@article{c534df743a634292956446e2eac152d8,
title = "A big-data approach to understanding metabolic rate and response to obesity in laboratory mice",
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 2,329 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.",
author = "Corrigan, {June K.} and Deepti Ramachandran and Yuchen He and Palmer, {Colin J} and Jurczak, {Michael J} and Rui Chen and Bingshan Li and Friedline, {Randall H} and Kim, {Jason K} and Ramsey, {Jon J} and Louise Lantier and McGuinness, {Owen P} and Banks, {Alexander S.} and Collen Croniger and Sean Adams and Heni Brunengraber and Michael Jurczak and Li Kang and Kent Lloyd and Richard McIndoe and Silvana Obici and Jerry Shulman and Craig Warden and Thomas Gettys and David Wasserman and Knotts, {Trina A.} and Karl Kaiyala",
note = "Funding Information: Financial support for this work was provided by the NIDDK Mouse Metabolic Phenotyping Centers (National MMPC, RRID:SCR_008997, www.mmpc.org) under the MICROMouse Program, grant DK076169 (ASB) and R01DK107717 (ASB) Swiss National Science Foundation Postdoc Mobility grant to DR. We would like to acknowledge Drs. K. C. Kent Lloyd (National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) U24-DK092993, Gerald I. Shulman (NIDDK U24-DK059635), and Henri Brunengraber (NIDDK U24-DK076174), David H. Wasserman (U24-DK059637), Patrick Tso (NIDDK U24-DK059630), Jason Kim (U24-DK093000) and Richard McIndoe (U24-DK076169) for their support of this work. We would like to thank Christopher Jacobs and Rachael Ivison for fruitful discussions on bioinformatic approaches, Jeff and Terry Flier for critical reading of the manuscript. Publisher Copyright: {\textcopyright} 2020, eLife Sciences Publications Ltd. All rights reserved.",
year = "2020",
month = may,
day = "1",
doi = "10.7554/eLife.53560",
language = "English",
volume = "9",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications Ltd.",
}