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Carbon source availability drives nutrient utilization in CD8+ T cells
Open accessAbstract
How environmental nutrient availability impacts T cell metabolism and function remains poorly understood. Here, we report that the presence of physiologic carbon sources (PCSs) in cell culture medium broadly impacts glucose utilization by CD8 + T cells, independent of transcriptional changes in metabolic reprogramming. The presence of PCSs reduced glucose contribution to the TCA cycle and increased effector function of CD8 + T cells, with lactate directly fueling the TCA cycle. In fact, CD8 + T cells responding to Listeria infection preferentially consumed lactate over glucose as a TCA cycle substrate in vitro , with lactate enhancing T cell bioenergetic and biosynthetic capacity. Inhibiting lactate-dependent metabolism in CD8 + T cells by silencing lactate dehydrogenase A (Ldha) impaired both T cell metabolic homeostasis and proliferative expansion in vivo . Together, our data indicate that carbon source availability shapes T cell glucose metabolism and identifies lactate as a bioenergetic and biosynthetic fuel for CD8 + effector T cells.
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“Also, CD8+ T cells preferentially utilize non-glucose physiologic carbon sources as metabolic fuel, and their activation and effector function are regulated by carbon availability.9”
From SGLT2 inhibitor promotes ketogenesis to improve MASH by suppressing CD8+ T cell activation (2024) · cites this paper as [9]
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“Also, CD8+ T cells preferentially utilize non-glucose physiologic carbon sources as metabolic fuel, and their activation and effector function are regulated by carbon availability.9”
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Cites
A RAPID METHOD OF TOTAL LIPID EXTRACTION AND PURIFICATION
Bligh, E. G., & Dyer, W. J. (1959) · Canadian Journal of Biochemistry and Physiology · doi:10.1139/o59-099 · Cited by 54,751 · cited 1×
“…saline and centrifuged before being snap frozen on dry-ice and stored at −80°C. Metabolites were extracted by modified Bligh-Dyer extraction (Bligh and Dyer, 1959) by the addition of ice-cold methanol (A456, Fisher Scientific) directly to frozen cells, to which one volume of chloroform…”
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
A guide to immunometabolism for immunologists
O'Neill, L. A. J., Kishton, R. J., & Rathmell, J. (2016) · Nature Reviews Immunology · doi:10.1038/nri.2016.70 · Cited by 3,315
Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis
Chang, C.-H., Curtis, J. D., Maggi, L. B., Faubert, B., Villarino, A. V., O’Sullivan, D., Huang, S. C.-C., van der Windt, G. J. W., Blagih, J., Qiu, J., et al. (2013) · Cell · doi:10.1016/j.cell.2013.05.016 · Cited by 2,204
Self-citationThe transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation
Wang, R., Dillon, C. P., Shi, L. Z., Milasta, S., Carter, R., Finkelstein, D., McCormick, L. L., Fitzgerald, P., Chi, H., Munger, J., et al. (2011) · Immunity · doi:10.1016/j.immuni.2011.09.021 · Cited by 2,140 · cited 2×
“Upon activation, CD8 + T cells undergo extensive metabolic reprogramming characterized by transcriptional changes in metabolic gene expression (Wang et al., 2011).”
LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells
Brand, A., Singer, K., Koehl, G. E., Kolitzus, M., Schoenhammer, G., Thiel, A., Matos, C., Bruss, C., Klobuch, S., Peter, K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.011 · Cited by 2,039
Inhibitory effect of tumor cell-derived lactic acid on human T cells
Fischer, K., Hoffmann, P., Voelkl, S., Meidenbauer, N., Ammer, J., Edinger, M., Gottfried, E., Schwarz, S., Rothe, G., Hoves, S., et al. (2007) · Blood · doi:10.1182/blood-2006-07-035972 · Cited by 1,946
Glucose feeds the TCA cycle via circulating lactate
Hui, S., Ghergurovich, J. M., Morscher, R. J., Jang, C., Teng, X., Lu, W., Esparza, L. A., Reya, T., Zhan, L., Yanxiang Guo, J., et al. (2017) · Nature · doi:10.1038/nature24057 · Cited by 1,721
Transcriptional control of effector and memory CD8+ T cell differentiation
Kaech, S. M., & Cui, W. (2012) · Nature Reviews Immunology · doi:10.1038/nri3307 · Cited by 1,628
The CD28 Signaling Pathway Regulates Glucose Metabolism
Frauwirth, K. A., Riley, J. L., Harris, M. H., Parry, R. V., Rathmell, J. C., Plas, D. R., Elstrom, R. L., June, C. H., & Thompson, C. B. (2002) · Immunity · doi:10.1016/s1074-7613(02)00323-0 · Cited by 1,475 · cited 1×
“…taken up by activated T cells and is a major substrate fueling central carbon metabolism (i.e., glycolysis; the pentose phosphate pathway [PPP]; serine, glycine, and one-carbon [SGOC] metabolism; the TCA cycle; and nucleotide metabolism) in T cells (Frauwirth et al., 2002; Wang et al., 2011).”
Lactate Metabolism in Human Lung Tumors
Faubert, B., Li, K. Y., Cai, L., Hensley, C. T., Kim, J., Zacharias, L. G., Yang, C., Do, Q. N., Doucette, S., Burguete, D., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.019 · Cited by 1,343
Self-citationGAGE: generally applicable gene set enrichment for pathway analysis
Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D., & Woolf, P. J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-161 · Cited by 1,333
Metabolic support of tumour-infiltrating regulatory T cells by lactic acid
Watson, M. J., Vignali, P., Mullett, S. J., Overacre-Delgoffe, A. E., Peralta, R. M., Grebinoski, S., Menk, A. V., Rittenhouse, N., DePeaux, K., Whetstone, R. D., et al. (2021) · Nature · doi:10.1038/s41586-020-03045-2 · Cited by 1,319
Self-citationFoxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments
Angelin, A., Gil‐de‐Gómez, L., Dahiya, S., Jiao, J., Guo, L., Levine, M. H., Wang, Z., Quinn, W. J., Kopinski, P. K., Wang, L., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2016.12.018 · Cited by 1,304
Metabolic Instruction of Immunity
Buck, M. D., Sowell, R., Kaech, S. M., & Pearce, E. L. (2017) · Cell · doi:10.1016/j.cell.2017.04.004 · Cited by 1,270
Toll-like receptor–induced changes in glycolytic metabolism regulate dendritic cell activation
Krawczyk, C. M., Holowka, T., Sun, J., Blagih, J., Amiel, E., DeBerardinis, R. J., Cross, J. R., Jung, E., Thompson, C. B., Jones, R. G., et al. (2010) · Blood · doi:10.1182/blood-2009-10-249540 · Cited by 1,246
Self-citationMacintyre, A. N., Gerriets, V. A., Nichols, A., Michalek, R. D., Rudolph, M. C., Deoliveira, D., Anderson, S. M., Abel, E. D., Chen, B. J., Hale, L. P., et al. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.05.004 · Cited by 1,169
Beltra, J., Manne, S., Abdel-Hakeem, M. S., Kurachi, M., Giles, J. R., Chen, Z., Casella, V., Ngiow, S. F., Khan, O., Huang, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.04.014 · Cited by 1,139
Metabolic heterogeneity in human lung tumors
Hensley, C. T., Faubert, B., Yuan, Q., Lev-Cohain, N., Jin, E., Kim, J., Jiang, L., Ko, B., Skelton, R., Loudat, L., et al. (2016) · Cell · doi:10.1016/j.cell.2015.12.034 · Cited by 1,138
Self-citationEverts, B., Amiel, E., Huang, S. C.-C., Smith, A. M., Chang, C.-H., Lam, W. Y., Redmann, V., Freitas, T. C., Blagih, J., van der Windt, G. J. W., et al. (2014) · Nature Immunology · doi:10.1038/ni.2833 · Cited by 1,064
Self-citationEffector and Memory CTL Differentiation
Williams, M. A., & Bevan, M. J. (2006) · Annual Review of Immunology · doi:10.1146/annurev.immunol.25.022106.141548 · Cited by 980
Scharping, N. E., Rivadeneira, D. B., Menk, A. V., Vignali, P. D. A., Ford, B. R., Rittenhouse, N. L., Peralta, R., Wang, Y., Wang, Y., DePeaux, K., et al. (2021) · Nature Immunology · doi:10.1038/s41590-020-00834-9 · Cited by 897
We need to talk about the Warburg effect
DeBerardinis, R. J., & Chandel, N. S. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0172-2 · Cited by 887
Self-citationMetabolomics and Isotope Tracing
Jang, C., Chen, L., & Rabinowitz, J. D. (2018) · Cell · doi:10.1016/j.cell.2018.03.055 · Cited by 841
Environment impacts the metabolic dependencies of Ras-driven non-small cell lung cancer
Davidson, S. M., Papagiannakopoulos, T., Olenchock, B. A., Heyman, J. E., Keibler, M. A., Luengo, A., Bauer, M. R., Jha, A. K., O’Brien, J. P., Pierce, K. A., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.01.007 · Cited by 737
Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides
Birsoy, K., Possemato, R., Lorbeer, F. K., Bayraktar, E. C., Thiru, P., Yucel, B., Wang, T., Chen, W. W., Clish, C. B., & Sabatini, D. M. (2014) · Nature · doi:10.1038/nature13110 · Cited by 710
The Energy Sensor AMPK Regulates T Cell Metabolic Adaptation and Effector Responses In Vivo
Blagih, J., Coulombe, F., Vincent, E. E., Dupuy, F., Galicia-Vázquez, G., Yurchenko, E., Raissi, T. C., van der Windt, G. J. W., Viollet, B., Pearce, E. L., et al. (2015) · Immunity · doi:10.1016/j.immuni.2014.12.030 · Cited by 679
Self-citationSerine is an essential metabolite for effector T cell expansion
Ma, E. H., Bantug, G., Griss, T., Condotta, S., Johnson, R. M., Samborska, B., Mainolfi, N., Suri, V., Guak, H., Balmer, M. L., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2016.12.011 · Cited by 657
Self-citationCantor, J. R., Abu-Remaileh, M., Kanarek, N., Freinkman, E., Gao, X., Louissaint, A., Lewis, C. A., & Sabatini, D. M. (2017) · Cell · doi:10.1016/j.cell.2017.03.023 · Cited by 638
Argüello, R. J., Combes, A. J., Char, R., Gigan, J. P., Baaziz, A. I., Bousiquot, E., Camosseto, V., Samad, B., Tsui, J. L., Yan, P., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.11.007 · Cited by 607
Sullivan, M. R., Danai, L. V., Lewis, C. A., Chan, S. H., Gui, D. Y., Kunchok, T., Dennstedt, E. A., Vander Heiden, M. G., & Muir, A. (2019) · eLife · doi:10.7554/elife.44235 · Cited by 588
Mookerjee, S. A., Gerencser, A. A., Nicholls, D. G., & Brand, M. D. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.774471 · Cited by 550
Single-cell RNA-seq reveals TOX as a key regulator of CD8+ T cell persistence in chronic infection
Yao, C., Sun, H.-W., Lacey, N. E., Ji, Y., Moseman, E. A., Shih, H.-Y., Heuston, E. F., Kirby, M., Anderson, S., Cheng, J., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0403-4 · Cited by 549
Increased demand for NAD(+) relative to ATP drives aerobic glycolysis
Luengo, A., Li, Z., Gui, D. Y., Sullivan, L. B., Zagorulya, M., Do, B. T., Ferreira, R., Naamati, A., Ali, A., Lewis, C. A., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.12.012 · Cited by 547
Metabolic heterogeneity confers differences in melanoma metastatic potential
Tasdogan, A., Faubert, B., Ramesh, V., Ubellacker, J. M., Shen, B., Solmonson, A., Murphy, M. M., Gu, Z., Gu, W., Martin, M., et al. (2020) · Nature · doi:10.1038/s41586-019-1847-2 · Cited by 509
Self-citationMitochondrial control of immunity: beyond ATP
Mehta, M., Weinberg, S. E., & Chandel, N. S. (2017) · Nature reviews. Immunology · doi:10.1038/nri.2017.66 · Cited by 409
Improving the metabolic fidelity of cancer models with a physiological cell culture medium
Voorde, J. V., Ackermann, T., Pfetzer, N., Sumpton, D. P., Mackay, G., Kalna, G., Nixon, C., Blyth, K., Gottlieb, E., & Tardito, S. (2019) · Science Advances · doi:10.1126/sciadv.aau7314 · Cited by 400
INCA: a computational platform for isotopically non-stationary metabolic flux analysis
Young, J. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu015 · Cited by 393
Ma, E. H., Verway, M. J., Johnson, R. M., Roy, D. G., Steadman, M., Hayes, S., Williams, K. S., Sheldon, R. D., Samborska, B., Kosinski, P. A., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.09.003 · Cited by 379
Self-citationLactate Limits T Cell Proliferation via the NAD(H) Redox State
Quinn, W. J., Jiao, J., TeSlaa, T. A., Stadanlick, J. E., Wang, Z., Wang, L., Akimova, T., Angelin, A., Schäfer, P. M., Cully, M. D., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108500 · Cited by 376
Glycolysis fuels phosphoinositide 3-kinase signaling to bolster T cell immunity
Xu, K., Yin, N., Peng, M., Stamatiades, E. G., Shyu, A., Li, P., Zhang, X., Do, M. H., Wang, Z., Capistrano, K. J., et al. (2021) · Science · doi:10.1126/science.abb2683 · Cited by 369
Balmer, M. L., Ma, E. H., Bantug, G. R., Grählert, J., Pfister, S., Glatter, T., Jauch, A., Dimeloe, S., Slack, E., Dehio, P., et al. (2016) · Immunity · doi:10.1016/j.immuni.2016.03.016 · Cited by 361
Self-citationMethionine Metabolism Shapes T Helper Cell Responses through Regulation of Epigenetic Reprogramming
Roy, D. G., Chen, J., Mamane, V. H., Ma, E. H., Muhire, B. M., Sheldon, R. D., Shorstova, T., Koning, R., Johnson, R. M., Esaulova, E., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.01.006 · Cited by 361
Self-citationGlucose Availability Regulates IFN-γ Production and p70S6 Kinase Activation in CD8+ Effector T Cells
Cham, C. M., & Gajewski, T. F. (2005) · The Journal of Immunology · doi:10.4049/jimmunol.174.8.4670 · Cited by 359
Inosine is an alternative carbon source for CD8+-T-cell function under glucose restriction
Wang, T., Gnanaprakasam, J. R., Chen, X., Kang, S., Xu, X., Sun, H., Liu, L., Rodgers, H., Miller, E., Cassel, T., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0219-4 · Cited by 343
Immunometabolic Interplay in the Tumor Microenvironment
Kaymak, I., Williams, K. S., Cantor, J. R., & Jones, R. G. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.004 · Cited by 333
Self-citationCD8+ T cell contraction is controlled by early inflammation
Badovinac, V. P., Porter, B. B., & Harty, J. T. (2004) · Nature Immunology · doi:10.1038/ni1098 · Cited by 317
Metabolic adaptation of lymphocytes in immunity and disease
Chapman, N. M., & Chi, H. (2022) · Immunity · doi:10.1016/j.immuni.2021.12.012 · Cited by 317
Single-cell metabolic profiling of human cytotoxic T cells
Hartmann, F. J., Mrdjen, D., McCaffrey, E., Glass, D. R., Greenwald, N. F., Bharadwaj, A., Khair, Z., Verberk, S. G. S., Baranski, A., Baskar, R., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0651-8 · Cited by 315
Systems-level analysis of mechanisms regulating yeast metabolic flux
Hackett, S. R., Zanotelli, V. R., Xu, W., Goya, J., Park, J. O., Perlman, D. H., Gibney, P. A., Botstein, D., Storey, J. D., & Rabinowitz, J. D. (2016) · Science · doi:10.1126/science.aaf2786 · Cited by 303
Quantitative Fluxomics of Circulating Metabolites
Hui, S. T., Cowan, A. J., Zeng, X., Yang, L., TeSlaa, T. A., Li, X., Bartman, C., Zhang, Z., Jang, C., Wang, L., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.07.013 · Cited by 274
TOX is expressed by exhausted and polyfunctional human effector memory CD8 + T cells
Sekine, T., Perez‐Potti, A., Nguyen, S., Gorin, J., Wu, V. H., Emma Gostick, D., Llewellyn‐Lacey, S., Hammer, Q., Falck‐Jones, S., Vangeti, S., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.aba7918 · Cited by 247
Hermans, D., Gautam, S., García‐Cañaveras, J. C., Gromer, D. J., Mitra, S., Spolski, R. J., Li, P., Christensen, S. M., Nguyen, R., Lin, J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920413117 · Cited by 232
Metformin Antagonizes Cancer Cell Proliferation by Suppressing Mitochondrial-Dependent Biosynthesis
Griss, T., Vincent, E. E., Egnatchik, R. A., Chen, J., Ma, E. H., Faubert, B., Viollet, B., DeBerardinis, R. J., & Jones, R. G. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002309 · Cited by 216
Self-citationT Cell Activation Depends on Extracellular Alanine
Ron‐Harel, N., Ghergurovich, J. M., Notarangelo, G., LaFleur, M. W., Tsubosaka, Y., Sharpe, A. H., Rabinowitz, J. D., & Haigis, M. C. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.034 · Cited by 201
Different T Cell Receptor Signals Determine CD8 + Memory Versus Effector Development
Teixeiro, E., Daniëls, M. A., Hamilton, S. E., Schrum, A. G., Bragado, R., Jameson, S. C., & Palmer, E. (2009) · Science · doi:10.1126/science.1163612 · Cited by 199
Pathogen-Induced Inflammatory Environment Controls Effector and Memory CD8+ T Cell Differentiation
Obar, J. J., Jellison, E. R., Sheridan, B. S., Blair, D. A., Pham, Q.-M., Zickovich, J. M., & Lefrançois, L. (2011) · The Journal of Immunology · doi:10.4049/jimmunol.1102335 · Cited by 172
Sizing up the key determinants of the CD8+ T cell response
Tscharke, D. C., Croft, N. P., Doherty, P. C., & Gruta, N. L. L. (2015) · Nature reviews. Immunology · doi:10.1038/nri3905 · Cited by 149
Biochemical Underpinnings of Immune Cell Metabolic Phenotypes
Olenchock, B. A., Rathmell, J. C., & Heiden, M. G. V. (2017) · Immunity · doi:10.1016/j.immuni.2017.04.013 · Cited by 141
CRISPR screens in physiologic medium reveal conditionally essential genes in human cells
Rossiter, N. J., Huggler, K. S., Adelmann, C. H., Keys, H. R., Soens, R. W., Sabatini, D. M., & Cantor, J. R. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.02.005 · Cited by 138
Levine, L. S., Hiam-Galvez, K. J., Marquez, D. M., Tenvooren, I., Madden, M. Z., Contreras, D. C., Dahunsi, D. O., Irish, J. M., Oluwole, O. O., Rathmell, J. C., et al. (2020) · Immunity · doi:10.1016/j.immuni.2021.02.018 · Cited by 132
Muir, A., Danai, L. V., & Heiden, M. G. V. (2018) · Disease Models & Mechanisms · doi:10.1242/dmm.035758 · Cited by 124
Lercher, A., Bhattacharya, A., Popa, A., Caldera, M., Schlapansky, M. F., Baazim, H., Agerer, B., Gürtl, B., Kosack, L., Májek, P., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.10.014 · Cited by 124
Chen, R., Bélanger, S., Frederick, M. A., Li, B., Johnston, R. J., Xiao, N., Liu, Y., Sharma, S., Peters, B., Rao, A., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.08.002 · Cited by 118
Cantor, J. R. (2019) · Trends in Cell Biology · doi:10.1016/j.tcb.2019.08.009 · Cited by 107
FluxFix: automatic isotopologue normalization for metabolic tracer analysis
Trefely, S., Ashwell, P., & Snyder, N. W. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1360-7 · Cited by 102
Human Plasma-like Medium Improves T Lymphocyte Activation
Leney-Greene, M. A., Boddapati, A. K., Su, H. C., Cantor, J. R., & Lenardo, M. J. (2019) · iScience · doi:10.1016/j.isci.2019.100759 · Cited by 70
MMMDB: Mouse Multiple Tissue Metabolome Database
Sugimoto, M., Ikeda, S., Niigata, K., Tomita, M., Sato, H., & Soga, T. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1170 · Cited by 67
Circulating metabolite homeostasis achieved through mass action
Li, X., Hui, S. T., Mirek, E. T., Jonsson, W. O., Anthony, T. G., Lee, W. D., Zeng, X., Jang, C., & Rabinowitz, J. D. (2022) · Nature Metabolism · doi:10.1038/s42255-021-00517-1 · Cited by 49
Clonal expansion of vaccine-elicited T cells is independent of aerobic glycolysis
Klarquist, J. S., Chitrakar, A., Pennock, N. D., Kilgore, A. M., Blain, T. J., Zheng, C., Danhorn, T., Walton, K., Jiang, L., Sun, J., et al. (2018) · Science Immunology · doi:10.1126/sciimmunol.aas9822 · Cited by 47
Sheldon, R. D., Ma, E. H., DeCamp, L. M., Williams, K. S., & Jones, R. G. (2021) · Nature Protocols · doi:10.1038/s41596-021-00586-2 · Cited by 43
Self-citationMacPherson, S., Keyes, S., Kilgour, M. K., Smazynski, J., Chan, V., Sudderth, J., Turcotte, T., Devlieger, A., Yu, J. Z., Huggler, K. S., et al. (2022) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2022.02.004 · Cited by 42
Self-citationRambotti, P., & Davis, S. (1981) · Blood · doi:10.1182/blood.v57.2.324.324 · Cited by 31
Primary Sequence and Glycation at Lysine-548 of Bovine Serum Albumin
Wada, Y. (1996) · Journal of Mass Spectrometry · doi:10.1002/(sici)1096-9888(199603)31:3 · Cited by 19
LDH isozymes of human T and B lymphocytes
Ringoir, S. M., & Plum, J. R. (1975) · Clinica Chimica Acta · doi:10.1016/0009-8981(75)90081-9 · Cited by 18
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Metadata
- Authors
- Kaymak, I., Luda, K. M., Duimstra, L. R., Ma, E. H., Longo, J., Dahabieh, M. S., Faubert, B., Oswald, B. M., Watson, M. J., Kitchen-Goosen, S. M., DeCamp, L. M., Compton, S. E., Fu, Z., DeBerardinis, R. J., Williams, K. S., Sheldon, R. D., & Jones, R. G.
- Affiliations
- Van Andel Institute (US)
- University of Copenhagen (DK)
- University of Chicago (US)
- Children's Medical Center (US)
- Corresponding author
- Jones, Russell G.
- ORCID
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 34
- Issue
- 9
- Pages
- 1298-1311.e6
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC10068808
- OpenAlex
- W4292111508
- Cited by
- 160
- References
- 75
- Citations by year
- 2026: 40 · 2025: 43 · 2024: 41 · 2023: 31 · 2022: 5
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Funders
- Van Andel Research Institute
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases
- National Cancer Institute Division of Cancer Prevention
- National Cancer Institute Division of Cancer Epidemiology and Genetics (R35CA22044901)
- The Paul G Allen Frontiers Group
- Howard Hughes Medical Institute
- Fonds de Recherche du Québec - Santé
- National Cancer Institute (T32CA251066-01A1)
- National Institute of Allergy and Infectious Diseases (R01AI165722)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- CD8+ T cells, effector T cells, carbon source availability, nutrient utilization, T cell metabolism, glucose metabolism, T cell activation
- MeSH terms
- Nutrients, Carbon, Glucose, CD8-Positive T-Lymphocytes, Lactic Acid
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