Abstract
BackgroundRespiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.ResultsHere we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs.ConclusionCompared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.
Highlights
Respiratory epithelial cells are the primary target of influenza virus infection in human
We propose a new framework called systematic identification of transcriptional and post-transcriptional regulations (SITPR), which stands for Systematic Identification of Transcriptional and Post-transcriptional Regulations, to identify the regulatory relationships by exploiting both curated knowledge and time-course expression data of mRNAs and miRNAs
Background regulatory network obtained for human Instead of inferring regulatory network only from expression profiling data, we first build a background regulatory network for human, and identify the activated regulatory relationships during influenza A virus (IAV) infection using the mRNA and miRNA time-course expression datasets
Summary
Respiratory epithelial cells are the primary target of influenza virus infection in human. IAV RNA segments that encode the hemagglutinin (HA) and neuraminidase (NA) proteins can undergo mutation (antigenic drift) or reassortment (antigenic shift), resulting in new viral strains that humans may lack the heterologous immunity against (e.g., the pandemic H1N1 2009 [2]). In such circumstances, the cell-mediated and humoral immune responses are primary, and a better understanding of the molecular mechanisms of immune responses to IAV infection becomes necessary to the development of more effective prevention and treatment strategies. ‘M5’, ‘M8’ and ‘M10’ are not statistically significant in the background regulatory network, while they become significant in the activated regulatory network
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.