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Mol Metab. 2015 Jun 20;4(9):593-604. doi: 10.1016/j.molmet.2015.06.006. eCollection 2015 Sep.

Dissecting diabetes/metabolic disease mechanisms using pluripotent stem cells and genome editing tools.

Author information

1
Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA 02215, USA ; Discovery Research Division, Institute of Molecular and Cell Biology, Proteos, Singapore 138673, Singapore ; School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore ; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.
2
Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA 02215, USA.
3
Section of Epidemiology and Genetics, Joslin Diabetes Center, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02215, USA.

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.

KEYWORDS:

CRISPR/Cas; Diabetes; Disease modeling; Genome editing; Metabolic disease; Pluripotent stem cells

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