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 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 Hardikar

doi:10.1016/j.isci.2021.102379

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

iScience. 2021 Mar 31;24(4):102379. doi: 10.1016/j.isci.2021.102379. eCollection 2021 Apr 23.

Authors

Wilson K M Wong^{1

2}, Mugdha V Joglekar^{1

2}, Vijit Saini^{1

3}, Guozhi Jiang⁴, Charlotte X Dong^{1

2}, Alissa Chaitarvornkit^{1

2}, Grzegorz J Maciag⁵, Dario Gerace³, Ryan J Farr^{1

2}, Sarang N Satoor^{1

2}, Subhshri Sahu^{1

2}, Tejaswini Sharangdhar^{1

2}, Asma S Ahmed^{1

2}, Yi Vee Chew⁶, David Liuwantara⁶, Benjamin Heng⁷, Chai K Lim⁷, Julie Hunter⁸, Andrzej S Januszewski⁹, Anja E Sørensen⁵, Ammira S A Akil¹⁰, Jennifer R Gamble⁸, Thomas Loudovaris¹¹, Thomas W Kay¹¹, Helen E Thomas¹¹, Philip J O'Connell⁶, Gilles J Guillemin⁷, David Martin¹², Ann M Simpson³, Wayne J Hawthorne⁶, Louise T Dalgaard⁵, Ronald C W Ma⁴, Anandwardhan A Hardikar^{1

2

5}

Affiliations

¹ Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Narellan Road & Gilchrist Drive, Campbelltown, NSW 2560, Australia.
² Diabetes and Islet Biology group, Faculty of Medicine and Health, University of Sydney, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
³ School of Life Sciences and the Centre for Health Technologies, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
⁴ Department of Medicine and Therapeutics, and Hong Kong Institute of Diabetes and Obesity, and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, Special Administrative Region, China.
⁵ Department of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark.
⁶ Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, 176 Hawkesbury Road, Westmead, NSW 2145, Australia.
⁷ Faculty of Medicine Health and Human Sciences, Macquarie University, Sydney, NSW 2019, Australia.
⁸ Centre for the Endothelium, Vascular Biology Program, Centenary Institute, University of Sydney Medical School, Locked Bag #6, Newtown, NSW 2042, Australia.
⁹ NHMRC Clinical Trials Centre, University of Sydney, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
¹⁰ Department of Human Genetics-Precision Medicine Program, Sidra Medicine, P.O. Box 26999, Doha, Qatar.
¹¹ St Vincent's Institute and The University of Melbourne Department of Medicine, 9 Princes Street, Fitzroy, VIC, Australia.
¹² Upper GI Surgery, Strathfield Hospital, 2/3 Everton Road, Strathfield, NSW 2135, Australia.

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.

Keywords: Computational Bioinformatics; Pathophysiology; Transcriptomics.