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Multi-omics profiling reveals microRNA-mediated insulin signaling networks.
Lin, Yang-Chi-Dung; Huang, Hsi-Yuan; Shrestha, Sirjana; Chou, Chih-Hung; Chen, Yen-Hua; Chen, Chi-Ru; Hong, Hsiao-Chin; Li, Jing; Chang, Yi-An; Chiew, Men-Yee; Huang, Ya-Rong; Tu, Siang-Jyun; Sun, Ting-Hsuan; Weng, Shun-Long; Tseng, Ching-Ping; Huang, Hsien-Da.
Affiliation
  • Lin YC; School of Life and Health Sciences, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Huang HY; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Shrestha S; School of Life and Health Sciences, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Chou CH; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Chen YH; Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Chen CR; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Hong HC; Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Li J; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Chang YA; Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY, 10021, USA.
  • Chiew MY; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Huang YR; School of Life and Health Sciences, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Tu SJ; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Sun TH; School of Life and Health Sciences, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Weng SL; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Longgang District, Shenzhen, 518172, Guangdong Province, China.
  • Tseng CP; Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
  • Huang HD; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.
BMC Bioinformatics ; 21(Suppl 13): 389, 2020 Sep 17.
Article in En | MEDLINE | ID: mdl-32938376
ABSTRACT

BACKGROUND:

MicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells.

RESULTS:

We applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network.

CONCLUSIONS:

In this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation / MicroRNAs / Gene Regulatory Networks / Insulin Type of study: Prognostic_studies Limits: Humans Language: En Journal: BMC Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation / MicroRNAs / Gene Regulatory Networks / Insulin Type of study: Prognostic_studies Limits: Humans Language: En Journal: BMC Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: China