Dynamic modularity of host protein interaction networks in Salmonella Typhi infection

Abstract

Background: Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood.

Methods: We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified.

Results: We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever.

Conclusions: Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi.

Figure 1. Salmonella Typhi infection specific dynamic subnetworks.

Co-expression merged with protein interaction networks, specifically altered during acute phase of S. Typhi infection. The round and diamond shaped nodes represent the hubs and their interacting partners, respectively. Out of 81, 6 yellow coloured hubs (IL2RA, SKP2, CIT, DTNA, CUL4A and SOCS7) represent increased Avg. PCC, while reaming 75 violet coloured (∼93%) hubs had decreased Avg. PCC during acute phase of S. Typhi infection.

Figure 2. Network of the interacting partners of CCR1 representing the differences in dynamic network properties during S. Typhi infection.

The edges were labelled with respective PCC values of individual interactors during different perturbances. The four conditions mentioned are A. Control (normal host cell), B. S. Typhi infection, C. other bacteremic (non-typhoid Salmonella, Klebsiella spp and Acinetobacter spp) infections and D. Leukemia. Hub CCR1 and four interactors (e.g, CCL15, CCL26, CCL8 and TPST1) showed unique co-expression patterns (lower PCC values corresponding to correlated expression between protein pairs) specific to S. Typhi infection.

Figure 3. Network of the interacting partners of IRS2 representing differences in dynamic network properties in S. Typhi infection.

The edges were labelled with respective PCC values of individual interactors during different perturbances. The four mentioned conditions are A. Control (normal host cell), B. S. Typhi infection, C. other bacteremic (non-typhoid Salmonella, Klebsiella spp and Acinetobacter spp) infections, D. Leukaemia. Hub IRS2 and five interactors (e.g, IL4R, JAK3, PIK3CD, SHC1 and TYK2) showed unique co-expression pattern (lower PCC values corresponding to correlated expression between protein pairs) specific to S. Typhi infection.