Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

Abstract

BackgroundThe immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.ResultsWe applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.ConclusionsOur results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

Global Transcriptome Analysis in Influenza-Infected Mouse Lungs Reveals the Kinetics of Innate and Adaptive Host Immune Responses

“…We already performed a first analysis of the transcriptome changes at early time points after infection [43]. Further studies are ongoing including later time points and cell-specific gene signatures.…”

Inference of dynamic networks using time-course data

“…For continuous transitions of network structures, this method penalizes gene expression data from distant time points more than those from close time points using kernel function. Dimitrakopoulou et al [44] used TV-DBN to reconstruct dynamic GINs from time-course data measured at every day after the infection of mouse with influenza A virus up to 5 days. They first identified 3500 DEGs and clustered them into 35 groups using k-means clustering.…”

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

“…As described in Pommerenke et al [23] -called hereafter reference study, C57BL/6J mice were infected with a mouse-adapted influenza A virus (PR8). Three replicates, from three individually infected mice, were taken for each time point after infection (1,2,3,5,8,10,14,18,22,26,30,40, 60 days) and nine replicates from three mockinfected mice (day 0). The complete dataset is accessible through ArrayExpress database under the accession number E-MTAB-764.…”

“…With the advent of time series microarray experiments, many standard clustering algorithms were recruited for analysis such as hierarchical clustering, k-means [1] and self-organizing maps [2], which however treat measurements taken at different time points as independent, ignoring the sequential nature of time series data. Lately, a significant number of methods were designed specifically for the time series analysis [3][4][5][6].…”