Hi-Jack: a novel computational framework for pathway-based inference of host–pathogen interactions
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/594174
MetadataShow full item record
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)
- Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.
- Authors: Mei S, Flemington EK, Zhang K
- Issue date: 2018 Jun 28
- Genetic-and-Epigenetic Interspecies Networks for Cross-Talk Mechanisms in Human Macrophages and Dendritic Cells during MTB Infection.
- Authors: Li CW, Lee YL, Chen BS
- Issue date: 2016
- Predicting and analyzing interactions between Mycobacterium tuberculosis and its human host.
- Authors: Rapanoel HA, Mazandu GK, Mulder NJ
- Issue date: 2013
- Cholesterol catabolism as a therapeutic target in Mycobacterium tuberculosis.
- Authors: Ouellet H, Johnston JB, de Montellano PR
- Issue date: 2011 Nov
- Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs.
- Authors: Huo T, Liu W, Guo Y, Yang C, Lin J, Rao Z
- Issue date: 2015 Mar 26