Validation of targets linked to weight gain in C57BL/6 mice and translational aspects in humans
Year: 2026
Type: article
Abstract: Obesity is linked to complex interactions between an organism's genetic background and its environment. Mouse models have been invaluable research tools to study obesity, with C57BL/6N being widely studied, given its propensity for weight gain on a high-fat diet (HFD). Despite this, the genetic basis of its susceptibility is poorly understood. In a partner paper we have identified SNP variants in 11 genes causing susceptibility to, or protection against, HFD-induced obesity in male C57BL/6N mice explaining 87% of the individual variation in fat gain. Surprisingly, most of them were maximally expressed outside the brain. In this study, we further validate some of these targets. We demonstrate that tissue-specific inhibition of Nova1 and Mchr1 in muscle, Nr1i3 in liver, and Ahr in lung and liver led to resistance to HFD-induced obesity or glucose intolerance. Further RNAseq analysis revealed that mTOR, Acox1, IL-15, and eIF2a signals are positioned downstream of these genes, potentially mediating their effects on obesity susceptibility. Importantly, variants in eight of the genes identified here have been previously implicated in human obesity, suggesting that a deeper understanding of the genetic/environment interactions of model systems can inform this complex human disease. Our work confirms that body fat regulation is likely more complex than current models which focus on the brain-adipose axis suggest.
Institutions: Chinese Academy of Sciences, Inner Mongolia University, Institute of Genetics and Developmental Biology, AGH University of Krakow, Yantai University, China Medical University, Peking University Third Hospital, China Medical University, Shenzhen Institutes of Advanced Technology, University of Aberdeen, Shenzhen University of Advanced Technology
Language: English
Subfield: Endocrine and Autonomic Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7223112862
