Coordinated regulation of virulence during systemic infection of Salmonella enterica serovar Typhimurium

Abstract

To cause a systemic infection, Salmonella must respond to many environmental cues during mouse infection and express specific subsets of genes in a temporal and spatial manner, but the regulatory pathways are poorly established. To unravel how micro-environmental signals are processed and integrated into coordinated action, we constructed in-frame non-polar deletions of 83 regulators inferred to play a role in Salmonella enteriditis Typhimurium (STM) virulence and tested them in three virulence assays (intraperitoneal [i.p.], and intragastric [i.g.] infection in BALB/c mice, and persistence in 129X1/SvJ mice). Overall, 35 regulators were identified whose absence attenuated virulence in at least one assay, and of those, 14 regulators were required for systemic mouse infection, the most stringent virulence assay. As a first step towards understanding the interplay between a pathogen and its host from a systems biology standpoint, we focused on these 14 genes. Transcriptional profiles were obtained for deletions of each of these 14 regulators grown under four different environmental conditions. These results, as well as publicly available transcriptional profiles, were analyzed using both network inference and cluster analysis algorithms. The analysis predicts a regulatory network in which all 14 regulators control the same set of genes necessary for Salmonella to cause systemic infection. We tested the regulatory model by expressing a subset of the regulators in trans and monitoring transcription of 7 known virulence factors located within Salmonella pathogenicity island 2 (SPI-2). These experiments validated the regulatory model and showed that the response regulator SsrB and the MarR type regulator, SlyA, are the terminal regulators in a cascade that integrates multiple signals. Furthermore, experiments to demonstrate epistatic relationships showed that SsrB can replace SlyA and, in some cases, SlyA can replace SsrB for expression of SPI-2 encoded virulence factors.

Figure 1. Attenuation of S. typhimurium strains encoding deletions of the indicated regulatory gene.

A. Five BALB/c mice were i.p. infected with ∼200 cfu (approximately 100× LD₅₀) of the Salmonella strain indicated and observed for 21 days. The percentages of surviving mice are shown for each strain. Salmonella strains lacking smpB, hnr, csrA, fruR, or crp caused death of some mice during the observation period and the other strains indicated with an asterisk resulted in no deaths. B. Each Salmonella strain was administered i.p. to 3 groups of 3 BALB/c mice at 1×10², 1×10⁴, and 1×10⁶ cfu respectively and monitored for 1 month to estimate the approximate LD₅₀ values.

Figure 2. Survival of Salmonella strains containing mutations in each of 14 virulence regulators in primary macrophages.

Primary bone marrow-derived macrophages were prepared as described in Materials and Methods and infected at an input MOI of 100 by low speed centrifugation onto cells in identically seeded wells (on average our conditions result in 1–2 intracellular bacteria per cell). The bacteria were opsonized in 10% normal mouse serum for 20 min prior to infection. Intracellular survival was measured at 30 min (grey), 2 h (white) and 18 h (black) post infection from at least three independent assays. Times refer to the time after centrifugation for macrophage infection. The results suggest that there is not a one to one correlation between systemic mouse infection and survival/replication in primary macrophages.

Figure 3. Transcriptional profiles of SPI-2 genes encoding components of the type III secretion apparatus.

In each case we have computed the ratio of the microarray results for a given gene in a specific mutant background to the results of the parental strain grown under identical conditions. The results were determined under four different growth conditions (from left to right rich medium (LB) logarithmic phase, LB stationary phase, acidic minimal medium 1 (AMM1) and AMM2). Rows correspond to individual genes located within SPI-2 while columns represent regulator mutants. A crp deletion was excluded from transcriptional profiling for AMM1 because it does not grow under this condition. Blue represents a decrease in the mutant and yellow an increase using the computer program Genesis for display (log₂ values; [94]). Thus blue indicates genes that are positively regulated (i.e. the level of expression decreases in the corresponding mutation compared to the parent). The results show that the 14 virulence regulators under study activate expression of most genes located in SPI-2 but the effect is only observed when bacteria are grown in minimal acidic media.

Figure 4. Validation of microarray results by qRT-PCR for 7 genes located within SPI-2.

The bottom part of the figure shows a map of the type III secretion system located within SPI-2 with the genes that were quantified colored the same as the corresponding bars above. The top part of the figure shows expression of 7 SPI-2 genes in each regulator mutant in comparison to the parental strain when grown in AMM1. The results are plotted on a logarithmic scale. Values were normalized using gyrB mRNA level and represent the average of RNA prepared from three independent biological samples. The results show that mutations in 6 regulators (himD, phoP/phoQ, ssrA/ssrB, slyA, csrA, and ompR/envZ) strongly decrease expression of SPI-2, in 5 strains there is a significant decrease (hnr, rpoE, smpB, crp, hfq) while in the remaining 3 no decrease or even an increase is observed for fruR (the others showing no change are spvR and rpoS).

Figure 5. Co-clustering of genes showing similar patterns of regulation.

The expression profiles for all genes were input to SEBINI and analyzed using the CLR algorithm . The results shown in the figure use a force-directed network layout algorithm where genes (shown as small colored circles) are generally closer together when their statistical association, and thus degree of predicted co-regulation, is stronger (the cutoff for the genes shown is 5 standard deviations from the mean or greater; p<.0001). The results were visualized as a network of similarity relationships using Cytoscape . Insert cluster shows the same analysis but on a different data set derived from the NIH sponsored gene expression omnibus GEO (cutoff score 5). Co-clustered genes are described in Table 2.

Figure 6. A model for regulatory interactions.

A. Transcription of all 14 regulatory genes in each regulator mutant background was determined by qRT-PCR using RNA isolated from bacteria grown in AMM1. Horizontal axis represents the genes under study; vertical represents an in-frame deletion of the regulator indicated. Values shown were normalized to gyrB mRNA level. Color intensity represents an average expression ratio between mutant and parent in a log₂ scale from three independent RNA samples; blue and yellow blocks indicate activation and repression by corresponding regulators respectively. Results are displayed in Genesis (log₂) . B. Cytoscape image of the hierarchical order of regulation under SPI-2 inducing conditions was constructed based on qRT-PCR data as described in Lee et al. . Nodes indicate regulators or SPI-2 and red and blue arrows indicate activation and repression described as “edges”. Six SPI-2 genes (ssaE, sseA, sscA, ssaG, ssaH, and ssaN) were used in the matrix construction and depicted as one node in the network for the simplicity. The arrows or edges do not distinguish between direct and indirect effects except as determined experimentally. A similar regulatory hierarchy was predicted using the CLR algorithm data and matrix analysis data based on all of the expression data and is included as Figure S3.

Figure 7. Determination of epistatic relationships among the regulators.

A. To determine if expressing ssrB in trans can compensate for mutations in the 14 regulators we monitored transcription of 7 SPI-2 genes by qRT-PCR (triplicate biological samples, normalized results shown in each case compared to the empty vector control). Each strain containing a specific regulator deletion was transformed with pBAD30SsrB or pBAD30 and grown under AMM1 condition in the presence of 0.02% L-arabinose for 4 hours. The results are presented as expression ratio comparing the strain that over-expresses ssrB to the empty vector control displayed in a logarithmic scale. The values shown are averages from three separate RNA samples and grouped into three parts based on the magnitude of the effect. The results show that ssrB transcription is sufficient to up regulate transcription of these 7 SPI-2 genes in each mutant background. The magnitude of the effect varied from several hundred-fold to 2-fold for crp. B. ssrB was expressed in strains missing the response regulator (ssrB), the signal sensor (ssrA) or both and ß-galactosidase expression was monitored using P_ssaG::lacZ (pFssaGTC; see Materials and Methods for details). The results show that ssrA is not necessary for ssrB to induce transcription of downstream genes (in this case ssaG) at least when ssrB is over-expressed. C. Complementation with pBAD30SlyA was performed in the same way as in Fig. 7 A in the 15 strains being investigated. The results show that transcription of slyA can suppress mutations in the other regulators although the results are strongest for himD, phoP/phoQ, ssrA/ssrB, and ompR/envZ. For all other regulators the effect of over-expression of slyA varied between the five SPI-2 encoded promoters and the genes that they express. D. slyA expression increases transcription of some SPI-2 genes even when ssrB is deleted. SsrB-independent SlyA activation on SPI-2 was further tested by slyA expression in double deletions of himD/ssrAB, phoPQ/ssrAB, slyA/ssrAB, csrA/ssrAB, and ompRenvZ/ssrAB. Expression fold compared to the parent strain harboring the empty vector is displayed. The results show a dichotomy for SlyA-mediated transcription of different operons located within SPI-2; a pronounced effect is observed for operons that encode ssaB-E and sseA-F but not for the two that encode the major structural components ssaG-U.