Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.
Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.
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系统鉴定甲型流感病毒感染期间人呼吸道上皮细胞的转录和转录后调控
DOI：
10.1186/1471-2105-15-336
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发表时间：
2014-10-04
影响因子：
3
通讯作者：
Miao H
中科院分区：
文献类型：
--
作者：
Liu ZP;Wu H;Zhu J;Miao H
研究背景呼吸道上皮细胞是人类流感病毒感染的主要靶标。然而，气道上皮细胞对病毒感染反应的分子机制尚不完全清楚。揭示全基因组转录和转录后调控关系可以进一步增进我们对这个问题的理解，从而推动开发新颖且更有效的计算方法来同时推断转录和转录后调控网络。结果在这里，我们提出了一个名为SITPR的新框架来研究转录因子（TF）、微小RNA（miRNA）和靶基因之间的相互作用。简而言之，全基因组范围内的背景调控网络（约 23,000 个节点和约 370,000 个潜在相互作用）是根据策划的知识和算法预测构建的，其中转录和转录后调控关系的识别是锚定的。为了将相关计算问题的维度减少到可承受的大小，使用了几种拓扑和基于数据的方法。此外，我们提出了约束 LASSO 公式，并将其与动态贝叶斯网络（DBN）模型相结合，以从时程表达数据中识别激活的调节关系。我们对不同规模网络的模拟研究表明，所提出的框架可以有效地确定 TF、miRNA 和目标基因之间的真正调控；此外，我们将 SITPR 与几种选定的最先进算法进行比较，以进一步评估其性能。通过将SITPR框架应用于人肺上皮A549细胞响应A/Mexico/InDRE4487/2009 (H1N1)病毒感染而生成的mRNA和miRNA表达数据，我们能够检测激活的转录和转录后调控关系以及重要的调控基序。结论与其他代表性的最先进算法相比，所提出的SITPR框架可以更有效地识别激活的转录和转录后调控关系。转录后调控同时来自给定的背景网络。 SITPR的思想普遍适用于人类细胞基因调控网络的分析。人类呼吸道上皮细胞获得的结果表明转录、转录后调控及其协同作用在针对 IAV 感染的先天免疫反应中的重要性。
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.
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