A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

Abstract

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

A systems approach to understanding human rhinovirus and influenza virus infection

“…Omnibus (accession GSE55271, Schuler et al, 2014;accession GSE28904, Naim et al unpublished;accession GSE27973, Proud et al, 2012) or influenza virus (Li et al, 2011;Mitchell et al, 2013;Josset et al, 2014; see also accession GSE48466, Gerlach et al, 2013)], but co-infection with both viruses in a well-controlled in vitro system has not been explored nor has an extensive time-course of host cell transcriptional changes following viral infection of a respiratory cell been investigated. Thus in order to better understand the host respiratory cellular transcriptional response to either rhinovirus, influenza virus and co-infection with both viruses, we performed a detailed time-course analysis of transcriptional changes following infection of the human bronchial epithelial cell line .…”

From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development

“…Not using feature reduction makes the scale of the network inference problem intractable for Bayesian network modeling (in terms of computational time, robustness, and identifiability), and thus necessitates the use of a different class of models or algorithms to identify relationships in the data. We will consider as a representative example the CLR method, briefly explained in Section 2.2, and previously used in multiple systems biology studies of pathogens [21; 22; 23]. We have run CLR with the original data (1000 features × 35 observations), assuming all features as the possible “transcription factors” in the algorithm, meaning that any gene can have a connection to any other gene.…”

“…CLR, or context likelihood of relatedness [20], has been used in multiple systems biology studies of infectious disease [21; 22; 23]. This approach calculates mutual information between variables and identifies as most important those pairs of variables where the mutual information between those variables is statistically significant compared to the background distribution of all mutual information values involving either of the variables in the pair.…”

Comprehensive Proteomics Identification of IFN-λ3-regulated Antiviral Proteins in HBV-transfected Cells

“…Finally, CHID1 was the single most up-regulated protein in our study on IFN-3 treatment. Though not prominent in the viral literature, CHID1 is known to bind LPS (52) and is seen to be down-regulated in several viral studies (53)(54)(55)(56).…”