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

Wilson K. M. Wong, Mugdha V. Joglekar, Vijit Saini, Guozhi Jiang, Charlotte X. Dong, Alissa Chaitarvornkit, Grzegorz J. Maciag, Dario Gerace, Ryan J. Farr, Sarang N. Satoor, Subhshri Sahu, Tejaswini Sharangdhar, Asma S. Ahmed, Yi Vee Chew, David Liuwantara, Benjamin Heng, Chai K. Lim, Julie Hunter, Andrzej S. Januszewski, Anja E. SørensenAmmira 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. Hardikar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Article number102379
Pages (from-to)1-19
Number of pages47
JournaliScience
Volume24
Issue number4
DOIs
Publication statusPublished - 23 Apr 2021

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Computational Bioinformatics
  • Pathophysiology
  • Transcriptomics

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