ML is funded by the Department of Medicine at UCLA, pilot grants from P30 DK 41301 (UCLA:DDRC NIH) and P30 DK063491 (UCSD-UCLA DERC).

All experiments were approved by the Institutional Animal Care and Use Committee at the University of California, Los Angeles (UCLA). Mice were administered their diet and water ad libitum in the metabolic cage, housed in a temperature-controlled environment (~21-22 or 30 &#176;C) with a 12h light/dark cycle. 8 week-old female mice fed a high-fat diet or chow diet for 8 weeks were used for this study.
1. Measurement of the Basal Metabolic Rate (BMR)
<ol>
	<li>Measure the total body weight of the mouse using a weight scale with accuracy in the range of 0.1 g. 
	NOTE: This must be done before housing the mice in metabolic cages and after the 2-3 days of acclimation period to the metabolic cages.</li>
	<li>Measure the body composition, including fat and lean mass in non-anesthetized mice, using an appropriate body composition analysis system (see Table of Materials). 
	NOTE: These measurements are needed to determine energy expenditure and are executed in parallel to total body weight measurements (step 1.1).</li>
	<li>Set up the metabolic cages and start the acclimation period. 
	NOTE: The metabolic cages system includes an enclosure that allows the user to control the housing temperature and light of 12 cages (Figure 1A,B). Each cage has a water bottle, a feeder, and a grid (Figure 1C). The grid separates the mouse from the bottom of the cage, allowing feces collection. Once the cage is installed on each pre-determined space, a lid that seals the cage includes the water bottle slot, the tubing sampling air, the airflow system, and the physical activity sensor (Figure 1D).
	<ol>
		<li>Turn on the temperature enclosure, the airflow system, and computer 2 h before starting the assay.</li>
		<li>After 2 h, open the software controlling the enclosure (see Table of Materials) and the airflow and let the software test the computer&#39;s communication with the equipment. 
		NOTE: Oxymax software was used for the present work.</li>
		<li>Once communication is established, click File, then Open Experiment Configuration (Figure 2A) and select the experiment configuration predesigned by the vendor (or set up from a previous assay).</li>
		<li>Click on Experiment, then click on Properties, which will open the Experiment Properties window (Figure 2B).</li>
		<li>In the Properties window, set up the parameters of the environmental enclosure, including the Ambient Temperature (21 &#176;C) and the 12 h Light cycles. 
		NOTE: Keeping the software open and running allows air to flow into the cages and the enclosure to maintain the selected temperature and light cycles. Thus, the entire system can operate with mice inside the cages for several days, even without measuring oxygen and CO2.</li>
		<li>Click on Experiment, then click on Setup, and the Experiment Setup window will open, where the parameters of each metabolic cage are defined.</li>
		<li>Assign each mouse ID to the individual cage where the mouse is housed (Figure 2C).</li>
		<li>Include the lean mass or total body weight for each mouse only if no differences in body weight are observed between groups. 
		NOTE: Obtaining raw values of oxygen consumption and energy expenditure facilitates ANCOVA analyses.</li>
		<li>Set the airflow rate to the metabolic cage at 0.5-0.6 L/min.</li>
		<li>On the Experiment Setup window, select the file saving path and name. Select the backup directory (Figure 2D).</li>
		<li>Add a pre-weighted amount of food to the feeders that covers at least food intake for 1 day. 
		NOTE: If the cages have integrated scales, the food can be added directly, and the software will record it.</li>
		<li>Add the water bottles. Check that the bottle is sealed correctly and does not leak.</li>
		<li>24 h after adding the food, weigh the food that is left on the cage. 
		NOTE: The grams of food added minus the grams of food left will measure food intake.</li>
		<li>Start the oxygen, CO2, and activity measurements (step 1.4.10) once the food intake values are the same as those of mice housed in regular cages. 
		NOTE: Here, the acclimation period (usually 2-3 days) is completed, and energy expenditure measurements can start.</li>
	</ol>
	</li>
	<li>Indirect calorimetry and activity measurements to assess energy expenditure
	<ol>
		<li>Measure the body weight, fat, and lean mass of all mice before starting the measurements. 
		NOTE: These are the body weight and lean mass values used to perform ANCOVA analyses.</li>
		<li>Calibrate the CLAMS system&#39;s O2 and CO2 Zirconia-based detector (see Table of Materials) with the recommended oxygen concentration; always re-calibrate the detector before starting a new experiment.</li>
		<li>Use a calibration gas of known composition (20.50% oxygen and 0.50% CO2). 
		NOTE: Gas suppliers often refer to this gas as &#34;Primary Standard Grade.&#34;</li>
		<li>Turn on and ensure that the tank output pressure is at 5-10 psi.</li>
		<li>Open the Calibration Utility software for calibrating and testing the gas sensors (Figure 2E). Click on Experiment, then Calibrate.</li>
		<li>Press Start. Then, wait for the sensors to be tested and for the software to ask the user to turn the knobs of the gas sensor (Figure 2F) until the value of O2 identity is 1 (Figure 2G-H). Click Next when the step is complete. 
		NOTE: If the Calibration Utility performs all current steps, the calibration will automatically proceed to the next step when the progress bar is filled.</li>
		<li>Verify the calibration results when all steps have been completed, and the results are presented.</li>
		<li>Turn off the calibration gas.</li>
		<li>Change food and add sufficient food&#160;for a 48-72 h period. 
		NOTE: Although cages could be opened during the energy expenditure measurements to monitor the body weight and change the food daily, mice can be stressed by these manipulations, and measurements are lost when the cages are opened. Thus, it is recommended to avoid any manipulation during the measurement period.</li>
		<li>In the software, click on Experiment and then Run to start the oxygen, CO2, and activity measurements (Figure 3A). 
		NOTE: The execution of the measurements can be tracked in real-time in a box located at the bottom left section of the software (red rectangle, Figure 3B). The red rectangle in Figure 3B shows that the system measures cage #1 at interval #3, namely the 3rd measurement. One measurement in one cage can take about 1 min. Thus, with 12 cages connected, oxygen consumption can be measured approximately every 12 min. Continuous measurements for a minimum of 48 h are recommended.</li>
		<li>Stop the experiment by clicking Experiment and&#160;then Stop (Figure 3C).</li>
		<li>Open the cages, weigh the mice and the food. Collect the feces to calculate the number of calories and lipids excreted over the 48-72 h measurements period. 
		NOTE: Feces can be stored at -20 &#176;C for later analyses. These cages cannot be effectively used to collect urine.</li>
		<li>Click on Experiments, then Export, and Export all subjects as a CSV file (Figure 3D). 
		NOTE: To facilitate ANCOVA analyses, it is essential to export raw Oxygen consumption (VO2) and CO2 production (VCO2)&#160;values without being normalized by the body weight.</li>
	</ol>
	</li>
	<li>Data analysis and quality control
	<ol>
		<li>In the exported CSV sheet (Figure 3D) (from step 1.4.13), use the raw values of the oxygen consumption (VO2) and CO2&#160;production (VCO2) measured every 12 min in the 2-3 day period that are automatically listed by the software and include a timestamp, namely the hour and date when they were measured. 
		NOTE: VO2 and VCO2 values will be automatically corrected if body weight or lean mass values are added.</li>
		<li>In the exported CSV sheet, use the raw values of the respiratory exchange ratio (RER: VCO2/VO2) that are automatically calculated and listed by the software according to their timestamp. 
		NOTE: Values close to 1 show that the mouse primarily oxidizes carbohydrates, while values closer to 0.7 represent that the mouse is mainly oxidizing fat. RER above 1 can occur during anaerobic exercise, as the body expels more CO2 to compensate for the acidosis caused by lactate. RER higher than 1 can indicate stress. The exported CSV file also contains the raw values from Energy expenditure (EE) or Heat production in calories per minute per mouse, measured every 12 min in the 2-3 days. Here, all the listed values include a timestamp.</li>
		<li>As single EE values per mouse are needed for an ANCOVA, average the EE values recorded between 09:00-16:00 for the light (day) phase and 19:00-04:00 for the dark (night) phase per mouse and day. 
		NOTE: This can be done manually using Excel or Graph Pad. Selecting these two-time windows avoids averaging the intermediate, gradual, and unstable EE values associated with the light-dark phase transition.</li>
		<li>For a 48 h period, calculate the average of the two daylight values and the two dark phase values per mouse using Excel or Graph Pad.</li>
		<li>To quantify the total physical activity, use Excel or Graph Pad to sum the x, y, and z beam break counts measured in the metabolic cages and listed in the CSV file for each mouse. 
		NOTE: The x,y,z total activity is calculated by doing first the average value of each X, Y, Z per mouse and cycle. Then, the sum of each average X, Y, Z values per mouse and cycle is determined to plot the data as in Figure 5E&#160;(being the average of 2 days).</li>
		<li>Alternatively, represent the data showing each measurement value over time, which generates curves that illustrate the changes in EE during the transition from light to dark cycles. 
		&#8203;NOTE: Please refer to the discussion section on how and when to perform ANCOVA analyses, and the different formulas used to calculate VO2, VCO2, and EE are provided in Supplementary File 1.</li>
	</ol>
	</li>
</ol>
2. Measurement of the capacity of thermogenic adipocytes to expend energy
<ol>
	<li>Set up the measurements and mouse treatments. See step 1 for details on the experimental preparations to monitor oxygen consumption, as thermogenic capacity in adipocytes is determined indirectly by oxygen consumption following steps 2.1.1-2.2.2. 
	NOTE: This protocol requires mouse anesthesia and acute treatment with the beta-3 receptor agonist CL-316,243 (see Table of Materials), giving a rapid assessment of BA thermogenic capacity.
	<ol>
		<li>Perform body composition analysis and weigh the mice. Turn on the CLAMS, set up the temperature at 30 &#176;C (thermoneutrality), and wait for 2 h for the whole system to warm up.</li>
		<li>Set up the rest of the assay conditions, including light, assign mouse ID to each cage and add body weight value of each mouse in the corresponding cage if no difference in body weight is observed between groups.</li>
		<li>Calibrate the oxygen/CO2 detector as in steps 1.4.2-1.4.7.</li>
		<li>Start the Experiment in the software.</li>
		<li>Inject each mouse with pentobarbital (60-120 mg/kg) and place each mouse in their assigned metabolic cage (step 2.1.2). 
		NOTE: The dose of pentobarbital required to maintain mice asleep at thermoneutrality (30 &#176;C) varies with the mouse strain and genotype. It is recommended to test different pentobarbital doses from 50 to 120 mg/kg and chose the one keeping the mouse anesthetized during 2-3 h at 30 &#176;C. Efficient anesthesia is essential to remove the contribution of physical activity to energy expenditure.</li>
		<li>To ensure anesthesia, observe mice after pentobarbital injection until they are completely asleep and their decreased oxygen consumption rates become steady.</li>
		<li>Wait to obtain at least 3 stable consecutive oxygen consumption rates before injecting CL-316,243. 
		NOTE: While waiting for the stabilization of oxygen consumption, prepare the syringes with CL-316,243 for each mouse (1 mg/kg).</li>
		<li>Open cage #1 and inject CL-316,243 subcutaneously immediately after one VO2 and VCO2 measurement occurred in cage #1. Return the mouse to cage #1 immediately after injection. 
		NOTE: Measurements are being indicated in real-time in the bottom-left section of the software (Figure 3B, red rectangle).</li>
		<li>Wait for cage #2 to be measured (Figure 3B, red rectangle) and then proceed as in step 2.1.8 for cage #2. 
		NOTE: Injecting CL-316,243 immediately after a measurement allows maintaining the time constant between injections. For instance, if there are 12 mice/cages running, with measurements collected in individual cages sequentially and the collection lasting 55 s per cage, then you should inject one mouse every minute. With these injection rates, the first measurement will occur after 12 min after injection in all 12 cages.</li>
		<li>Continue the energy expenditure measurements until the energy expenditure values plateau for 5-6 consecutive measurements, usually 90-180 min after injection. 
		NOTE: Mice could wake up from the anesthesia during the experiments. These mice need to be removed from the analysis. Therefore, testing pentobarbital doses beforehand will increase the efficiency of the studies.</li>
		<li>Stop the energy expenditure measurements, but keep mice at their cages at 30 &#176;C, until they wake up.</li>
		<li>After mice are fully awake, inspect the health of the mice and return them to their initial cages.</li>
		<li>Export the data of each mouse as a CSV file using the equipment software, as described in section 1.4.13.</li>
	</ol>
	</li>
	<li>Data analysis 
	NOTE: The data analysis was performed by Excel or Graphpad
	<ol>
		<li>Plot the 3-5 consecutive values of VO2, VCO2, and EE that are stable and constant over time, as these are the values representing the metabolic rate when mice are fully anesthetized.</li>
		<li>Then, plot the first and the following consecutive VO2, VCO2, and EE measurements obtained after injection. 
		NOTE: The absolute values of EE and the fold increase in EE induced by injection indicate BA thermogenic function7.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Rolfe, D. F., Brown, G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+energy+utilization+and+molecular+origin+of+standard+metabolic+rate+in+mammals.">Cellular energy utilization and molecular origin of standard metabolic rate in mammals.</a> Physiological Reviews. 77 (3), 731-758 (1997).</li><li>Heymsfield, S. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+energy+expenditure:+advances+in+organ-tissue+prediction+models.">Human energy expenditure: advances in organ-tissue prediction models.</a> Obesity Reviews. 19 (9), 1177-1188 (2018).</li><li>Kummitha, C. M., Kalhan, S. C., Saidel, G. M., Lai, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relating+tissue/organ+energy+expenditure+to+metabolic+fluxes+in+mouse+and+human:+experimental+data+integrated+with+mathematical+modeling.">Relating tissue/organ energy expenditure to metabolic fluxes in mouse and human: experimental data integrated with mathematical modeling.</a> Physiological Reports. 2 (9), 12159 (2014).</li><li>Tschop, M. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+analysis+of+mouse+energy+metabolism.">A guide to analysis of mouse energy metabolism.</a> Nature. 9 (1), 57-63 (2011).</li><li>Mina, A. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CalR:+A+Web-Based+Analysis+Tool+for+Indirect+Calorimetry+Experiments.">CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments.</a> Cell Metabolism. 28 (4), 656-666 (2018).</li><li>Shum, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ABCB10+exports+mitochondrial+biliverdin,+driving+metabolic+maladaptation+in+obesity.">ABCB10 exports mitochondrial biliverdin, driving metabolic maladaptation in obesity.</a> Science Translational Medicine. 13 (594), (2021).</li><li>Assali, E. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NCLX+prevents+cell+death+during+adrenergic+activation+of+the+brown+adipose+tissue.">NCLX prevents cell death during adrenergic activation of the brown adipose tissue.</a> Nature Communication. 11 (1), 3347 (2020).</li><li>Clark, J. D., Gebhart, G. F., Gonder, J. C., Keeling, M. E., Kohn, D. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Special+Report:+The+1996+Guide+for+the+Care+and+Use+of+Laboratory+Animals.">Special Report: The 1996 Guide for the Care and Use of Laboratory Animals.</a> ILAR Journal. 38 (1), 41-48 (1997).</li><li>Schena, G., Caplan, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Everything+You+Always+Wanted+to+Know+about+beta3-AR+*+(*+But+were+afraid+to+ask).">Everything You Always Wanted to Know about beta3-AR * (* But were afraid to ask).</a> Cells. 8 (4), 357 (2019).</li><li>Granneman, J. G., Burnazi, M., Zhu, Z., Schwamb, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=White+adipose+tissue+contributes+to+UCP1-independent+thermogenesis.">White adipose tissue contributes to UCP1-independent thermogenesis.</a> American Journal of Physiology-Endocrinology and Metabolism. 285 (6), 1230-1236 (2003).</li><li>Szentirmai, E., Kapas, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+the+brown+adipose+tissue+in+beta3-adrenergic+receptor+activation-induced+sleep,+metabolic+and+feeding+responses.">The role of the brown adipose tissue in beta3-adrenergic receptor activation-induced sleep, metabolic and feeding responses.</a> Scientific Reports. 7 (1), 958 (2017).</li></ol>

The authors declare no conflict of interest to this protocol paper. M.L. is a co-founder and consultant for Enspire Bio LLC.

Indirect calorimetry has been used for years to assess whole-body energy expenditure4. This protocol described herein provides a straightforward method of measuring the basal metabolic rate and determining BA thermogenic capacity in vivo using metabolic cages.
The indirect calorimetry method described here confirms that dividing energy expenditure values by body weight values can be misleading. For example, it can conclude that energy expenditure is systematica...

Metabolism can be defined as the integration of the biochemical reactions responsible for nutrient uptake, storage, transformation, and breakdown that cells use to grow and perform their functions. Metabolic reactions transform the energy contained in nutrients into a form that can be used by cells to synthesize new molecules and execute work. These biochemical reactions are inherently inefficient in transforming this energy into a usable form to sustain life1. Such inefficiency results in energy dissipation in the form of heat, with this heat production being used to quantify the Standard Metabolic Rate (SMR) of an organism1. The Standard condition was classically defined as heat production occurring in an awake but resting adult, not ingesting or digesting food, at thermoneutrality and without any stress1. The Basal Metabolic Rate (BMR) or basal energy expenditure in mice is referred to as the SMR but in individuals ingesting and digesting food under mild thermal stress (ambient temperatures 21-22 &#176;C)1. The challenges and difficulties of directly measuring heat production made indirect calorimetry, namely calculating heat production from oxygen consumption measurements, to become the most popular approach to determine the BMR. Calculating the BMR from oxygen consumption is possible because the oxidation of nutrients by mitochondria to synthesize ATP is responsible for 72% of the total oxygen consumed in an organism, with 8% of total oxygen consumption also occurring in mitochondria but without generating ATP (uncoupled respiration)1. The majority of the remaining 20% of oxygen consumed can be attributed to nutrient oxidation in other subcellular locations (peroxisomal fatty acid oxidation), anabolic processes, and reactive oxygen species formation1. Thus, in 1907, Lusk established an equation, based on empirical measurements, widely used to transform oxygen consumption and CO2 production into energy dissipation as heat. In humans, the brain accounts for ~25% of the BMR, the musculoskeletal system for ~18.4%, the liver for ~20 %, the heart for ~10%, and the adipose tissue for ~3-7%2. In mice, the tissue contribution to BMR is slightly different, with the brain representing ~6.5%, the skeletal muscle ~13%, the liver ~52%, the heart ~3.7%, and adipose tissue ~5%3.
Remarkably, the biochemical reactions defining the BMR are not fixed and change in response to different needs, such as external work (physical activity), development (tissue growth), internal stresses (counteracting infections, injuries, tissue turnover), and changes in ambient temperature (cold defense)1. Some organisms actively recruit processes to generate heat in cold exposure, implying that heat produced by metabolism is not just an accidental byproduct. Instead, evolution selected regulatory mechanisms that could specifically upregulate heat production by changing the rate of metabolic reactions1. Thus, these same oxygen consumption measurements can be used to determine the capacity of an organism to generate heat in response to cold.
Two major processes contribute to heat generation upon cold exposure. The first one is shivering, which generates heat by increasing mitochondrial oxidative phosphorylation and glycolysis in muscle to cover the physical work done by involuntary muscle contraction. Therefore, cold exposure will increase oxygen consumption in muscles1. The second is Non-Shivering Thermogenesis, which occurs through an increase in oxygen consumption in brown and beige adipocytes (BA). Dissipation of energy into heat in BA is mediated by the mitochondrial uncoupling protein 1 (UCP1), which allows proton re-entry into the mitochondrial matrix, decreasing the mitochondrial proton gradient. The dissipation of the mitochondrial proton gradient by UCP1 increases heat production by the elevation in electron transfer and oxygen consumption and the energy released by proton dissipation per se without generating ATP (uncoupled). Moreover, thermogenic BA can recruit additional mechanisms that elevate oxygen consumption without causing a large dissipation in the proton gradient, by activating futile oxidative ATP synthesis and consumption cycles. The metabolic cages described here, namely the CLAMS-Oxymax system from Columbus Instruments, offer the possibility to measure energy expenditure at different ambient temperatures. However, to determine BA thermogenic capacity using whole-body oxygen consumption measurements, one needs to: (1) eliminate the contribution of shivering, and other non-BA metabolic processes to energy expenditure, and (2) specifically activate BA thermogenic activity in vivo. Thus, a second protocol describes how to selectively activate BA in vivo using pharmacology in anesthetized mice at thermoneutrality (30 &#176;C), with anesthesia and thermoneutrality limiting other non-BA thermogenic processes (i.e., physical activity). The pharmacological strategy to activate BA is treating mice with the &#946;3-adrenergic receptor agonist CL-316,246. The reason is that cold exposure promotes a sympathetic response releasing norepinephrine to activate &#946;-adrenergic receptors in BA, which activates UCP1 and fat oxidation. Furthermore, &#946;3-adrenergic receptor expression is highly enriched in adipose tissue in mice.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CLAMS-Oxymax System</td>
			<td>Columbus Instruments</td>
			<td>CLAMS-center feeder-ENC</td>
			<td>Including enviromental enclosure and Zirconia oxygen sensor</td>
		</tr>
		<tr>
			<td>Desktop PC with Oxymax Software</td>
			<td>HP/Columbus</td>
			<td>N/A</td>
			<td>PC needed to be purchased separately</td>
		</tr>
		<tr>
			<td>Drierite jug (Calcium Sulfate with Cobalt Chloride Indicator)</td>
			<td>Fisher Scientific</td>
			<td>23-116681</td>
			<td>Needed to dry the gas entering the oxygen sensor, humidity can damage the sensor</td>
		</tr>
		<tr>
			<td>NMR for body composition</td>
			<td>Echo-MRI</td>
			<td>Echo-MRI 100</td>
			<td>Measure lean and fat mass in alive mice. It is necessary for ANCOVA analyses.</td>
		</tr>
		<tr>
			<td>CL-316-243</td>
			<td>Sigma</td>
			<td>C5976</td>
			<td>Injected to the mice subcutaneously to activate thermogenesis</td>
		</tr>
		<tr>
			<td>High fat diet</td>
			<td>Research Diets</td>
			<td>D12266B</td>
			<td>Provided to the mice prior and during measurements</td>
		</tr>
		<tr>
			<td>Pentobarbital/Nembutal</td>
			<td>Pharmacy at DLAM</td>
			<td>N/A</td>
			<td>Anesthesia for the mice</td>
		</tr>
		<tr>
			<td>Primary standard grade gas (tank and regulator)</td>
			<td>Praxair</td>
			<td>NI CD5000O6P-K/PRS 2012-2331-590</td>
			<td>20.50% Oxygen, 0.50% CO2 balanced with nitrogen used for calibration</td>
		</tr>
	</tbody>
</table>

determining basal energy expenditure and the capacity of thermogenic adipocytes to expend energy in obese mice

Energy expenditure measurements are necessary to understand how changes in metabolism can lead to obesity. Basal energy expenditure can be determined in mice by measuring whole-body oxygen consumption, CO2 production, and physical activity using metabolic cages. Thermogenic brown/beige adipocytes (BA) contribute significantly to rodent energy expenditure, particularly at low ambient temperatures. Here, measurements of basal energy expenditure and total BA capacity to expend energy in obese mice are described in two detailed protocols: the first explaining how to set up the assay to measure basal energy expenditure using analysis of covariance (ANCOVA), a necessary analysis given that energy expenditure co-varies with body mass. The second protocol describes how to measure BA energy expenditure capacity in vivo in mice. This procedure involves anesthesia, needed to limit expenditure caused by physical activity, followed by the injection of beta3-adrenergic agonist, CL-316,243, which activates energy expenditure in BA. These two protocols and their limitations are described in sufficient detail to allow a successful first experiment.

Figure 4 shows VO2, VCO2, Heat production/Energy expenditure (EE), Respiratory Exchange Ratio (RER), and X, Y, Z physical activity values obtained using the metabolic cages of the CLAMS system. The VO2 and VCO2 provided by the CLAMS system is the volume of gas (mL) per minute and can be already divided by the body weight or the lean mass values by entering these weight values in the CLAMS software before starting the measurements. However, body wei...

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Determining Basal Energy Expenditure and the Capacity of Thermogenic Adipocytes to Expend Energy in Obese Mice

This manuscript describes a protocol to measure the basal metabolic rate and the oxidative capacity of thermogenic adipocytes in obese mice.

Energy expenditure measurements are necessary to understand how changes in metabolism can lead to obesity. Basal energy expenditure can be determined in ...

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4.8K Views.  Universite Laval. This manuscript describes a protocol to measure the basal metabolic rate and the oxidative capacity of thermogenic adipocytes in obese mice.

Video: Determining Basal Energy Expenditure and the Capacity of Thermogenic Adipocytes to Expend Energy in Obese Mice

Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana

Light mediates an array of developmental and adaptive processes throughout the life cycle of a plant. Plants utilize light-absorbing molecules called photoreceptors to sense and adapt to light. The red/far-red light-absorbing phytochrome photoreceptors have been studied extensively. Phytochromes exist as a family of proteins with distinct and overlapping functions in all higher plant systems in which they have been studied1. Phytochrome-mediated light responses, which range from seed germination through flowering and senescence, are often localized to specific plant tissues or organs2. Despite the discovery and elucidation of individual and redundant phytochrome functions through mutational analyses, conclusive reports on distinct sites of photoperception and the molecular mechanisms of localized pools of phytochromes that mediate spatial-specific phytochrome responses are limited. We designed experiments based on the hypotheses that specific sites of phytochrome photoperception regulate tissue- and organ-specific aspects of photomorphogenesis, and that localized phytochrome pools engage distinct subsets of downstream target genes in cell-to-cell signaling. We developed a biochemical approach to selectively reduce functional phytochromes in an organ- or tissue-specific manner within transgenic plants. Our studies are based on a bipartite enhancer-trap approach that results in transactivation of the expression of a gene under control of the Upstream Activation Sequence (UAS) element by the transcriptional activator GAL43. The biliverdin reductase (BVR) gene under the control of the UAS is silently maintained in the absence of GAL4 transactivation in the UAS-BVR parent4. Genetic crosses between a UAS-BVR transgenic line and a GAL4-GFP enhancer trap line result in specific expression of the BVR gene in cells marked by GFP expression4. BVR accumulation in Arabidopsis plants results in phytochrome chromophore deficiency in planta5-7. Thus, transgenic plants that we have produced exhibit GAL4-dependent activation of the BVR gene, resulting in the biochemical inactivation of phytochrome, as well as GAL4-dependent GFP expression. Photobiological and molecular genetic analyses of BVR transgenic lines are yielding insight into tissue- and organ-specific phytochrome-mediated responses that have been associated with corresponding sites of photoperception4, 7, 8. Fluorescence Activated Cell Sorting (FACS) of GFP-positive, enhancer-trap-induced BVR-expressing plant protoplasts coupled with cell-type-specific gene expression profiling through microarray analysis is being used to identify putative downstream target genes involved in mediating spatial-specific phytochrome responses. This research is expanding our understanding of sites of light perception, the mechanisms through which various tissues or organs cooperate in light-regulated plant growth and development, and advancing the molecular dissection of complex phytochrome-mediated cell-to-cell signaling cascades.

Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana

The molecular basis of spatial-specific phytochrome responses is being investigated using transgenic plants that exhibit tissue- and organ-specific phytochrome deficiencies. The isolation of specific cells exhibiting induced phytochrome chromophore depletion by Fluorescence-Activated Cell Sorting followed by microarray analyses is being utilized to identify genes involved in spatial-specific phytochrome responses.

Light mediates an array of developmental and adaptive processes throughout the life cycle of a plant. Plants utilize light-absorbing molecules called ...

OLIgo Mass Profiling (OLIMP) of Extracellular Polysaccharides

The direct contact of cells to the environment is mediated in many organisms by an extracellular matrix. One common aspect of extracellular matrices is that they contain complex sugar moieties in form of glycoproteins, proteoglycans, and/or polysaccharides. Examples include the extracellular matrix of humans and animal cells consisting mainly of fibrillar proteins and proteoglycans or the polysaccharide based cell walls of plants and fungi, and the proteoglycan/glycolipid based cell walls of bacteria. All these glycostructures play vital roles in cell-to-cell and cell-to-environment communication and signalling.
An extraordinary complex example of an extracellular matrix is present in the walls of higher plant cells. Their wall is made almost entirely of sugars, up to 75% dry weight, and consists of the most abundant biopolymers present on this planet. Therefore, research is conducted how to utilize these materials best as a carbon-neutral renewable resource to replace petrochemicals derived from fossil fuel. The main challenge for fuel conversion remains the recalcitrance of walls to enzymatic or chemical degradation due to the unique glycostructures present in this unique biocomposite.
Here, we present a method for the rapid and sensitive analysis of plant cell wall glycostructures. This method OLIgo Mass Profiling (OLIMP) is based the enzymatic release of oligosaccharides from wall materials facilitating specific glycosylhydrolases and subsequent analysis of the solubilized oligosaccharide mixtures using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS)1 (Figure 1). OLIMP requires walls of only 5000 cells for a complete analysis, can be performed on the tissue itself2, and is amenable to high-throughput analyses3. While the absolute amount of the solubilized oligosaccharides cannot be determined by OLIMP the relative abundance of the various oligosaccharide ions can be delineated from the mass spectra giving insights about the substitution-pattern of the native polysaccharide present in the wall.
OLIMP can be used to analyze a wide variety of wall polymers, limited only by the availability of specific enzymes4. For example, for the analysis of polymers present in the plant cell wall enzymes are available to analyse the hemicelluloses xyloglucan using a xyloglucanase5, 11, 12, 13, xylan using an endo-&beta;-(1-4)-xylanase 6,7, or for pectic polysaccharides using a combination of a polygalacturonase and a methylesterase 8. Furthermore, using the same principles of OLIMP glycosylhydrolase and even glycosyltransferase activities can be monitored and determined 9.

OLIgo Mass Profiling OLIMP of Extracellular Polysaccharides

A rapid way is described to gain insights into the structure of polysaccharides in an extracellular matrix. The method takes advantage of the specificity of glycosylhydrolases and the sensitivity of mass spectrometry allowing minute amounts of materials to be analyzed. This technique is adaptable to be used directly on tissue itself.

The direct contact of cells to the environment is mediated in many organisms by an extracellular matrix. One common aspect of extracellular matrices is ...

Patch Clamp and Perfusion Techniques for Studying Ion Channels Expressed in Xenopus oocytes

The protocol presented here is designed to study the activation of the large conductance, voltage- and Ca2+-activated K+ (BK) channels. The protocol may also be used to study the structure-function relationship for other ion channels and neurotransmitter receptors1. BK channels are widely expressed in different tissues and have been implicated in many physiological functions, including regulation of smooth muscle contraction, frequency tuning of inner hair cells and regulation of neurotransmitter release2-6. BK channels are activated by membrane depolarization and by intracellular Ca2+ and Mg2+ 6-9. Therefore, the protocol is designed to control both the membrane voltage and the intracellular solution. In this protocol, messenger RNA of BK channels is injected into Xenopus laevis oocytes (stage V-VI) followed by 2-5 days of incubation at 18&deg;C10-13. Membrane patches that contain single or multiple BK channels are excised with the inside-out configuration using patch clamp techniques10-13. The intracellular side of the patch is perfused with desired solutions during recording so that the channel activation under different conditions can be examined. To summarize, the mRNA of BK channels is injected into Xenopus laevis oocytes to express channel proteins on the oocyte membrane; patch clamp techniques are used to record currents flowing through the channels under controlled voltage and intracellular solutions.

Patch Clamp and Perfusion Techniques for Studying Ion Channels Expressed in Xenopus oocytes

Ionic current of BK channels is recorded using patch clamp techniques. BK channels are expressed in Xenopus oocytes by injecting messenger RNA. The intracellular solution during patch clamp recordings is controlled by a perfusion system.

The protocol presented here is designed to study the activation of the large conductance, voltage- and Ca2+-activated K+ (BK) channels. The protocol may ...

Determining the Contribution of the Energy Systems During Exercise

One of the most important aspects of the metabolic demand is the relative contribution of the energy systems to the total energy required for a given physical activity. Although some sports are relatively easy to be reproduced in a laboratory (e.g., running and cycling), a number of sports are much more difficult to be reproduced and studied in controlled situations. This method presents how to assess the differential contribution of the energy systems in sports that are difficult to mimic in controlled laboratory conditions. The concepts shown here can be adapted to virtually any sport.
The following physiologic variables will be needed: rest oxygen consumption, exercise oxygen consumption, post-exercise oxygen consumption, rest plasma lactate concentration and post-exercise plasma peak lactate. To calculate the contribution of the aerobic metabolism, you will need the oxygen consumption at rest and during the exercise. By using the trapezoidal method, calculate the area under the curve of oxygen consumption during exercise, subtracting the area corresponding to the rest oxygen consumption. To calculate the contribution of the alactic anaerobic metabolism, the post-exercise oxygen consumption curve has to be adjusted to a mono or a bi-exponential model (chosen by the one that best fits). Then, use the terms of the fitted equation to calculate anaerobic alactic metabolism, as follows: ATP-CP metabolism = A1 (mL . s-1) x t1 (s). Finally, to calculate the contribution of the lactic anaerobic system, multiply peak plasma lactate by 3 and by the athlete&#x2019;s body mass (the result in mL is then converted to L and into kJ).
The method can be used for both continuous and intermittent exercise. This is a very interesting approach as it can be adapted to exercises and sports that are difficult to be mimicked in controlled environments. Also, this is the only available method capable of distinguishing the contribution of three different energy systems. Thus, the method allows the study of sports with great similarity to real situations, providing desirable ecological validity to the study.

This protocol allows researchers focused on exercise and sports sciences to determine the relative contribution of three different energy systems to the total energy expenditure during a large variety of exercises.

One of the most important aspects of the metabolic demand is the relative contribution of the energy systems to the total energy required for a given ...

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine cell central metabolism and discover new enzymes is 13C-assisted metabolism analysis 1. This technique is based on isotopic labeling, whereby microbes are fed with a 13C labeled substrates. By tracing the atom transition paths between metabolites in the biochemical network, we can determine functional pathways and discover new enzymes. 
As a complementary method to transcriptomics and proteomics, approaches for isotopomer-assisted analysis of metabolic pathways contain three major steps 2. First, we grow cells with 13C labeled substrates. In this step, the composition of the medium and the selection of labeled substrates are two key factors. To avoid measurement noises from non-labeled carbon in nutrient supplements, a minimal medium with a sole carbon source is required. Further, the choice of a labeled substrate is based on how effectively it will elucidate the pathway being analyzed. Because novel enzymes often involve different reaction stereochemistry or intermediate products, in general, singly labeled carbon substrates are more informative for detection of novel pathways than uniformly labeled ones for detection of novel pathways3, 4. Second, we analyze amino acid labeling patterns using GC-MS. Amino acids are abundant in protein and thus can be obtained from biomass hydrolysis. Amino acids can be derivatized by N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS) before GC separation. TBDMS derivatized amino acids can be fragmented by MS and result in different arrays of fragments. Based on the mass to charge (m/z) ratio of fragmented and unfragmented amino acids, we can deduce the possible labeled patterns of the central metabolites that are precursors of the amino acids. Third, we trace 13C carbon transitions in the proposed pathways and, based on the isotopomer data, confirm whether these pathways are active 2. Measurement of amino acids provides isotopic labeling information about eight crucial precursor metabolites in the central metabolism. These metabolic key nodes can reflect the functions of associated central pathways.
13C-assisted metabolism analysis via proteinogenic amino acids can be widely used for functional characterization of poorly-characterized microbial metabolism1. In this protocol, we will use Cyanothece 51142 as the model strain to demonstrate the use of labeled carbon substrates for discovering new enzymatic functions.

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

13C-isotope labeling is a useful technique for determining the cell central metabolism for various types of microorganisms. After cells have been cultured with a specific labeled substrate, GC-MS measurement can reveal functional metabolic pathways based on unique labeling patterns in proteinogenic amino acids.

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine ...

Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live cells. BRET is the non-radiative transfer of energy from a 'donor' luciferase enzyme to an 'acceptor' fluorescent protein. In the most common configuration of this assay, the donor is Renilla reniformis luciferase and the acceptor is Yellow Fluorescent Protein (YFP). Because the efficiency of energy transfer is strongly distance-dependent, observation of the BRET phenomenon requires that the donor and acceptor be in close proximity. To test for an interaction between two proteins of interest in cultured mammalian cells, one protein is expressed as a fusion with luciferase and the second as a fusion with YFP. An interaction between the two proteins of interest may bring the donor and acceptor sufficiently close for energy transfer to occur. Compared to other techniques for investigating protein-protein interactions, the BRET assay is sensitive, requires little hands-on time and few reagents, and is able to detect interactions which are weak, transient, or dependent on the biochemical environment found within a live cell. It is therefore an ideal approach for confirming putative interactions suggested by yeast two-hybrid or mass spectrometry proteomics studies, and in addition it is well-suited for mapping interacting regions, assessing the effect of post-translational modifications on protein-protein interactions, and evaluating the impact of mutations identified in patient DNA.

Interactions between proteins are fundamental to all cellular processes. Using Bioluminescence Resonance Energy Transfer, the interaction between a pair of proteins can be monitored in live cells and in real time. Furthermore, the effects of potentially pathogenic mutations can be assessed.

Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of energy homeostasis and obesity. The small size of zebrafish allows for assays on embryos to be conducted in a 96- or 384-well plate format, Morpholino and CRISPR based technologies promote ease of genetic manipulation, and drug treatment by bath application is viable. Moreover, zebrafish are ideal for forward genetic screens allowing for novel gene discovery. Given the relative novelty of zebrafish as a model for obesity, it is necessary to develop tools that fully exploit these benefits. Herein, we describe a method to measure energy expenditure in thousands of embryonic zebrafish simultaneously. We have developed a whole animal microplate platform in which we use 96-well plates to isolate individual fish and we assess cumulative NADH2 production using the commercially available cell culture viability reagent alamarBlue. In poikilotherms the relationship between NADH2 production and energy expenditure is tightly linked. This energy expenditure assay creates the potential to rapidly screen pharmacological or genetic manipulations that directly alter energy expenditure or alter the response to an applied drug (e.g. insulin sensitizers).

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism for the study of energy homeostasis. By utilizing a NADH2 sensitive redox indicator, alamar Blue, we have developed an assay that measures the metabolic rate of zebrafish larvae in a 96 well plate format and can be applied to drug or gene discovery.

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of ...

Measurement of Energy Metabolism in Explanted Retinal Tissue Using Extracellular Flux Analysis

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while maintaining transparency of the visual axis. Perturbations to this delicate balance cause blinding illnesses, such as diabetic retinopathy. Therefore, the understanding of energy metabolism changes in the retina during disease is imperative to the development of rational therapies for various causes of vison loss. The recent advent of commercially-available extracellular flux analyzers has made the study of retinal energy metabolism more accessible. This protocol describes the use of such an analyzer to measure contributions to retinal energy supply through its two principle arms - oxidative phosphorylation and glycolysis - by quantifying changes in oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) as proxies for these pathways. This technique is readily performed in explanted retinal tissue, facilitating assessment of responses to multiple pharmacologic agents in a single experiment. Metabolic signatures in retinas from animals lacking rod photoreceptor signaling are compared to wild-type controls using this method. A major limitation in this technique is the lack of ability to discriminate between light-adapted and dark-adapted energy utilization, an important physiologic consideration in retinal tissue.

This technique describes real time recording of oxygen consumption and extracellular acidification rates in explanted mouse retinal tissues using an extracellular flux analyzer.

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while ...

A Labor-saving and Repeatable Touch-force Signaling Mutant Screen Protocol for the Study of Thigmomorphogenesis of a Model Plant Arabidopsis thaliana

Plants responding to both intracellular and extracellular mechanical stimulations (or force signals) and develop special morphological changes, a called thigmomorphogenesis. In past decades, several signaling components have been identified and reported for being involved in the mechanotransduction (e.g., calcium ion binding proteins and jasmonic acid biosynthesis enzymes). However, the relatively slow pace of research in the study of force signaling or thigmomorphogenesis is largely attributed to two reasons: the requirement for laborious human hand-manipulated touch induction of thigmomorphogenesis and the force strength errors associated with people&#8217;s hand-touch. To enhance the efficiency of external force loading on a plant organism, an automatic touch-force loading machine was built. This robotic arm-driven hair brush touches provide a labor-saving and easily repeatable touch-force simulation, unlimited rounds of touch repetition and adjustable touch strength. This hair touch-force loading machine can be used for both large scale screening of touch-force signaling mutants and the phenomics study of plant thigmomorphogenesis. In addition, touch materials such as human hair, can be replaced with other natural materials like animal hair, silk threads and cotton fibers. The automated moving arms on the machine may be equipped with water sprinkling nozzles and air blowers to mimic the natural forces of rain drops and wind, respectively. By using this automatic hair touch-force loading machine in combination with the hand-performed cotton swab touching, we have investigated the touch response of two force signaling mutants, MAP KINASE KINASE 1 (MKK1) and MKK2 plants. The phenomes of the touch-force loaded wild type plants and two mutants were evaluated statistically. They have exhibited significant differences in touch response.

A Labor-saving and Repeatable Touch-force Signaling Mutant Screen Protocol for the Study of Thigmomorphogenesis of a Model Plant Arabidopsis thaliana

A gentle touch-force loading machine is built from human hair brushes, robotic arms and a controller. The hair brushes are driven by robotic arms installed on the machine and move periodically to apply touch-force on plants. The strength of machine-driven hair touches is comparable to that of manually applied touches.

Plants responding to both intracellular and extracellular mechanical stimulations (or force signals) and develop special morphological changes, a called ...