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A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274785/ https://www.ncbi.nlm.nih.gov/pubmed/32356724 http://dx.doi.org/10.7554/eLife.53560 |
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author | 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 Lantier, Louise McGuinness, Owen P Banks, Alexander S |
author_facet | 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 Lantier, Louise McGuinness, Owen P Banks, Alexander S |
author_sort | Corrigan, June K |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7274785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-72747852020-06-09 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 Lantier, Louise McGuinness, Owen P Banks, Alexander S eLife Human Biology and Medicine 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. eLife Sciences Publications, Ltd 2020-05-01 /pmc/articles/PMC7274785/ /pubmed/32356724 http://dx.doi.org/10.7554/eLife.53560 Text en © 2020, Corrigan et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Human Biology and Medicine 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 Lantier, Louise McGuinness, Owen P Banks, Alexander S A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title | A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title_full | A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title_fullStr | A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title_full_unstemmed | A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title_short | A big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
title_sort | big-data approach to understanding metabolic rate and response to obesity in laboratory mice |
topic | Human Biology and Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274785/ https://www.ncbi.nlm.nih.gov/pubmed/32356724 http://dx.doi.org/10.7554/eLife.53560 |
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