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accession-icon SRP070673
Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers Elovl2 as a novel regulator of glucose-induced insulin secretion
  • organism-icon Mus musculus
  • sample-icon 383 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Defective insulin secretion by pancreatic ß cells underlies the development of type 2 diabetes (T2D). High fat diet-fed mice are commonly used to study diabetes progression, but studies are usually limited to a single strain, such as C57Bl/6J. Here, we use a systems biology approach to integrate large phenotypic and islet transcriptomic data sets from six commonly used strains fed a high fat or regular chow diet to identify genes associated with glucose intolerance and insulin secretion. One of these genes is Elovl2, encoding very long chain fatty acid elongase 2. ELOVL2 is responsible for the synthesis of the polyunsaturated fatty acid, docosahexaenoic acid (DHA). We show that DHA rescues glucose-induced insulin secretion and cytosolic Ca2+ influx impaired by glucolipotoxicity, and that Elovl2 over-expression is able to restore the insulin secretion defect under these conditions. We propose that increased endogenous DHA levels resulting from Elovl2 up-regulation counteracts the insulin secretion defect associated with glucolipotoxicity. Although we focus our experimental validation on Elovl2, the comprehensive data set and integrative network model we used to identify this candidate gene represents an important novel resource to dissect the molecular aetiology of ß cell failure in murine models. Overall design: 6 mouse strains, 4 time points, 2 diets

Publication Title

Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for <i>Elovl2</i> in glucose-induced insulin secretion.

Sample Metadata Fields

Specimen part, Cell line, Subject, Time

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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