文章基本信息

标题：Machine learning workflows identify a microRNA signature of insulin transcription in human tissues
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作者：Wilson K.M. Wong ; Mugdha V. Joglekar ; Vijit Saini 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：4
页码：1-47
DOI：10.1016/j.isci.2021.102379
语种：English
出版社：Elsevier
摘要：SummaryDicer 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.Graphical abstractDisplay OmittedHighlights•Unbiased machine learning workflow ranks miRNAs associated with insulin transcription•Forced expression of top-ranked miRNAs drives pro-endocrine program in progenitor cells•Knockdown of top-ranked miRNAs retards insulin gene transcription in human islets•Insulin transcript-associated miRNAs are reduced in islets of donors with type 2 diabetesPathophysiology; Computational Bioinformatics; Transcriptomics