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

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

Western Sydney University

Research output: Contribution to journal › Article › peer-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. Whereas 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
Article number	102379
Number of pages	47
Journal	iScience
Volume	24
Issue number	4
DOIs	https://doi.org/10.1016/j.isci.2021.102379
Publication status	Published - 2021

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Wong, W. K. M., Joglekar, M. V., Saini, V., Jiang, G., Dong, C. X., Chaitarvornkit, A., Maciag, G. J., Gerace, D., Farr, R. J., Satoor, S. N., Sahu, S., Sharangdhar, T., Ahmed, A. S., Chew, Y. V., Liuwantara, D., Heng, B., Lim, C. K., Hunter, J., Januszewski, A. S., ... Hardikar, A. A. (2021). Machine learning workflows identify a microRNA signature of insulin transcription in human tissues. iScience, 24(4), Article 102379. https://doi.org/10.1016/j.isci.2021.102379

@article{04bc28f463f4485d831031a3629b46ae,

title = "Machine learning workflows identify a microRNA signature of insulin transcription in human tissues",

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. Whereas 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.",

author = "Wong, {Wilson K. M.} and Joglekar, {Mugdha V.} and Vijit Saini and Guozhi Jiang and Dong, {Charlotte X.} and Alissa Chaitarvornkit and Maciag, {Grzegorz J.} and Dario Gerace and Farr, {Ryan J.} and Satoor, {Sarang N.} and Subhshri Sahu and Tejaswini Sharangdhar and Ahmed, {Asma S.} and Chew, {Yi Vee} and David Liuwantara and Benjamin Heng and Lim, {Chai K.} and Julie Hunter and Januszewski, {Andrzej S.} and S{\o}rensen, {Anja E.} and Akil, {Ammira S. A.} and Gamble, {Jennifer R.} and Thomas Loudovaris and Kay, {Thomas W.} and Thomas, {Helen E.} and O'Connell, {Philip J.} and Guillemin, {Gilles J.} and David Martin and Simpson, {Ann M.} and Hawthorne, {Wayne J.} and Dalgaard, {Louise T.} and Ma, {Ronald C. W.} and Hardikar, {Anandwardhan A.}",

year = "2021",

doi = "10.1016/j.isci.2021.102379",

language = "English",

volume = "24",

journal = "iScience",

issn = "2589-0042",

publisher = "Cell Press",

number = "4",

}

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, Ma, RCW & Hardikar, AA 2021, 'Machine learning workflows identify a microRNA signature of insulin transcription in human tissues', iScience, vol. 24, no. 4, 102379. https://doi.org/10.1016/j.isci.2021.102379

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.

PY - 2021

Y1 - 2021

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. Whereas 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. Whereas 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.

UR - http://hdl.handle.net/1959.7/uws:59361

U2 - 10.1016/j.isci.2021.102379

DO - 10.1016/j.isci.2021.102379

M3 - Article

SN - 2589-0042

VL - 24

JO - iScience

JF - iScience

IS - 4

M1 - 102379

ER -

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

Abstract

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