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Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions.

Schleker S, Kshirsagar M, Klein-Seetharaman J - Front Microbiol (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Klein-Seetharaman Laboratory, Division of Metabolic and Vascular Health, Warwick Medical School, University of Warwick , Coventry, UK ; Department of Molecular Phytomedicine, Institute of Crop Science and Resource Conservation, University of Bonn , Bonn, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

Overview of metabolic processes putatively targeted and known to be repressed by S. Typhimurium effectors. MapMan (Thimm et al., 2004) analysis providing a metabolism overview of (A) predicted Arabidopsis targets of S. Typhimurium effectors predicted with cut-offs of 1 (voting score) and 0.98 (probability aggregated score) by the KMM–SVM model (Kshirsagar et al., 2015) and (B)Arabidopsis genes experimentally identified to be upregulated upon infection with S. Typhimurium prgH– vs. WT (Schikora et al., 2011). Each small square displays a predicted Arabidopsis target of S. Typhimurium (A) or an upregulated Arabidopsis gene (B). In (B), the color intensity visualizes the degree of upregulation.
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Figure 1: Overview of metabolic processes putatively targeted and known to be repressed by S. Typhimurium effectors. MapMan (Thimm et al., 2004) analysis providing a metabolism overview of (A) predicted Arabidopsis targets of S. Typhimurium effectors predicted with cut-offs of 1 (voting score) and 0.98 (probability aggregated score) by the KMM–SVM model (Kshirsagar et al., 2015) and (B)Arabidopsis genes experimentally identified to be upregulated upon infection with S. Typhimurium prgH– vs. WT (Schikora et al., 2011). Each small square displays a predicted Arabidopsis target of S. Typhimurium (A) or an upregulated Arabidopsis gene (B). In (B), the color intensity visualizes the degree of upregulation.

Mentions: The KMM–SVM predictions indicate that S. Typhimurium effectors heavily target Arabidopsis metabolic and biosynthetic processes (Kshirsagar et al., 2015). For visualization, we utilized MapMan (Thimm et al., 2004), a software tool that assigns Arabidopsis proteins to specific plant processes and pathways, to see what metabolic pathways are putatively interfered with by S. Typhimurium effectors. A comparison with available transcriptomic data revealed a clear overlap in the pathways predicted to be targeted by S. Typhimurium effectors (Figure 1A) and those identified to involve genes that are upregulated upon S. Typhimurium prgH– vs. WT infection (Figure 1B; Schikora et al., 2011). In Figure 1A, every small blue square displays one Arabidopsis protein predicted by KMM–SVM to be targeted by one or more S. Typhimurium effectors. In Figure 1B, Arabidopsis genes known to be upregulated during S. Typhimurium prgH– vs. WT infection are visualized by white to blue small squares depending on the degree of upregulation. Thus, the darker blue the square is, the more efficiently this gene is suppressed by S. Typhimurium TTSS-1 effectors. In conclusion, the metabolic processes S. Typhimurium most intensively interferes with include those related to the cell wall, lipids, light reactions, tetrapyrrole, and secondary metabolism of, e.g., terpenes (Figure 1B).


Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions.

Schleker S, Kshirsagar M, Klein-Seetharaman J - Front Microbiol (2015)

Overview of metabolic processes putatively targeted and known to be repressed by S. Typhimurium effectors. MapMan (Thimm et al., 2004) analysis providing a metabolism overview of (A) predicted Arabidopsis targets of S. Typhimurium effectors predicted with cut-offs of 1 (voting score) and 0.98 (probability aggregated score) by the KMM–SVM model (Kshirsagar et al., 2015) and (B)Arabidopsis genes experimentally identified to be upregulated upon infection with S. Typhimurium prgH– vs. WT (Schikora et al., 2011). Each small square displays a predicted Arabidopsis target of S. Typhimurium (A) or an upregulated Arabidopsis gene (B). In (B), the color intensity visualizes the degree of upregulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4309195&req=5

Figure 1: Overview of metabolic processes putatively targeted and known to be repressed by S. Typhimurium effectors. MapMan (Thimm et al., 2004) analysis providing a metabolism overview of (A) predicted Arabidopsis targets of S. Typhimurium effectors predicted with cut-offs of 1 (voting score) and 0.98 (probability aggregated score) by the KMM–SVM model (Kshirsagar et al., 2015) and (B)Arabidopsis genes experimentally identified to be upregulated upon infection with S. Typhimurium prgH– vs. WT (Schikora et al., 2011). Each small square displays a predicted Arabidopsis target of S. Typhimurium (A) or an upregulated Arabidopsis gene (B). In (B), the color intensity visualizes the degree of upregulation.
Mentions: The KMM–SVM predictions indicate that S. Typhimurium effectors heavily target Arabidopsis metabolic and biosynthetic processes (Kshirsagar et al., 2015). For visualization, we utilized MapMan (Thimm et al., 2004), a software tool that assigns Arabidopsis proteins to specific plant processes and pathways, to see what metabolic pathways are putatively interfered with by S. Typhimurium effectors. A comparison with available transcriptomic data revealed a clear overlap in the pathways predicted to be targeted by S. Typhimurium effectors (Figure 1A) and those identified to involve genes that are upregulated upon S. Typhimurium prgH– vs. WT infection (Figure 1B; Schikora et al., 2011). In Figure 1A, every small blue square displays one Arabidopsis protein predicted by KMM–SVM to be targeted by one or more S. Typhimurium effectors. In Figure 1B, Arabidopsis genes known to be upregulated during S. Typhimurium prgH– vs. WT infection are visualized by white to blue small squares depending on the degree of upregulation. Thus, the darker blue the square is, the more efficiently this gene is suppressed by S. Typhimurium TTSS-1 effectors. In conclusion, the metabolic processes S. Typhimurium most intensively interferes with include those related to the cell wall, lipids, light reactions, tetrapyrrole, and secondary metabolism of, e.g., terpenes (Figure 1B).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Klein-Seetharaman Laboratory, Division of Metabolic and Vascular Health, Warwick Medical School, University of Warwick , Coventry, UK ; Department of Molecular Phytomedicine, Institute of Crop Science and Resource Conservation, University of Bonn , Bonn, Germany.

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.

No MeSH data available.


Related in: MedlinePlus