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 language | English (US) |
|---|---|
| Article number | 102379 |
| Journal | iScience |
| Volume | 24 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 23 2021 |
| Externally published | Yes |
Keywords
- Computational Bioinformatics
- Pathophysiology
- Transcriptomics
ASJC Scopus subject areas
- General
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Machine learning workflows identify a microRNA signature of insulin transcription in human tissues. / Wong, Wilson K.M.; Joglekar, Mugdha V.; Saini, Vijit; Jiang, Guozhi; Dong, Charlotte X.; Chaitarvornkit, Alissa; Maciag, Grzegorz J.; Gerace, Dario; Farr, Ryan J.; Satoor, Sarang N.; Sahu, Subhshri; Sharangdhar, Tejaswini; Ahmed, Asma S.; Chew, Yi Vee; Liuwantara, David; Heng, Benjamin; Lim, Chai K.; Hunter, Julie; Januszewski, Andrzej S.; Sørensen, Anja E.; Akil, Ammira S.A.; Gamble, Jennifer R.; Loudovaris, Thomas; Kay, Thomas W.; Thomas, Helen E.; O'Connell, Philip J.; Guillemin, Gilles J.; Martin, David; Simpson, Ann M.; Hawthorne, Wayne J.; Dalgaard, Louise T.; Ma, Ronald C.W.; Hardikar, Anandwardhan A.
In: iScience, Vol. 24, No. 4, 102379, 23.04.2021.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Machine learning workflows identify a microRNA signature of insulin transcription in human tissues
AU - Wong, Wilson K.M.
AU - Joglekar, Mugdha V.
AU - Saini, Vijit
AU - Jiang, Guozhi
AU - Dong, Charlotte X.
AU - Chaitarvornkit, Alissa
AU - Maciag, Grzegorz J.
AU - Gerace, Dario
AU - Farr, Ryan J.
AU - Satoor, Sarang N.
AU - Sahu, Subhshri
AU - Sharangdhar, Tejaswini
AU - Ahmed, Asma S.
AU - Chew, Yi Vee
AU - Liuwantara, David
AU - Heng, Benjamin
AU - Lim, Chai K.
AU - Hunter, Julie
AU - Januszewski, Andrzej S.
AU - Sørensen, Anja E.
AU - Akil, Ammira S.A.
AU - Gamble, Jennifer R.
AU - Loudovaris, Thomas
AU - Kay, Thomas W.
AU - Thomas, Helen E.
AU - O'Connell, Philip J.
AU - Guillemin, Gilles J.
AU - Martin, David
AU - Simpson, Ann M.
AU - Hawthorne, Wayne J.
AU - Dalgaard, Louise T.
AU - Ma, Ronald C.W.
AU - Hardikar, Anandwardhan A.
N1 - Funding Information: The research presented herein has been funded through grants from the Australian Research Council Future Fellowship (FT110100254), the Juvenile Diabetes Research Foundation (JDRF) Australia T1D Clinical Research Network (JDRF/4-CDA2016-228-MB), and the University of Sydney CDIP grants to A.A.H. A.A.H, L.T.D. and A.E.S. are also supported through visiting professorships from the Danish Diabetes Academy, funded by the Novo Nordisk Foundation, grant number NNF17SA0031406 (2016-18 and 2019-22). W.K.M.W. acknowledges previous support from the Australian Postgraduate Award, University of Sydney, JDRF Australia PhD top-up award, and current funding through JDRF Australia/Helmsley Charitable Trust. M.V.J. was supported through a JDRF USA advanced post-doctoral award (3-APF-2016-178-A-N) and currently a transition award from JDRF, USA. J.R.G. holds the Wenkart Chair of the Endothelium. P.O.C. was an NHMRC senior practitioner fellow and funded by an NHMRC program grant and the JDRF. The St Vincent's Institute receives support from the Operational Infrastructure Support Scheme of the Government of Victoria. R.C.W.M. acknowledges support from the RGC Theme-based Research Scheme (T12-402/13N) and Research Impact Fund (R4012-18), the Focused Innovation Scheme, and Faculty Postdoctoral Scheme of the Chinese University of Hong Kong. A.E.S. was supported through a Danish Diabetes Academy post-doctoral grant, supported by the Novo Nordisk Foundation. We thank Ms. Cody Lee-Maynard, Ms. Dana AlRijjal, and Dr. Najeeb Syed for assistance in laboratory analytical work. A.A.H. acknowledges interaction(s) with Dr. Khalid Fakhro, Ms. Shihana Fathima, Prof. Alicia J. Jenkins, Prof. Anthony C. Keech, Prof. Val Gebski and infrastructure support from the NHMRC CTC, Faculty of Medicine & Health, University of Sydney; Australia, School of Medicine, Western Sydney University, Australia; Western Sydney University, Ingham Institute, Liverpool; Australia and the Rebecca L. Cooper Medical Research Foundation. The support of all surgical team members contributing to the consenting and acquisition of research tissue samples, all organ donors, and supporting family members is gratefully acknowledged. Conceptualization, A.A.H.; methodology, A.A.H. W.K.M.W. and M.V.J.; software, W.K.M.W. G.J. G.J.M. C.X.D. A.C. A.S.A. A.S.J. A.E.S. A.S.A. L.T.D. and R.C.W.M.; validation, W.K.M.W. M.V.J. V.S. and A.A.H.; formal analysis, W.K.M.W. M.V.J. V.S. G.J. A.E.S. L.T.D. R.C.W.M. and A.A.H.; investigation, W.K.M.W. M.V.J. V.S. D.G. R.J.F. S.N.S. S.S. T.S. and D.L.; resources, W.K.M.W. M.V.J. V.S. D.G. S.N.S. Y.V.C. B.H. C.K.L. J.H. J.R.G. T.L. T.W.K. H.E.T. P.J.O. G.J.G. D.M. A.M.S. W.J.H. and A.A.H.; data curation, G.J. Y.V.C. W.J.H. and R.C.W.M.; writing ? original draft, W.K.M.W. M.V.J. and A.A.H.; writing ? review & editing, all authors; visualization, A.A.H.; supervision, A.A.H.; project administration, A.A.H.; funding acquisition, A.A.H. A patent application (WO2019000017A1) was filed. Funding Information: The research presented herein has been funded through grants from the Australian Research Council Future Fellowship ( FT110100254 ), the Juvenile Diabetes Research Foundation (JDRF) Australia T1D Clinical Research Network ( JDRF/4-CDA2016-228-MB ), and the University of Sydney CDIP grants to A.A.H. A.A.H, L.T.D., and A.E.S. are also supported through visiting professorships from the Danish Diabetes Academy , funded by the Novo Nordisk Foundation , grant number NNF17SA0031406 (2016-18 and 2019-22). W.K.M.W. acknowledges previous support from the Australian Postgraduate Award, University of Sydney , JDRF Australia PhD top-up award, and current funding through JDRF Australia/Helmsley Charitable Trust. M.V.J. was supported through a JDRF USA advanced post-doctoral award (3-APF-2016-178-A-N) and currently a transition award from JDRF, USA. J.R.G. holds the Wenkart Chair of the Endothelium. P.O.C. was an NHMRC senior practitioner fellow and funded by an NHMRC program grant and the JDRF. The St Vincent's Institute receives support from the Operational Infrastructure Support Scheme of the Government of Victoria . R.C.W.M. acknowledges support from the RGC Theme-based Research Scheme ( T12-402/13N ) and Research Impact Fund ( R4012-18 ), the Focused Innovation Scheme, and Faculty Postdoctoral Scheme of the Chinese University of Hong Kong. A.E.S. was supported through a Danish Diabetes Academy post-doctoral grant, supported by the Novo Nordisk Foundation . We thank Ms. Cody Lee-Maynard, Ms. Dana AlRijjal, and Dr. Najeeb Syed for assistance in laboratory analytical work. A.A.H. acknowledges interaction(s) with Dr. Khalid Fakhro, Ms. Shihana Fathima, Prof. Alicia J. Jenkins, Prof. Anthony C. Keech, Prof. Val Gebski and infrastructure support from the NHMRC CTC, Faculty of Medicine & Health , University of Sydney ; Australia, School of Medicine , Western Sydney University, Australia ; Western Sydney University, Ingham Institute , Liverpool; Australia and the Rebecca L. Cooper Medical Research Foundation. The support of all surgical team members contributing to the consenting and acquisition of research tissue samples, all organ donors, and supporting family members is gratefully acknowledged. Publisher Copyright: © 2021 The Authors
PY - 2021/4/23
Y1 - 2021/4/23
N2 - 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.
AB - 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.
KW - Computational Bioinformatics
KW - Pathophysiology
KW - Transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85104302184&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104302184&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2021.102379
DO - 10.1016/j.isci.2021.102379
M3 - Article
AN - SCOPUS:85104302184
VL - 24
JO - iScience
JF - iScience
SN - 2589-0042
IS - 4
M1 - 102379
ER -