Title:	Machine learning workflows identify a microRNA signature of insulin transcription in human tissues
Authors:	Wong, WKM Joglekar, MV Saini, V Jiang, GZ Dong, CX Chaitarvornkit, A Maciag, GJ Gerace, D Farr, RJ Satoor, SN Sahu, S Sharangdhar, T Ahmed, AS Chew, YV Liuwantara, D Heng, B Lim, CK Hunter, J Januszewski, AS Sorensen, AE Akil, ASA Gamble, JR Loudovaris, T Kay, TW Thomas, HE O’Connell, PJ Guillemin, GJ Martin, D Simpson, AM Hawthorne, WJ Dalgaard, LT Ma, RCW Hardikar, AA
Issue Year:	2021
Publisher	CELL PRESS
Series	ISCIENCE:
Abstract	Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic beta-cell function. Studies to identify specific microRNA( s) associated with human (pro-)endocrine gene expression are needed. We profiled microRNAs and key pancreatic genes in 353 human tissue samples. Machine learning workflows identified microRNAs associated with (pro-)insulin transcripts in a discovery set of islets (n = 30) and insulin-negative tissues (n = 62). This microRNA signature was validated in remaining 261 tissues that include nine islet samples from individuals with type 2 diabetes. Top eight microRNAs (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, and -429-3p) were confirmed to be associated with and predictive of (pro-)insulin transcript levels. Use of doxycycline-inducible microRNA-overexpressing human pancreatic duct cell lines confirmed the regulatory roles of thesemicroRNAs in (pro-)endocrine gene expression. Knockdown of these microRNAs in human islet cells reduced (pro-)insulin transcript abundance. Our data provide specific microRNAs to further study microRNA-mRNA interactions in regulating insulin transcription.
URI:	https://publications.svi.edu.au/publications/6782
Other Identifiers	10.1016/j.isci.2021.102379
Publication type	Article