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Browsing by Author "Teng, Mingxiang"
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Item miR2Disease: a manually curated database for microRNA deregulation in human disease(Oxford Academic, 2008-10-15) Jiang, Qinghua; Wang, Yadong; Hao, Yangyang; Juan, Liran; Teng, Mingxiang; Zhang, Xinjun; Li, Meimei; Wang, Guohua; Liu, Yunlong; Medicine, School of Medicine‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.Item A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer.(BioMed Central, 2011-05-09) Shen, Changyu; Huang, Yiwen; Liu, Yunlong; Wang, Guohua; Zhao, Yuming; Wang, Zhiping; Teng, Mingxiang; Wang, Yadong; Flockhart, David A.; Skaar, Todd C.; Yan, Pearlly; Nephew, Kenneth P.; Huang, Tim Hm; Li, LangBACKGROUND: Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. RESULTS: We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ERα target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ERα regulatory network was unresponsive to 17β-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations. CONCLUSIONS: We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.Item Signal Transducers and Activators of Transcription-1 (STAT1) Regulates microRNA Transcription in Interferon γ-Stimulated HeLa Cells(Public Library of Science, 2010-07-26) Wang, Guohua; Wang, Yadong; Teng, Mingxiang; Zhang, Denan; Li, Lang; Liu, Yunlong; Medical and Molecular Genetics, School of MedicineBackground Constructing and modeling the gene regulatory network is one of the central themes of systems biology. With the growing understanding of the mechanism of microRNA biogenesis and its biological function, establishing a microRNA-mediated gene regulatory network is not only desirable but also achievable. Methodology In this study, we propose a bioinformatics strategy to construct the microRNA-mediated regulatory network using genome-wide binding patterns of transcription factor(s) and RNA polymerase II (RPol II), derived using chromatin immunoprecipitation following next generation sequencing (ChIP-seq) technology. Our strategy includes three key steps, identification of transcription start sites and promoter regions of primary microRNA transcripts using RPol II binding patterns, selection of cooperating transcription factors that collaboratively function with the transcription factors targeted by ChIP-seq assay, and construction of the network that contains regulatory cascades of both transcription factors and microRNAs. Principal Findings Using CAMDA (Critical Assessment of Massive Data Analysis) 2009 data set that includes ChIP-seq data on RPol II and STAT1 (signal transducers and activators of transcription 1) in HeLa S3 cells in control condition and with interferon γ stimulation, we first identified promoter regions of 83 microRNAs in HeLa cells. We then identified two potential STAT1 collaborating factors, AP-1 and C/EBP (CCAAT enhancer-binding proteins), and further established eight feedback network elements that may regulate cellular response during interferon γ stimulation. Conclusions This study offers a bioinformatics strategy to provide testable hypotheses on the mechanisms of microRNA-mediated transcriptional regulation, based upon genome-wide protein-DNA interaction data derived from ChIP-seq experiments.