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Viral perturbations of host networks reflect disease etiology.

ABSTRACT: Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

SUBMITTER: Gulbahce N

PROVIDER: S-EPMC3386155 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Viral perturbations of host networks reflect disease etiology.

Gulbahce Natali N Yan Han H Dricot Amélie A Padi Megha M Byrdsong Danielle D Franchi Rachel R Lee Deok-Sun DS Rozenblatt-Rosen Orit O Mar Jessica C JC Calderwood Michael A MA Baldwin Amy A Zhao Bo B Santhanam Balaji B Braun Pascal P Simonis Nicolas N Huh Kyung-Won KW Hellner Karin K Grace Miranda M Chen Alyce A Rubio Renee R Marto Jarrod A JA Christakis Nicholas A NA Kieff Elliott E Roth Frederick P FP Roecklein-Canfield Jennifer J Decaprio James A JA Cusick Michael E ME Quackenbush John J Hill David E DE Münger Karl K Vidal Marc M Barabási Albert-László AL

PLoS computational biology 20120628 6

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral protei ...[more]

PMID: 22761553

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Project description:The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.

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Viral perturbations of host networks reflect disease etiology.

Publications

Viral perturbations of host networks reflect disease etiology.

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