Protein level identification of the Listeria monocytogenes Sigma H, Sigma L, and Sigma C regulons

Abstract

Background

Transcriptional regulation by alternative sigma (σ) factors represents an important mechanism that allows bacteria to rapidly regulate transcript and protein levels in response to changing environmental conditions. While the role of the alternative σ factor σ^B has been comparatively well characterized in L. monocytogenes, our understanding of the roles of the three other L. monocytogenes alternative σ factors is still limited. In this study, we employed a quantitative proteomics approach using Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) to characterize the L. monocytogenes σ^L, σ^H, and σ^C protein regulons. Proteomic comparisons used a quadruple alternative σ factor mutant strain (ΔBCHL) and strains expressing a single alternative σ factor (i.e., σ^L, σ^H, and σ^C; strains ΔBCH, ΔBCL, and ΔBHL) to eliminate potential redundancies between σ factors.

Results

Among the three alternative σ factors studied here, σ^H provides positive regulation for the largest number of proteins, consistent with previous transcriptomic studies, while σ^L appears to contribute to negative regulation of a number of proteins. σ^C was found to regulate a small number of proteins in L. monocytogenes grown to stationary phase at 37°C. Proteins identified as being regulated by multiple alternative σ factors include MptA, which is a component of a PTS system with a potential role in regulation of PrfA activity.

Conclusions

This study provides initial insights into global regulation of protein production by the L. monocytogenes alternative σ factors σ^L, σ^H, and σ^C. While, among these σ factors, σ^H appears to positively regulate the largest number of proteins, we also identified PTS systems that appear to be co-regulated by multiple alternative σ factors. Future studies should not only explore potential roles of alternative σ factors in activating a “cascade” of PTS systems that potentially regulate PrfA, but also may want to explore the σ^L and σ^C regulons under different environmental conditions to identify conditions where these σ factors may regulate larger numbers of proteins or genes.

Background

The foodborne pathogen Listeria monocytogenes uses complex regulatory mechanisms to adapt to a variety of environmental conditions and to cause listeriosis, a life-threatening infection, in humans and animals. A key mechanism used by L. monocytogenes to regulate transcript and protein levels in order to adapt to changing environmental conditions is through alternative sigma (σ) factors. Alternative σ factors reprogram the RNA polymerase holoenzyme to recognize specific promoters and hence allow for rapid induction of transcription of potentially large groups of genes under specific environmental conditions [1]. In L. monocytogenes, four alternative σ factors, σ^B, σ^C, σ^H, and σ^L_, have been identified. However, σ^C has only been described in L. monocytogenes strains that group into lineage II, a well defined phylogenetic group that includes serotypes 1/2a and 1/2c [2-4]. A number of studies that have explored σ^B-mediated stress response as well as σ^B-mediated gene expression and protein production in L. monocytogenes[1,5-16] have shown that this alternative σ factor controls a large regulon and contributes to both stress response and virulence.

σ^H, σ^L, and σ^C have not been as extensively characterized as σ^B in L. monocytogenes, at least partially because studies to date have only identified limited phenotypic consequences of null mutations in these σ factors in L. monocytogenes. Among these three alternative σ factors, σ^H appears to control the largest regulon; Chaturongakul et al. (2011) identified 97 and 72 genes as positively and negatively regulated by σ^H, respectively, in L. monocytogenes strain 10403S [7]. While a L. monocytogenes EGD-e sigH mutant was reported to have significantly impaired growth in minimal medium and under alkaline stress conditions as well as slightly reduced virulence potential in a mouse model [17], phenotypic studies in a L. monocytogenes 10403S ΔsigH strain did not find evidence for an effect of this mutation on virulence in a guinea pig model, cell invasion and intracellular growth, or resistance to heat stress [7]. With regard to σ^L, 31 and 20 genes were identified as positively and negatively regulated, respectively, by this σ factor, in L. monocytogenes 10403S [7]. A more recent study in L. monocytogenes EGD-e identified 237 and 203 genes as positively regulated by σ^L when the parent and ΔsigL mutant strains were grown at 3°C and 37°C, respectively; most of the 47 genes that showed positive regulation by σ^L under both temperatures were located within prophage A118 [18]. Phenotypic and gene expression studies also support a potential contribution of σ^L to L. monocytogenes growth under different stress conditions, most notably osmotic and low temperature stress [19,20]. L. monocytogenes σ^L has also been reported to be involved in resistance to the antimicrobial peptide mesentericin Y105 [21]. Finally, studies conducted to date on the L. monocytogenes σ^C regulon typically identified few genes as σ^C-dependent. Chaturongakul et al. (2011) were only able to identify and confirm, by qRT-PCR, a single gene (lmo0422) as σ^C-dependent; lmo0422, which encodes LstR, a lineage II specific thermal regulator, is in the same operon as sigC and this finding is consistent with previous data suggesting that the sigC operon is auto-regulated [3,7]. Zhang et al. (2005) also found some evidence that σ^C may contribute to thermal resistance in the L. monocytogenes lineage II strain 10403S, when grown to log phase [3]; by contrast, Chaturongakul et al. (2011) did not find any evidence for reduced heat resistance when an independent L. monocytogenes 10403S ΔsigC strain was grown to stationary phase prior to heat exposure [7].

Previous studies [7] have suggested considerable overlap between different L. monocytogenes alternative σ factor regulons (e.g., between the σ^B and the σ^H regulon), suggesting the potential for redundancies as well as compensation for deletion of a single alternative σ factor by other σ factors. We thus hypothesized that an experimental approach that eliminates these potential redundancies is needed to gain a better understanding of the roles of σ^C, σ^H, and σ^L in regulating production of specific proteins in L. monocytogenes. As an experimental approach, we selected to create an L. monocytogenes 10403S quadruple mutant with a non-polar deletion of all four genes that encode alternative σ factors (i.e., strain ΔBCHL) as well as corresponding mutants with deletions of three alternative σ factors (ΔBCH, ΔBCL, and ΔBHL), which thus expressed only σ^L, σ^H, and σ^C, respectively. These strains were then used for proteomic comparisons between the quadruple mutant strain and the three different strains expressing only a single alternative σ factor. We particularly focused on exploring the contributions of these alternative σ factors to regulating protein production as, despite availability of a number of proteomics data sets on the σ^B regulon [15,16], only a single proteomics study on the σ^L regulon is available [22]. While alternative σ factors directly regulate transcription of genes, it is increasingly clear that alternative σ factors also make important indirect contributions to protein production via mechanisms other than transcriptional activation of a σ factor dependent promoter upstream of a protein encoding gene, including through regulation of non-coding RNAs or through direct transcriptional up-regulation of a protein that in turn, directly or indirectly, affects production of other proteins [23]. The goal of this proteomics study was thus to specifically identify additional post-transcriptional regulatory pathways that are linked to the action of alternative σ factors in L. monocytogenes.

Results and discussion

Proteomic comparisons between L. monocytogenes mutants expressing only σ^L, σ^H, and σ^C and a quadruple mutant that does not express any alternative σ factors, all grown to stationary phase at 37°C, showed that (i) σ^H provides, among these three alternative σ factors, positive regulation for the largest number of proteins, consistent with previous transcriptomic studies [7]; (ii) σ^L appears to contribute to negative regulation of a number of proteins; (iii) σ^C regulates a small number of proteins in L. monocytogenes grown to stationary phase at 37°C; and (iv) proteins regulated by multiple alternative σ factors include MptA, which has a potential role in regulation of PrfA.

σ^H positively regulates a large number of proteins and appears to directly and indirectly contribute to transport and metabolism of β-glucosides

Our proteomic comparison identified 15 proteins as positively regulated by σ^H, as supported by higher protein levels (Fold change (FC) ≥ 1.5; p-value^c (p^c) < 0.05) in L. monocytogenes ΔBCL as compared to the ΔBCHL strain (Table 1); four of these 15 proteins also showed higher levels in the parent strain (which expresses all four alternative σ factors) as compared to the quadruple mutant. Overall, positive fold changes for these proteins (in ΔBCL versus ΔBCHL) ranged from 1.55 to 3.39. These 15 proteins represented nine role categories (e.g., “energy metabolism”; “amino acid biosynthesis”; “transport and binding proteins”, see Figure 1); a Monte Carlo simulation of Fisher’s exact test did not find a significant association between positively regulated genes and role categories (p = 0.06); however, individual Fisher’s exact tests did show overrepresentation of proteins in the role category “amino acid biosynthesis” among the 15 proteins that were found to be positively regulated by σ^H (with a significant p-value; p < 0.01; Odds Ratio = 6.26). Some of the 15 proteins positively regulated by σ^H have likely roles in stress adaptation and virulence, including Lmo1439 (superoxide dismutase, SodA) [24] and Lmo0096 (mannose-specific PTS system IIAB component, MptA), which has been linked to regulation of the virulence gene regulator PrfA [25]. Previously reported transcriptomic studies [7] only identified the coding gene for one of these 15 proteins (i.e., Lmo1454) as σ^H-dependent; lmo1454 (rpoD) was also identified as preceded by a σ^H consensus promoter, suggesting direct transcriptional regulation by σ^H. In addition, the coding gene for Lmo2487, one of these 15 proteins, is in an operon with lmo2485, which was previously reported to be positively regulated by σ^H, even though no upstream σ^H consensus promoter was identified, suggesting indirect regulation [7]. RNA-Seq data from our group (unpublished data) found clear evidence (FC > 5 and likelihood of being positively regulated by σ^H > 0.95) for σ^H-dependent transcript levels for only two of the genes encoding these 15 proteins, including lmo1454 and lmo0239; importantly, RNA-Seq data allow for quantification with similar sensitivity as qRT-PCR [14]. lmo1454 thus has been consistently identified as a gene that is directly up-regulated by σ^H, as supported by proteomics and transcriptomic studies and identification of an upstream σ^H-dependent promoter. Many of the other proteins identified here as showing σ^H-dependent production, on the other hand, appear to be regulated indirectly by σ^H, possibly at the post-transcriptional level. While future efforts will be needed to confirm σ^H-dependent production of these proteins (e.g., through Western blot or translational reporter fusions) and to explore the mechanisms of regulation, our data identified and further characterized a σ^H-dependent pathway that involves indirect effects of σ^H. Specifically, we found that both Lmo0027 (a component of a β-glucoside specific PTS system) and BglA (a β-glucosidase) showed higher protein levels in the presence of σ^H. As lmo0027 is preceded by a σ^H consensus promoter, these findings suggest a model where σ^H directly activates transcription of lmo0027, which facilitates PTS-based import of beta-glucosides into the cell. We hypothesize that these β-glucosides then lead to an increase in the levels of BglA (through a yet to be defined mechanism), facilitating the use of β-glucosides in downstream pathways involved in energy acquisition (e.g., glycolysis, the pentose phosphate pathway).

Table 1.

Proteins found to be differentially regulated by σ^H, as determined by a proteomic comparison between L. monocytogenes 10403S ΔBCL and ΔBCHL

Proteina	Fold change Δ BCL /ΔBCHL	Description	Gene name	Role categoryb	Sub-Role categoryb	Promoterd	Sigma factor
Proteins with positive fold change (>1.5) and p < 0.05 (indicating positive regulation by σH)
Lmo0027	1.55	beta-glucoside-specificPTS system IIABC component	lmo0027	Transport and binding proteins	Carbohydrates, organic alcohols, and acids	aggacacgtgtatgcgtggagtcctcgaatga	SigmaH
				Amino acid biosynthesis	Aromatic amino acid family
				Energy metabolism	Pyruvate dehydrogenase
Lmo0096	3.39	mannose-specific PTS system IIAB component ManL	mptA	Energy metabolism	Pyruvate dehydrogenase	tggcacagaacttgca	SigmaL
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo0239	1.82	cysteinyl-tRNA synthetase	cysS	Protein synthesis	tRNA aminoacylation	ttgcaaggaattttattgctgttataatag	SigmaA
Lmo0319	1.77	beta-glucosidase	bglA	Energy metabolism	Sugars	N/A	N/A
Lmo0356	2.16	YhhX family oxidoreductase	lmo0356	Energy metabolism	Fermentation	tggctaagtacagcgctagtgtagtactat	SigmaA
				Energy metabolism	Electron transport
				Central intermediary metabolism	Other
Lmo1001	1.65	hypothetical protein	lmo1001	Unclassified	Role category not yet assigned	N/A	N/A
Lmo1070	2.18	similar to B. subtilis YlaN protein	lmo1070	Hypothetical proteins	Conserved	ttgcgtggcaaataaattatgctatact	SigmaA
Lmo1255	1.60	trehalose-specific PTS system IIBC component	lmo1255	Energy metabolism	Pyruvate dehydrogenase	ttgcgctttcaactgatttatagtatagt	SigmaA
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo1439	1.66	superoxide dismutase	sodA	Cellular processes	Detoxification	ttgcaagcatttagggagcatggtaggct	SigmaA
						gtttaacttttgagtttcagggaaa	SigmaB
Lmo1454c	1.85	RNA polymerase sigma factor RpoD	rpoD	Transcription	Transcription factors	gttttaaaaccgctaaatgatggtat	SigmaB
						aggacttttgctttttgtggcgaatat	SigmaH
						ttgactttttagcaaaaatacagtatctt	SigmaA
Lmo2006	1.60	acetolactate synthase catabolic	alsS	Amino acid biosynthesis	Aspartate family	ttgcaataattcttttgagtagtataat	SigmaA
				Amino acid biosynthesis	Pyruvate family
Lmo2064	2.01	large conductance mechanosensitive channel protein	mscL	Cellular processes	Adaptations to atypical conditions	tttcacatcgcagttagatgttttatact	SigmaA
Lmo2487	1.65	hypothetical protein	lmo2487	Hypothetical proteins	Conserved	N/A	N/A
Lmo2614	2.05	50S ribosomal protein L30	rpmD	Protein synthesis	Ribosomal proteins: synthesis and modification	ttgattactacccctaacccgtgtataat	SigmaA
Lmo2621	1.63	50S ribosomal protein L24	rplX	Protein synthesis	Ribosomal proteins: synthesis and modification	ttgattactacccctaacccgtgtataat	SigmaA
Proteins with negative fold change (< -1.5) and p < 0.05 (indicating negative regulation by σH)
Lmo1877	−1.61	formate-tetrahydrofolate ligase	fhs	Amino acid biosynthesis	Aspartate family
				Protein synthesis	tRNA aminoacylation
				Amino acid biosynthesis	Histidine family
				Purines, pyrimidines, nucleosides, and nucleotides	Purine ribonucleotide biosynthesis
				Biosynthesis of cofactors, prosthetic groups, and carriers	Pantothenate and coenzyme A
Lmo2094	−7.35	hypothetical protein	lmo2094	Energy metabolism	Sugars
Lmo2097	−3.17	galactitol-specific PTS system IIB component	lmo2097	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo2098	−2.33	galactitol-specific PTS system IIA component	lmo2098	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids

^aProtein names are based on the L. monocytogenes EGD-e locus.

^bRole Categories and Sub-Role categories are based on JCVI classification [26].

^cReported as positively and directly regulated by σ^H in Chaturongakul et al., 2011 [7].

^dPromoters were identified based on RNA-Seq data (Orsi et al., unpublished) or previously published data. -10 and -35 (σ^A, σ^B, σ^H) and -12 and -24 (σ^L) regions are underlined. N/A indicates that a promoter was not identified.

Figure 1.

Functional role category classification of alternative σ factor dependent proteins. Functional role category classification of σ^H positively-regulated (blue), σ^H negatively-regulated (red), σ^C positively-regulated (green), σ^C negatively-regulated (purple), σ^L positively-regulated (turquoise), and σ^L negatively-regulated (gray) proteins; and proteins with higher levels in L. monocytogenes parent strain 10403S (PAR.) compared to ΔBCHL (yellow) and lower levels in PAR. compared to ΔBCHL (orange). Role category numbers correspond to: (1) Amino acid biosynthesis; (2) Biosynthesis of cofactors, prosthetic groups, and carriers; (3) Cell envelope; (4) Cellular processes; (5) Central intermediary metabolism; (6) Energy metabolism; (7) Fatty acid and phospholipid metabolism; (8) Hypothetical proteins; (9) Protein fate; (10) Protein synthesis; (11) Purines, pyrimidines, nucleosides, and nucleotides; (12) Regulatory functions; (13) Transcription; (14) Transport and binding proteins; (15) Unclassified; (16) Unknown function; (17) Viral functions. One protein may be classified into more than one role category. Statistical analysis of contingency tables for regulons with > 10 proteins (i.e., proteins positively regulated by σ^H; proteins negatively regulated by σ^L; proteins with higher or lower levels in the parent strain) found that role categories were not randomly distributed among proteins negatively regulated by σ^L and proteins with lower levels in the parent strain.

Our proteomic comparison also identified four proteins that showed lower levels in the strain expressing σ^H, suggesting (indirect) negative regulation by σ^H; three of these four proteins also showed lower levels in the parent strain (which expresses all four alternative σ factors) as compared to the quadruple mutant. None of the genes encoding these proteins showed significantly higher transcript levels in a ΔsigH strain in a transcriptomic study [7]. However, the coding gene for Lmo1877, one of these four proteins, is in an operon with lmo1876, which was previously reported to be negatively regulated by σ^H[7]. Overall, global indirect down-regulation of proteins by σ^H does not seem to play an important role in stationary phase L. monocytogenes 10403S.

σ^L appears to contribute to negative regulation of a number of proteins

Our proteomic comparison identified only two proteins (Lmo0096 and Lmo2006) as positively regulated by σ^L, as supported by higher protein levels (FC ≥ 1.5; p^c < 0.05) in L. monocytogenes ΔBCH as compared to the ΔBCHL strain (Table 2). Both of these proteins also showed higher levels in the parent strain (which expresses all four alternative σ factors) as compared to the quadruple mutant. Lmo0096 (MptA) is annotated as the mannose-specific PTS system IIAB component, while Lmo2006 (AlsS) is annotated as an acetolactate synthase. Both lmo0096 and lmo2006 have previously been reported to be positively regulated by σ^L at the transcriptomic level [18]. Lmo0096 was also reported as showing lower levels in an L. monocytogenes EGD-e rpoN (σ^L) mutant in a 2-DE based proteomic analysis [22] and the lmo0096 gene was found to be preceded by a putative σ^L consensus promoter in the same study, further supporting positive regulation of the gene encoding this protein by σ^L.

Table 2.

Proteins found to be differentially regulated by σ^L, as determined by a proteomic comparison between L. monocytogenes 10403S ΔBCH and ΔBCHL

Proteina	Fold change Δ BCH /ΔBCHL	Description	Gene name	Role categoryb	Sub-Role categoryb
Proteins with positive fold change (>1.5) and p < 0.05 (indicating positive regulation by σL)
Lmo0096d,f	64.16	mannose-specific PTS system IIAB component ManL	mptA	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo2006g	3.41	acetolactate synthase catabolic	alsS	Amino acid biosynthesis	Aspartate family
				Amino acid biosynthesis	Pyruvate family
Proteins with negative fold change (< -1.5) and p < 0.05 (indicating negative regulation by σL)
Lmo0027c,e	−3.62	beta-glucoside-specific PTS system IIABC component	lmo0027	Transport and binding proteins	Carbohydrates, organic alcohols, and acids
				Amino acid biosynthesis	Aromatic amino acid family
				Energy metabolism	Pyruvate dehydrogenase
Lmo0130	−3.64	hypothetical protein	lmo0130	Unclassified	Role category not yet assigned
Lmo0178	−2.07	hypothetical protein	lmo0178	Regulatory functions	Other
Lmo0181	−3.25	multiple sugar transport system substrate-binding protein	lmo0181	Transport and binding proteins	Unknown substrate
Lmo0260	−1.68	hydrolase	lmo0260	Hypothetical proteins	Conserved
Lmo0278	−1.67	maltose/maltodextrin transport system ATP-binding protein	lmo0278	Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo0319c,e	−2.96	beta-glucosidase	bglA	Energy metabolism	Sugars
Lmo0343	−3.94	transaldolase	tal2	Energy metabolism	Pentose phosphate pathway
Lmo0344	−4.69	short chain dehydrogenase	lmo0344	Energy metabolism	Biosynthesis and degradation of polysaccharides
Lmo0345	−6.04	ribose 5-phosphate isomerase B	lmo0345	Energy metabolism	Pentose phosphate pathway
Lmo0346	−2.74	triosephosphate isomerase	tpiA2	Energy metabolism	Glycolysis/gluconeogenesis
Lmo0348	−2.41	dihydroxyacetone kinase	lmo0348	Fatty acid and phospholipid metabolism	Biosynthesis
				Energy metabolism	Sugars
Lmo0391	−1.67	hypothetical protein	lmo0391
Lmo0401	−2.16	alpha-mannosidase	lmo0401	Unclassified	Role category not yet assigned
Lmo0517e	−3.21	phosphoglycerate mutase	lmo0517	Energy metabolism	Glycolysis/gluconeogenesis
Lmo0521	−2.23	6-phospho-beta-glucosidase	lmo0521	Energy metabolism	Sugars
Lmo0536	−1.97	6-phospho-beta-glucosidase	lmo0536	Central intermediary metabolism	Other
Lmo0574	−1.65	6-phospho-beta-glucosidase GmuD	lmo0574	Central intermediary metabolism	Other
Lmo0640	−1.78	oxidoreductase	lmo0640	Energy metabolism	Fermentation
				Central intermediary metabolism	Other
				Energy metabolism	Electron transport
Lmo0643	−2.61	transaldolase	lmo0643	Energy metabolism	Pentose phosphate pathway
Lmo0689	−1.71	chemotaxis protein CheV	lmo0689	Cellular processes	Chemotaxis and motility
Lmo0690	−2.44	flagellin	flaA	Cellular processes	Chemotaxis and motility
Lmo0692	−1.66	chemotaxis protein CheA	cheA	Cellular processes	Chemotaxis and motility
Lmo0813	−2.04	fructokinase	lmo0813	Energy metabolism	Sugars
Lmo0930	−1.88	hypothetical protein	lmo0930	Unclassified	Role category not yet assigned
Lmo1242	−1.59	hypothetical protein	lmo1242	Hypothetical proteins	Conserved
Lmo1254	−2.10	alpha-phosphotrehalase	lmo1254	Energy metabolism	Biosynthesis and degradation of polysaccharides
Lmo1348	−2.42	glycine cleavage system T protein	gcvT	Energy metabolism	Amino acids and amines
Lmo1349	−2.68	glycine cleavage system P-protein	gcvPA	Energy metabolism	Amino acids and amines
				Central intermediary metabolism	Other
Lmo1350e	−2.11	glycine dehydrogenase subunit 2	gcvPB	Central intermediary metabolism	Other
				Energy metabolism	Amino acids and amines
Lmo1388e	−2.02	ABC transport system	tcsA	Unclassified	Role category not yet assigned
Lmo1389	−2.32	simple sugar transport system ATP-binding protein	lmo1389	Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo1538e	−1.89	glycerol kinase	glpK	Energy metabolism	Other
Lmo1699	−1.92	Methyl-accepting chemotaxis protein	lmo1699	Cellular processes	Chemotaxis and motility
Lmo1730	−2.55	lactose/L-arabinose transport system substrate-binding protein	lmo1730	Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo1791	−1.75	hypothetical protein	lmo1791
Lmo1812	−1.70	L-serine dehydratase iron-sulfur-dependent alpha subunit	lmo1812	Energy metabolism	Amino acids and amines
				Energy metabolism	Glycolysis/gluconeogenesis
Lmo1856	−1.65	purine nucleoside phosphorylase	deoD	Purines, pyrimidines, nucleosides, and nucleotides	Salvage of nucleosides and nucleotides
Lmo1860	−1.64	peptide-methionine (S)-S-oxide reductase	msrA	Protein fate	Protein modification and repair
Lmo1877	−2.14	formate-tetrahydrofolate ligase	fhs	Amino acid biosynthesis	Aspartate family
				Protein synthesis	tRNA aminoacylation
				Amino acid biosynthesis	Histidine family
				Purines, pyrimidines, nucleosides, and nucleotides	Purine ribonucleotide biosynthesis
				Biosynthesis of cofactors, prosthetic groups, and carriers	Pantothenate and coenzyme A
Lmo1954e	−1.97	phosphopentomutase	deoB	Purines, pyrimidines, nucleosides, and nucleotides	Salvage of nucleosides and nucleotides
Lmo1993	−1.81	pyrimidine-nucleoside phosphorylase	pdp	Purines, pyrimidines, nucleosides, and nucleotides	Salvage of nucleosides and nucleotides
Lmo2094	−28.99	hypothetical protein	lmo2094	Energy metabolism	Sugars
Lmo2097	−12.12	galactitol-specific PTS system IIB component	lmo2097	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo2098	−3.96	galactitol-specific PTS system IIA component	lmo2098	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Lmo2160	−2.37	sugar phosphate isomerase/epimerase	lmo2160	Hypothetical proteins	Conserved
Lmo2161	−2.58	hypothetical protein	lmo2161	Hypothetical proteins	Conserved
Lmo2362	−1.87	glutamate/gamma-aminobutyrate antiporter	lmo2362	Transport and binding proteins	Amino acids, peptides and amines
Lmo2425	−1.59	glycine cleavage system H protein	gcvH	Energy metabolism	Amino acids and amines
Lmo2481	−1.52	pyrophosphatase PpaX	ppaX	Central intermediary metabolism	Other
Lmo2529	−1.72	ATP synthase F1 beta subunit	atpD2	Energy metabolism	ATP-proton motive force interconversion
Lmo2648	−2.50	hypothetical protein	lmo2648	Unclassified	Role category not yet assigned
Lmo2664	−1.72	L-iditol 2-dehydrogenase	lmo2664	Central intermediary metabolism	Other
				Energy metabolism	Glycolysis/gluconeogenesis
				Energy metabolism	Electron transport
				Energy metabolism	TCA cycle
				Energy metabolism	Fermentation
Lmo2696	−2.68	dihydroxyacetone kinase L subunit	lmo2696	Energy metabolism	Sugars
				Fatty acid and phospholipid metabolism	Biosynthesis
Lmo2697	−3.10	dihydroxyacetone kinase	lmo2697	Hypothetical proteins	Conserved
Lmo2743	−2.71	transaldolase	tal1	Energy metabolism	Pentose phosphate pathway

^aProtein names are based on the L. monocytogenes EGD-e locus.

^bRole Categories and Sub-Role categories are based on JCVI classification [26].

^cReported as negatively regulated by σ^L in Chaturongakul et al., 2011 [7].

^dReported as downregulated in a rpoN (σ^L) mutant compared to wildtype L. monocytogenes EGD-e in Arous et al., 2004 [22].

^eReported as upregulated in a rpoN (σ^L) mutant compared to wildtype L. monocytogenes EGD-e in Arous et al., 2004 [22].

^fPreceded by a putative σ^L promoter; tggcacagaacttgca; -12 and -24 regions are underlined.

^gPreceded by a putative σ^A promoter; ttgcaataattcttttgagtagtataat; -10 and -35 regions are underlined.

A total of 56 proteins showed lower levels in the presence of σ^L (in the comparison between the ΔBCH and the ΔBCHL strain), suggesting indirect negative regulation of these proteins by σ^L (Table 2); two of the genes encoding these proteins had previously been shown to have higher transcript levels in a ΔsigL null mutant as compared to a parent strain, further supporting negative regulation by σ^L[7]. Twenty-one of the proteins with evidence for negative regulation by σ^L also showed lower protein levels in the parent strain as compared to the ΔBCHL strain (Additional file 1: Table S1), further supporting their negative regulation. Four of these 21 proteins as well as three other proteins found to be negatively regulated by σ^L in this study were also reported as showing higher transcript levels in an L. monocytogenes EGD-e rpoN (σ^L) mutant [22] (Table 2), supporting their negative regulation by σ^L. Overall, the 56 proteins identified here as negatively regulated by σ^L represented 13 role categories (e.g., energy metabolism, transport and binding proteins, central intermediary metabolism), including 31 proteins in the energy metabolism role category; statistical analyses showed overrepresentation of the role category “energy metabolism” (p < 0.01; Odds Ratio = 5.6) among these 56 proteins. Specific proteins identified as negatively regulated by σ^L included flagellin (FlaA), chemotaxis protein CheA, and a glutamate-γ-aminobutyric acid (GABA) antiporter (Lmo2362, GadC, GadT2), which have known roles in stress adaptation or virulence in L. monocytogenes[1,27].

σ^C regulates a small number of proteins

Previous studies indicated a role for σ^C in L. monocytogenes thermal adaptive response as well as in cold adaptation [3,13], however only a few genes have been identified as part of the σ^C regulon [7]. Similarly, we were only able to identify one protein, Lmo0096, that showed higher protein levels (FC ≥ 1.5; p^c < 0.05) in the presence of σ^C (i.e., the comparison between the ΔBHL and the ΔBCHL strain; Table 3). Lmo0096 has been previously reported to be induced under cold stress in L. monocytogenes[28], supporting a role of σ^C in response to temperature stress in the bacterium. By comparison, the transcriptomic study by Chaturongakul et al., 2011 only identified lmo0422, which is in the same operon as sigC (lmo0423), as positively regulated by σ^C[7].

Table 3.

Proteins found to be differentially regulated by σ^C, as determined by a proteomic comparison between L. monocytogenes 10403S ΔBHL and ΔBCHL

Proteina	Fold change ΔBHL/ΔBCHL	Description	Gene name	Role categoryb	Sub-Role categoryb
Proteins with positive fold change (>1.5) and p < 0.05 (indicating positive regulation by σC)
Lmo0096c	3.19	mannose-specific PTS system IIAB component ManL	mptA	Energy metabolism	Pyruvate dehydrogenase
				Amino acid biosynthesis	Aromatic amino acid family
				Transport and binding proteins	Carbohydrates, organic alcohols, and acids
Proteins with negative fold change (< -1.5) and p < 0.05 (indicating negative regulation by σC)
Lmo2094	−1.82	hypothetical protein	lmo2094	Energy metabolism	Sugars
Lmo1902	−1.61	3-methyl-2-oxobutanoate hydroxymethyltransferase	panB	Biosynthesis of cofactors, prosthetic groups, and carriers	Pantothenate and coenzyme A

^aProtein names are based on the L. monocytogenes EGD-e locus.

^bRole Categories and Sub-Role categories are based on JCVI classification [26].

^cPreceded by a putative σ^L promoter; tggcacagaacttgca; -12 and -24 regions are underlined.

We also identified two proteins, Lmo2094 and Lmo1902, that showed higher protein levels in the absence of σ^C, suggesting negative regulation of these proteins by σ^C (Table 3). By comparison, the transcriptomic study by Chaturongakul et al. (2011) identified three different genes, representing two operons (lmo1854; lmo2185 and lmo2186), that showed lower transcript levels in the parent strain compared to the ΔsigC mutant, suggesting negative regulation by σ^C[7]. While our data are consistent with previous findings of a limited σ^C regulon in L. monocytogenes 10403S, it is possible that σ^C- dependent gene regulation only occurs under specific conditions (e.g., heat stress [3]) and that more complete identification of the σ^C regulon requires transcriptomic and proteomic studies under specific conditions that remain to be defined. In addition, future experiments using an L. monocytogenes strain that expresses sigC from an inducible promoter may also allow for identification of additional proteins that show σ^C-dependent production; this strategy applied to other alternative σ factors may also allow for identification of additional proteins that show σ^H- or σ^L-dependent production.

Proteins regulated by multiple alternative σ factors include MptA, which has a potential role in regulation of PrfA

Our data reported here also provided an opportunity to gather further insight into genes and proteins that are co-regulated by multiple σ factors and, consequently, into regulatory networks among different alternative σ factors. To facilitate these analyses, we also compared the protein levels between the L. monocytogenes parent strain and the ΔBCHL strain (which does not express any alternative σ factors). This analysis identified (i) 33 proteins that showed significantly higher levels (FC ≥ 1.5; p^c < 0.05) in the parent strain as compared to the ΔBCHL strain (Additional file 1: Table S1) and (ii) 44 proteins that show lower levels in the parent as compared to the ΔBCHL mutant (Additional file 1: Table S1). Approximately 40% of the proteins that showed differential production (either up or down) are involved in energy metabolism and transport and binding functions (Figure 1). Among the 33 proteins that showed higher levels in the parent strain, (i) two were also found to be positively regulated by σ^H; (ii) one was also positively regulated by σ^H and σ^L, and (iii) one was also positively regulated by σ^H, σ^L and σ^C (Figure 2; Table 4). In addition, 12 of the 29 proteins that were found to be positively regulated in the parent strain, were also found to be positively regulated by σ^B in a recent proteomics study, which compared L. monocytogenes parent strain 10403S and ΔsigB mutant grown to stationary phase under the same conditions as used here [23]. While these 12 proteins likely represent proteins that are positively regulated by σ^B, the other 17 proteins that showed higher levels in the parent strain as compared to the ΔBCHL strain, but were not identified as positively regulated by any of the alternative σ factors, represent candidate proteins for redundant co-regulation by multiple alternative σ factors. Future experiments using an L. monocytogenes strain that only expresses σ^B (i.e., a ΔCHL strain) may help to not only further define the σ^B regulon, but also allow for further refinement of genes and proteins co-regulated by multiple alternative σ factors. Regulatory redundancy among multiple alternative σ factors has also previously been demonstrated through analyses of Bacillus subtilis alternative σ factor mutants; in particular, certain phenotypes displayed by a B. subtilis triple alternative σ factor deletion mutant were not found among single or double mutants of each of the three alternative σ factors, suggesting regulatory overlaps [29].

Figure 2.

Venn diagram of proteins identified as showing higher protein levels in comparisons of (i) L. monocytogenes parent strain 10403S (PAR.) and Δ BCHL ; (ii) ΔBCH and ΔBCHL (identifying genes positively regulated by σ^L); ΔBCL and ΔBCHL (identifying genes positively regulated by σ^H); and ΔBHL and ΔBCHL (identifying genes positively regulated by σ^C). Twelve of the 29 proteins that were found to be positively regulated in the parent strain were also found to be positively regulated by σ^B in a recent proteomics study, which compared L. monocytogenes parent strain 10403S and a ΔsigB mutant [23]; these proteins include Lmo2748, Lmo2213, Lmo2158, Lmo2047, Lmo1830, Lmo0913, Lmo0796, Lmo0794, Lmo0722, Lmo0654, Lmo0539, and Lmo0265. The 17 proteins that show higher levels in the parent strain as compared to the ΔBCHL strain, but were not identified as positively regulated by any of the alternative σ factors include Lmo1540, Lmo2610, Lmo1422, Lmo1421, Lmo1602, Lmo1426, Lmo1428, Lmo2205, Lmo2398, Lmo1601, Lmo0554, Lmo1634, Lmo0110, Lmo2558, Lmo0783, Lmo0134, and Lmo0098.

Table 4.

Proteins found to be differentially regulated by at least two of the three alternative sigma factors studied here

	Regulation byb			Regulation by σBc	Differential levels in comparison between parent and ΔBCHL
Proteina	σH	σL	σC
Lmo0027	+	-	NDR	NDR	-
Lmo0096 (MptA)	+	+	+	NDR	+
Lmo0319 (BglA)	+	-	NDR	NDR	-
Lmo1877 (Fhs)	-	-	NDR	NDR	-
Lmo2006 (AlsS)	+	+	NDR	NDR	+
Lmo2094	-	-	-	NDR	-
Lmo2097	-	-	NDR	NDR	-
Lmo2098	-	-	NDR	NDR	NDR

^aWhere available, protein name is shown in parenthesis.

^bProteins that were identified here as positively (+) or negatively (−) regulated (absolute FC > 1.5; p < 0.05) by a given σ factor are shown; NDR (“not differentially regulated”) indicates that a protein was not found to be differentially regulated between strains with and without a given alternative σ factor.

^cData for proteins differentially regulated by σ^B were obtained from Mujahid et al. [23]; this study compared protein levels between the 10403S parent strain and an isogenic ΔsigB strain.

Among the 44 proteins that showed lower levels in the parent strain as compared to the ΔBCHL mutant (Additional file 1: Table S1), (i) two also showed evidence for negative regulation by σ^H and σ^L (Lmo2097 and Lmo1877); (ii) one also showed evidence for negative regulation by σ^H, σ^L, and σ^C (Lmo2094; located in the same operon as lmo2097). Among these 44 proteins, statistical analyses showed overrepresentation of three role categories, including (i) “energy metabolism” (p < 0.01; Odds Ratio = 3.02), (ii) “biosynthesis of cofactors, prosthetic groups, and carriers” (p = 0.04; Odds Ratio = 2.72), and (iii) “purines, pyrimidines, nucleosides, and nucleotides” (p = 0.04; Odds Ratio = 3.29), as well as underrepresentation of the role category “hypothetical proteins” (p = 0.01; Odds Ratio = 0.208).

Overall, our data provide additional evidence that a number of genes and proteins are co-regulated by more than one σ factor. This is consistent with previous microarray studies [7] that have reported considerable overlaps between σ factor regulons in L. monocytogenes, in particular between the σ^H and the σ^B regulon. We also identified some proteins with particularly striking patterns of co-regulation, including (i) members of the lmo2093-lmo2099 operon, specifically Lmo2094, which was found to be negatively regulated by σ^H, σ^L, and σ^C and Lmo2097 and Lmo2098, which were found to be negatively regulated by σ^H and σ^L (Table 4) and (ii) MptA (Lmo0096), which was found to be positively regulated by σ^H, σ^L, and σ^C (Table 4). Lmo2094 shows particularly striking negative regulation of protein production by σ^H, σ^L, and σ^C with respective fold changes of −7.35, -28.99, and −1.82. Although Lmo2094 is annotated as a fuculose-phosphate aldolase, it is part of an operon in which most of the other genes with assigned functions are annotated as being involved in the galactitol degradation pathway. Specifically, the lmo2093 to lmo2099 operon encodes components of a putative PTS galactitol family permease [30], including the PTS system galactitol-specific enzyme IIC (Lmo2096), IIB (Lmo2097), and IIA (Lmo2098) components, as well as a transcription antiterminator (Lmo2099), a tagatose-6-phosphate kinase/1-phosphofructokinase (Lmo2095), an L-fuculose-phosphate aldolase (Lmo2094), and a hypothetical protein (Lmo2093). Therefore, it is possible that Lmo2094 is also involved in this pathway functioning as a tagatose-1,6-biphosphate aldolase. This enzyme converts tagatose-1,6,-biphosphate into glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, which allows both tagatose and galactitol to be used as energy sources for glycolysis [31].

MptA, a component of a permease of the PTS mannose–fructose–sorbose family, which is another one of the seven PTS families of L. monocytogenes[30], showed the highest fold change in the ΔBCH strain as compared to the ΔBCHL strain, supporting σ^L dependent protein levels (FC = 64.16); fold changes supporting σ^H and σ^C dependent protein levels were 3.39 and 3.19, respectively. MptA is encoded by a gene that is part of a three-gene operon (mptACD[32], which also has been designated as manLMN[25]); these three genes encode a mannose-specific PTS system IIAB component, a mannose-specific PTS system IIC component, and a mannose-specific PTS system IID component, respectively [25,32]. Recently, it was suggested that during glucose uptake, MptA dephosphorylates, which directly, or indirectly, inhibits PrfA, the major positive regulator of L. monocytogenes virulence genes [25]. These findings thus provide for a hypothesis that redundant upregulation of MptA, through multiple alternative σ factors, may provide a critical initial step towards inactivation of PrfA.

Conclusions

Transcriptional regulation through the interplay between alternative σ factors represents an important component of L. monocytogenes stress response systems and the ability of this pathogen to regulate gene expression during infection. In addition to transcriptional regulation, alternative σ factors may also regulate protein production post-transcriptionally and/or post-translationally. To allow for further insights into the roles of different alternative σ factors in L. monocytogenes, we thus completed a global evaluation of alternative σ factor-dependent protein production patterns in L. monocytogenes stationary phase cells. In concert with previous transcriptomic studies, our data not only provide a further refinement of our understanding of the alternative σ factor regulons in this important pathogen, but also provide clear evidence for co-regulation, by multiple σ factors, of different PTS systems, including one PTS system that has been suggested to be linked to regulation of PrfA. Co-regulation by multiple σ factors can provide sensitive means for fine-tuning of gene expression and protein production under different environmental conditions, as well as redundancy that can ensure gene expression and protein production under different conditions. Consistent with the goals of this study, many of the proteins that were identified as showing production dependent on the presence of alternative σ factors appear to represent indirect regulation by a given σ factor, which will require future confirmation by protein based methods (e.g., Western blots, translational fusions).

Methods

Bacterial strains, mutant construction, and growth conditions

Splicing by overlap extension (SOE) PCR and allelic exchange mutagenesis was used to construct ΔBCL, ΔBHL, ΔBCH, and ΔBCHL mutant strains in an L. monocytogenes 10403S background as described previously [13] (Additional file 2: Table S2). All mutations were confirmed by PCR amplification and sequencing of the PCR product. Strains were grown to stationary phase in BHI at 37°C as described previously [33].

Protein isolation, iTRAQ labeling, and Nano-scale reverse phase chromatography and tandem mass spectrometry (nanoLC-MS/MS)

Protein isolation, digestion, and iTRAQ labeling were performed as previously described [33]. Briefly, proteins were isolated from a 25 ml culture of L. monocytogenes stationary phase cells. A noninterfering protein assay kit (Calbiochem) and 1D SDS-PAGE were used to verify protein concentration and quality. A total of 100 μg protein of each sample was denatured, reduced, and the cysteine residues were blocked. Protein samples were then digested with sequence-grade-modified trypsin at 37°C for 16 h, and protein digestion efficiency was assessed by SDS-PAGE. Tryptic peptides from L. monocytogenes parent strain 10403S and ΔBCL, ΔBHL, ΔBCH, and ΔBCHL mutant strains were each labeled with iTRAQ reagents, according to the manufacturer’s protocols.

Four labeled protein samples were combined for a single run and fractionated via Isoelectric focusing OffGel electrophoresis (OGE) using an Agilent 3100 OFFGEL Fractionator (Agilent, G3100AA), and subsequent nanoLC-MS/MS was carried out using a LTQ-Orbitrap Velos (Thermo-Fisher Scientific) mass spectrometer as previously described [33]. Two separate biological replicates of the entire proteomics experiment were run for each strain.

Protein identification and data analysis

All MS and MS/MS raw spectra from iTRAQ experiments were processed using Proteome Discoverer 1.1 for subsequent database search using in-house licensed Mascot Daemon; quantitative processing, protein identification, and data analysis were conducted as previously described [33].

The biological replicates of each experiment were analyzed independently. As described in [33], the Wilcoxon signed rank test was applied to peptide ratios for each identified protein to determine significant changes between strains. The Fisher’s Combined Probability Test was then used to combine FDR adjusted Wilcoxon p-values from each replicate into one test statistic for every protein to obtain a combined p-value (p-value^c). Proteins with peptide ratios exhibiting a Fisher’s Combined Probability Test p-value^c < 0.05 and an iTRAQ protein ratio ≥ 1.5 in both replicates were considered significantly differentially expressed. Statistical analyses were conducted using R statistical software.

A Monte Carlo simulation of Fisher’s exact test was used to determine whether the distribution of role categories among proteins identified as differentially regulated by a given σ factor was different from the role category distribution that would be expected by chance (based on the role category primary annotation for all L. monocytogenes EGD-e genes [26]). Individual Fisher’s exact tests were then used to determine whether individual role categories were over- or under- represented; uncorrected p-values were reported, allowing readers to apply corrections if deemed appropriate. Analyses were performed using all role categories assigned to a given gene in the JCVI-CMR L. monocytogenes EGD-e database. Analyses were only performed for regulons that contained 10 or more proteins (i.e., proteins positively regulated by σ^H; proteins negatively regulated by σ^L; proteins with higher or lower levels in the parent strain).

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

SM performed the experimental work and most of the data analysis that was not carried out at the Cornell Proteomics and Mass Spectrometry Core Facility and drafted the manuscript. RHO contributed to analysis of the data and helped to revise the manuscript. MW and KJB conceived of the study, and participated in its design and coordination and helped to draft or revise the manuscript. All authors read and approved the final manuscript.

Supplementary Material

Additional file 1: Table S1

Proteins found to be differentially produced between L. monocytogenes parent strain 10403S and ΔBCHL.

Additional file 2: Table S2

Strains used in this study.

Acknowledgements

This work was funded by NIH-NIAID R01 AI052151 (K.J.B.). S. M. was partially supported by a New York Sea Grant Scholar Fellowship (RSHH-15). We thank the Proteomics and Mass Spectrometry Core Facility at Cornell University for their services and Dr. James Booth for assistance with statistical analyses.