Literature DB >> 24262216

Identification of transcription factors for drug-associated gene modules and biomedical implications.

Min Xiong1, Bin Li, Qiang Zhu, Yun-Xing Wang, Hong-Yu Zhang.   

Abstract

MOTIVATION: One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown.
RESULTS: In this study, by using biclustering approach FABIA (factor analysis for bicluster acquisition), we generate 49 modules for gene expression profiles on 1309 agent treatments. These modules are of biological relevance in terms of functional enrichment, drug-drug interactions and 3D proximity in chromatins. By using the information of drug targets (some of which are TFs) and biological regulation, the links between 28 modules and 12 specific TFs, such as estrogen receptors (ERs), nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma, can be established. Some of the links are supported by 3D transcriptional regulation data [derived from ChIA-PET (chromatin interaction analysis using paired-end tags) experiments] and drug mode of action as well. The relationships between modules and TFs provide new clues to interpreting biological regulation mechanisms, in particular, the lipid metabolism regulation by ERα. In addition, the links between natural products (e.g. polyphenols) and their associated modules and TFs are helpful to elucidate their polypharmacological effects in terms of activating specific TFs, such as ERs, nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma.

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Year:  2013        PMID: 24262216     DOI: 10.1093/bioinformatics/btt683

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

Review 1.  It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.

Authors:  Juan Xie; Anjun Ma; Anne Fennell; Qin Ma; Jing Zhao
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

2.  Elucidating pharmacological mechanisms of natural medicines by biclustering analysis of the gene expression profile: a case study on curcumin and Si-Wu-Tang.

Authors:  Yuan Quan; Bin Li; You-Min Sun; Hong-Yu Zhang
Journal:  Int J Mol Sci       Date:  2014-12-29       Impact factor: 5.923

3.  Heat Diffusion Kernel Algorithm-Based Interpretation of the Disease Intervention Mechanism for DHA.

Authors:  Yuan Quan; Hong-Yu Zhang; Jiang-Hui Xiong; Rui-Feng Xu; Min Gao
Journal:  Genes (Basel)       Date:  2020-07-07       Impact factor: 4.096

4.  Identification of Non-Electrophilic Nrf2 Activators from Approved Drugs.

Authors:  Qing-Ye Zhang; Xin-Yi Chu; Ling-Han Jiang; Meng-Yuan Liu; Zhi-Ling Mei; Hong-Yu Zhang
Journal:  Molecules       Date:  2017-05-26       Impact factor: 4.411

5.  Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates.

Authors:  Yuan Quan; Zhi-Hui Luo; Qing-Yong Yang; Jiang Li; Qiang Zhu; Ye-Mao Liu; Bo-Min Lv; Ze-Jia Cui; Xuan Qin; Yan-Hua Xu; Li-Da Zhu; Hong-Yu Zhang
Journal:  Front Genet       Date:  2019-05-29       Impact factor: 4.599

6.  Identification of NUDT5 Inhibitors From Approved Drugs.

Authors:  Xin-Yu Tong; Xuan Liao; Min Gao; Bo-Min Lv; Xiao-Hui Chen; Xin-Yi Chu; Qing-Ye Zhang; Hong-Yu Zhang
Journal:  Front Mol Biosci       Date:  2020-03-31

7.  Elucidating polypharmacological mechanisms of polyphenols by gene module profile analysis.

Authors:  Bin Li; Min Xiong; Hong-Yu Zhang
Journal:  Int J Mol Sci       Date:  2014-06-25       Impact factor: 5.923

8.  Quantitative Identification of Compound-Dependent On-Modules and Differential Allosteric Modules From Homologous Ischemic Networks.

Authors:  B Li; J Liu; Y Y Zhang; P Q Wang; Y N Yu; R X Kang; H L Wu; X X Zhang; Z Wang; Y Y Wang
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-10-19

9.  Rectified factor networks for biclustering of omics data.

Authors:  Djork-Arné Clevert; Thomas Unterthiner; Gundula Povysil; Sepp Hochreiter
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937

10.  A Machine Learning Method for Drug Combination Prediction.

Authors:  Jiang Li; Xin-Yu Tong; Li-Da Zhu; Hong-Yu Zhang
Journal:  Front Genet       Date:  2020-08-25       Impact factor: 4.599

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