Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104324
Title: Dissecting diabetes/metabolic disease mechanisms using pluripotent stem cells and genome editing tools
Authors: Teo, Adrian Kee Keong
Gupta, Manoj K.
Doria, Alessandro
Kulkarni, Rohit N.
Keywords: Diabetes; Metabolic disease; Pluripotent stem cells; Genome editing; CRISPR/Cas; Disease modeling
Issue Date: 2015
Source: Teo, A. K. K., Gupta, M. K., Doria, A., & Kulkarni, R. N. (2015). Dissecting diabetes/metabolic disease mechanisms using pluripotent stem cells and genome editing tools. Molecular Metabolism, 4(9), 593-604.
Series/Report no.: Molecular Metabolism
Abstract: Background: Diabetes and metabolic syndromes are chronic, devastating diseases with increasing prevalence. Human pluripotent stem cells are gaining popularity in their usage for human in vitro disease modeling. With recent rapid advances in genome editing tools, these cells can now be genetically manipulated with relative ease to study how genes and gene variants contribute to diabetes and metabolic syndromes. Scope of review: We highlight the diabetes and metabolic genes and gene variants, which could potentially be studied, using two powerful technologies – human pluripotent stem cells (hPSCs) and genome editing tools – to aid the elucidation of yet elusive mechanisms underlying these complex diseases. Major conclusions: hPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes. The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.
URI: https://hdl.handle.net/10356/104324
http://hdl.handle.net/10220/38812
ISSN: 2212-8778
DOI: 10.1016/j.molmet.2015.06.006
Schools: School of Biological Sciences 
Rights: © 2015 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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