Role of SPI-1 secreted effectors in acute bovine response to Salmonella enterica Serovar Typhimurium: a systems biology analysis approach

Abstract

Salmonella enterica Serovar Typhimurium (S. Typhimurium) causes enterocolitis with diarrhea and polymorphonuclear cell (PMN) influx into the intestinal mucosa in humans and calves. The Salmonella Type III Secretion System (T3SS) encoded at Pathogenicity Island I translocates Salmonella effector proteins SipA, SopA, SopB, SopD, and SopE2 into epithelial cells and is required for induction of diarrhea. These effector proteins act together to induce intestinal fluid secretion and transcription of C-X-C chemokines, recruiting PMNs to the infection site. While individual molecular interactions of the effectors with cultured host cells have been characterized, their combined role in intestinal fluid secretion and inflammation is less understood. We hypothesized that comparison of the bovine intestinal mucosal response to wild type Salmonella and a SipA, SopABDE2 effector mutant relative to uninfected bovine ileum would reveal heretofore unidentified diarrhea-associated host cellular pathways. To determine the coordinated effects of these virulence factors, a bovine ligated ileal loop model was used to measure responses to wild type S. Typhimurium (WT) and a ΔsipA, sopABDE2 mutant (MUT) across 12 hours of infection using a bovine microarray. Data were analyzed using standard microarray analysis and a dynamic bayesian network modeling approach (DBN). Both analytical methods confirmed increased expression of immune response genes to Salmonella infection and novel gene expression. Gene expression changes mapped to 219 molecular interaction pathways and 1620 gene ontology groups. Bayesian network modeling identified effects of infection on several interrelated signaling pathways including MAPK, Phosphatidylinositol, mTOR, Calcium, Toll-like Receptor, CCR3, Wnt, TGF-β, and Regulation of Actin Cytoskeleton and Apoptosis that were used to model of host-pathogen interactions. Comparison of WT and MUT demonstrated significantly different patterns of host response at early time points of infection (15 minutes, 30 minutes and one hour) within phosphatidylinositol, CCR3, Wnt, and TGF-β signaling pathways and the regulation of actin cytoskeleton pathway.

Figure 1. Invasion of bovine intestinal mucosa by S. enterica serovar Typhimurium.

Data are plotted as the geometric means from four independent assays. The bars represent the standard errors. The percent invasion normalized to the inoculum for wild type S. Typhimurium is represented by the black bars and the ΔsipA, sopABDE2 mutant is represented by the grey bars. Asterisks indicate a significant difference in invasion compared with the wild type positive control (p≤0.05).

Figure 2. Comparison of gene expression analysis by microarray with quantitative real time PCR.

Microarray (black bars) and qRT-PCR (gray bars) fold-changes for both an up-regulated gene, CCL2 (A) and a down-regulated gene, ApoC3 (B) are represented. The means of 4 independent assays using RNA from ligated ileal loops inoculated with wild type Salmonella Typhimurium (IR715), both microarrays and qRT-PCR, performed in duplicate are shown.

Figure 3. Gene expression changes in wild type S. enterica serovar Typhimurium and ΔsipA, sopABDE2 mutant inoculated ileal loops measured by quantitative real time PCR.

Real time PCR data were calculated using the Δ (Δ) CT method with wild type and mutant compared to negative control loops. Glyceraldehyde 3 phosphate dehydrogenase (GAPDH) was used as the housekeeping gene for normalization. Black bars represent wild type S. Typhimurium. Gray bars represent the ΔsipA, sopABDE2 mutant. Expression was measured for CCL2 (A), PLAU (B), TLR4 (C), CCL8 (D), F3 (E), v-FOS (F), Apoliporotein C3 (G), and IL-6 (H). Means +/- Standard Deviation (error bars) of 4 independent assays performed in duplicate are shown.

Figure 4. Wild type S. enterica serovar Typhimurium Toll-like Receptor Model with Mechanistic Genes.

This is an example of the DBN model showing the mechanistic genes indicated by concentric rings around the node. The network model reveals the parent and child relationships and the state of gene expression for any selected time point. Increasing up regulation is indicated by the gradient colors from light yellow to dark red and down regulation is from light green to dark green. The red rings indicate mechanistic genes unique to the WT condition, while the blue rings indicate those in common with the MUT condition.

Figure 5. Pathway scores in bovine intestinal loops inoculated with wild type S. enterica serovar Typhimurium and the ΔsipA, sopABDE2 mutant.

Heat map comparison of pathway scores for each host condition by sampling time point post infection was generated. Pathways were limited to the categories of cell mobility, cell communication, cell growth and death, infectious diseases, immune system, membrane transport, and signal transduction were compared. The score magnitudes are shown as a gradient color from light to bright red for induced and from light to bright green for suppressed pathway activity. Comparisons between wild type inoculated loops and control loops are labeled WT; between the ΔsipA, sopABDE2 mutant inoculated loops and control loops inoculated with LB broth are labeled MUT; and between wild type and the ΔsipA, sopABDE2 mutant inoculated loops are labeled WT vs. MUT.

Figure 6. Temporal differences between pathways scores for wild type S. enterica serovar Typhimurium and the ΔsipA, sopABDE2 mutant.

Pathway activation in wild type and mutant inoculated loops was compared and evaluated for temporal changes. Pathways activated in early time points (15 minutes, 30 minutes, and 60 minutes) were summarized (A) and pathways activated in late time points (120, 240, and 480 minutes) were summarized (B). Pathways were limited to the categories of cell mobility, cell communication, cell growth and death, infectious diseases, immune system, membrane transport, and signal transduction. The score magnitudes are shown as a gradient color from light to bright red for induced and from light to bright green for suppressed pathway activity.

Figure 7. Temporal differences within a subset of pathways that differentiate infection with wild type S. enterica serovar Typhimurium and a ΔsipA, sopABDE2 mutant.

Temporal changes in pathway activation in wild type and mutant inoculated loops were evaluated in a subset of pathways. Pathways were limited to the categories of cell mobility, cell communication, cell growth and death, infectious diseases, immune system, membrane transport, and signal transduction. The score magnitudes are shown as a gradient color from light to bright red for induced and from light to bright green for suppressed pathway activity. Comparisons between wild type inoculated loops and control loops are labeled WT; between the ΔsipA, sopABDE2 mutant inoculated loops and control loops inoculated with LB broth are labeled MUT; and between wild type and the ΔsipA, sopABDE2 mutant inoculated loops are labeled W v M.

Figure 8. Dynamic Bayesian Network Disease Model of wild type S. enterica serovar Typhimurium.

A host disease model was constructed by combining the network structures of the top ten scoring pathways for WT S. Typhimurium. The key mechanistic genes are labeled with their common gene names. The blue rings indicated mechanistic genes (|Bayesian z-score| >2.24) as derived from the scoring described for the Dynamic Bayesian Gene Group Activation technique (File S1).