Machine learning workflows identify a microRNA signature of insulin transcription in human tissues

• DOI: 10.1016/j.isci.2021.102379
• Publication Date: 2021-04-01T05:52:25Z
• Authors: Wilson K.M. Wong; Mugdha V. Joglekar; Vijit Saini; Guozhi Jiang; Charlotte X. Dong; Alissa Chaitarvor...; Grzegorz J. Maciag; Dario Gerace; Ryan J. Farr; Sarang N. Satoor; Subhshri Sahu; Tejaswini Sharang...; Asma S. Ahmed; Yi Vee Chew; David Liuwantara; Benjamin Heng; Chai K. Lim; Julie Hunter; Andrzej S. Janusz...; Anja E. Sørensen; Ammira S.A. Akil; Jennifer R. Gamble; Thomas Loudovaris; Thomas W. Kay; Helen E. Thomas; Philip J. O'Connell; Gilles J. Guillemin; David Martin; Ann M. Simpson; Wayne J. Hawthorne; Louise T. Dalgaard; Ronald C.W. Ma; Anandwardhan A. H...
• Keywords: Signature (topology)

Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic β-cell function. Studies to identify specific microRNA(s) associated with human (pro-)endocrine gene expression are needed. We profiled and genes 353 tissue samples. Machine learning workflows identified (pro-)insulin transcripts discovery set of islets (n = 30) insulin-negative tissues 62). This microRNA signature was validated remaining 261 that include nine islet samples from individuals type 2 diabetes. Top eight (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, -429-3p) were confirmed be predictive transcript levels. Use doxycycline-inducible microRNA-overexpressing duct cell lines the regulatory roles these expression. Knockdown cells reduced abundance. Our data provide further study microRNA-mRNA interactions regulating insulin transcription.