Browsing by Author "Wang, Guohua"
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Item AKT Alters Genome-Wide Estrogen Receptor α Binding and Impacts Estrogen Signaling in Breast Cancer(American Society for Microbiology, 2008-12) Bhat-Nakshatri, Poornima; Wang, Guohua; Appaiah, Hitesh; Luktuke, Nikhil; Carroll, Jason S.; Geistlinger, Tim R.; Brown, Myles; Badve, Sunil; Liu, Yunlong; Nakshatri, HarikrishnaEstrogen regulates several biological processes through estrogen receptor α (ERα) and ERβ. ERα-estrogen signaling is additionally controlled by extracellular signal activated kinases such as AKT. In this study, we analyzed the effect of AKT on genome-wide ERα binding in MCF-7 breast cancer cells. Parental and AKT-overexpressing cells displayed 4,349 and 4,359 ERα binding sites, respectively, with ∼60% overlap. In both cell types, ∼40% of estrogen-regulated genes associate with ERα binding sites; a similar percentage of estrogen-regulated genes are differentially expressed in two cell types. Based on pathway analysis, these differentially estrogen-regulated genes are linked to transforming growth factor β (TGF-β), NF-κB, and E2F pathways. Consistent with this, the two cell types responded differently to TGF-β treatment: parental cells, but not AKT-overexpressing cells, required estrogen to overcome growth inhibition. Combining the ERα DNA-binding pattern with gene expression data from primary tumors revealed specific effects of AKT on ERα binding and estrogen-regulated expression of genes that define prognostic subgroups and tamoxifen sensitivity of ERα-positive breast cancer. These results suggest a unique role of AKT in modulating estrogen signaling in ERα-positive breast cancers and highlights how extracellular signal activated kinases can change the landscape of transcription factor binding to the genome.Item Bioinformatics Methods and Biological Interpretation for Next-Generation Sequencing Data(Hindawi, 2015-09-07) Wang, Guohua; Liu, Yunlong; Zhu, Dongxiao; Klau, Gunnar W.; Feng, Weixing; Department of Medical & Molecular Genetics, IU School of MedicineItem Estradiol-regulated microRNAs control estradiol response in breast cancer cells(Oxford University Press, 2009-08) Bhat-Nakshatri, Poornima; Wang, Guohua; Collins, Nikail R.; Thomson, Michael J.; Geistlinger, Tim R.; Carroll, Jason S.; Brown, Myles; Hammond, Scott; Srour, Edward F.; Liu, Yunlong; Nakshatri, HarikrishnaEstradiol (E2) regulates gene expression at the transcriptional level by functioning as a ligand for estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ). E2-inducible proteins c-Myc and E2Fs are required for optimal ERα activity and secondary estrogen responses, respectively. We show that E2 induces 21 microRNAs and represses seven microRNAs in MCF-7 breast cancer cells; these microRNAs have the potential to control 420 E2-regulated and 757 non-E2-regulated mRNAs at the post-transcriptional level. The serine/threonine kinase, AKT, alters E2-regulated expression of microRNAs. E2 induced the expression of eight Let-7 family members, miR-98 and miR-21 microRNAs; these microRNAs reduced the levels of c-Myc and E2F2 proteins. Dicer, a ribonuclease III enzyme required for microRNA processing, is also an E2-inducible gene. Several E2-regulated microRNA genes are associated with ERα-binding sites or located in the intragenic region of estrogen-regulated genes. We propose that the clinical course of ERα-positive breast cancers is dependent on the balance between E2-regulated tumor-suppressor microRNAs and oncogenic microRNAs. Additionally, our studies reveal a negative-regulatory loop controlling E2 response through microRNAs as well as differences in E2-induced transcriptome and proteome.Item Identification of transcription factor and microRNA binding sites in responsible to fetal alcohol syndrome(BioMed Central, 2008-03-20) Wang, Guohua; Wang, Xin; Wang, Yadong; Yang, Jack Y.; Li, Lang; Nephew, Kenneth P.; Edenberg, Howard J.; Zhou, Feng C.; Liu, Yunlong; Medicine, School of MedicineThis is a first report, using our MotifModeler informatics program, to simultaneously identify transcription factor (TF) and microRNA (miRNA) binding sites from gene expression microarray data. Based on the assumption that gene expression is controlled by combinatorial effects of transcription factors binding in the 5'-upstream regulatory region and miRNAs binding in the 3'-untranslated region (3'-UTR), we developed a model for (1) predicting the most influential cis-acting elements under a given biological condition, and (2) estimating the effects of those elements on gene expression levels. The regulatory regions, TF and miRNA, which mediate the differential genes expression in fetal alcohol syndrome were unknown; microarray data from alcohol exposure paradigm was used. The model predicted strong inhibitory effects of 5' cis-acting elements and stimulatory effects of 3'-UTR under alcohol treatment. Current predictive model derived a key hypothesis for the first time a novel role of miRNAs in gene expression changes associated with abnormal mouse embryo development after alcohol exposure. This suggests that disturbance of miRNA functions may contribute to the alcohol-induced developmental deficiencies.Item MeDReaders: a database for transcription factors that bind to methylated DNA(Oxford Academic, 2018-01-04) Wang, Guohua; Luo, Ximei; Wang, Jianan; Wan, Jun; Xia, Shuli; Zhu, Heng; Qian, Jiang; Wang, Yadong; Medical and Molecular Genetics, School of MedicineUnderstanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.Item Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network(Hindawi, 2017) Zhao, Yuming; Wang, Fang; Chen, Su; Wan, Jun; Wang, Guohua; Medical and Molecular Genetics, School of MedicineMicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.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 Model-based Comparative Prediction of Transcription-Factor Binding Motifs in Anabolic Responses in Bone.(Elsevier, 2007) Chen, Andy B.; Hamamura, Kazunori; Wang, Guohua; Xing, Weirong; Mohan, Subburaman; Yokota, Hiroki; Liu, Yunlong; Department of Biomedical Engineering, School of Engineering and TechnologyUnderstanding the regulatory mechanism that controls the alteration of global gene expression patterns continues to be a challenging task in computational biology. We previously developed an ant algorithm, a biologically-inspired computational technique for microarray data, and predicted putative transcription-factor binding motifs (TFBMs) through mimicking interactive behaviors of natural ants. Here we extended the algorithm into a set of web-based software, Ant Modeler, and applied it to investigate the transcriptional mechanism underlying bone formation. Mechanical loading and administration of bone morphogenic proteins (BMPs) are two known treatments to strengthen bone. We addressed a question: Is there any TFBM that stimulates both “anabolic responses of mechanical loading” and “BMP-mediated osteogenic signaling”? Although there is no significant overlap among genes in the two responses, a comparative model-based analysis suggests that the two independent osteogenic processes employ common TFBMs, such as a stress responsive element and a motif for peroxisome proliferator-activated receptor (PPAR). The post-modeling in vitro analysis using mouse osteoblast cells supported involvements of the predicted TFBMs such as PPAR, Ikaros 3, and LMO2 in response to mechanical loading. Taken together, the results would be useful to derive a set of testable hypotheses and examine the role of specific regulators in complex transcriptional control of bone formation.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 RNA Polymerase II Binding Patterns Reveal Genomic Regions Involved in MicroRNA Gene Regulation(Public Library of Science, 2010-11-02) Wang, Guohua; Wang, Yadong; Shen, Changyu; Huangn, Yi-wen; Huang, Kun; Huang, Tim H. M.; Nephew, Kenneth P.; Li, Lang; Liu, Yunlong; Medicine, School of MedicineMicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of pri-microRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states.