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- A big-data approach to understanding metabolic rate and response to obesity in laboratory mice.
A big-data approach to understanding metabolic rate and response to obesity in laboratory mice.
Citation | “A Big-Data Approach To Understanding Metabolic Rate And Response To Obesity In Laboratory Mice.”. Elife. . |
Center | Vanderbilt University |
Author | June K Corrigan, Deepti Ramachandran, Yuchen He, Colin J Palmer, Michael J Jurczak, Rui Chen, Bingshan Li, Randall H Friedline, Jason K Kim, Jon J Ramsey, Louise Lantier, Owen P McGuinness, Mouse Metabolic Phenotyping Center Energy Balance Working Group, Alexander S Banks |
Keywords | energy expenditure, food intake, Genetics, human biology, medicine, metabolic rate, mouse, neuroscience, obesity, thermogenesis |
Abstract |
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. |
Year of Publication |
2020
|
Journal |
eLife
|
Volume |
9
|
Date Published |
05/2020
|
ISSN Number |
2050-084X
|
DOI |
10.7554/eLife.53560
|
Alternate Journal |
Elife
|
PMID |
32356724
|
PMCID |
PMC7274785
|
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