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Technologies and approaches to elucidate and model the virulence program of salmonella.

TitleTechnologies and approaches to elucidate and model the virulence program of salmonella.
Publication TypeJournal Article
Year of Publication2011
AuthorsMcDermott JE, Yoon H, Nakayasu ES, Metz TO, Hyduke DR, Kidwai AS, Palsson BO, Adkins JN, Heffron F
JournalFront Microbiol
Abstract

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

DOI10.3389/fmicb.2011.00121
Alternate JournalFront Microbiol
PubMed ID21687430
PubMed Central IDPMC3108385
Grant ListR01 AI022933-25 / AI / NIAID NIH HHS / United States
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