bioRxivPhysiologyPreprint
Modeling metabolic disease susceptibility and resilience in genetically diverse mice
Candice N. BakeriD, Jeffrey M. HarderiD, Daniel A. SkellyiD, Isabella Gerdes GyuriczaiD, Margaret Gaca, Matthew J. VincentiD, Allison IngallsiD, Mark P. KelleriD, Alan AttieiD, Madeleine Braun, Michael L. StitzeliD, Edison T. LiuiD, Nadia A RosenthaliD, Gary A. ChurchilliD
Abstract
Model organisms have provided critical insights into the basic biology of metabolic disorders, however, one of the greatest limitations to translation has been the absence of the genetic heterogeneity that is characteristic of human populations. We examined metabolic health across three genetically diverse mouse strains fed control (low fat, no sucrose) or unhealthy (high fat, high sucrose) diets and observed a wide range of metabolic responses from overt type 2 diabetes in NZO/HlLtJ mice, to obesity and glucose intolerance in C57BL/6J mice, to complete resilience in CAST/EiJ mice. Analysis of multi-tissue gene expression revealed strain- and tissue-specific responses to diet, with strongest responses in white adipose tissue and pancreatic islets. In pancreatic islets, diet response was limited to just NZO mice, which showed high levels of inflammation and associated {beta} cell dysfunction. Adipose tissue was responsive to diet across all three strains and revealed both common and strain-specific changes in inflammatory and metabolic pathways. Using a complementary outbred mouse resource, we mapped genetic loci associated with strain-specific diet responses, including a monocyte regulatory locus on mChr19. This multi-strain approach to modeling metabolic disease revealed a prominent role of white adipose tissue and lipid-associated inflammation in the determination of individual disease risk in response to unhealthy diets.
- organism: mouse
- assay: bulk RNA-seq
- research focus: metabolism & bioenergetics
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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