Abstract
Salmonellosis is the most frequent foodborne disease worldwide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella–human interactions can be transferred to the Salmonella–plant system. Reviewed are recent publications on analysis and prediction of Salmonella–host interactomes. Putative protein–protein interactions (PPIs) between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host–pathogen communication are discussed.
Highlights
Salmonella are Gram-negative bacteria comprising more than 2500 known serovars (Abraham et al, 2012)
Putative protein–protein interactions (PPIs) between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources
WORK Due to the lack of direct experimental data on Salmonella– Arabidopsis and limited data on the Salmonella–human interactions, we here utilized published predictions of these interactomes. This is a limitation as we cannot ascertain the reliability of the predictions without further testing
Summary
Salmonella are Gram-negative bacteria comprising more than 2500 known serovars (Abraham et al, 2012). As no data on known Salmonella–plant PPIs is available, this technique builds a prediction model based on the known Salmonella–human PPIs (Schleker et al, 2012b) and knowledge of plant interactions with other pathogenic bacteria such as E. coli (Kshirsagar et al, 2015). When comparing Salmonella WT and the prgH− mutant, a large portion of Arabidopsis genes upregulated in the prgH− mutant were involved in the ubiquitin-dependent protein degradation process as well as cell wall, defense response and WRKY transcription factor clusters.
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