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

Wong, WKM; Joglekar, MV; Saini, V; Jiang, G; 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; Sørensen, AE; Akil, ASA; Gamble, JR; Loudovaris, T; Kay, TW; Thomas, HE; O’Connell, PJ; Guillemin, GJ; Martin, D; Simpson, AM; Hawthorne, WJ; Dalgaard, LT; C.W., R; Hardikar, AA

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

Wong, WKM Joglekar, MV Saini, V Jiang, G 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 Sørensen, AE Akil, ASA Gamble, JR Loudovaris, T Kay, TW Thomas, HE O’Connell, PJ Guillemin, GJ Martin, D Simpson, AM Hawthorne, WJ Dalgaard, LT C.W., R Hardikar, AA

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: iScience, 2021, 24, (4), pp. 102379
Issue Date:: 2021-03-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (7.63 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wong, WKM
dc.contributor.author	Joglekar, MV
dc.contributor.author	Saini, V
dc.contributor.author	Jiang, G
dc.contributor.author	Dong, CX
dc.contributor.author	Chaitarvornkit, A
dc.contributor.author	Maciag, GJ
dc.contributor.author	Gerace, D https://orcid.org/0000-0001-9154-0624
dc.contributor.author	Farr, RJ
dc.contributor.author	Satoor, SN
dc.contributor.author	Sahu, S
dc.contributor.author	Sharangdhar, T
dc.contributor.author	Ahmed, AS
dc.contributor.author	Chew, YV
dc.contributor.author	Liuwantara, D
dc.contributor.author	Heng, B
dc.contributor.author	Lim, CK
dc.contributor.author	Hunter, J
dc.contributor.author	Januszewski, AS
dc.contributor.author	Sørensen, AE
dc.contributor.author	Akil, ASA
dc.contributor.author	Gamble, JR
dc.contributor.author	Loudovaris, T
dc.contributor.author	Kay, TW
dc.contributor.author	Thomas, HE
dc.contributor.author	O’Connell, PJ
dc.contributor.author	Guillemin, GJ
dc.contributor.author	Martin, D
dc.contributor.author	Simpson, AM
dc.contributor.author	Hawthorne, WJ
dc.contributor.author	Dalgaard, LT
dc.contributor.author	C.W., R
dc.contributor.author	Hardikar, AA
dc.date.accessioned	2022-01-25T17:38:34Z
dc.date.available	2021-03-29
dc.date.available	2022-01-25T17:38:34Z
dc.date.issued	2021-03-01
dc.identifier.citation	iScience, 2021, 24, (4), pp. 102379
dc.identifier.issn	2589-0042
dc.identifier.issn	2589-0042
dc.identifier.uri	http://hdl.handle.net/10453/153578
dc.description.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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	iScience
dc.relation.isbasedon	10.1016/j.isci.2021.102379
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine learning workflows identify a microRNA signature of insulin transcription in human tissues
dc.type	Journal Article
utslib.citation.volume	24
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-25T17:38:27Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	24
utslib.citation.issue	4

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153578