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

Wilson W.K. Wong; Mugdha Joglekar; Vijit Saini; Guozhi Jiang; Charlotte Dong; Alissa Chaitarvornki; Grzegorz Maciag; Dario Gerace; Ryan Farr; Sarang Satoor; Subhshri  Sahu; Tejaswini Sharangdhar; Asma S Ahmed; Yi Vee Chew; David Liuwantara; Benjamin Heng; Chai K. Lim; Julie Hunter; Andrzej Januszewski; Anja Elaine Sørensen; Ammira Akil; Jennifer Gamble; Thomas Loudovaris; Thomas W Kay; Helen Thomas; Philip O'Connell; Gilles Guillemin; David Martin; Ann Simpson; Wayne Hawthorne; Louise Torp Dalgaard; Ronald C Ma; Anandwardhan Awadhoot Hardikar

doi:10.1016/j.isci.2021.102379

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

Wilson W.K. Wong, Mugdha Joglekar, Vijit Saini, Guozhi Jiang, Charlotte Dong, Alissa Chaitarvornki, Grzegorz Maciag, Dario Gerace, Ryan Farr, Sarang Satoor, Subhshri Sahu, Tejaswini Sharangdhar, Asma S Ahmed, Yi Vee Chew, David Liuwantara, Benjamin Heng, Chai K. Lim, Julie Hunter, Andrzej Januszewski, Anja Elaine SørensenAmmira Akil, Jennifer Gamble, Thomas Loudovaris, Thomas W Kay, Helen Thomas, Philip O'Connell, Gilles Guillemin, David Martin, Ann Simpson, Wayne Hawthorne, Louise Torp Dalgaard, Ronald C Ma, Anandwardhan Awadhoot Hardikar^*

^*Corresponding author

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › 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. 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.

Originalsprog	Engelsk
Artikelnummer	102379
Tidsskrift	iScience
Vol/bind	24
Udgave nummer	4
DOI	https://doi.org/10.1016/j.isci.2021.102379
Status	Udgivet - 23 apr. 2021

Link til dokument

10.1016/j.isci.2021.102379Licens: CC BY-NC-ND

Machine learning workflows identifya microRNA signature of insulintranscription in human tissuesForlagets udgivne version, 7,46 MBLicens: CC BY-NC-ND

Citer dette

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

@article{11b782527e4f472498e9b38c70b52b54,

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. 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",

author = "Wong, {Wilson W.K.} and Mugdha Joglekar and Vijit Saini and Guozhi Jiang and Charlotte Dong and Alissa Chaitarvornki and Grzegorz Maciag and Dario Gerace and Ryan Farr and Sarang Satoor 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 Andrzej Januszewski and S{\o}rensen, {Anja Elaine} and Ammira Akil and Jennifer Gamble and Thomas Loudovaris and Kay, {Thomas W} and Helen Thomas and Philip O'Connell and Gilles Guillemin and David Martin and Ann Simpson and Wayne Hawthorne and Dalgaard, {Louise Torp} and Ma, {Ronald C} and Hardikar, {Anandwardhan Awadhoot}",

year = "2021",

month = apr,

day = "23",

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

language = "English",

volume = "24",

journal = "iScience",

issn = "2589-0042",

number = "4",

}

Wong, WWK, Joglekar, M, Saini, V, Jiang, G, Dong, C, Chaitarvornki, A, Maciag, G, Gerace, D, Farr, R, Satoor, S, Sahu, S, Sharangdhar, T, Ahmed, AS, Chew, YV, Liuwantara, D, Heng, B, Lim, CK, Hunter, J, Januszewski, A, Sørensen, AE, Akil, A, Gamble, J, Loudovaris, T, Kay, TW, Thomas, H, O'Connell, P, Guillemin, G, Martin, D, Simpson, A, Hawthorne, W, Dalgaard, LT, Ma, RC & Hardikar, AA 2021, 'Machine learning workflows identify a microRNA signature of insulin transcription in human tissues', iScience, bind 24, nr. 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 W.K.

AU - Joglekar, Mugdha

AU - Saini, Vijit

AU - Jiang, Guozhi

AU - Dong, Charlotte

AU - Chaitarvornki, Alissa

AU - Maciag, Grzegorz

AU - Gerace, Dario

AU - Farr, Ryan

AU - Satoor, Sarang

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

AU - Sørensen, Anja Elaine

AU - Akil, Ammira

AU - Gamble, Jennifer

AU - Loudovaris, Thomas

AU - Kay, Thomas W

AU - Thomas, Helen

AU - O'Connell, Philip

AU - Guillemin, Gilles

AU - Martin, David

AU - Simpson, Ann

AU - Hawthorne, Wayne

AU - Dalgaard, Louise Torp

AU - Ma, Ronald C

AU - Hardikar, Anandwardhan Awadhoot

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

U2 - 10.1016/j.isci.2021.102379

DO - 10.1016/j.isci.2021.102379

M3 - Journal article

SN - 2589-0042

VL - 24

JO - iScience

JF - iScience

IS - 4

M1 - 102379

ER -