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
Hardikar, AA
Issue Year: 2021
Publisher CELL PRESS
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