We acknowledge the contributions of Sarah S. Chang, BS, for the initial cell culture and preparation of MH-S cells for experimentation. This work was supported by NIAAA R01-AA026086 to SMY (ORCID: 0000-0001-9309-0233), NIAAA F31-F31AA029938 to KMC, and NIGMS T32-GM008602 to Randy Hall, Department of Pharmacology and Chemical Biology, Emory University. The contents of this report do not represent the views of the Department of Veterans Affairs or the US Government.

1. Chronic EtOH exposure in vitro
<ol>
	<li>Culture MH-S cells, a mouse alveolar macrophage cell line, in complete media containing RPMI-1640 with 10% fetal bovine serum, 1% penicillin/streptomycin, 11.9 mM sodium bicarbonate, 40 mg/mL gentamicin, and 50 &#181;M 2-mercaptoethanol at 37&#176;C with 5% CO2 in a humidified incubator.</li>
	<li>Treat cultured MH-S cells with or without EtOH (0.8 mg/mL or 0.08%) for 72 h, where EtOH should be changed every 24 h. Incubate EtOH-treated MH-S cells at 37 &#176;C with 5% CO2 in a separate humidified incubator containing 0.08% EtOH in the incubator water.</li>
</ol>
2. Plating MH-S cells for assessing glutamine as a fuel source using an extracellular flux bioanalyzer
<ol>
	<li>Ensure that untreated control (Con)- and chronic EtOH-treated MH-S cells are grown to at least 50% confluency (70%-90% confluency is ideal) in T-75 flasks to passage into a 96-well extracellular flux microculture plate. Prepare a plate map for 5-6 technical replicates per biological replicate of Con and EtOH experimental conditions and media controls for each to compare against BPTES injections (four groups total for biological n= 1). </li>
	<li>Rinse cells with media or phosphate-buffered saline.</li>
	<li>Add 6 mL of serum-containing media to the flask.</li>
	<li>Scrape cells until they are detached and the bottom of the plate is clear. Trypsin is not needed for MH-S cell detachment but may be helpful in other cell types.</li>
	<li>Separate the cells using a serological pipet or 1 mL pipette.</li>
	<li>Transfer the suspended cells to a 15 mL conical tube and centrifuge them at 200 x g for 6 min.</li>
	<li>Aspirate the supernatant.</li>
	<li>Add 1 mL of media and resuspend the cells thoroughly.</li>
	<li>Transfer 10 &#181;L to a microcentrifuge tube and add 10 &#181;L of trypan blue. Mix well.</li>
	<li>Transfer 10 &#181;L of the cell/trypan blue mixture to one side of a hemocytometer or cell counter.</li>
	<li>Count the number of cells using a light microscope or cell counter.</li>
	<li>Calculate the cell density as an average live cell count per milliliter.</li>
	<li>Make calculations for the dilutions that are needed: 
	Number of wells needed x 10000-15000 cells = total number of cells needed. 
	Number of wells needed x 80 &#181;L = total volume of resuspended cells needed.</li>
	<li>Apply 80 &#181;L of cells to the necessary wells of the 96-well extracellular flux microculture plate.</li>
	<li>Leave the corner wells completely devoid of any cells or media. These wells will be used for background measurements.</li>
	<li>Incubate cells in a 37 &#176;C humidified incubator with 5% CO2 overnight to allow cells to adhere and equilibrate in the extracellular flux microculture plate.</li>
	<li>Treat MH-S cells in the 96-well extracellular flux microculture plate for 72 h using step 1.</li>
	<li>At least 4 h and up to 24 h before running the extracellular flux bioanalyzer experiment, hydrate a 96-well extracellular flux pak cartridge.
	<ol>
		<li>Remove the top portion of the extracellular flux pak and place the probed portion of the flux pak face up on the bench. Apply 200 &#181;L of extracellular flux calibrant solution to each well in the bottom part of the extracellular flux pak cartridge.</li>
		<li>Put the sections together and wrap the extracellular flux pak cartridge in parafilm. Place it in a 37 &#176;C non-CO2 humidified incubator or 37 &#176;C bead bath.</li>
	</ol>
	</li>
	<li>Turn on the computer containing the assay design and open the running/analysis software attached to the extracellular flux bioanalyzer instrument to allow it to warm up at least 4 h prior to performing the experiment.</li>
</ol>
3. Using an extracellular flux bioanalyzer to assess glutamine as a fuel source for mitochondrial respiration in MH-S cells
NOTE: The general overview of this procedure is shown in Figure 1.
<ol>
	<li>Check Con- and EtOH-treated cultured MH-S cells under a light microscope and ensure that cells are healthy and adhered to the wells in a monolayer. Ensure that the cells are at least 70% confluent (90% confluency is ideal) to perform the assay reliably.</li>
	<li>Prepare the extracellular flux base medium with the following supplements:
	<ol>
		<li>Aliquot 35 mL of extracellular flux base medium into a 50 mL tube.</li>
		<li>Add 350 &#181;L of 100 mM sodium pyruvate (1 mM final concentration), 350 &#181;L D-glucose (10 mM final concentration), and 350 &#181;L of GlutaMAX, which is more shelf-stable compared to L-glutamine (2 mM final concentration). 
		NOTE: Fresh L-glutamine may also be used at the same concentration.</li>
		<li>Warm the solution to 37 &#176;C and ensure the pH is 7.4 &#177; 0.05 at 37 &#176;C.</li>
	</ol>
	</li>
	<li>Gently aspirate out the growth media from the extracellular microculture plate. Leave as little media as possible without aspirating cells from the bottom. Wash once with a maximum of 50 &#181;L of the extracellular flux-supplemented base medium.</li>
	<li>Add 180 &#181;L of extracellular flux supplemented base medium to each well and incubate the extracellular microculture plate in a 37 &#176;C non-CO2 humidified incubator for 30 min to 1 h to allow for equilibration.</li>
	<li>Dissolve glutamine oxidation test reagents:
	<ol>
		<li>Into the BPTES tube, add 700 &#181;L of extracellular flux-supplemented base medium to make a 120 mM stock solution. Pipette up and down 10 times to properly solubilize the compound. 
		CAUTION: BPTES causes skin and eye irritation and may cause respiratory irritation. Wear protective gloves, eye protection, and face protection when handling. Dispose of contents and containers to an approved waste disposal facility.</li>
		<li>Into the oligomycin tube, add 630 &#181;L of extracellular flux supplemented base medium to make a 100 mM stock solution. Pipette up and down 10 times to properly solubilize the compound.</li>
		<li>Into the FCCP tube, add 720 &#181;L of extracellular flux-supplemented base medium to make a 100 mM stock solution. Pipette up and down 10 times to properly solubilize the compound. 
		CAUTION: FCCP is harmful if swallowed, causes severe skin burns and eye damage, may cause an allergic skin reaction, and may cause long-lasting harmful effects to aquatic life. Wear protective gloves, protective clothing, eye protection, and face protection when handling. Dispose of contents and containers to an approved waste disposal facility.</li>
		<li>Into the R/A tube, add 540 &#181;L of extracellular flux-supplemented base medium to make a 50 mM stock solution. Pipette up and down 10 times to properly solubilize the compound. 
		CAUTION: Rotenone is fatal if swallowed or if inhaled, causes skin irritation, causes serious eye irritation, may cause respiratory irritation, and is very toxic to aquatic life with long-lasting effects. Wear protective gloves, eye protection, face protection, and respiratory protection. Dispose of contents and containers to an approved waste disposal facility. Antimycin A is fatal if swallowed and is very toxic to aquatic life, with long-lasting effects. Dispose of contents and containers to an approved waste disposal facility.</li>
	</ol>
	</li>
	<li>Prepare working concentrations of substrate oxidation and mitochondrial stress test reagents:
	<ol>
		<li>For BPTES, mix 1,500 &#181;L of extracellular flux supplemented base medium with 500 &#181;L of 120 mM BPTES stock solution to make a 30 &#181;M BPTES working solution.</li>
		<li>For oligomycin, mix 2,520 &#181;L of extracellular flux supplemented base medium with 480 &#181;L of 100 mM oligomycin to make a 16 mM oligomycin working solution.</li>
		<li>For FCCP, mix 2,865 &#181;L of extracellular flux supplemented base medium with 135 &#181;L of 100 mM FCCP to make a 4.5 mM FCCP working solution.</li>
		<li>For R/A, mix 2,700 &#181;L of extracellular flux supplemented base medium with 300 &#181;L of 50 mM R/A to make a 5 mM R/A working solution.</li>
	</ol>
	</li>
	<li>Load the ports of the upper of the extracellular flux pak cartridge with the following:
	<ol>
		<li>Load the background and media control wells with 20 &#181;L of extracellular flux supplemented base medium into port A, 22 &#181;L of oligomycin into port B, 25 &#181;L of FCCP into port C, and 27 &#181;L of R/A into port D.</li>
		<li>Load the cell-containing wells with 20 &#181;L of media control or BPTES into port A (final concentration of 3 &#181;M for BPTES), 22 &#181;L of oligomycin into port B (final concentration of 2 &#181;M for oligomycin), 25 &#181;L of FCCP into port C (final concentration of 0.5 &#181;M for FCCP), and 27 &#181;L of R/A (final concentration of 0.5 &#181;M for R/A) into port D.</li>
	</ol>
	</li>
	<li>Perform the extracellular flux bioanalyzer experiment using the computer software program for assay design and analysis that is attached to the instrument using the following timing and injection strategy:
	<ol>
		<li>For the injection with port A for BPTES, set the number of measurements to 6. Set the mix time to 3 min, with a 0-min wait time and a 3-min measurement time.</li>
		<li>For the injection with port B for oligomycin, set the number of measurements to 3. Set the mix time to 3 min, with a 0-min wait time and a 3-min measurement time.</li>
		<li>For the injection with port C for FCCP, set the number of measurements to 3. Set the mix time to 3 min, with a 6-min wait time and a 3-min measurement time.</li>
		<li>For the injection with port D for R/A, set the number of measurements to 3. Set the mix time to 3 min, with a 0-min wait time and a 3-min measurement time.</li>
		<li>Review all set background, media control, and experimental group wells. Save and run the assay. First, load the extracellular flux pak with A-D injections. Calibration will occur until the cell plate is ready to load.</li>
		<li>Remove the cell plate immediately at the end of the assay. Save results to analyze later on another device.</li>
		<li>Check if the cells are still adhered to using a light microscope. Wash 1x with phosphate-buffered saline and lyse cells with a preferred cell lysis buffer. Store at -80 &#176;C or assay immediately for protein concentration.</li>
	</ol>
	</li>
</ol>
4. Analysis of results to assess glutamine as a fuel source for mitochondrial respiration in MH-S cells
<ol>
	<li>Extract protein from cells in each well to normalize oxygen consumption rate (OCR) data to protein:
	<ol>
		<li>Calculate the concentration (nanograms/microliter [ng/&#181;L]) of protein per well.</li>
		<li>Calculate the average protein concentration (ng/&#181;L) for the entire extracellular flux microculture plate.</li>
		<li>Divide each well&#39;s protein concentration by the average plate&#39;s protein concentration to get a normalization factor.</li>
		<li>Apply the normalization factor to the data file of interest using the computer software program for assay design and analysis.</li>
	</ol>
	</li>
	<li>Ensure that the OCR values of all background wells are close to 0.</li>
	<li>Deselect any cell wells where OCR is &#60; 10 at baseline. Data will not be deleted, but highlighted wells will show up in the exported data file.</li>
	<li>Export the data from the computer software program for assay design and analysis into a computer spreadsheet program.</li>
	<li>Open the file in the computer spreadsheet program and sort all data by decreasing the values of OCR. Delete background wells, wells that were previously deselected (and had not been deleted), any wells with low cell viability, wells with low cell confluency, wells without full injection strategy completion, wells with inaccurate protein concentration readings, wells with baseline OCR &#60; 10, and any other predetermined criteria.</li>
	<li>Sort all remaining data by measurement, then by group, and then by OCR.</li>
	<li>Average each biological N&#39;s technical replicates independently by measurement. Ensure there is a minimum of 18 different values (3 baseline, 3 after media/BPTES, 3 after oligomycin, 3 after FCCP, and 6 after rotenone/antimycin A) per experimental group per biological N after averaging the technical replicates. Do this for each biological N separately so that the standard deviation or standard error of the mean can be calculated later for analyzed data.</li>
	<li>Copy all averaged values into a new spreadsheet, separated by experimental group.</li>
	<li>Average the OCR values by injection strategy in each experimental group.</li>
	<li>Calculate parameters of mitochondrial bioenergetics based on oxygen consumption:
	<ol>
		<li>Calculate basal respiration:
		<ol>
			<li>Calculate basal mitochondrial respiration as follows: 
			Basal mitochondrial respiration (pmol O2 consumed) = ([AVG OCR measurements #1, 2, 3 - AVG OCR measurements #16, 17, 18] x [Time #3 - Time #1]) 
			&#8203;NOTE: Basal respiration should be similar between media control and BPTES groups because this is before the inhibitor is injected.</li>
			<li>Calculate basal mitochondrial respiration not dependent on glutamine as follows: 
			Basal mitochondrial respiration not dependent on glutamine (pmol O2 consumed) = ([AVG OCR measurements #4, 5, 6, 7, 8, 9 - AVG OCR measurements #16, 17, 18] x [Time #9 - Time #4]) 
			NOTE: Basal mitochondrial respiration not dependent on glutamine should be less than basal mitochondrial respiration unless cells are not heavily dependent on glutamine.</li>
			<li>Calculate basal mitochondrial respiration dependent on glutamine as follows: 
			Basal mitochondrial respiration dependent on glutamine (pmol O2 consumed) = ([AVG OCR measurements #1, 2, 3 - AVG OCR measurements #4, 5, 6, 7, 8, 9] x [Time #9 - Time #4]) 
			&#8203;NOTE: Total basal mitochondrial respiration should not be subtracted from basal mitochondrial respiration, and it should not be dependent on glutamine since these are calculated at different time points.</li>
		</ol>
		</li>
		<li>Calculate ATP-linked respiration as follows:
		<ol>
			<li>Calculate total mitochondrial ATP-linked respiration from all fuels as follows: 
			Total mitochondrial ATP-linked respiration from all fuels (pmol O2 consumed) = ([AVG OCR measurements #1, 2, 3 - AVG OCR measurements #10, 11, 12] x [Time #12 - Time #10]) 
			&#8203;NOTE: The average OCR for measurements #10, 11, and 12 should be similar between media control and BPTES groups since oligomycin should strongly inhibit ATP synthase, and inhibiting one fuel pathway should not change the total ATP-linked respiration.</li>
			<li>Calculate ATP-linked mitochondrial respiration not dependent on glutamine as follows: 
			ATP-linked mitochondrial respiration not dependent on glutamine (pmol O2 consumed) = ([AVG OCR measurements #4, 5, 6, 7, 8, 9 - AVG OCR measurements #10, 11, 12] x [Time #12 - Time #10]) 
			NOTE: ATP-linked mitochondrial respiration not dependent on glutamine should be less than ATP-linked mitochondrial respiration unless cells are not heavily dependent on glutamine.</li>
			<li>Calculate ATP-linked mitochondrial respiration dependent on glutamine as follows: 
			&#8203;ATP-linked mitochondrial respiration dependent on glutamine (pmol O2 consumed) = Total ATP-linked mitochondrial respiration from all fuels - ATP-linked mitochondrial respiration not dependent on glutamine</li>
		</ol>
		</li>
		<li>Calculate maximal respiration as follows:
		<ol>
			<li>Calculate total maximal mitochondrial respiration as follows: 
			&#8203;Total maximal mitochondrial respiration (pmol O2 consumed) = ([AVG OCR measurements #13, 14, 15 - AVG OCR measurements #16, 17, 18] x [Time #15 - Time #13])</li>
			<li>Calculate maximal mitochondrial respiration not dependent on glutamine as follows: 
			&#8203;Maximal mitochondrial respiration not dependent on glutamine (pmol O2 consumed) = ([AVG OCR measurements #13, 14, 15 of BPTES groups - AVG OCR measurements #16, 17, 18 of BPTES groups] x [Time #15 - #Time 13])</li>
			<li>Calculate maximal mitochondrial respiration dependent on glutamine as follows: 
			&#8203;Maximal mitochondrial respiration dependent on glutamine (pmol O2 consumed) = Total maximal mitochondrial respiration from all fuels - Maximal mitochondrial respiration not dependent on glutamine 
			&#8203;NOTE: This calculation may be used to determine cell dependency for the spare capacity of glutamine compared to other fuels. The greater the maximal mitochondrial respiration, the greater the dependency on glutamine, which can be expressed as BPTES groups - control groups to show a loss in maximal mitochondrial respiration or as a percentage of total respiration.</li>
		</ol>
		</li>
		<li>Calculate spare respiratory capacity as follows:
		<ol>
			<li>Calculate total spare respiratory capacity as follows: 
			Total spare respiratory capacity (pmol O2 consumed) = ([AVG OCR measurements #13, 14, 15 - AVG OCR measurements #1, 2, 3] x [Time #15 - Time #13])</li>
			<li>Calculate spare respiratory capacity not dependent on glutamine as follows: 
			&#8203;Spare respiratory capacity not dependent on glutamine (pmol O2 consumed) = ([AVG OCR measurements #13, 14, 15 of BPTES groups - AVG OCR measurements #1, 2, 3 of BPTES groups] x [Time #15 - Time #13])</li>
			<li>Calculate spare respiratory capacity dependent on glutamine as follows: 
			Spare respiratory capacity dependent on glutamine (pmol O2 consumed) = Total spare respiratory capacity from all fuels - Spare respiratory capacity not dependent on glutamine 
			NOTE: This calculation represents the flexibility of cells dependent on glutamine and if they can achieve greater respiration without glutamine when needed.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Subtract media controls by inhibitor groups and normalize pmol O2 consumed relative to the control group if desired.</li>
	<li>Create graphs of OCR bioenergetic profiles (pmol O2/min), basal respiration (pmol O2), ATP-linked respiration (pmol O2), maximal respiration (pmol O2), and spare respiratory capacity (pmol O2) dependent on glutamine.</li>
	<li>Depict mitochondrial bioenergetic endpoint values in bar graphs or express as means of the inhibitor groups relative to the media control groups. If each biological N is calculated separately, then calculate the standard deviation or standard error of the mean for analyzed data across all biological Ns.</li>
</ol>
5. Statistical analysis
<ol>
	<li>Perform all statistical analyses using appropriate data analysis software.</li>
	<li>For linear points of OCR bioenergetic profiles, perform two-way ANOVA followed by Tukey&#39;s post hoc analyses to compare data from these four experimental groups with two factors (treatment and injection strategy): Con + media, Con + BPTES, EtOH + media, and EtOH + BPTES.</li>
	<li>For bar graphs of basal respiration, ATP-linked respiration, maximal respiration, and spare respiratory capacity dependent on glutamine, conduct Student&#39;s t-test analyses to compare data from two experimental groups, Con + BPTES, and EtOH + BPTES.</li>
	<li>Present data as means &#177; standard error of the mean (SEM). Consider p &#60; 0.05 statistically significant.</li>
</ol>

Ethanol Impairs AM Bioenergetics by Shifting to Glutamine-Dependent Respiration

<ol>
	<li>. 2021 National Survey on Drug Use and Health (NSDUH). Table 5.6A - Alcohol Use Disorder in Past Year: Among People Aged 12 or Older; by Age Group and Demographic Characteristics, Numbers in Thousands, 2021 Available from: <a target="_blank" href="https://www.samhsa.gov/data/sites/default/files/reports/rpt39441/NSDUHDetailedTabs2021/NSDUHDetailedTabs2021/NSDUHDetTabsSect5pe2021.htm">https://www.samhsa.gov/data/sites/default/files/reports/rpt39441/NSDUHDetailedTabs2021/NSDUHDetailedTabs2021/NSDUHDetTabsSect5pe2021.htm</a> (2021)</li><li>Everts, R. J., 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=Nosocomial+pneumonia+in+adult+general+medical+and+surgical+patients+at+Christchurch+Hospital.">Nosocomial pneumonia in adult general medical and surgical patients at Christchurch Hospital.</a> N Z Med J. 113 (1111), 221-224 (2000).</li><li>de Roux, 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=Impact+of+alcohol+abuse+in+the+etiology+and+severity+of+community-acquired+pneumonia.">Impact of alcohol abuse in the etiology and severity of community-acquired pneumonia.</a> Chest. 129 (5), 1219-1225 (2006).</li><li>Moss, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epidemiology+of+sepsis:+race,+sex,+and+chronic+alcohol+abuse.">Epidemiology of sepsis: race, sex, and chronic alcohol abuse.</a> Clin Infect Dis. 41 (Suppl 7), S490-S497 (2005).</li><li>Fernandez-Sola, J., 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=High+alcohol+intake+as+a+risk+and+prognostic+factor+for+community-acquired+pneumonia.">High alcohol intake as a risk and prognostic factor for community-acquired pneumonia.</a> Arch Intern Med. 155 (15), 1649-1654 (1995).</li><li>Yeligar, S. M., Harris, F. L., Hart, C. M., Brown, 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=Glutathione+attenuates+ethanol-induced+alveolar+macrophage+oxidative+stress+and+dysfunction+by+downregulating+NADPH+oxidases.">Glutathione attenuates ethanol-induced alveolar macrophage oxidative stress and dysfunction by downregulating NADPH oxidases.</a> Am J Physiol Lung Cell Mol Physiol. 306 (5), L429-L441 (2014).</li><li>Yeligar, S. M., Mehta, A. J., Harris, F. L., Brown, L. A., Hart, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peroxisome+proliferator-activated+receptor+gamma+regulates+chronic+alcohol-induced+alveolar+macrophage+dysfunction.">Peroxisome proliferator-activated receptor gamma regulates chronic alcohol-induced alveolar macrophage dysfunction.</a> Am J Respir Cell Mol Biol. 55 (1), 35-46 (2016).</li><li>Liang, Y., Harris, F. L., Brown, 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=Alcohol+induced+mitochondrial+oxidative+stress+and+alveolar+macrophage+dysfunction.">Alcohol induced mitochondrial oxidative stress and alveolar macrophage dysfunction.</a> Biomed Res Int. 2014, 371593 (2014).</li><li>Liang, Y., Harris, F. L., Jones, D. P., Brown, 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=Alcohol+induces+mitochondrial+redox+imbalance+in+alveolar+macrophages.">Alcohol induces mitochondrial redox imbalance in alveolar macrophages.</a> Free Radic Biol Med. 65, 1427-1434 (2013).</li><li>Morris, N. L., Harris, F. L., Brown, L. A. S., Yeligar, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alcohol+induces+mitochondrial+derangements+in+alveolar+macrophages+by+upregulating+NADPH+oxidase+4.">Alcohol induces mitochondrial derangements in alveolar macrophages by upregulating NADPH oxidase 4.</a> Alcohol. 90, 27-38 (2021).</li><li>Crotty, K. M., Kabir, S. A., Chang, S. S., Mehta, A. J., Yeligar, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pioglitazone+reverses+alcohol-induced+alterations+in+alveolar+macrophage+mitochondrial+phenotype.">Pioglitazone reverses alcohol-induced alterations in alveolar macrophage mitochondrial phenotype.</a> Alcohol Clin Exp Res (Hoboken). 48 (5), 810-826 (2024).</li><li>Demine, S., Renard, P., Arnould, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mitochondrial+uncoupling:+A+key+controller+of+biological+processes+in+physiology+and+diseases.">Mitochondrial uncoupling: A key controller of biological processes in physiology and diseases.</a> Cells. 8 (8), 795 (2019).</li><li>Hill, B. G., 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=Integration+of+cellular+bioenergetics+with+mitochondrial+quality+control+and+autophagy.">Integration of cellular bioenergetics with mitochondrial quality control and autophagy.</a> Biol Chem. 393 (12), 1485-1512 (2012).</li><li>Chacko, B. K., 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=The+Bioenergetic+Health+Index:+a+new+concept+in+mitochondrial+translational+research.">The Bioenergetic Health Index: a new concept in mitochondrial translational research.</a> Clin Sci (Lond). 127 (6), 367-373 (2014).</li><li>Fan, Y., 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=Analyzing+mitochondrial+respiration+of+human+induced+pluripotent+stem+cell-derived+myeloid+progenitors+using+Seahorse+technology.">Analyzing mitochondrial respiration of human induced pluripotent stem cell-derived myeloid progenitors using Seahorse technology.</a> STAR Protoc. 4 (1), 102073 (2023).</li><li>Gu, X., Ma, Y., Liu, Y., Wan, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+mitochondrial+respiration+in+adherent+cells+by+Seahorse+XF96+Cell+Mito+Stress+Test.">Measurement of mitochondrial respiration in adherent cells by Seahorse XF96 Cell Mito Stress Test.</a> STAR Protoc. 2 (1), 100245 (2021).</li><li>Plitzko, B., Loesgen, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+oxygen+consumption+rate+(OCR)+and+extracellular+acidification+rate+(ECAR)+in+culture+cells+for+assessment+of+the+energy+metabolism.">Measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in culture cells for assessment of the energy metabolism.</a> Bio Protoc. 8 (10), e2850 (2018).</li><li>Winnica, D., 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=Bioenergetic+differences+in+the+airway+epithelium+of+lean+versus+obese+asthmatics+are+driven+by+nitric+oxide+and+reflected+in+circulating+platelets.">Bioenergetic differences in the airway epithelium of lean versus obese asthmatics are driven by nitric oxide and reflected in circulating platelets.</a> Antioxid Redox Signal. 31 (10), 673-686 (2019).</li><li>Nickens, K. P., Wikstrom, J. D., Shirihai, O. S., Patierno, S. R., Ceryak, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bioenergetic+profile+of+non-transformed+fibroblasts+uncovers+a+link+between+death-resistance+and+enhanced+spare+respiratory+capacity.">A bioenergetic profile of non-transformed fibroblasts uncovers a link between death-resistance and enhanced spare respiratory capacity.</a> Mitochondrion. 13 (6), 662-667 (2013).</li><li>Mdaki, K. S., Larsen, T. D., Weaver, L. J., Baack, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age+related+bioenergetics+profiles+in+isolated+rat+cardiomyocytes+using+extracellular+flux+analyses.">Age related bioenergetics profiles in isolated rat cardiomyocytes using extracellular flux analyses.</a> PLoS One. 11 (2), e0149002 (2016).</li><li>Dranka, B. P., 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=Assessing+bioenergetic+function+in+response+to+oxidative+stress+by+metabolic+profiling.">Assessing bioenergetic function in response to oxidative stress by metabolic profiling.</a> Free Radic Biol Med. 51 (9), 1621-1635 (2011).</li><li>Chacko, B. K., 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=Methods+for+defining+distinct+bioenergetic+profiles+in+platelets,+lymphocytes,+monocytes,+and+neutrophils,+and+the+oxidative+burst+from+human+blood.">Methods for defining distinct bioenergetic profiles in platelets, lymphocytes, monocytes, and neutrophils, and the oxidative burst from human blood.</a> Lab Invest. 93 (6), 690-700 (2013).</li><li>Tyrrell, D. J., Bharadwaj, M. S., Jorgensen, M. J., Register, T. C., Molina, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Blood+cell+respirometry+is+associated+with+skeletal+and+cardiac+muscle+bioenergetics:+Implications+for+a+minimally+invasive+biomarker+of+mitochondrial+health.">Blood cell respirometry is associated with skeletal and cardiac muscle bioenergetics: Implications for a minimally invasive biomarker of mitochondrial health.</a> Redox Biol. 10, 65-77 (2016).</li><li>Shah-Simpson, S., Pereira, C. F., Dumoulin, P. C., Caradonna, K. L., Burleigh, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioenergetic+profiling+of+Trypanosoma+cruzi+life+stages+using+Seahorse+extracellular+flux+technology.">Bioenergetic profiling of Trypanosoma cruzi life stages using Seahorse extracellular flux technology.</a> Mol Biochem Parasitol. 208 (2), 91-95 (2016).</li><li>Nicholls, D. G., 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=Bioenergetic+profile+experiment+using+C2C12+myoblast+cells.">Bioenergetic profile experiment using C2C12 myoblast cells.</a> J Vis Exp. 46, e2511 (2010).</li><li>Chance, B., Williams, G. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Respiratory+enzymes+in+oxidative+phosphorylation.+I.+Kinetics+of+oxygen+utilization.">Respiratory enzymes in oxidative phosphorylation. I. Kinetics of oxygen utilization.</a> J Biol Chem. 217 (1), 383-393 (1955).</li><li>Gerencser, A. 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=Quantitative+microplate-based+respirometry+with+correction+for+oxygen+diffusion.">Quantitative microplate-based respirometry with correction for oxygen diffusion.</a> Anal Chem. 81 (16), 6868-6878 (2009).</li><li>Yeligar, S. 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=PPARgamma+regulates+mitochondrial+structure+and+function+and+human+pulmonary+artery+smooth+muscle+cell+proliferation.">PPARgamma regulates mitochondrial structure and function and human pulmonary artery smooth muscle cell proliferation.</a> Am J Respir Cell Mol Biol. 58 (5), 648-657 (2018).</li><li>Yeligar, S., Tsukamoto, H., Kalra, V. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ethanol-induced+expression+of+ET-1+and+ET-BR+in+liver+sinusoidal+endothelial+cells+and+human+endothelial+cells+involves+hypoxia-inducible+factor-1alpha+and+microrNA-199.">Ethanol-induced expression of ET-1 and ET-BR in liver sinusoidal endothelial cells and human endothelial cells involves hypoxia-inducible factor-1alpha and microrNA-199.</a> J Immunol. 183 (8), 5232-5243 (2009).</li><li>Yeligar, S. M., Harris, F. L., Brown, L. A. S., Hart, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pharmacological+reversal+of+post-transcriptional+alterations+implicated+in+alcohol-induced+alveolar+macrophage+dysfunction.">Pharmacological reversal of post-transcriptional alterations implicated in alcohol-induced alveolar macrophage dysfunction.</a> Alcohol. 106, 30-43 (2023).</li></ol>

All authors do not have any relevant financial conflicts of interest to report.

The data presented herein are additional biological replicates for untreated and chronic EtOH-exposed MH-S cells treated with media control or BPTES, which are published in Crotty et al.11. The protocol described is used to assess the dependency of EtOH-exposed MH-S cells on glutamine as a fuel source for mitochondrial respiration and bioenergetics using an extracellular flux bioanalyzer. There are several critical steps in the protocol. Firstly, MH-S cell formation and confluency are important. M...

Alcohol use disorder (AUD) affects over 28 million people in the United States1. People with AUD are 2-4 times more likely to develop respiratory infections2,3, increasing risk for premature morbidity and mortality3,4,5. Alcohol misuse increases susceptibility to respiratory infections, in part through suppressing lung immune function. Alveolar macrophages (AMs) are the first line of cellular defense against pathogens in the lower lung, where they phagocytize and clear inhaled microbes. However, chronic alcohol consumption impairs the capacity of AMs to engulf and kill pathogens6,7.
The immune functions of AMs, such as phagocytosis, are energy-demanding processes that require metabolically active pathways necessary for high adenosine triphosphate (ATP) generation. Mitochondrial oxidative phosphorylation is the most efficient cellular process for producing ATP, but chronic ethanol (EtOH) exposure increases mitochondrial-derived oxidative stress, which can result in mitochondrial dysfunction in AMs8,9,10. Mitochondrial respiration measured using cellular oxygen consumption rate (OCR) can be quantified using an extracellular flux bioanalyzer in real-time. Mitochondrial respiration profiles assessed in response to oligomycin (ATP synthase inhibitor), carbonyl cyanide- p-trifluoromethoxyphenylhydrazone (FCCP, proton uncoupler), and rotenone/antimycin A (R/A, mitochondrial complex I and III inhibitors, respectively) are used to determine mitochondrial bioenergetics. Chronic EtOH decreases mitochondrial bioenergetics in AMs, as evidenced by diminished mitochondrial basal respiration, ATP-linked respiration, maximal respiration, and spare respiratory capacity10.
Cells have various levels of dependency on sources of fuels, such as pyruvate, glutamine, or fatty acids, for ATP production. They also have a level of flexibility whereby they can switch fuel usage to regulate cellular bioenergetics. Glutaminolysis is a key pathway for mitochondrial respiration, particularly when pyruvate is shunted towards lactic acid production or when fatty acids are insufficient. Our recent study showed that chronic EtOH exposure increases AM dependency on glutamine and decreases AM flexibility to use glutamine and other fuel sources for mitochondrial respiration11. These results, along with data demonstrating that treatment of EtOH-exposed MH-S cells with the pyruvate oxidation inhibitor UK5099 did not alter mitochondrial bioenergetics compared to media control-exposed MH-S cells treated with UK5099, suggested a shift away from pyruvate-dependent respiration and towards glutamine-dependent respiration in EtOH MH-S cells. However, this shift is insufficient to meet AM bioenergetic demands11. Herein, we describe the methods to assess glutamine as a fuel source for mitochondrial respiration in chronic EtOH-exposed AMs using an extracellular flux bioanalyzer. This protocol was created and optimized for a 96-well (11.04 mm2 area) format and should be adjusted for larger cell culture well sizes. Glutamine dependency for mitochondrial bioenergetics is determined using bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide (BPTES, inhibitor of glutaminase 1) to selectively inhibit the enzymatic conversion of glutamine to glutamate. The data presented in this study are additional biological replicates for the media control- and BPTES-treated experimental groups of untreated control- and chronic EtOH-exposed mouse AM cell line, MH-S cells, that are published in Crotty et al.11.

<table><tbody><tr><td>15 mL conical tube&amp;nbsp;</td><td>VWR International, Inc.</td><td>21008-670</td><td>Used for preparing stock concentrations of mitochondria-related reagents.</td></tr><tr><td>2-mercaptoethanol&amp;nbsp;</td><td>Sigma Aldrich Co.</td><td>M6250</td><td>Used for culturing MH-S cells.</td></tr><tr><td>Cell scraper</td><td>Dot Scientific, Inc.</td><td>70-1180</td><td>Used for scraping MH-S cells from T-75 flasks.</td></tr><tr><td>Countess 3 FL Instrument: cell counter</td><td>Fisher Scientific Company</td><td>AMQAF2000</td><td>Used for counting the number of MH-S cells to plate for experiments.</td></tr><tr><td>D-glucose&amp;nbsp;</td><td>Sigma Aldrich Co.</td><td>G8270</td><td>Used for the extracellular flux base medium.</td></tr><tr><td>Ethanol</td><td>Fisher Scientific Company</td><td>4355720</td><td>Experimental treatment for MH-S cells.</td></tr><tr><td>Fetal bovine serum</td><td>Sciencell Research Laboratories</td><td>500</td><td>Used for culturing MH-S cells.</td></tr><tr><td>Gentamicin</td><td>Sigma Aldrich Co.</td><td>G1397</td><td>Used for culturing MH-S cells.</td></tr><tr><td>GlutaMAX</td><td>Thermo Fisher Scientific</td><td>35050061</td><td>Used for the extracellular flux base medium.</td></tr><tr><td>GraphPad Prism 10.2.3</td><td>GraphPad Software</td><td>N/A</td><td>Software for statistical analysis. Downloadable after purchase at https://www.graphpad.com/features.</td></tr><tr><td>MH-S cells</td><td>American Type Culture Collection</td><td>CRL-2019</td><td>Mouse alveolar macrophage cell line.</td></tr><tr><td>Microcentrifuge tube&amp;nbsp;</td><td>USA Scientific, Inc.</td><td>4036-3212</td><td>Used for preparing working concentrations of mitochondria-related reagents.</td></tr><tr><td>Microsoft 365 Excel: computer spreadsheet program</td><td>Microsoft</td><td>N/A</td><td>Software for data organization and mitochondrial bioenergetics calculations. Downloadable after purchase at https://www.microsoft.com/en-us/microsoft-365/excel.</td></tr><tr><td>Penicillin/streptomycin</td><td>Fisher Scientific Company</td><td>15140122</td><td>Used for culturing MH-S cells.</td></tr><tr><td>Phosphate buffered saline</td><td>VWR International, Inc.</td><td>45000-446</td><td>Used for washing MH-S cells.</td></tr><tr><td>RPMI-1640</td><td>VWR International, Inc.</td><td>45000-396</td><td>Used for culturing MH-S cells.</td></tr><tr><td>Seahorse Wave Pro Software: computer software program for assay design and analysis</td><td>Agilent Technologies, Inc.</td><td>N/A</td><td>The computer is attached to the Seahorse XF Pro Analyzer instrument. Downloadable from&amp;nbsp; https://www.agilent.com/en/product/cell-analysis/real-time-cell-metabolic-analysis/xf-software/software-download-for-seahorse-wave-pro-software?productURL=https%3A%2F%2Fwww.agilent.com%2Fen%2Fproduct%2Fcell-analysis%2Freal-time-cell-metabolic-analysis%2Fxf-software%2Fseahorse-wave-pro-software-2007523.</td></tr><tr><td>Seahorse XF Base Medium: extracellular flux base medium, pH 7.4</td><td>Agilent Technologies, Inc.</td><td>103334-100</td><td>Used for preparing stock and working concentrations of mitochondria-related reagents and culturing MH-S cells prior to experimental runs.</td></tr><tr><td>Seahorse XF Glutamine Oxidation Stress Test Kit</td><td>Agilent Technologies, Inc.</td><td>103674-100</td><td>Contains stock BPTES, oligomycin, FCCP, and rotenone/antimycin A.</td></tr><tr><td>Seahorse XF Pro Analyzer: extracellular flux bioanalyzer</td><td>Agilent Technologies, Inc.</td><td>N/A</td><td>Extracellular flux bioanalyzer.</td></tr><tr><td>Seahorse XFe96 FluxPak: 96-well extracellular flux pak cartridges, 96-well extracellular flux microculture plates, and calibrant solution</td><td>Agilent Technologies, Inc.</td><td>102416-100</td><td>Used to prepare MH-S cells for assays using an extracellular flux bioanalyzer.</td></tr><tr><td>Serological pipet&amp;nbsp;</td><td>Santa Cruz Biotechnology, Inc.</td><td>sc-550678</td><td>Used for removing media from MH-S cells.</td></tr><tr><td>Sodium bicarbonate</td><td>Sigma Aldrich Co.</td><td>S6014</td><td>Used for culturing MH-S cells.</td></tr><tr><td>Sodium pyruvate&amp;nbsp;</td><td>Sigma Aldrich Co.</td><td>P4562</td><td>Used for the extracellular flux base medium.</td></tr><tr><td>T-75 flasks</td><td>Santa Cruz Biotechnology, Inc.</td><td>sc-200263</td><td>Used for culturing MH-S cells.</td></tr></tbody></table>

assessment of glutamine as a fuel source for alveolar macrophages exposed to chronic ethanol using an extracellular flux bioanalyzer

Alveolar macrophages (AMs) are the first line of cellular defense in the lower airway against pathogens. However, chronic and excessive alcohol use impairs the ability of AMs to phagocytize and clear pathogens from the alveolar space, in part through dysregulated fuel metabolism and bioenergetics. Our prior work has shown that chronic ethanol (EtOH) consumption impairs mitochondrial bioenergetics and increases lactate levels in AMs. Further, we recently demonstrated that EtOH increases glutamine dependency and glutamine-dependent maximal respiration while decreasing flexibility, shifting away from pyruvate-dependent respiration and towards glutamine-dependent respiration. Glutaminolysis is an important compensatory pathway for mitochondrial respiration when pyruvate is used for lactic acid production or when other fuel sources are insufficient. Using a mouse AM cell line, MH-S cells, exposed to either no EtOH or EtOH (0.08%) for 72 h, we determined the dependency of mitochondrial respiration and bioenergetics on glutamine as a fuel source using an extracellular flux bioanalyzer. Real-time measures were done in response to bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide (BPTES), an inhibitor of glutaminase 1, which prevents the enzymatic conversion of glutamine to glutamate, in media vehicle or in response to vehicle alone, followed by testing mitochondrial stress. The step-by-step protocol provided herein describes our methods and calculations for analyzing average levels of glutamine-dependent basal mitochondrial respiration, mitochondrial ATP-linked respiration, maximal mitochondrial respiration, and mitochondrial spare respiratory capacity across multiple biological and experimental replicates.

Chronic EtOH exposure decreases glutamine dependency in MH-S cells. 
Glutaminolysis is a critical pathway for mitochondrial respiration, supporting glutamine as an important fuel source for cellular bioenergetics. To determine whether chronic exposure to EtOH alters the dependency of MH-S cells on glutamine as a fuel source for mitochondrial respiration, MH-S cells were treated with no EtOH control (Con) or EtOH, and OCR was measured over time in response to serial injections of reagents associated ...

Assessment of Glutamine as a Fuel Source for Alveolar Macrophages Exposed to Chronic Ethanol Using an Extracellular Flux Bioanalyzer

Alcohol misuse impairs alveolar macrophage (AM) immunity due to suppressed mitochondrial respiration and bioenergetics. We recently demonstrated that ethanol (EtOH) exposure increases glutamine dependency for mitochondrial respiration in AMs. Herein, methods are provided to determine the usage of glutamine for mitochondrial respiration in EtOH-treated AMs using an extracellular flux bioanalyzer.

Alveolar macrophages (AMs) are the first line of cellular defense in the lower airway against pathogens. However, chronic and excessive alcohol use ...

assessment-glutamine-as-fuel-source-for-alveolar-macrophages-exposed

Research

JoVE Journal

Immunology and Infection

233 Views.  Emory University. Alcohol misuse impairs alveolar macrophage (AM) immunity due to suppressed mitochondrial respiration and bioenergetics. We recently demonstrated that ethanol (EtOH) exposure increases glutamine dependency for mitochondrial respiration in AMs. Herein, methods are provided to determine the usage of glutamine for mitochondrial respiration in EtOH-treated AMs using an extracellular flux bioanalyzer. 

Video: Assessment of Glutamine as a Fuel Source for Alveolar Macrophages Exposed to Chronic Ethanol Using an Extracellular Flux Bioanalyzer

FIBS-enabled Noninvasive Metabolic Profiling

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be validated for predictive purposes. Ideally such systems would be low volume, which precludes sampling and destructive analyses when time course data are to be obtained. What is needed is an in situ monitoring tool which can report the necessary information in real-time and noninvasively. An interesting option is the use of fluorescent, protein-based in vivo biological sensors as reporters of intracellular concentrations. One particular class of in vivo biosensors that has found applications in metabolite quantification is based on F&#246;rster Resonance Energy Transfer (FRET) between two fluorescent proteins connected by a ligand binding domain. FRET integrated biological sensors (FIBS) are constitutively produced within the cell line, they have fast response times and their spectral characteristics change based on the concentration of metabolite within the cell. In this paper, the method for constructing Chinese hamster ovary (CHO) cell lines that constitutively express a FIBS for glucose and glutamine and calibrating the FIBS in vivo in batch cell culture in order to enable future quantification of intracellular metabolite concentration is described. Data from fed-batch CHO cell cultures demonstrates that the FIBS was able in each case to detect the resulting change in the intracellular concentration. Using the fluorescent signal from the FIBS and the previously constructed calibration curve, the intracellular concentration was accurately determined as confirmed by an independent enzymatic assay.

A description of how to calibrate F&#246;rster Resonance Energy Transfer integrated biological sensors (FIBS) for in situ metabolic profiling is presented.&#160;The FIBS can be used to measure intracellular levels of metabolites noninvasively aiding in the development of metabolic models and high throughput screening of bioprocess conditions.

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be ...

Bioengineering

Glutamine Flux Imaging Using Genetically Encoded Sensors

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for biological molecules became available in the past 15 years, some of which became indispensable tools that are used routinely in many laboratories.
One of the exciting applications of genetically encoded sensors is the use of these sensors in investigating cellular transport processes. Properties of transporters such as kinetics and substrate specificities can be investigated at a cellular level, providing possibilities for cell-type specific analyses of transport activities. In this article, we will demonstrate how transporter dynamics can be observed using genetically encoded glutamine sensor as an example. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed.

This article will demonstrate how to monitor glutamine dynamics in live cells using FRET. Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed for genetically encoded glutamine sensors.

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for ...

Metabolic Characterization of Polarized M1 and M2 Bone Marrow-derived Macrophages Using Real-time Extracellular Flux Analysis

Specific metabolic pathways are increasingly being recognized as critical hallmarks of macrophage subsets. While LPS-induced classically activated M1 or M(LPS) macrophages are pro-inflammatory, IL-4 induces alternative macrophage activation and these so-called M2 or M(IL-4) support resolution of inflammation and wound healing. Recent evidence shows the crucial role of metabolic reprogramming in the regulation of M1 and M2 macrophage polarization. 
In this manuscript, an extracellular flux analyzer is applied to assess the metabolic characteristics of naive, M1 and M2 polarized mouse bone marrow-derived macrophages. This instrument uses pH and oxygen sensors to measure the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), which can be related to glycolytic and mitochondrial oxidative metabolism. As such, both glycolysis and mitochondrial oxidative metabolism can be measured in real-time in one single assay.
Using this technique, we demonstrate here that inflammatory M1 macrophages display enhanced glycolytic metabolism and reduced mitochondrial activity. Conversely, anti-inflammatory M2 macrophages show high mitochondrial oxidative phosphorylation (OXPHOS) and are characterized by an enhanced spare respiratory capacity (SRC). 
The presented functional assay serves as a framework to investigate how particular cytokines, pharmacological compounds, gene knock outs or other interventions affect the macrophage&#8217;s metabolic phenotype and inflammatory status.

Metabolic reprogramming is a characteristic and prerequisite for M1 and M2 macrophage polarization. This manuscript describes an assay for the measurement of fundamental parameters of glycolysis and mitochondrial function in mouse bone marrow-derived macrophages. This tool can be applied to investigate how particular factors affect the macrophage&#8217;s metabolism and phenotype.

Specific metabolic pathways are increasingly being recognized as critical hallmarks of macrophage subsets. While LPS-induced classically activated M1 or ...

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in cellular and mammalian models. Here, we describe detailed methods to perform stable isotopic profiling of intermediary metabolism and metabolic flux in the nematode, Caenorhabditis elegans. Methods are described for profiling whole worm free amino acids, labeled carbon dioxide, labeled organic acids, and labeled amino acids in animals exposed to stable isotopes either from early development on nematode growth media agar plates or beginning as young adults while exposed to various pharmacologic treatments in liquid culture. Free amino acids are quantified by high performance liquid chromatography (HPLC) in whole worm aliquots extracted in 4% perchloric acid. Universally labeled 13C-glucose or 1,6-13C2-glucose is utilized as the stable isotopic precursor whose labeled carbon is traced by mass spectrometry in carbon dioxide (both atmospheric and dissolved) as well as in metabolites indicative of flux through glycolysis, pyruvate metabolism, and the tricarboxylic acid cycle. Representative results are included to demonstrate effects of isotope exposure time, various bacterial clearing protocols, and alternative worm disruption methods in wild-type nematodes, as well as the relative extent of isotopic incorporation in mitochondrial complex III mutant worms (isp-1(qm150)) relative to wild-type worms. Application of stable isotopic profiling in living nematodes provides a novel capacity to investigate at the whole animal level real-time metabolic alterations that are caused by individual genetic disorders and/or pharmacologic therapies.

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling by gas chromatography mass spectrometric analysis of intermediary metabolic flux is described in the nematode, Caenorhabditis elegans. Methods are detailed for assessing isotopic enrichment in carbon dioxide, organic acids, and amino acids following isotope exposure either during development on agar plates or during adulthood in liquid culture.

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in ...

Biology

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell&#39;s survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

The protocols describe high-performance liquid chromatography methods coupled to refractive index or mass spectrometric detection for studying metabolic reactions in complex lysate-based cell-free systems.

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the ...

A Comprehensive High-Efficiency Protocol for Isolation, Culture, Polarization, and Glycolytic Characterization of Bone Marrow-Derived Macrophages

Macrophages are among the most important antigen-presenting cells. Many subsets of macrophages have been identified with unique metabolic signatures. Macrophages are commonly classified as M1-like (inflammatory) and M2-like (anti-inflammatory) subtypes. M1-like macrophages are pro-inflammatory macrophages that get activated by LPS and/or pro-inflammatory cytokines such as INF-&#947;, IL-12 &#38; IL-2. M1-like polarized macrophages are involved in various diseases by mediating the host&#39;s defense to a variety of bacteria and viruses. That is very important to study LPS induced M1-like macrophages and their metabolic states in inflammatory diseases. M2-like macrophages are considered anti-inflammatory macrophages, activated by anti-inflammatory cytokines and stimulators. Under the pro-inflammatory state, macrophages show increased glycolysis in glycolytic function. The glycolytic function has been actively investigated in the context of glycolysis, glycolytic capacity, glycolytic reserve, compensatory glycolysis, or non-glycolytic acidification using extracellular flux (XF) analyzers.
This paper demonstrates how to assess the glycolytic states in real-time with easy-to-follow steps when the bone marrow-derived macrophages (BMDMs) are respiring, consuming, and producing energy. Using specific inhibitors and activators of glycolysis in this protocol, we show how to obtain a systemic and complete view of glycolytic metabolic processes in the cells and provide more accurate and realistic results. To be able to measure multiple glycolytic phenotypes, we provide an easy, sensitive, DNA-based normalization method for polarization assessment of BMDMs. Culturing, activation/polarization and identification of the phenotype and metabolic state of the BMDMs are crucial techniques that can help to investigate many different types of diseases.
In this paper, we polarized the na&#239;ve M0 macrophages to M1-like and M2-like macrophages with LPS and IL4, respectively, and measured a comprehensive set of glycolytic parameters in BMDMs in real-time and longitudinally over time, using extracellular flux analysis and glycolytic activators and inhibitors.

This protocol provides detailed and comprehensive methods for the isolation, culture, polarization, and measurement of the glycolytic metabolic state of live bone marrow-derived macrophages (BMDMs). This paper provides step-by-step instructions with realistic visual illustrations for workflow and glycolytic assessment of BMDMs in real-time.

Macrophages are among the most important antigen-presenting cells. Many subsets of macrophages have been identified with unique metabolic signatures. ...

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis

Mitochondria are essential organelles for the cellular metabolism and survival. A variety of key events take place in mitochondria, such as cellular respiration, oxidative metabolism, signal transduction, and apoptosis. Consequently, mitochondrial dysfunction is reported to play an important role in the antifungal drug tolerance and virulence of pathogenic fungi. Recent data have also led to the recognition of the importance of the mitochondria as an important contributor to fungal pathogenesis. Despite the importance of the mitochondria in fungal biology, standardized methods to understand its function are poorly developed. Here, we present a procedure to study the basal oxygen consumption rate (OCR), a measure of mitochondrial respiration, and extracellular acidification rates (ECAR), a measure of glycolytic function in C. albicans strains. The method described herein can be applied to any Candidaspp. strains without the need to purify mitochondria from the intact fungal cells. Furthermore, this protocol can also be customized to screen for inhibitors of mitochondrial function in C. albicans strains.

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis

Here, we present a stepwise protocol to investigate the mitochondrial respiration and glycolytic function in Candida Albicans using an extra flux analyzer.

Mitochondria are essential organelles for the cellular metabolism and survival. A variety of key events take place in mitochondria, such as cellular ...

Medicine

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes

Metabolic reprogramming is a hallmark of monocyte/macrophage activation and polarization between pro- and anti-inflammatory states. For example, pro-inflammatory (i.e., M1-like) monocytes/macrophages display more reliance on anaerobic glycolysis and less reliance on mitochondrial oxidative phosphorylation, whereas anti-inflammatory (M2-like) macrophages display more reliance on glucose and fatty acid oxidation in the mitochondria. Here, we describe in-depth protocols for extracting macrophages from the two major monocyte/macrophage reservoirs in the body, the spleen and bone marrow, as well as injured tissues such as the heart following myocardial infarction.
Macrophages or monocytes are extracted by immunomagnetic sorting by using antibody-tagged microbeads, which easily bind to cells without compromising their phenotypes. The extracted cells are then cultured in 96-well plates, followed by extracellular flux analysis using a metabolic flux analyzer. Both glycolysis and mitochondrial oxidative phosphorylation can be measured simultaneously in small numbers of cells (as little as 2-3 &#215; 105 cells). This method can easily be performed in 1 day and produces reliable and repeatable results. Ultimately, these methods help to enhance our understanding of metabolic changes during immune and inflammatory responses to injury and disease, which could lead to the development of novel therapeutic targets for immunometabolic pathways.

Here, we describe in detail methods for extracting macrophages from the bone marrow, spleen, and infarcted heart, and subsequently assessing metabolic flux in live cells.

Metabolic reprogramming is a hallmark of monocyte/macrophage activation and polarization between pro- and anti-inflammatory states. For example, ...

Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both mitochondrial (respiration) and non-mitochondrial (other redox) reactions consume oxygen, but these reactions can be easily distinguished by chemical inhibition of mitochondrial respiration. However, while mitochondrial oxygen consumption is an unambiguous and direct measurement of respiration rate2, the same is not true for extracellular acid production and its relationship to glycolytic rate 3-6. Extracellular acid produced by cells is derived from both lactate, produced by anaerobic glycolysis, and CO2, produced in the citric acid cycle during respiration. For glycolysis, the conversion of glucose to lactate- + H+ and the export of products into the assay medium is the source of glycolytic acidification. For respiration, the export of CO2, hydration to H2CO3 and dissociation to HCO3- + H+ is the source of respiratory acidification. The proportions of glycolytic and respiratory acidification depend on the experimental conditions, including cell type and substrate(s) provided, and can range from nearly 100% glycolytic acidification to nearly 100% respiratory acidification 6. Here, we demonstrate the data collection and calculation methods needed to determine respiratory and glycolytic contributions to total extracellular acidification by whole cells in culture using C2C12 myoblast cells as a model.

Glycolysis is a defining metabolic marker in multiple biological systems. Monitoring glycolysis by measuring the extracellular flux of H+ is common, but requires correction to be quantitative and unambiguous. Here, we demonstrate how to gather and correct extracellular flux data to distinguish between respiratory and glycolytic sources of extracellular acidification.

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both ...

Using Real-Time Cell Metabolic Flux Analyzer to Monitor Osteoblast Bioenergetics

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, prevent fracture. In the interest to both optimize peak bone mass and combat various musculoskeletal diseases (i.e., post-menopausal osteoporosis, anorexia nervosa, type 1 and 2 diabetes mellitus), incredible efforts have been made in the field of bone biology to fully characterize osteoblasts throughout their differentiation process. Given the primary role of mature osteoblasts to secrete matrix proteins and mineralization vesicles, it has been noted that these processes take an incredible amount of cellular energy, or adenosine triphosphate (ATP). The overall cellular energy status is often referred to as cellular bioenergetics, and it includes a series of metabolic reactions that sense substrate availability to derive ATP to meet cellular needs. Therefore, the current method details the process of isolating primary, murine bone marrow stromal cells (BMSCs) and monitoring their bioenergetic status using the Real-time cell metabolic flux analyzer at various stages in osteoblast differentiation. Importantly, these data have demonstrated that the metabolic profile changes dramatically throughout osteoblast differentiation. Thus, using this physiologically relevant cell type is required to fully appreciate how a cell's bioenergetic status can regulate the overall function.

Real-time cell metabolic flux assay measures the oxygen consumption rate and extracellular acidification rate, which corresponds to mitochondrial and glycolytic adenosine triphosphate production, using pH and oxygen sensors. The manuscript explains a method to understand the energy status of osteoblasts and the characterization and interpretation of the cellular bioenergetic status.

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, ...

Assessment of Glutamine as a Fuel Source for Alveolar Macrophages Exposed to Chronic Ethanol Using an Extracellular Flux Bioanalyzer

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A Comprehensive High-Efficiency Protocol for Isolation, Culture, Polarization, and Glycolytic Characterization of Bone Marrow-Derived Macrophages

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes

Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Using Real-Time Cell Metabolic Flux Analyzer to Monitor Osteoblast Bioenergetics