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

Wilson W.K. Wong, Mugdha Joglekar, Vijit Saini, Guozhi Jiang, Charlotte Dong, Alissa Chaitarvornki, Grzegorz Maciag, Dario Gerace, Ryan Farr, Sarang Satoor, Subhshri Sahu, Tejaswini Sharangdhar, Asma S Ahmed, Yi Vee Chew, David Liuwantara, Benjamin Heng, Chai K. Lim, Julie Hunter, Andrzej Januszewski, Anja Elaine SørensenAmmira Akil, Jennifer Gamble, Thomas Loudovaris, Thomas W Kay, Helen Thomas, Philip O'Connell, Gilles Guillemin, David Martin, Ann Simpson, Wayne Hawthorne, Louise Torp Dalgaard, Ronald C Ma, Anandwardhan Awadhoot Hardikar*

*Corresponding author

Publikation: Bidrag til tidsskriftTidsskriftartikelpeer review

Abstract

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 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 these microRNAs 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.
OriginalsprogEngelsk
Artikelnummer102379
TidsskriftiScience
Vol/bind24
Udgave nummer4
DOI
StatusUdgivet - 23 apr. 2021

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