ARHGEF26 enhances Salmonella invasion and inflammation in cells and mice

Abstract

Salmonella hijack host machinery in order to invade cells and establish infection. While considerable work has described the role of host proteins in invasion, much less is known regarding how natural variation in these invasion-Associated host proteins affects Salmonella pathogenesis. Here we leveraged a candidate cellular GWAS screen to identify natural genetic variation in the ARHGEF26 (Rho Guanine Nucleotide Exchange Factor 26) gene that renders lymphoblastoid cells susceptible to Salmonella Typhi and Typhimurium invasion. Experimental follow-up redefined ARHGEF26's role in Salmonella epithelial cell infection. Specifically, we identified complex serovar-by-host interactions whereby ARHGEF26 stimulation of S. Typhi and S. Typhimurium invasion into host cells varied in magnitude and effector-dependence based on host cell type. While ARHGEF26 regulated SopB-and SopE-mediated S. Typhi (but not S. Typhimurium) infection of HeLa cells, the largest effect of ARHGEF26 was observed with S. Typhimurium in polarized MDCK cells through a SopBand SopE2-independent mechanism. In both cell types, knockdown of the ARHGEF26-associated protein DLG1 resulted in a similar phenotype and serovar specificity. Importantly, we show that ARHGEF26 plays a critical role in S. Typhimurium pathogenesis by contributing to bacterial burden in the enteric fever murine model, as well as inflammation in the colitis infection model. In the enteric fever model, SopB and SopE2 are required for the effects of Arhgef26 deletion on bacterial burden, and the impact of sopB and sopE2 deletion in turn required ARHGEF26. In contrast, SopB and SopE2 were not required for the impacts of Arhgef26 deletion on colitis. A role for ARHGEF26 on inflammation was also seen in cells, as knockdown reduced IL-8 production in HeLa cells. Together, these data reveal pleiotropic roles for ARHGEF26 during infection and highlight that many of the interactions that occur during infection that are thought to be well understood likely have underappreciated complexity.