The DOI matches its Crossref record; 164 of the 164 references it cites were found in a registry or an index.
Metabolic modeling of single Th17 cells reveals regulators of autoimmunity
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citation
Cited by
Sorted by citations
Items 201–300 of 523
Metabolism in stem cell driven leukaemia: parallels between haematopoiesis and immunity
Rattigan, K. M., Zarou, M. M., & Helgason, G. V. (2023) · Blood · doi:10.1182/blood.2022018258 · Cited by 16
Ramos, J. M. P., Castellani, G., Kviatcovsky, D., Croese, T., Tsitsou-Kampeli, A., Burgaletto, C., Abellanas, M. A., Cahalon, L., Colaiuta, S. P., Salame, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58494-y · Cited by 18
Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues
Hartmann, F. (2025) · European Journal of Immunology · doi:10.1002/eji.70094 · Cited by 15
Lupus Susceptibility Loci Predispose Mice to Clonal Lymphocytic Responses and Myeloid Expansion
Akama‐Garren, E. H., & Carroll, M. C. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2200098 · Cited by 12
Self-citationSui, H. X., Deng, W., Chai, Q., Han, B., Zhang, Y., Wei, Z., Li, Z., Wang, T., Feng, J., Yuan, M., et al. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.11.008 · Cited by 13
The kinase ITK controls a Ca 2+ -mediated switch that balances T H 17 and T reg cell differentiation
Anannya, O., Huang, W., & August, A. (2024) · Science Signaling · doi:10.1126/scisignal.adh2381 · Cited by 20
Lysine demethylase 6 (KDM6): A promising therapeutic target in autoimmune disorders and cancer
Nastaranpour, M., Damara, A., Grabbe, S., & Shahneh, F. Z. (2025) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2025.118254 · Cited by 13
Multi‐Omics and ‐Organ Insights into Energy Metabolic Adaptations in Early Sepsis Onset
Xu, L., Zhou, Z., Schäuble, S., Vivas, W., Dlubatz, K., Bauer, M. W., Weis, S., Singer, M., Lukaszewski, R. A., & Panagiotou, G. (2025) · Advanced Science · doi:10.1002/advs.202504418 · Cited by 13
Rong, W., & Zhou, Y. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1710474 · Cited by 14
Oral pathogens meet the gut microbiome: new mechanistic insights on systemic disease
Gan, G., Chen, R., Zheng, P., Long, K., Ma, K. K., Sulaiman, J. E., & Huang, X. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1673512 · Cited by 16
Masuyama, S., Mizui, M., Morita, M., Shigeki, T., Kato, H., Yamamoto, T., Sakaguchi, Y., Inoue, K., Namba‐Hamano, T., Matsui, I., et al. (2024) · Clinical Immunology · doi:10.1016/j.clim.2024.110357 · Cited by 12
Polyamine: A metabolic compass for T helper cell fate direction
Shi, H., & Chi, H. (2021) · Cell · doi:10.1016/j.cell.2021.07.012 · Cited by 12
DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease
Vecellio, M. L., Paraboschi, E. M., Ceribelli, A., Isailovic, N., Motta, F., Cardamone, G., Robusto, M., Asselta, R., Brescianini, S., Sacrini, F., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.778677 · Cited by 13
T cell phenotype switching in autoimmune disorders: Clinical significance of targeting metabolism
Barberis, M., & López, A. R. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.898 · Cited by 12
Alterations in Lymphocytic Metabolism—An Emerging Hallmark of MS Pathophysiology?
Greeck, V. B., Williams, S. K., Haas, J., Wildemann, B., & Fairless, R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032094 · Cited by 14
Prognostic biomarker IL17A correlated with immune infiltrates in head and neck cancer
Yu, M., Qian, X. X., Li, G., Cheng, Z., & Lin, Z. (2022) · World Journal of Surgical Oncology · doi:10.1186/s12957-022-02703-1 · Cited by 11
Metabolic adaption of mucosal macrophages: Is metabolism a driver of persistence across tissues?
Bennett, C. L., & Perona‐Wright, G. (2023) · Mucosal Immunology · doi:10.1016/j.mucimm.2023.06.006 · Cited by 11
Single-cell omics analysis with genome-scale metabolic modeling
Chen, Y., Gustafsson, J., Yang, J., Nielsen, J., & Kerkhoven, E. J. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103078 · Cited by 12
HSP90β shapes the fate of Th17 cells with the help of glycolysis‐controlled methylation modification
Yang, L., Zhu, J., Li, S., Zeng, X., Xue, X., Dai, Y., & Wei, Z. (2024) · British Journal of Pharmacology · doi:10.1111/bph.16432 · Cited by 11
Moraly, J., Kondo, T., Benzaoui, M., DuSold, J., Talluri, S., Pouzolles, M., Chien, C. D., Dardalhon, V., & Taylor, N. N. (2024) · Molecular Oncology · doi:10.1002/1878-0261.13691 · Cited by 12
Zhong, X., Wu, H., Zhang, W., Shi, Y., Gwack, Y., Xue, H., & Sun, Z. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114951 · Cited by 11
Unraveling the metabolic heterogeneity and commonality in senescent cells using systems modeling
Li, G., Li, Y., Yu, Q., Zhou, Q.-Q., Zhang, R., Weng, C.-J., Ge, M., & Kong, Q.-P. (2025) · Life Medicine · doi:10.1093/lifemedi/lnaf003 · Cited by 11
Vitamin A retinoic acid contributes to muscle stem cell and mitochondrial function loss in old age
Fraczek, P. M., Duran, P., Yang, B. A., Ferre, V., Alawieh, L., Castor‐Macias, J. A., Wong, V. T., Guzman, S. D., Piotto, C., Itsani, K., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.183706 · Cited by 12
Zhang, E., Ding, X., Zhang, J., Liu, W., Liu, G., Li, M., Liu, X., Wang, Y., Zhang, F., Li, B., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217887 · Cited by 11
Liang, H., Fu, W., Yu, W., Cao, Z., Liu, E., Sun, F., Kong, X., Gao, Y., & Zhou, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1061448 · Cited by 11
Li, Z., Guo, Z., Yang, Z., Yang, B., Hu, Y., Xie, X., Zong, Z., Chen, Z., Zhang, K., Zhao, P., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115611 · Cited by 10
Jones, M. G., Rosen, Y., & Yosef, N. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100200 · Cited by 10
Self-citationQin, X., Ha, S., Chen, L., Zhang, T., & Li, M. (2023) · Nutrients · doi:10.3390/nu15234882 · Cited by 11
Wang, X., Wang, H., Lu, Z., Liu, X., Chai, W., Wang, W., Feng, J., Shen, Y., Yang, W., Cheng, H., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-24-1509 · Cited by 10
Biases in machine-learning models of human single-cell data
Willem, T., Shitov, V. A., Luecken, M. D., Kilbertus, N., Bauer, S., Piraud, M., Buyx, A., & Theis, F. J. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01619-8 · Cited by 12
John, R. K., Vogel, S. P., Zia, S., Lee, K. V., Nguyen, A. T., Espin, A. T., Fenrich, K. K., Ng, C., Schmidt, E. K. A., Vavrek, R., et al. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03379-6 · Cited by 10
Eduardo, M. B., Cottone, G., McCloskey, C. W., Liu, S., Palma, F. R., Zappia, M. P., Islam, A. B. M. M. K., Gao, P., Setya, J., Dennis, S., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads9182 · Cited by 11
Dessauge, E., Brauge, B., Léonard, S., Beyou, A., Laurent, C., Isen, V., Barbier, N., Monvoisin, C., Lejeune, T., Destin, J., et al. (2025) · Blood Advances · doi:10.1182/bloodadvances.2024015616 · Cited by 11
Zoccheddu, M., Suga, K., Seng, A., Svensson, M. N. D., Dutta, P., Panahandeh, S., Makinde, H. M., Ro, M., Wang, Y., Kim, H., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adm7800 · Cited by 10
Hegarty, C., Neto, N. G. B., Cahill, P. A., & Floudas, A. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.08.005 · Cited by 9
Zhu, Y., Chen, X., Zou, Y., Su, L., Yan, X., Zhu, X., Hou, Y., Liu, M., Jiang, W., Zou, C. P., et al. (2024) · Phytomedicine · doi:10.1016/j.phymed.2024.155544 · Cited by 10
LL37/self-DNA complexes mediate monocyte reprogramming
Damara, A., Wegner, J., Trzeciak, E. R., Kolb, A., Nastaranpour, M., Khatri, R., Tuettenberg, A., Kramer, D., Grabbe, S., & Shahneh, F. Z. (2024) · Clinical Immunology · doi:10.1016/j.clim.2024.110287 · Cited by 8
Lv, Y., Li, Z., Shi, L., Jian, H., Yang, F., Qiu, J., Li, C., Xiao, P., Ruan, W., Li, H., et al. (2024) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-24-00037 · Cited by 9
Zhang, M., Gou, Z., Qu, Y., & Su, X. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1456891 · Cited by 9
Sharma, R., Wu, K., Han, K., Russo, A. C., Dagur, P. K., Combs, C. A., Yao, X., Levine, S. J., & Sack, M. N. (2024) · Allergy · doi:10.1111/all.16461 · Cited by 9
Inferring metabolic objectives and trade-offs in single cells during embryogenesis
Lin, D.-W., Zhang, L., Zhang, J., & Chandrasekaran, S. (2025) · Cell Systems · doi:10.1016/j.cels.2024.12.005 · Cited by 10
The emerging roles of the urea cycle in tumor microenvironment and therapies
Zhou, J., Sun, X., & Jiang, P. (2025) · Trends in cancer · doi:10.1016/j.trecan.2025.08.007 · Cited by 9
Decoding immunometabolism with next-generation tools: lessons from dendritic cells and T cells
Wang, Y., Wang, L., & Ho, P. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00569-z · Cited by 10
Tang, Y., Zhao, Y., Chen, Z., Shi, X., Zhou, Y., Li, L., Xiao, F., Zhu, X., Qing, Y., Mo, Y., et al. (2025) · Cellular and Molecular Immunology · doi:10.1038/s41423-025-01356-w · Cited by 13
Saravia, J., Chapman, N. M., Sun, Y., Meng, X., Risch, I., Li, W., Guy, C. S., Shi, H., Hu, H., Dhungana, Y., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.ads9456 · Cited by 11
Therapeutic opportunities to modulate immune tolerance through the metabolism-chromatin axis
Ferrara, A. L., Liotti, A., Pezone, A., & Rosa, V. D. (2022) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2022.04.002 · Cited by 9
Pacheco, M. P., Ji, J., Prohaska, T., Garcia, M. M., & Sauter, T. (2022) · Metabolites · doi:10.3390/metabo12121211 · Cited by 9
Immunoinformatics: Pushing the boundaries of immunology research and medicine
Chatanaka, M. K., Ulndreaj, A., Sohaei, D., & Prassas, I. (2021) · ImmunoInformatics · doi:10.1016/j.immuno.2021.100007 · Cited by 10
Mosure, S., Wilson, A. N., & Solt, L. A. (2022) · Immunometabolism · doi:10.20900/immunometab20220006 · Cited by 10
Zhao, Q., Zhang, T., & Yang, H. (2022) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2022.03.034 · Cited by 11
Elevated enteric putrescine suppresses differentiation of intestinal germinal center B cells
Wei, X., Shi, S., Lu, Z., Li, C., Xu, X., Chai, J., Liu, X., Hu, T., & Wang, B. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.111544 · Cited by 8
Application of Metabolomics across the Spectrum of Pulmonary and Critical Care Medicine
Simpson, C. E., Ledford, J. G., & Liu, G. (2024) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2024-0080ps · Cited by 8
New Insights and Implications of Cell–Cell Interactions in Developmental Biology
Wu, G., Liang, Y., Xi, Q., & Zuo, Y. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26093997 · Cited by 8
Donohoe, E., Canning, A., Johnston, E., Moosavizadeh, S., Wang, J., Leahy, M. J., Treacy, O., Ryan, A. E., & Ritter, T. (2025) · Stem Cell Research & Therapy · doi:10.1186/s13287-025-04504-1 · Cited by 8
Digital twins for in vivo metabolic flux estimations in patients with brain cancer
Meghdadi, B., Al‐Holou, W. N., Scott, A. J., Mittal, A., Liang, N., Palavalasa, S., Achreja, A., O’Brien, A. M., Do, K., Wu, Z., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.10.022 · Cited by 8
Shao, M., Shi, M., Du, J., Pei, X., Gu, B.-B., & Yi, F. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.829760 · Cited by 8
Autophagy-inducing nutritional interventions in experimental and clinical oncology
Hofer, S. J., Kroemer, G., & Kepp, O. (2022) · International review of cytology · doi:10.1016/bs.ircmb.2022.08.003 · Cited by 8
Liu, P.-W., Lin, J., Hou, R., Cai, Z., Gong, Y., He, P., & Yang, J. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107926 · Cited by 7
Atlas of Cell Repertoire Within Neointimal Lesions Is Metabolically Altered in Hypertensive Rats
Sun, X., Wu, J., Zhang, X., Xie, C., Wei, H., Li, P., Yang, Y., Yuan, H., Cai, J., Xiao, Q., et al. (2024) · Hypertension · doi:10.1161/hypertensionaha.123.22057 · Cited by 7
Deciphering metabolic crosstalk in context: lessons from inflammatory diseases
Verheijen, F. W., Minh, T. T. N., Chang, J., Broere, F., Zaal, E. A., & Berkers, C. R. (2024) · Molecular Oncology · doi:10.1002/1878-0261.13588 · Cited by 8
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Steach, H. R., Viswanath, S., He, Y., Zhang, X., Ivanova, N. B., Hirn, M., Perlmutter, M., & Krishnaswamy, S. (2024) · Lecture notes in computer science · doi:10.1007/978-1-0716-3989-4_15 · Cited by 7
Sun, J., Shi, S., Sun, C., Wang, J., Yang, X., Yang, Z., Xu, J., & Zhang, S. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05635-4 · Cited by 7
Fu, X., Zhao, Y., Cui, X., Huang, S., Lv, Y., Li, C., Gong, F., Yang, Z., Yang, X., & Xiao, R. (2025) · Angiogenesis · doi:10.1007/s10456-025-09970-y · Cited by 9
Enhanced flux potential analysis links changes in enzyme expression to metabolic flux
Li, X., Walhout, A. J. M., & Yilmaz, L. S. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00090-9 · Cited by 7
McCreery, C. V., Alessi, D., Mollo, K., Fasano, A., & Zomorrodi, A. R. (2025) · BMC Medicine · doi:10.1186/s12916-025-03854-0 · Cited by 6
Liao, W., Hu, R., Ji, Y., Zhong, Z., Huang, X., Cai, T., Zhou, C., Wang, Y., Ye, Z., & Yang, P. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.156660 · Cited by 7
Ding, H., Xue, L., Zhu, S., Wang, Z., Gui, H., Zhang, G., Tang, N., Ren, H., & Chen, D. (2025) · Smart Medicine · doi:10.1002/smmd.70009 · Cited by 7
Biological and prognostic insights into the prostaglandin D2 signaling axis in lung adenocarcinoma
Liu, Q., Chen, H., Tang, D., Zhang, H., Chen, S., Meng, Y., Zheng, B., Liu, F., Zhou, J., & Zhang, W. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1562261 · Cited by 7
Sun, X., Wu, J., He, T., Yao, M., Li, Q., Weng, C., Peng, L., Xiao, Q., Lu, Y., Yuan, H., et al. (2025) · Circulation · doi:10.1161/circulationaha.125.073691 · Cited by 7
Zhang, S., Shi, Q., Wu, Y., Chen, X., Li, J., Chen, P., Wu, Y., Li, X., Zheng, S., Zhang, J., et al. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c09864 · Cited by 7
HIVEP1 aggravates NASH by reprogramming polyamine metabolism in T H 17 cells
Ren, Y., Liu, X., Feng, M., Zhao, J., Duan, Y., Dong, G., Gao, H., Hao, X., Wang, Q. L., Yao, J., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adn1150 · Cited by 9
Prado, D. D. S., Kutuva, A. R., Cattley, R. T., Sonego, A. B., Sutariya, P., Elbushra, I. M., Wu, S., Lee, M., Cumberland, R. L., Mehta, I. D., et al. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03591-4 · Cited by 8
Fan, Y., Xiao, B., Zhou, H., Liu, Z., Wang, L., Jin, W., Yin, Y., & Mao, X. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.108031 · Cited by 7
Harbison, R. A., Pandey, R., Considine, M., Leone, R. D., Stewart, T. M., Erbe, R., Mandal, R., Burns, M. R., Casero, R. A., Seiwert, T. Y., et al. (2022) · Cancer Research Communications · doi:10.1158/2767-9764.crc-22-0061 · Cited by 7
Zheng, X., Li, Z., Meng, Q.-Q., Li, W. S., Li, Q., Xie, L., Xiao, Y., Xu, Q., & Chen, Y.-Y. (2023) · International Immunopharmacology · doi:10.1016/j.intimp.2023.111204 · Cited by 7
Hepatitis C Virus Dysregulates Polyamine and Proline Metabolism and Perturbs the Urea Cycle
Zakirova, N. F., Khomich, O. A., Smirnova, O. A., Molle, J., Duponchel, S., Yanvarev, D. V., Треворович, В.-Э. В., Monnier, L., Grigorov, B., Ivanova, O. N., et al. (2024) · Cells · doi:10.3390/cells13121036 · Cited by 7
Rosario, S. R., Smith, R. J., Patnaik, S. K., Liu, S., Barbi, J. J., & Yendamuri, S. (2022) · Cancer & Metabolism · doi:10.1186/s40170-022-00292-x · Cited by 7
The Polyamine Putrescine Is a Positive Regulator of Group 3 Innate Lymphocyte Activation
Sah, P., & Zenewicz, L. A. (2023) · ImmunoHorizons · doi:10.4049/immunohorizons.2200097 · Cited by 7
Transcriptomes and metabolism define mouse and human MAIT cell heterogeneityPreprint
Chandra, S., Ascui, G., Riffelmacher, T., Chawla, A., Ramírez-Suástegui, C., Castelan, V. O. C., Seumois, G., Simon, H. A., Murray, M. P., Seo, G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.20.473182 · Cited by 7
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. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.03.569754 · Cited by 7
Huang, H., Yang, Y., Zhang, Q., Yang, Y., Xiong, Z., Mao, S., Song, T., Wang, Y., Liu, Z., Bu, H., et al. (2024) · eLife · doi:10.7554/elife.101731 · Cited by 7
Yang, X., Ding, Z., Peng, L., Song, Q., Zhang, D., Cui, F., Xia, C., Li, K., Yin, H., Li, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34364-9 · Cited by 6
Ma, W., Li, Y., Hu, Y., Liu, X., Guo, Z., Zhong, B., Qiu, H., & Li, J. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1482925 · Cited by 6
Ai, C., Li, H., Wang, C., Ji, Y., Wallace, D. C., Qian, J., Zhu, Y., & Guan, M. (2025) · JCI Insight · doi:10.1172/jci.insight.188962 · Cited by 7
Self-citationInterplay between gut microbiota and intestinal lipid metabolism: mechanisms and implications
Hang, B., & Wang, Y. (2025) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b2500102 · Cited by 7
Ou, Q., Niu, L., Wang, D., Yao, G., Ren, Q., Li, Z., Mao, X., Teng, W., Chen, Z., Xiang, A. P., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02442-1 · Cited by 6
Gong, Y., & Weinberg, S. E. (2025) · Biomolecules · doi:10.3390/biom15121687 · Cited by 6
Contextualizing TH17 cells in cancer
Pang, D., Bertocchi, A., Powrie, F. M., & Pohin, M. (2026) · Nature reviews. Immunology · doi:10.1038/s41577-025-01250-6 · Cited by 7
The Th17/Treg axis: a key to understanding and treating autoimmune disorders
Xie, X., Liu, Y., & Li, H. (2026) · Open Life Sciences · doi:10.1515/biol-2025-1285 · Cited by 6
Metabolic reprogramming of Th17/Treg imbalance in autoimmune thyroid diseases
Zhou, M., Wu, W., Liu, Y., Wang, S. Q., & Peng, H. (2026) · Cell Communication and Signaling · doi:10.1186/s12964-026-02915-y · Cited by 6
MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism
Chapman, S. P., Brunet, T., Mourier, A., & Habermann, B. H. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae172 · Cited by 5
Microbiome–Immune Interaction in Pulmonary Arterial Hypertension: What Have We Missed?
Zhou, X., Tian, W., Gu, S., Rabinovitch, M., Nicolls, M. R., & Snyder, M. P. (2025) · Research · doi:10.34133/research.0669 · Cited by 6
Characterizing control of memory CD8 T cell differentiation by BTB-ZF transcription factor Zbtb20
Preiss, N. K., Kamal, Y., Wilkins, O. M., Li, C., Kolling, F., Trask, H. W., Usherwood, Y.-K., Cheng, C., Frost, H. R., & Usherwood, E. J. (2023) · Life Science Alliance · doi:10.26508/lsa.202201683 · Cited by 7
Wang, T., & Gao, Y. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.59 · Cited by 7
Phua, Y. L., D’Annibale, O., Karunanidhi, A., Mohsen, A.-W. A., Kirmse, B., Dobrowolski, S. F., & Vockley, J. (2024) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2024.108495 · Cited by 7
Li, X., Yilmaz, L. S., & Walhout, A. J. M. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100407 · Cited by 6
An, S., Hou, S., Xu, F., Yan, H., Zhang, W., Xiang, J., Chen, H., Zhang, H., Dong, L., Sun, X., et al. (2024) · Advanced Science · doi:10.1002/advs.202412222 · Cited by 5
Chen, J., Zhu, L., Cui, Z., Zhang, Y., Jia, R., Zhou, D., Hu, B., Zhong, W., Xu, J., Zhang, L., et al. (2025) · Journal of Clinical Investigation · doi:10.1172/jci183559 · Cited by 5
The glycolytic reaction PGAM restrains Th17 pathogenicity and Th17-dependent autoimmunity
Wang, C., Wagner, A., Fessler, J., DeTomaso, D., Zaghouani, S., Zhou, Y., Pierce, K. A., Sobel, R. A., Clish, C. B., Yosef, N., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115799 · Cited by 5
Self-citationYuan, Y., Tian, Y., Zhao, Z., Yang, G., Wang, J., Zhu, F., Xue, G., Gao, F., Liu, Y., & Fan, H. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.157441 · Cited by 6
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review