Hi-Jack: a novel computational framework for pathway-based inference of host–pathogen interactions
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
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AbstractMotivation: Pathogens infect their host and hijack the host machinery to produce more progeny pathogens. Obligate intracellular pathogens, in particular, require resources of the host to replicate. Therefore, infections by these pathogens lead to alterations in the metabolism of the host, shifting in favor of pathogen protein production. Some computational identification of mechanisms of host-pathogen interactions have been proposed, but it seems the problem has yet to be approached from the metabolite-hijacking angle. Results: We propose a novel computational framework, Hi-Jack, for inferring pathway-based interactions between a host and a pathogen that relies on the idea of metabolite hijacking. Hi-Jack searches metabolic network data from hosts and pathogens, and identifies candidate reactions where hijacking occurs. A novel scoring function ranks candidate hijacked reactions and identifies pathways in the host that interact with pathways in the pathogen, as well as the associated frequent hijacked metabolites. We also describe host-pathogen interaction principles that can be used in the future for subsequent studies. Our case study on Mycobacterium tuberculosis (Mtb) revealed pathways in human-e.g. carbohydrate metabolism, lipids metabolism and pathways related to amino acids metabolism-that are likely to be hijacked by the pathogen. In addition, we report interesting potential pathway interconnections between human and Mtb such as linkage of human fatty acid biosynthesis with Mtb biosynthesis of unsaturated fatty acids, or linkage of human pentose phosphate pathway with lipopolysaccharide biosynthesis in Mtb. © The Author 2015. Published by Oxford University Press. All rights reserved.
CitationKleftogiannis D, Wong L, Archer JAC, Kalnis P (2015) Hi-Jack: a novel computational framework for pathway-based inference of host–pathogen interactions. Bioinformatics 31: 2332–2339. Available: http://dx.doi.org/10.1093/bioinformatics/btv138.
PublisherOxford University Press (OUP)
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