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    Date Issued2020 (1)2019 (1)Author
    Banks, Alexander S. (2)
    Corrigan, June K. (2)Friedline, Randall H. (2)He, Yuchen (2)Jurczak, Michael J. (2)View MoreUMass Chan AffiliationDivision of Endocrinology, Metabolism, and Diabetes, Department of Medicine (2)Program in Molecular Medicine (2)Document TypeJournal Article (1)Preprint (1)KeywordBiochemical Phenomena, Metabolism, and Nutrition (2)Nutritional and Metabolic Diseases (2)obesity (2)Bioinformatics (1)body weight (1)View MoreJournalbioRxiv (1)eLife (1)

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    A big-data approach to understanding metabolic rate and response to obesity in laboratory mice

    Corrigan, June K.; Ramachandran, Deepti; He, Yuchen; Palmer, Colin J.; Jurczak, Michael J.; Chen, Rui; Li, Bingshan; Friedline, Randall H.; Kim, Jason K.; Ramsey, Jon J.; et al. (2020-05-01)
    Maintaining a healthy body weight requires an exquisite balance between energy intake and energy expenditure. To understand the genetic and environmental factors that contribute to the regulation of body weight, an important first step is to establish the normal range of metabolic values and primary sources contributing to variability. Energy metabolism is measured by powerful and sensitive indirect calorimetry devices. Analysis of nearly 10,000 wild-type mice from two large-scale experiments revealed that the largest variation in energy expenditure is due to body composition, ambient temperature, and institutional site of experimentation. We also analyze variation in 2329 knockout strains and establish a reference for the magnitude of metabolic changes. Based on these findings, we provide suggestions for how best to design and conduct energy balance experiments in rodents. These recommendations will move us closer to the goal of a centralized physiological repository to foster transparency, rigor and reproducibility in metabolic physiology experimentation.
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    A big-data approach to understanding metabolic rate and response to obesity in laboratory mice [preprint]

    Corrigan, June K.; Ramachandran, Deepti; He, Yuchen; Palmer, Colin; Jurczak, Michael J.; Li, Bingshan; Friedline, Randall H.; Kim, Jason K.; Ramsey, Jon J.; Lantier, Louise; et al. (2019-11-12)
    Maintaining a healthy body weight requires an exquisite balance between energy intake and energy expenditure. In humans and in laboratory mice these factors are experimentally measured by powerful and sensitive indirect calorimetry devices. To understand the genetic and environmental factors that contribute to the regulation of body weight, an important first step is to establish the normal range of metabolic values and primary sources contributing to variability in results. Here we examine indirect calorimetry results from two experimental mouse projects, the Mouse Metabolic Phenotyping Centers and International Mouse Phenotyping Consortium to develop insights into large-scale trends in mammalian metabolism. Analysis of nearly 10,000 wildtype mice revealed that the largest experimental variances are consequences of institutional site. This institutional effect on variation eclipsed those of housing temperature, body mass, locomotor activity, sex, or season. We do not find support for the claim that female mice have greater metabolic variation than male mice. An analysis of these factors shows a normal distribution for energy expenditure in the phenotypic analysis of 2,246 knockout strains and establishes a reference for the magnitude of metabolic changes. Using this framework, we examine knockout strains with known metabolic phenotypes. We compare these effects with common environmental challenges including age, and exercise. We further examine the distribution of metabolic phenotypes exhibited by knockout strains of genes corresponding to GWAS obesity susceptibility loci. Based on these findings, we provide suggestions for how best to design and conduct energy balance experiments in rodents, as well as how to analyze and report data from these studies. These recommendations will move us closer to the goal of a centralized physiological repository to foster transparency, rigor and reproducibility in metabolic physiology experimentation.
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