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 An all-to-all approach to the identification of sequence-specific readers for epigenetic DNA modifications on cytosine(Springer Nature, 2021-02-04) Song, Guang; Wang, Guohua; Luo, Ximei; Cheng, Ying; Song, Qifeng; Wan, Jun; Moore, Cedric; Song, Hongjun; Jin, Peng; Qian, Jiang; Zhu, Heng; Medical and Molecular Genetics, School of MedicineEpigenetic modifications of DNA play important roles in many biological processes. Identifying readers of these epigenetic marks is a critical step towards understanding the underlying mechanisms. Here, we present an all-to-all approach, dubbed digital affinity profiling via proximity ligation (DAPPL), to simultaneously profile human TF-DNA interactions using mixtures of random DNA libraries carrying different epigenetic modifications (i.e., 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine) on CpG dinucleotides. Many proteins that recognize consensus sequences carrying these modifications in symmetric and/or hemi-modified forms are identified. We further demonstrate that the modifications in different sequence contexts could either enhance or suppress TF binding activity. Moreover, many modifications can affect TF binding specificity. Furthermore, symmetric modifications show a stronger effect in either enhancing or suppressing TF-DNA interactions than hemi-modifications. Finally, in vivo evidence suggests that USF1 and USF2 might regulate transcription via hydroxymethylcytosine-binding activity in weak enhancers in human embryonic stem cells.Item Author Correction: An all-to-all approach to the identification of sequence-specific readers for epigenetic DNA modifications on cytosine(Springer Nature, 2021-02-23) Song, Guang; Wang, Guohua; Luo, Ximei; Cheng, Ying; Song, Qifeng; Wan, Jun; Moore, Cedric; Song, Hongjun; Jin, Peng; Qian, Jiang; Zhu, Heng; Medical and Molecular Genetics, School of MedicineCorrection to: Nature Communications 10.1038/s41467-021-20950-w, published online 04 February 2021. In the original version of this Article, the “Methods” section “Genome-wide hmC profiling of human embryonic stem cell H1” incorrectly stated “"Human embryonic stem cell H1 was purchased from WiCell Research Institute (WiCell) and the ethics approval was obtained from the Robert-Koch Institute, Berlin, Germany.”. Ethical approval was not required for the use of hESC H1 cells purchased from WiCell Research Institute. The statement has been corrected to “Human embryonic stem cell H1 was purchased from WiCell Research Institute (WiCell).” This has been corrected in the HTML and PDF version of this Article.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 Empirical Bayes Model Comparisons for Differential Methylation Analysis(Hindawi, 2012) Teng, Mingxiang; Wang, Yadong; Kim, Seongho; Li, Lang; Shen, Changyu; Wang, Guohua; Liu, Yunlong; Huang, Tim H.M.; Nephew, Kenneth P.; Balch, Curt; Obstetrics and Gynecology, School of MedicineA number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division-(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.Item 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 regulatory regions of bidirectional genes in cervical cancer(Springer Nature, 2013) Wang, Guohua; Qi, Ke; Zhao, Yuming; Li, Yu; Juan, Liran; Teng, Mingxiang; Li, Lang; Liu, Yunlong; Wang, Yadong; Medical and Molecular Genetics, School of MedicineBackground: Bidirectional promoters are shared promoter sequences between divergent gene pair (genes proximal to each other on opposite strands), and can regulate the genes in both directions. In the human genome, > 10% of protein-coding genes are arranged head-to-head on opposite strands, with transcription start sites that are separated by < 1,000 base pairs. Many transcription factor binding sites occur in the bidirectional promoters that influence the expression of 2 opposite genes. Recently, RNA polymerase II (RPol II) ChIP-seq data are used to identify the promoters of coding genes and non-coding RNAs. However, a bidirectional promoter with RPol II ChIP-Seq data has not been found. Results: In some bidirectional promoter regions, the RPol II forms a bi-peak shape, which indicates that 2 promoters are located in the bidirectional region. We have developed a computational approach to identify the regulatory regions of all divergent gene pairs using genome-wide RPol II binding patterns derived from ChIP-seq data, based upon the assumption that the distribution of RPol II binding patterns around the bidirectional promoters are accumulated by RPol II binding of 2 promoters. In HeLa S3 cells, 249 promoter pairs and 1094 single promoters were identified, of which 76 promoters cover only positive genes, 86 promoters cover only negative genes, and 932 promoters cover 2 genes. Gene expression levels and STAT1 binding sites for different promoter categories were therefore examined. Conclusions: The regulatory region of bidirectional promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription. From gene expression and transcription factor binding site analysis, the promoters in bidirectional regions may regulate the closest gene, and STAT1 is involved in primary promoter.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.