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Metabolic modeling of single Th17 cells reveals regulators of autoimmunity
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Abstract
Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.
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“the non-pathogenic/homeostatic and pathogenic Th17 cell phenotypes studied in the mouse models may mirror the physiological roles of human Th17 cell subsets that are important for immunity against distinct bacteria and fungi”
From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [65]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationBettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., & Kuchroo, V. K. (2006) · Nature · doi:10.1038/nature04753 · Cited by 6,992
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Holbert, C. E., Cullen, M., Casero, R. A., & Stewart, T. M. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00473-2 · Cited by 378
Principles and therapeutic applications of adaptive immunity
Chi, H., Pepper, M., & Thomas, P. G. (2024) · Cell · doi:10.1016/j.cell.2024.03.037 · Cited by 394
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Chapman, N. M., & Chi, H. (2022) · Immunity · doi:10.1016/j.immuni.2021.12.012 · Cited by 317
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Schnell, A., Littman, D. R., & Kuchroo, V. K. (2023) · Nature Immunology · doi:10.1038/s41590-022-01387-9 · Cited by 286
Self-citationStem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity
Schnell, A., Huang, L., Singer, M., Singaraju, A., Barilla, R. M., Regan, B. M. L., Bollhagen, A., Thakore, P. I., Dionne, D., Delorey, T. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.018 · Cited by 268
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Alghamdi, N., Chang, W., Dang, P., Lu, X., Wan, C., Gampala, S., Huang, Z., Wang, J., Ma, Q., Zang, Y., et al. (2021) · Genome Research · doi:10.1101/gr.271205.120 · Cited by 271
Uzquiano, A., Kedaigle, A. J., Pigoni, M., Paulsen, B. D. S., Adiconis, X., Kim, K., Faits, T., Nagaraja, S., Antón-Bolaños, N., Gerhardinger, C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.010 · Cited by 257
Self-citationCellular metabolism regulates the differentiation and function of T-cell subsets
Ma, S., Ming, Y., Wu, J., & Cui, G. (2024) · Cellular and Molecular Immunology · doi:10.1038/s41423-024-01148-8 · Cited by 276
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Wu, H., González-Villalobos, R. A., Yao, X., Reilly, D. F., Chen, T., Rankin, M. M., Myshkin, E., Breyer, M. D., & Humphreys, B. D. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.05.010 · Cited by 254
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Kirschenbaum, D. S., Xie, K., Ingelfinger, F., Katzenelenbogen, Y., Abadie, K., Look, T., Sheban, F., Phan, T. S., Li, B., Zwicky, P., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.032 · Cited by 224
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Bader, J. E., Wolf, M. M., Lupica-Tondo, G. L., Madden, M. Z., Reinfeld, B. I., Arner, E. N., Hathaway, E. S., Steiner, K. K., Needle, G. A., Hatem, Z., et al. (2024) · Nature · doi:10.1038/s41586-024-07529-3 · Cited by 191
Pathogenesis and treatment of Sjogren’s syndrome: Review and update
Zhan, Q., Zhang, J., Lin, Y., Chen, W., Fan, X., & Zhang, D. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1127417 · Cited by 179
The role of Th17 cells in inflammatory bowel disease and the research progress
Chen, L., Ruan, G., Cheng, Y., Yi, A., Chen, D., & Wei, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1055914 · Cited by 163
Aso, K., Kono, M., Kanda, M., Kudo, Y., Sakiyama, K., Hisada, R., Karino, K., Ueda, Y., Nakazawa, D., Fujieda, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36594-x · Cited by 143
Zhao, L., Zhang, X., Zhou, Y., Fu, K., Lau, H. C.-H., Chun, T. W.-Y., Cheung, A. H. K., Coker, O. O., Wei, H., Wu, W. K. K., et al. (2022) · Oncogene · doi:10.1038/s41388-022-02395-7 · Cited by 145
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Lee, M.-S., Dennis, C., Naqvi, I., Dailey, L., Lorzadeh, A., Ye, G., Zaytouni, T., Adler, A., Hitchcock, D. S., Lin, L., et al. (2023) · Nature · doi:10.1038/s41586-023-05891-2 · Cited by 143
Self-citationAn exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer’s brains
Millet, A., Ledo, J. H., & Tavazoie, S. F. (2024) · Immunity · doi:10.1016/j.immuni.2023.12.001 · Cited by 132
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Takeuchi, T., Nakanishi, Y., & Ohno, H. (2024) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090222-102035 · Cited by 143
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Canè, S., Geiger, R., & Bronte, V. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-024-01098-2 · Cited by 140
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Liu, X., Ren, B., Ren, J., Gu, M., You, L., & Zhao, Y.-P. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01760-1 · Cited by 147
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Chen, S., Cui, W., Chi, Z., Xiao, Q., Hu, T., Ye, Q., Zhu, K., Yu, W., Wang, Z., Yu, C., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.08.016 · Cited by 123
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Chen, D., Wang, Y., Vijay, G. K. M., Fu, S., Nash, C. W., Xu, D., He, D., Salomonis, N., Singh, H., & Xu, H. (2021) · Nature Immunology · doi:10.1038/s41590-021-00936-y · Cited by 121
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Brockmann, L., Tran, A., Huang, Y., Edwards, M., Ronda, C., Wang, H. H., & Ivanov, I. I. (2023) · Immunity · doi:10.1016/j.immuni.2023.11.003 · Cited by 115
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Schibalski, R. S., Shulha, A. S., Tsao, B. P., Palygin, O., & Ilatovskaya, D. V. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00074.2024 · Cited by 118
Subudhi, I., Konieczny, P., Prystupa, A., Castillo, R., Sze-Tu, E., Xing, Y., Rosenblum, D., Reznikov, I., Sidhu, I., Loomis, C. A., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.04.022 · Cited by 110
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Chen, H., Tong, T., Lu, S., Ji, L., Xuan, B., Zhao, G., Yan, Y., Song, L., Zhao, L., Xie, Y., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.03.003 · Cited by 107
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Petrova, B., Maynard, A. G., Wang, P., & Kanarek, N. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105457 · Cited by 103
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Wilfahrt, D. N., & Delgoffe, G. M. (2024) · Nature Immunology · doi:10.1038/s41590-023-01733-5 · Cited by 112
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Huang, Y., Si, X., Shao, M., Teng, X., Xiao, G., & Huang, H. (2022) · Journal of Hematology & Oncology · doi:10.1186/s13045-022-01255-x · Cited by 110
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Baixauli, F., Piletič, K., Puleston, D. J., Villa, M., Field, C. S., Flachsmann, L. J., Quintana, A., Rana, N., Edwards-Hicks, J., Matsushita, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05264-1 · Cited by 102
Self-citationData analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 114
Selective remodelling of the adipose niche in obesity and weight loss
Miranda, A. M. A., McAllan, L., Mazzei, G., Andrew, I., Davies, I., Ertuğrul, M. S., Kenkre, J. S., Kudo, H., Carrelha, J., Patel, B., et al. (2025) · Nature · doi:10.1038/s41586-025-09233-2 · Cited by 112
Nutrients: Signal 4 in T cell immunity
Raynor, J. L., & Chi, H. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20221839 · Cited by 99
Zhi, Y., Wang, Q., Zi, M., Zhang, S., Ge, J., Liu, K., Lu, L., Fan, C., Yan, Q., Shi, L., et al. (2024) · Advanced Science · doi:10.1002/advs.202306515 · Cited by 99
INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation
Filippo, M. D., Pescini, D., Galuzzi, B. G., Bonanomi, M., Gaglio, D., Mangano, E., Consolandi, C., Alberghina, L., Vanoni, M., & Damiani, C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009337 · Cited by 83
Guimarães, G. R., Maklouf, G. R., Teixeira, C. E., Santos, L. D. O., Tessarollo, N. G., de Toledo, N. E., Serain, A. F., de Lanna, C. A., Pretti, M. A. M., da Cruz, J. G. V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49916-4 · Cited by 93
Chen, X., Jaiswal, A., Costliow, Z. A., Herbst, P., Creasey, E. A., Oshiro-Rapley, N., Daly, M. J., Carey, K. L., Graham, D. B., & Xavier, R. J. (2022) · Nature Immunology · doi:10.1038/s41590-022-01231-0 · Cited by 79
Sodium perturbs mitochondrial respiration and induces dysfunctional Tregs
Côrte‐Real, B. F., Hamad, I., Hornero, R. A., Geisberger, S., Roels, J., Zeebroeck, L. V., Dyczko, A., van Gisbergen, M. W., Kurniawan, H., Wagner, A., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.01.009 · Cited by 79
Self-citationNakhle, J., Khattar, K., Özkan, T., Boughlita, A., Moussa, D. A., Darlix, A., Lorcy, F., Rigau, V., Bauchet, L., Gerbal‐Chaloin, S., et al. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0144 · Cited by 76
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Zhao, C., Ma, Y., Zhang, M., Gao, X., Liang, W., Qin, Y., Fu, Y., Jia, M., Song, H., Gao, C., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.09.012 · Cited by 83
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Chia, T.-Y., Zolp, A., & Miska, J. M. (2022) · Cells · doi:10.3390/cells11050896 · Cited by 76
Lian, J., Liang, Y., Zhang, H., Lan, M., Ye, Z., Lin, B., Qiu, X., & Zeng, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.912279 · Cited by 75
Sen, P., & Orešič, M. (2023) · Metabolites · doi:10.3390/metabo13070855 · Cited by 75
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Yang, C. G., Geng, H., Yang, X., Ji, S., Liu, Z., Feng, H., Li, Q., Zhang, T., Zhang, S., Ma, X., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.10.008 · Cited by 75
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Zheng, R., Zhang, Y., Tsuji, T., Gao, X., Shamsi, F., Wagner, A., Yosef, N., Cui, K., Chen, H., Kiebish, M. A., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf569 · Cited by 71
Self-citationEffects of lactate on metabolism and differentiation of CD4+T cells
Zhang, Y., Xing, M.-L., Fang, H.-H., Li, W., Wu, L., & Chen, Z. (2023) · Molecular Immunology · doi:10.1016/j.molimm.2022.12.015 · Cited by 69
Cellular spermine targets JAK signaling to restrain cytokine-mediated autoimmunity
Xu, H., Zhang, X., Wang, X., Li, B., Yu, H., Quan, Y., Jiang, Y., You, Y., Wang, Y., Wen, M., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.05.025 · Cited by 76
Toward modeling metabolic state from single-cell transcriptomics
Hrovatin, K., Fischer, D. S., & Theis, F. J. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101396 · Cited by 70
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Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 70
Gustafsson, J., Anton, M., Roshanzamir, F., Jörnsten, R. J., Kerkhoven, E. J., Robinson, J. L., & Nielsen, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2217868120 · Cited by 69
Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
Zhang, C., Sánchez, B. J., Li, F., Eiden, C. W. Q., Scott, W. T., Liebal, U. W., Blank, L. M., Mengers, H. G., Anton, M., Rangel, A. E. T., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00060-7 · Cited by 83
Systems-based approaches to study immunometabolism
Purohit, V., Wagner, A., Yosef, N., & Kuchroo, V. K. (2022) · Cellular and Molecular Immunology · doi:10.1038/s41423-021-00783-9 · Cited by 64
Self-citationMetabolism Characteristics of Th17 and Regulatory T Cells in Autoimmune Diseases
Qin, Y., Gao, C., & Luo, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.828191 · Cited by 68
Shi, C., Zhang, J., Wang, H., Chen, C., Han, M., Gao, L., Tang, C., Sun, P., Zhao, X., Guo, F., et al. (2022) · Advanced Materials · doi:10.1002/adma.202210262 · Cited by 60
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Zou, D., Yin, Z., Yi, S. G., Wang, G., Guo, Y., Xiao, X., Li, S., Zhang, X., Gonzalez, N. M., Minze, L. J., et al. (2024) · Nature Immunology · doi:10.1038/s41590-023-01682-z · Cited by 69
Gut Microbial-Derived Metabolites as Immune Modulators of T Helper 17 and Regulatory T Cells
Calvo‐Barreiro, L., Zhang, L., Abdel‐Rahman, S. A., Naik, S. P., & Gabr, M. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021806 · Cited by 62
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Yoon, J.-H., Do, J.-S., Velankanni, P., Lee, C., & Kwon, H. (2023) · Immune Network · doi:10.4110/in.2023.23.e6 · Cited by 64
Fibroblasts in immune responses, inflammatory diseases and therapeutic implications
Zou, A. E., Kongthong, S., Mueller, A. A., & Brenner, M. B. (2025) · Nature Reviews Rheumatology · doi:10.1038/s41584-025-01259-0 · Cited by 66
Chang, H.-W., Lee, E. M., Wang, Y., Zhou, C., Pruss, K. M., Henrissat, S., Chen, R. Y., Kao, C.-Y., Hibberd, M. C., Lynn, H. M., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01628-7 · Cited by 60
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DeBerge, M., Chaudhary, R. K., Schroth, S. L., & Thorp, E. B. (2023) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2022.12.010 · Cited by 59
Thakore, P. I., Schnell, A., Zhao, M., Huang, L., Hou, Y., Christian, E., Zaghouani, S., Wang, C., Singh, V., Ma, S., et al. (2022) · doi:10.1101/2022.02.26.482041 · Cited by 57
Self-citationSpatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective
Xiao, Y., Li, Y., & Zhao, H. (2024) · Molecular Cancer · doi:10.1186/s12943-024-02113-9 · Cited by 61
Genome-scale models in human metabologenomics
Mardinoglu, A., & Palsson, B. Ø. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00768-0 · Cited by 54
Meyer, T., Shimon, D., Youssef, S., Yankovitz, G., Tessler, A., Chernobylsky, T., Gaoni-Yogev, A., Perelroizen, R., Budick-Harmelin, N., Steinman, L., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2211310119 · Cited by 57
Franchina, D. G., Kurniawan, H., Grusdat, M., Binsfeld, C., Guerra, L., Bonetti, L., Soriano‐Baguet, L., Ewen, A., Kobayashi, T., Farinelle, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29426-x · Cited by 52
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Wang, X., Sun, B. T., Wang, Y., Gao, P., Song, J., Chang, W., Xiao, Z., Xi, Y., Li, Z., An, F., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1333993 · Cited by 56
Hu, H., Li, H., Li, R., Liu, P., & Liu, H. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05450-x · Cited by 56
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Kundu, P., Beura, S., Mondal, S., Das, A. K., & Ghosh, A. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108400 · Cited by 53
Li, Q., Xiao, Y., Han, L., Luo, W., Dai, W., Fang, H., Wang, R., Xu, Y., Cai, S., Goel, A., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-00910-9 · Cited by 58
Treg cell: Critical role of regulatory T-cells in depression
Gao, X. W., Tang, Y., Kong, L., Fan, Y., Wang, C., & Wang, R. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106893 · Cited by 51
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Pawlak, M., DeTomaso, D., Schnell, A., Meyer zu Horste, G., Lee, Y., Nyman, J., Dionne, D., Regan, B. M. L., Singh, V., Delorey, T., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.08.007 · Cited by 49
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Kang, S., Liu, L., Wang, T., Cannon, M. V., Lin, P., Fan, T. W. ‐., Scott, D. A., Wu, H.-J. J., Lane, A. N., & Wang, R. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00638-1 · Cited by 48
Bassler, K. E., Fujii, W., Kapellos, T. S., Dudkin, E., Reusch, N., Horne, A., Reiz, B., Luecken, M. D., Osei-Sarpong, C., Warnat‐Herresthal, S., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.917232 · Cited by 53
Self-citationHong, M. M. Y., & Vareki, S. M. (2022) · Cancers · doi:10.3390/cancers14061580 · Cited by 52
Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections
Chi, X., Huang, M., Tu, H., Zhang, B., Lin, X., Xu, H., Dong, C., & Hu, X. (2023) · Science China Life Sciences · doi:10.1007/s11427-021-2187-3 · Cited by 47
Johnson, A., Laterra, J., & López-Bertoni, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.995498 · Cited by 45
The astrocyte-produced growth factor HB-EGF limits autoimmune CNS pathology
Linnerbauer, M., Lößlein, L., Vandrey, O., Peter, A., Han, Y., Tsaktanis, T., Wogram, E., Needhamsen, M., Kular, L., Nagel, L., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01756-6 · Cited by 46
Peng, V., Cao, S. “., Tršan, T., Bando, J. K., Ávila-Pacheco, J., Cleveland, J. L., Clish, C. B., Xavier, R. J., & Colonna, M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214900119 · Cited by 41
Self-citationDivergent metabolic programmes control two populations of MAIT cells that protect the lung
Riffelmacher, T., Murray, M. P., Wientjens, C., Chandra, S., Castelan, V. O. C., Chou, T.-F., McArdle, S., Dillingham, C. H., Devereaux, J., Nilsen, A., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01152-6 · Cited by 42
Targeting gut microbiota: new therapeutic opportunities in multiple sclerosis
Kujawa, D., Łaczmański, Ł., Budrewicz, S. P., Pokryszko‐Dragan, A., & Podbielska, M. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2274126 · Cited by 45
Metabolic rewiring and communication in cancer immunity
Chapman, N. M., & Chi, H. (2024) · Cell chemical biology · doi:10.1016/j.chembiol.2024.02.001 · Cited by 44
Puniya, B. L., Verma, M., Damiani, C., Bakr, S. H., & Dräger, A. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae090 · Cited by 47
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Wang, Z., Li, T. X., Jiang, Y., Ning, L., Zhu, X., Chen, X., Xuan, B., Zhou, Y., Ding, J., Ma, Y., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.01.007 · Cited by 46
Wang, L., Li, S., Wang, K., Wang, N., Liu, Q., Sun, Z., Wang, L., Wang, L., Liu, Q., Song, C., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.01.001 · Cited by 42
Tumor-produced ammonia is metabolized by regulatory T cells to further impede anti-tumor immunity
Gu, J., Yu, L., Chen, Q., Song, Z., Qian, Q., Liang, Y., Huang, T., Qiao, L., Li, X., Yu, M., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.034 · Cited by 47
Systems Immunology Approaches to Metabolism
Mogilenko, D. A., Sergushichev, A., & Artyomov, M. N. (2023) · Annual Review of Immunology · doi:10.1146/annurev-immunol-101220-031513 · Cited by 44
Wang, X., Huang, R., Wu, W., Xiong, J., Wen, Q., Zeng, Y., Chen, T., Li, J., Zhang, C., Zhong, J. F., et al. (2022) · Acta Biomaterialia · doi:10.1016/j.actbio.2022.11.007 · Cited by 39
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Prasad, R., Floyd, J. L., Dupont, M., Harbour, A., Adu-Agyeiwaah, Y., Asare‐Bediako, B., Chakraborty, D., Kichler, K., Rohella, A., Calzi, S. L., et al. (2022) · Circulation Research · doi:10.1161/circresaha.122.322003 · Cited by 41
Chu, W., Peng, W., Lu, Y., Liu, Y., Li, Q., Wang, H., Wang, L., Zhang, B., Liu, Z., Han, L., et al. (2024) · Advanced Science · doi:10.1002/advs.202403177 · Cited by 39
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Chen, H., You, R., Guo, J., Zhou, W., Chew, G., Devapragash, N., Loh, J. Z., Gesualdo, L., Li, Y., Jiang, Y., et al. (2024) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000328 · Cited by 41
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Chen, Y., Chen, K., Zhu, H., Qin, H., Liu, J., & Cao, X. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314561121 · Cited by 39
Chowdhury, N. U., Cephus, J.-Y., Pilier, E. H., Wolf, M. M., Madden, M. Z., Kuehnle, S. N., McKernan, K. E., Jennings, E. Q., Arner, E. N., Heintzman, D. R., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci177242 · Cited by 36
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Ma, S., Chi, X., Cai, Y., Ji, Z., Wang, S., Ren, J., & Liu, G. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-120642 · Cited by 37
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Kumar, V., & Stewart, J. H. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1125874 · Cited by 37
Tillett, B. J., Dwiyanto, J., Secombe, K. R., George, T., Zhang, V., Anderson, D., Duggan, E., Giri, R., Loo, D., Stoll, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58319-y · Cited by 41
Ren, H., Chen, Y., Zhu, Z., Xia, J., Liu, S., Hu, Y., Qin, X., Zhang, L., Ding, Y., Xia, S., et al. (2023) · Cell Death and Disease · doi:10.1038/s41419-023-05879-w · Cited by 34
Metadata
- Authors
- Wagner, A., Wang, C., Fessler, J., DeTomaso, D., Avila-Pacheco, J., Kaminski, J., Zaghouani, S., Christian, E., Thakore, P., Schellhaass, B., Akama-Garren, E., Pierce, K., Singh, V., Ron-Harel, N., Douglas, V. P., Bod, L., Schnell, A., Puleston, D., Sobel, R. A., Haigis, M., Pearce, E. L., Soleimani, M., Clish, C., Regev, A., Kuchroo, V. K., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Massachusetts Eye and Ear Infirmary (US)
- Max Planck Institute of Immunobiology and Epigenetics (DE)
- VA Palo Alto Health Care System (US)
- Harvard University (US)
- University of New Mexico (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Wang, Chao
- Kuchroo, Vijay K.
- Yosef, Nir
- ORCID
- Wagner, Allon
- Wang, Chao
- Fessler, Johannes
- DeTomaso, David
- Zaghouani, Sarah
- Christian, Elena
- Pierce, Kerry
- Singh, Vasundhara
- Douglas, Vivian Paraskevi
- Bod, Lloyd
- Schnell, Alexandra
- Puleston, Daniel
- Sobel, Raymond A.
- Haigis, Marcia
- Pearce, Erika L.
- Soleimani, Manoocher
- Clish, Clary
- Regev, Aviv
- Kuchroo, Vijay K.
- Yosef, Nir
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 184
- Issue
- 16
- Pages
- 4168-4185.e21
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC8621950
- OpenAlex
- W3174064560
- Cited by
- 524
- References
- 163
- Citations by year
- 2026: 105 · 2025: 118 · 2024: 111 · 2023: 92 · 2022: 75 · 2021: 18
- FWCI
- 27.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 369 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Chan Zuckerberg Initiative
- Australian Academy of Science
- Österreichischen Akademie der Wissenschaften
- NIH
- Canada First Research Excellence Fund (P01AI039671, P01AI056299, P01AI073748, R01AI144166, R01NS045937, RO1NS30843, TA-1605-08590)
- National Multiple Sclerosis Society
- National Institute of Mental Health (NIH5U19MH114821)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- Th17 cells, cellular metabolism, flux balance analysis, glycolysis, fatty acid oxidation, regulatory T cells, polyamine metabolism, arginine metabolism, Foxp3, central nervous system autoimmunity, multiple sclerosis, encephalitogenic T cells
- MeSH terms
- Epigenome, Diamine N-Acetyltransferase, Acetyltransferases, Adenosine Triphosphate, Aerobiosis, Algorithms, Eflornithine, Animals, Chromatin, Citric Acid Cycle, Encephalomyelitis, Autoimmune, Experimental, Fatty Acids, Glycolysis, Mice, Inbred C57BL, Models, Biological, Oxidation-Reduction, Putrescine, Autoimmunity, Cytokines, Mitochondrial Membrane Transport Proteins, T-Lymphocytes, Regulatory, Mice, Jumonji Domain-Containing Histone Demethylases, Th17 Cells, Single-Cell Analysis, Transcriptome
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