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iScience · 2021

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

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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

Аннотация

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.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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