About the Unit
The primary goal of the Mouse Metabolic Research Unit is to characterize mouse metabolic phenotypes. MMRU users currently have access to a Comprehensive Laboratory Animal Monitoring System (CLAMS, Columbus Instruments) for metabolic assessments, treadmills, and instruments for indirectly measuring body composition; i.e., lean body mass, fat mass and bone mineral content.
Instruments and Services
The Comprehensive Lab Animal Monitoring System (CLAMS) is a mouse monitoring system that can be customized to fulfill the needs of specific users/experimental designs. This system integrates a variety of non-invasive monitoring technologies that enable the continuous assessment of a number of metabolic and physiologic parameters in mice. The capabilities of our system include quantitative 24-hour monitoring of behavioral (e.g. spontaneous ambulatory or running wheel activity/locomotion, sleeping, and feeding patterns) and physiological (e.g. oxygen consumption, carbon dioxide production, respiratory exchange ratio, energy expenditure, body temperature, and exercise capacity) parameters, as well as automated control of food availability for meal feeding or pair-feeding.
For noninvasive measurement of body composition, the MMRU maintains instruments for dual x-ray absorptiometry (PIXImus, GE Healthcare) and quantitative magnetic resonance (QMR, Echo Medical).
For assessment of energy expenditure, food intake, activity, body temperature, a two lane metabolic treadmill, a six-lane treadmill, and a grip strength meter (Columbus Instruments) are available.
Benefits for Investigators
The MMRU enables investigators to monitor energy balance concurrently in a large number of mice in an affordable and interpretable manner. The capability to assess body composition and exercise capacity extends the possibility to accurately evaluate and understand components of energy balance. The MMRU expands upon the Children’s Nutrition Research Center's original calorimetry core established in 2002. The expertise in the performance and interpretation of metabolic studies of the CNRC calorimetry core (also includes human and pig indirect calorimetry facilities) remains within the MMRU and ensures that the data generated are consistently of high quality. Users can also benefit from the expertise of other MMRU scientists, who are available to help plan experimental designs and assist with data interpretation.
Unit References
Mina AI, LeClair RA, LeClair KB, Cohen DE, Lantier L, Banks AS. CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments. Cell Metab. 2018; 28(4):656-666. PMID: 30017358
Cortopassi MD, Ramachandran D, Rubio WB, Hochbaum D, Sabatini BL, Banks AS. Analysis of Thermogenesis Experiments with CalR. Methods Mol Biol. 2022;2448:43-72. PMID: 35167089
Even PC, Nadkarni NA. Indirect calorimetry in laboratory mice and rats: principles, practical considerations, interpretation and perspectives. Am J Physiol Regul Integr Comp Physiol. 2012;303:R459-76.
Tschöp MH, Speakman JR, Arch JR, Auwerx J, Brüning JC, Chan L, Eckel RH, Farese RV Jr, Galgani JE, Hambly C, Herman MA, Horvath TL, Kahn BB, Kozma SC, Maratos-Flier E, Müller TD, Münzberg H, Pfluger PT, Plum L, Reitman ML, Rahmouni K, Shulman GI, Thomas G, Kahn CR, Ravussin E. A guide to analysis of mouse energy metabolism. Nat Methods. 2011 Dec 28;9(1):57-63.
Pack AI, Galante RJ, Maislin G, Cater J, Metaxas D, Lu S, Zhang L, Von Smith R, Kay T, Lian J, Svenson K, and Peters LL. Novel method for high-throughput phenotyping of sleep in mice. Physiol. Genomics February 2007 28:(2) 232-238.
Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C: Determinants of 24-hour energy expenditure in man. Methods and results using a respiratory chamber. J Clin Invest 1986;78:1568-1578.
Jones AS, Johnson MS, Nagy TR. Validation of quantitative magnetic resonance for the determination of body composition of mice. Int J Body Compos Res. 2009;7(2):67-72.
Nagy TR et al., Precision and accuracy of DEXA for determining in vivo body composition of mice. Obesity Research 8: 392, 2000. Brommage R. Validation and calibration of DEXA body composition in mice. Am J Physiol Endo Metab 285: E454, 2003.
Ellacott KL, Morton GJ, Woods SC, Tso P, Schwartz MW. Assessment of feeding behavior in laboratory mice. Cell Metab. 2010 12(1):10-17.
Kaiyala KJ, Morton GJ, Leroux BG, Ogimoto K, Wisse B, Schwartz MW. Identification of body fat mass as a major determinant of metabolic rate in mice. Diabetes. 2010 59(7):1657-1666.
MacLean PS. Comment on: Kaiyala et al. (2010) Identification of body fat mass as a major determinant of metabolic rate in mice (Letter). Diabetes 2011;60:e3.
Response to Comment on: Kaiyala et al. (2010) Identification of Body Fat Mass as a Major Determinant of Metabolic Rate in Mice. Diabetes;59:1657-1666 Diabetes 2011 60:e4.
Weir, J. B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 109, 1–9.