate. Relative fold changes to 10403S at t=0 were calculated using the ΔΔ-C_t relative quantification method (Livak and Schmittgen, 2001). rpoB transcripts were used to normalize transcript levels.

FIG. 2.

Box plots of transcript levels, relative to transcript levels for 10403S at t=0, of σ^B-regulated genes gadD3 (A) and inlA (B), and CtsR-regulated genes lmo1138 (C) and clpB (D) before (t=0, black boxes) and after exposure (t=10 min, white boxes) to brain heart infusion (BHI)+6% NaCl (w/v) at 37°C, by persistence (N, non-persistent; P, persistent) and lineage (I, II) classifications. Boxes represent 25^th and 75^th percentiles with median line, and whiskers represent outermost data points that fall within upper quartile+1.5*(interquartile range) and lower quartile−1.5*(interquartile range) of individual data from two biological replicates per isolate. Outliers falling outside of this range are represented by points. Transcript levels were calculated using the ΔΔ-C_t relative quantification method (Livak and Schmittgen, 2001). rpoB transcripts were used to normalize transcript levels. Asterisks denote significant effects (p≤0.05) between groups linked by brackets as determined by two-way analysis of variance (ANOVA). Data were log transformed prior to two-way ANOVA.

Relative transcript levels of σ^B-regulated genes gadD3 and inlA after salt shock were 17.6–172.5 and 6.7–42.5, respectively (for the 13 strains from food processing plants), compared to 12.3 and 4.6 for 10403S. Relative transcript levels of CtsR-regulated genes lmo1138 and clpB were 11.7–87.6 and 10.3–77.6, respectively, compared to 5.8 and 5.3 for 10403S. These ranges indicate considerable variation of transcript levels in response to salt shock for the strains from food processing plants. Analysis of relative transcript levels after salt shock (t=10 min) indicated higher relative transcript levels of gadD3 (p=0.024; two-way ANOVA) for lineage I strains. Borderline significantly higher relative transcript levels of clpB (p=0.053; two-way ANOVA) were also observed in lineage I strains. Sequencing and analysis revealed no polymorphisms in the putative CtsR binding site upstream of clpB.

Comparison of salt shock induction of σ^B- and CtsR-regulated genes by persistence and lineage classification of L. monocytogenes strains

Individual induction fold changes of gadD3, inlA, lmo1138, and clpB (Fig. 3) in all strains from food processing plants were >1.0, indicating that the salt stress presented by our experimental conditions induced transcription of these genes. For σ^B-regulated genes, mean fold changes were 3.7–44.6 for gadD3 and 1.7–16.6 for inlA. Induction, after salt shock, of gadD3 and inlA in 10403S (9.8 and 4.4 fold change, respectively), but not in 10403SΔsigB (0.5 and 1.0 fold change, respectively), confirmed σ^B-regulated transcription of gadD3 and inlA in response to salt stress. A two-way ANOVA found no significant effects of persistence or lineage on induction of either gadD3 or inlA after salt shock (p>0.05, two-way ANOVA; Fig. 4).

FIG. 3.

Induction of σ^B-regulated genes gadD3 (A) and inlA (B), and CtsR-regulated genes lmo1138 (C) and clpB (D) after exposure to brain heart infusion (BHI)+6% NaCl (w/v) for 10 min at 37°C, by isolate. Genetic lineages of isolates are represented by bar color (lineage I, black; lineage II, gray). Bars represent mean fold changes of transcript levels after salt shock relative to transcript levels prior to salt shock, and error bars show standard deviations of fold changes of two biological replicates calculated using the Pfaffl relative quantification method (Pfaffl, 2001). rpoB transcripts were used to normalize transcript levels. Dashed lines denote fold change of 1.

FIG. 4.

Box plots of induction of σ^B-regulated genes gadD3 (A) and inlA (B), and CtsR-regulated genes lmo1138 (C) and clpB (D) after exposure to brain heart infusion (BHI)+6% NaCl (w/v) for 10 min at 37°C, by persistence (N, non-persistent; P, persistent) and lineage (I, II) classifications. Boxes represent 25^th and 75^th percentiles with median line, and whiskers represent outermost data points that fall within upper quartile+1.5*(interquartile range) and lower quartile – 1.5*(interquartile range) of individual data from two biological replicates per isolate. Outliers falling outside of this range are represented by points. Induction values were calculated using the Pfaffl relative quantification method (Pfaffl, 2001). rpoB transcripts were used to normalize transcript levels. Asterisks denote significant effects (p≤0.05) between groups linked by brackets as determined by two-way analysis of variance (ANOVA).

For CtsR-regulated genes, a two-way ANOVA found that induction of clpB after salt shock was significantly higher in non-persistent compared to persistent strains (p=0.020, two-way ANOVA; Fig. 4). No significant effects of persistence or lineage were observed on induction of lmo1138 after salt shock (p>0.05; two-way ANOVA). Transcript levels of lmo1138 and clpB did not increase after salt shock in the ΔctsR strain (fold change of 1.0 and 1.1, respectively) but increased in 10403S (4.5 and 3.9 fold change, respectively). Combined with the higher absolute transcript levels in the ΔctsR strain (as compared to the parent strain 10403S; Fig. 1), these data support that CtsR represses transcription of these genes in the absence of stress conditions (Hu et al., 2007; Nair et al., 2000) with complete derepression of lmo1138 and clpB before and after salt shock in the ΔctsR strain. Induction of lmo1138 and clpB after salt shock in 10403SΔsigB, with fold changes of 4.5 and 2.6, suggests that upregulation of these genes in response to salt shock is σ^B-independent.

Discussion

Characterization of exponential phase transcript levels and salt induction of four stress response genes among 13 different L. monocytogenes isolates, representing strains that persisted in food processing plants and strains with no evidence of persistence, was performed to evaluate stress response gene expression patterns as well as CtsR and σ^B activity in these strains. Overall, our data indicate that (i) the σ^B-dependent genes gadD3 and inlA and the CtsR-dependent genes lmo1138 and clpB are induced, across strains and lineages, after salt stress exposure; (ii) lineage I strains show higher transcript levels, as compared to lineage II strains, for some stress response genes (e.g., gadD3 and clpB, after salt stress exposure); and (iii) L. monocytogenes isolates representing persistent and non-persistent strain do not differ in transcript levels or induction of the stress responsive genes gadD3, inlA, and lmo1138.

σ^B-Dependent genes gadD3 and inlA and the CtsR-dependent genes lmo1138 and clpB are induced, across strains and lineages, after salt stress exposure

The salt stress conditions used in this study consistently led to an increase in transcript levels of σ^B- and CtsR-regulated genes in a broad range of L. monocytogenes strains comprised of 12 ribotypes and two lineages. All four genes studied here showed higher relative transcript levels after salt shock in strains from food processing plants compared to the potentially laboratory-adapted strain 10403S. Induction of the σ^B-regulated genes inlA and gadD3 across various salt concentrations (Hu et al., 2007; Olesen et al., 2009; Sue et al., 2004) shows that induction of σ^B-regulated genes occurs over a large range of osmolarity, which may induce cross-protection of L. monocytogenes cells from additional stresses such as sanitizer treatment, low temperatures, or bile stress by “priming” cells with active σ^B (Begley et al., 2002; Wemekamp-Kamphuis et al., 2004). In addition to induction of σ^B regulon members, we observed induction of CtsR-regulated genes in response to salt stress in all wildtype strains tested. Our finding that CtsR-regulated genes are induced by salt stress suggests that other stresses besides heat (Chastanet et al., 2004; Hu et al., 2007; Nair et al., 2000) lead to derepression of the CtsR regulon and indicates that some members of the CtsR regulon may contribute to salt stress response.

Lineage I strains show higher transcript levels, as compared to lineage II strains, for some stress response genes (i.e., gadD3 and clpB)

Among several lineage-specific differences in transcript levels of genes chosen in this study, we observed higher clpB transcript levels in exponential phase cells before salt exposure in lineage I strains as compared to lineage II strains, as well as higher gadD3 and clpB transcript levels after salt exposure in lineage I strains. GadD3 (Lmo2434) is one of three glutamate decarboxylases found in L. monocytogenes (Cotter et al., 2005) that have been demonstrated to facilitate L. monocytogenes survival in low pH conditions, including gastric fluid (Cotter et al., 2001, 2005). ClpB has been shown to be necessary for virulence in a mouse model (Chastanet et al., 2004). Our data suggest either higher transcription of these genes or that these mRNA transcripts are more stable in lineage I strains. As we found no sequence differences in the CtsR binding sites of clpB (Chastanet et al., 2004; Hu et al., 2007), genetic differences elsewhere in the chromosome are likely to be responsible for altered regulation of clpB in lineage I and II strains. It is also tempting to speculate that the difference in transcript levels between lineage I and II strains may relate to the potentially higher virulence potential of lineage I strains (Chen et al., 2006; Norton et al., 2001; Wiedmann et al., 1997), which are more commonly associated with human L. monocytogenes isolates, as opposed to lineage II isolates, which are overrepresented among food isolates in many countries (Gray et al., 2004). The lineage differences in transcript levels identified here further support that that genetic relatedness may affect stress gene and virulence factor expression in L. monocytogenes, as previously observed (Bergholz et al., 2010; Moorhead and Dykes, 2003; Oliver et al., 2010).

L. monocytogenes isolates representing persistent and non-persistent strains do not differ in transcript levels or induction of the stress responsive genes gadD3, inlA, and lmo1138

Our data found no evidence for a link between persistence and transcriptional response to one environmental stress, salt, as we observed only one gene, clpB, to be induced at significantly higher levels in non-persistent strains exposed to salt stress compared to persistent strains. Conceptual models explaining L. monocytogenes persistence in food processing environments include a model (Aase et al., 2000; Earnshaw and Lawrence, 1998; Kastbjerg and Gram, 2009; Lundén et al., 2008) that certain L. monocytogenes strains or subpopulations (Kastbjerg et al., 2009) have unique phenotypic characteristics (e.g., increased biofilm formation, sanitizer resistance, resistance to heat or acidic conditions) that facilitate establishment of a persistent population. An alternative model (Ferreira et al., 2011) is that most, if not all, L. monocytogenes can establish persistence if introduced into an appropriate niche (i.e., a location where they are protected from cleaning and sanitizers) at an opportune time. Some previous studies reported unique stress resistance phenotypes for persistent strains (Aase et al., 2000; Lundén et al., 2003a, 2008), supporting the first model. Other studies, however, did not identify any differences in stress resistance between persistent and non-persistent strains (Jensen et al., 2007; Kastbjerg and Gram, 2009), consistent with our observations here that did not find evidence for increased transcript levels or enhanced induction of stress response genes in persistent strains. Our data support the model that establishment of persistence is not a reflection of specific strain characteristics. Future studies using a larger number of strains from food processing environments are needed to further support this model. Use of whole genomic transcriptomic and proteomics approaches can also lead to insights on transcriptional differences, other than σ^B and CtsR regulation, that may exist between persistent and non-persistent strains.

Footnotes

Acknowledgments

This work was supported by the USDA National Research Initiative (grant 2005-35201-15330 to K.J.B.). D.R. was supported by the USDA National Needs Graduate Fellowship (competitive grant 2007-38420-17751 from the National Institute of Food and Agriculture).

Disclosure Statement

No competing financial interests exist.

References

Aase

, Sundheim

, Langsrud

, Rørvik

. Occurrence of and a possible mechanism for resistance to a quaternary ammonium compound in Listeria monocytogenes. Int J Food Microbiol, 2000; 62:57–63.

Becker

, Cetin

, Hutkins

, Benson

. Identification of the gene encoding the alternative sigma factor σ^B from Listeria monocytogenes and its role in osmotolerance. J Bacteriol, 1998; 180:4547–4554.

Begley

, Gahan

CGM

, Hill

. Bile stress response in Listeria monocytogenes LO28: Adaptation, cross-protection, and identification of genetic loci involved in bile resistance. Appl Environ Microbiol, 2002; 68:6005–6012.

Bergholz

, den Bakker

, Fortes

, Boor

, Wiedmann

. Salt stress phenotypes in Listeria monocytogenes vary by genetic lineage and temperature. Foodborne Pathog Dis, 2010; 7:7492–7498.

Bhagwat

, Phadke

, Wheeler

, Kalantre

, Gudipati

, Bhagwat

. Computational methods and evaluation of RNA stabilization reagents for genome-wide expression studies. J Microbiol Methods, 2003; 55:399–409.

Bishop

, Hinrichs

. Adoptive transfer of immunity to Listeria monocytogenes. The influence of in vitro stimulation on lymphocyte subset requirements. J Immunol, 1987; 139:2005–2009.

Boylan

, Redfield

, Brody

, Price

. Stress-induced activation of the σ^B transcription factor of Bacillus subtilis. J Bacteriol, 1993; 175:7931–7937.

Chastanet

, Derré

, Nair

, Msadek

. clpB, a novel member of the Listeria monocytogenes CtsR regulon, is involved in virulence but not in general stress tolerance. J Bacteriol, 2004; 186:1165–1174.

Chaturongakul

, Boor

. SigmaB activation under environmental and energy stress conditions in Listeria monocytogenes. Appl Environ Microbiol, 2006; 72:5197–5203.

10.

Chen

, Ross

, Gray

, Wiedmann

, Whiting

, Scott

. Attributing risk to Listeria monocytogenes subgroups: Dose response in relation to genetic lineages. J Food Prot, 2006; 69:335–344.

11.

Cotter

, Gahan

, Hill

. A glutamate decarboxylase system protects Listeria monocytogenes in gastric fluid. Mol Microbiol, 2001; 40:465–475.

12.

Cotter

, Ryan

, Gahan

, Hill

. Presence of GadD1 glutamate decarboxylase in selected Listeria monocytogenes strains is associated with an ability to grow at low pH. Appl Environ Microbiol, 2005; 71:2832–2839.

13.

Earnshaw

, Lawrence

. Sensitivity to commercial disinfectants, and the occurrence of plasmids within various Listeria monocytogenes genotypes isolated from poultry products and the poultry processing environment. J Appl Microbiol, 1998; 84:642–648.

14.

Ferreira

, O'Byrne

, Boor

. Role of σ^B in heat, ethanol, acid, and oxidative stress resistance and during carbon starvation in Listeria monocytogenes. Appl Environ Microbiol, 2001; 67:4454–4457.

15.

Ferreira

, Barbosa

, Stasiewicz

, Vongkamjan

, Moreno Switt

, Hogg

, Gibbs

, Teixeira

, Wiedmann

. Persistent Listeria monocytogenes in fermented meat sausage production facilities in Portugal represent diverse geno- and phenotypes. Appl Environ Microbiol, 2011; 77:2701–2715.

16.

Gray

, Zadoks

, Fortes

, Dogan

, Cai

, Chen

, Scott

, Gombas

, Boor

, Wiedmann

. Listeria monocytogenes isolates from foods and humans form distinct but overlapping populations. Appl Environ Microbiol, 2004; 70:5833–5841.

17.

Holah

, Bird

, Hall

. The microbial ecology of high-risk, chilled food factories; Evidence for persistent Listeria spp. and Escherichia coli strains. J Appl Microbiol, 2004; 97:68–77.

18.

, Raengpradub

, Schwab

, Loss

, Orsi

, Wiedmann

, Boor

. Phenotypic and transcriptomic analyses demonstrate interactions between the transcriptional regulators CtsR and σ^B in Listeria monocytogenes. Appl Environ Microbiol, 2007; 73:7967–7980.

19.

Jensen

, Larsen

, Ingmer

, Vogel

, Gram

. Sodium chloride enhances adherence and aggregation and strain variation influences invasiveness of Listeria monocytogenes strains. J Food Prot, 2007; 70:592–599.

20.

Joerger

, Chen

, Kniel

. Characterization of a spontaneous, pressure-tolerant Listeria monocytogenes Scott A ctsR deletion mutant. Foodborne Pathog Dis, 2006; 3:196–202.

21.

Karatzas

KAG

, Valdramidis

, Wells-Bennik

MHJ

. Contingency locus in ctsR of Listeria monocytogenes Scott A: A strategy for occurrence of abundant piezotolerant isolates within clonal populations. Appl Environ Microbiol, 2005; 71:8390–8396.

22.

Karatzas

KAG

, Wouters

, Gahan

CGM

, Hill

, Abee

, Bennik

MHJ

. The CtsR regulator of Listeria monocytogenes contains a variant glycine repeat region that affects piezotolerance, stress resistance, motility and virulence. Mol Microbiol, 2003; 49:1227–1238.

23.

Kastbjerg

, Gram

. Model systems allowing quantification of sensitivity to disinfectants and comparison of disinfectant susceptibility of persistent and presumed nonpersistent Listeria monocytogenes. J Appl Microbiol, 2009; 106:1667–1681.

24.

Kastbjerg

, Nielsen

, Arneborg

, Gram

. Response of Listeria monocytogenes to disinfection stress at the single-cell and population levels as monitored by intracellular pH measurements and viable-cell counts. Appl Environ Microbiol, 2009; 75:4550–4556.

25.

Kazmierczak

, Mithoe

, Boor

, Wiedmann

. Listeria monocytogenes σ^B regulates stress response and virulence functions. J Bacteriol, 2003; 185:5722–5734.

26.

Kazmierczak

, Wiedmann

, Boor

. Contributions of Listeria monocytogenes σ^B and PrfA to expression of virulence and stress response genes during extra- and intracellular growth. Microbiology, 2006; 152:1827.

27.

Lappi

, Thimothe

, Nightingale

, Gall

, Scott

, Wiedmann

. Longitudinal studies on Listeria in smoked fish plants: Impact of intervention strategies on contamination patterns. J Food Prot, 2004; 67:2500–2514.

28.

Lingnau

, Domann

, Hudel

, Bock

, Nichterlein

, Wehland

, Chakraborty

. Expression of the Listeria monocytogenes EGD inlA and inlB genes, whose products mediate bacterial entry into tissue culture cell lines, by PrfA-dependent and -independent mechanisms. Infect Immun, 1995; 63:3896–3903.

29.

Livak

, Schmittgen

. Analysis of relative gene expression data using real-time quantitative PCR and the 2^-ΔΔC _T Method. Methods, 2001; 25:402–408.

30.

Lundén

, Autio

, Markkula

, Hellstrom

, Korkeala

. Adaptive and cross-adaptive responses of persistent and non-persistent Listeria monocytogenes strains to disinfectants. Int J Food Microbiol, 2003a. 82:265–272.

31.

Lundén

, Autio

, Sjöberg

, Korkeala

. Persistent and nonpersistent Listeria monocytogenes contamination in meat and poultry processing plants. J Food Prot, 2003b. 66:2062–2069.

32.

Lundén

, Tolvanen

, Korkeala

. Acid and heat tolerance of persistent and nonpersistent Listeria monocytogenes food plant strains. Lett Appl Microbiol, 2008; 46:276–280.

33.

McGann

, Wiedmann

, Boor

. The alternative sigma factor σ^B and the virulence gene regulator PrfA both regulate transcription of Listeria monocytogenes internalins. Appl Environ Microbiol, 2007; 73:2919.

34.

Moorhead

, Dykes

. The role of the sigB gene in the general stress response of Listeria monocytogenes varies between a strain of serotype 1/2a and a strain of serotype 4c. Curr Microbiol, 2003; 46:461–466.

35.

Nadon

, Woodward

, Young

, Rodgers

, Wiedmann

. Correlations between molecular subtyping and serotyping of Listeria monocytogenes. J Clin Microbiol, 2001; 39:2704–2707.

36.

Nair

, Derré

, Msadek

, Gaillot

, Berche

. CtsR controls class III heat shock gene expression in the human pathogen Listeria monocytogenes. Mol Microbiol, 2000; 35:800–811.

37.

Norton

, Scarlett

, Horton

, Sue

, Thimothe

, Boor

, Wiedmann

. Characterization and pathogenic potential of Listeria monocytogenes isolates from the smoked fish industry. Appl Environ Microbiol, 2001; 67:646–653.

38.

Olesen

, Vogensen

, Jespersen

. Gene transcription and virulence potential of Listeria monocytogenes strains after exposure to acidic and NaCl stress. Foodborne Pathog Dis, 2009; 6:669–680.

39.

Oliver

, Orsi

, Wiedmann

, Boor

. Listeria monocytogenes σ^B has a small core regulon and a conserved role in virulence but makes differential contributions to stress tolerance across a diverse collection of strains. Appl Environ Microbiol, 2010; 76:4216–4232.

40.

Ollinger

, Wiedmann

, Boor

. σB- and PrfA-dependent transcription of genes previously classified as putative constituents of the Listeria monocytogenes PrfA regulon. Foodborne Pathog Dis, 2008; 5:281–293.

41.

Orsi

, Borowsky

, Lauer

, Young

, Nusbaum

, Galagan

, Birren

, Ivy

, Sun

, Graves

, Swaminathan

, Wiedmann

. Short-term genome evolution of Listeria monocytogenes in a non-controlled environment. BMC Genomics, 2008; 9:539.

42.

Pfaffl

. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res, 2001; 29:e45.

43.

Raengpradub

, Wiedmann

, Boor

. Comparative analysis of the σ^B-dependent stress responses in Listeria monocytogenes and Listeria innocua strains exposed to selected stress conditions. Appl Environ Microbiol, 2008; 74:158–171.

44.

Sue

, Fink

, Wiedmann

, Boor

. σ^B-Dependent gene induction and expression in Listeria monocytogenes during osmotic and acid stress conditions simulating the intestinal environment. Microbiology, 2004; 150:3843–3855.

45.

Wemekamp-Kamphuis

, Wouters

, de Leeuw

, Hain

, Chakraborty

, Abee

. Identification of sigma factor σ^B- controlled genes and their impact on acid stress, high hydrostatic pressure, and freeze survival in Listeria monocytogenes EGD-e. Appl Environ Microbiol, 2004; 70:3457–3466.

46.

Wiedmann

, Bruce

, Keating

, Johnson

, McDonough

, Batt

. Ribotypes and virulence gene polymorphisms suggest three distinct Listeria monocytogenes lineages with differences in pathogenic potential. Infect Immun, 1997; 65:2707–2716.

Salt Stress-Induced Transcription of σ B - and CtsR-Regulated Genes in Persistent and Non-persistent Listeria monocytogenes Strains from Food Processing Plants

Abstract

Introduction

Materials and Methods

Bacterial strains and growth conditions

Salt shock experiments

Total RNA isolation

TaqMan qRT-PCR

Statistical analysis

Results

Comparison of relative transcript levels of σB- and CtsR-regulated genes by persistence and lineage classifications of L. monocytogenes strains from food processing plants

Comparison of salt shock induction of σB- and CtsR-regulated genes by persistence and lineage classification of L. monocytogenes strains

Discussion

σB-Dependent genes gadD3 and inlA and the CtsR-dependent genes lmo1138 and clpB are induced, across strains and lineages, after salt stress exposure

Lineage I strains show higher transcript levels, as compared to lineage II strains, for some stress response genes (i.e., gadD3 and clpB)

L. monocytogenes isolates representing persistent and non-persistent strains do not differ in transcript levels or induction of the stress responsive genes gadD3, inlA, and lmo1138

Footnotes

Acknowledgments

Disclosure Statement

References

Comparison of relative transcript levels of σ^B- and CtsR-regulated genes by persistence and lineage classifications of L. monocytogenes strains from food processing plants

Comparison of salt shock induction of σ^B- and CtsR-regulated genes by persistence and lineage classification of L. monocytogenes strains

σ^B-Dependent genes gadD3 and inlA and the CtsR-dependent genes lmo1138 and clpB are induced, across strains and lineages, after salt stress exposure