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Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.

ABSTRACT: Respiratory 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.Here 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.Compared 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.

SUBMITTER: Liu ZP

PROVIDER: S-EPMC4287445 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.

Liu Zhi-Ping ZP Wu Hulin H Zhu Jian J Miao Hongyu H

BMC bioinformatics 20141004

<h4>Background</h4>Respiratory 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 po ...[more]

PMID: 25281301

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Project description:Airway epithelial cells and macrophages differ markedly in their responses to influenza A virus (IAV) infection. To investigate transcriptional responses underlying these differences, purified subsets of type II airway epithelial cells (ATII) and alveolar macrophages (AM) recovered from the lungs of mock- or IAV-infected mice at 9 h postinfection were subjected to RNA sequencing. This time point was chosen to allow for characterization of cell types first infected with the virus inoculum, prior to multicycle virus replication and the infiltration of inflammatory cells into the airways. In the absence of infection, AM predominantly expressed genes related to immunity, whereas ATII expressed genes consistent with their physiological roles in the lung. Following IAV infection, AM almost exclusively activated cell-intrinsic antiviral pathways that were dependent on interferon (IFN) regulatory factor 3/7 (IRF3/7) and/or type I IFN signaling. In contrast, IAV-infected ATII activated a broader range of physiological responses, including cell-intrinsic antiviral pathways, which were both independent of and dependent on IRF3/7 and/or type I IFN. These data suggest that transcriptional profiles hardwired during development are a major determinant underlying the different responses of ATII and AM to IAV infection.IMPORTANCE Airway epithelial cells (AEC) and airway macrophages (AM) represent major targets of influenza A virus (IAV) infection in the lung, yet the two cell types respond very differently to IAV infection. We have used RNA sequencing to define the host transcriptional responses in each cell type under steady-state conditions as well as following IAV infection. To do this, different cell subsets isolated from the lungs of mock- and IAV-infected mice were subjected to RNA sequencing. Under steady-state conditions, AM and AEC express distinct transcriptional activities, consistent with distinct physiological roles in the airways. Not surprisingly, these cells also exhibited major differences in transcriptional responses following IAV infection. These studies shed light on how the different transcriptional architectures of airway cells from two different lineages drive transcriptional responses to IAV infection.

Dataset Information

Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.

Publications

Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.

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