{"id":408031,"date":"2026-01-15T04:23:09","date_gmt":"2026-01-15T04:23:09","guid":{"rendered":"https:\/\/www.newsbeep.com\/us\/408031\/"},"modified":"2026-01-15T04:23:09","modified_gmt":"2026-01-15T04:23:09","slug":"the-ubiquitin-ligase-klhl6-drives-resistance-to-cd8-t-cell-dysfunction","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/us\/408031\/","title":{"rendered":"The ubiquitin ligase KLHL6 drives resistance to CD8+ T cell dysfunction"},"content":{"rendered":"<p>Mice<\/p>\n<p>Male and female mice were used for the study. CD45.1+ OT-I or P14 TCR-transgenic mice were housed together. CD45.2+ male and female C57BL\/6N or C57BL\/6JNifdc mice aged 6\u20138\u2009weeks were purchased from Vital River as recipients. Female NCG (NOD\/ShiLtJGpt-Prkdcem26Cd52Il2rgem26Cd22\/Gpt) mice aged 6\u20138\u2009weeks were purchased from GemPharmatech. Rosa26-Cas9 mice were provided as a gift from the W. Sheng laboratory at the University of Zhejiang. We crossed Rosa26-Cas9 mice with OT-I transgenic mice to generate Cas9+ OT-I mice for CRISPR\u2013Cas9 screening in tumour antigen-specific CD8+ T cells. Four-week-old Klhl6+\/\u2212 mice were purchased from Cyagen. The Klhl6+\/\u2212 mice were crossed with OT-I, P14 or C57BL\/6N mice to generate Klhl6\u2212\/\u2212 OT-I\/P14 mice or Klhl6\u2212\/\u2212 mice for subsequent experiments. All mice were kept in a specific-pathogen-free facility, and all animal experiments were performed with the approval of the Institutional Animal Care and Use Committee of Suzhou Institute of Systems Medicine (ISM-IACUC-0151-R and ISM-IACUC-20240098). Mice were housed in standard conditions, with 12\u2009h\/12\u2009h light\/dark cycles, a controlled temperature of 22\u201324\u2009\u00b0C and humidity of 60%, with unrestricted food and water availability, and were examined daily. All mice were used at 6\u201316\u2009weeks old. All tumour burdens did not exceed the permission of the Institutional Animal Care and Use Committee of Suzhou Institute of Systems Medicine. Age-matched and sex-matched mice were assigned randomly to experimental and control groups.<\/p>\n<p>Cell lines<\/p>\n<p>Human embryonic kidney 293T (HEK293T) cells were purchased from the American Type Culture Collection (ATCC, CRL-3216) and maintained in DMEM (Gibco, C11995500BT) supplemented with 10% fetal bovine serum (FBS) (Gibco, 16000044) and 1% penicillin\u2013streptomycin (P\/S) (Gibco, 15140122). The mouse melanoma cell line B16 was transduced to express OVA257-264 antigen\u00a0(a gift from\u00a0Bo Huang laboratory) and maintained in DMEM with 10% FBS and 1% P\/S. HepG2 cells (ATCC, HB-8065) were transduced to express human NY-ESO antigen (HepG2-ESO) and cultured in DMEM with 10% FBS and 1% P\/S. Jurkat (ATCC, TIB-152) and EL4 (ATCC, TIB-39) cell lines were cultured within the complete Roswell Park Memorial Institute (RPMI)-1640 medium supplemented with 10% FBS, 1% P\/S, 1% GlutaMAX (Gibco, 35050061), 10\u2009mM HEPES (Gibco, 15630130), 1% non-essential amino acids (Gibco, 11140076), 1\u2009mM sodium pyruvate (Gibco, 11360070) and 50\u2009\u03bcM \u03b2-mercaptoethanol (Sigma, M6250).\u00a0HEK293T, Jurkat, HepG2\u00a0and EL4 cells were pre-authenticated by ATCC by short tandem repeat (STR)\u00a0sequencing. B16-OVA cells were frequently monitored based on their morphological features but have not been authenticated by STR. All cell lines were routinely tested for mycoplasma contamination.<\/p>\n<p>Plasmids<\/p>\n<p>Mouse Klhl6, Tox and Ppargc1a genes were amplified from the complementary DNA (cDNA) library of mice OT-I T cells, and human KLHL6 and TOX genes were amplified from human peripheral blood mononuclear cells (PBMCs). Retroviral plasmid (MSGV-Thy1.1-Klhl6, MSGV-Thy1.1-Ppargc1a and MSGV-Thy1.1-Vector) and packaging vector (pCL-Eco) plasmid were used to produce retroviruses in HEK293T cells using 293 Transfection Reagent (Mirus, MIR 2700), which were then transduced into OT-I CD8+ T cells. The MESV-shCtrl-GFP (Addgene, 85587) was used for Tox or Pgam5 KD. Primer sequences used for Tox and Pgam5 KD can be found in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">6<\/a>. The retroviral plasmids (MSGV-NGFR-KLHL6, MSGV-Thy1.1-1G4 TCR and MSGV-NGFR-Vector) and packaging vectors (pHIT60 and RD114) were used to produce retroviruses, which were used to transduce PBMCs and Jurkat cells. Lentivirus vectors (pCCLc-MND-Thy1.1-Klhl6 and pCCLc-MND-Thy1.1-Vector) and packaging vector (PA2X and VSV-G) plasmids were used for lentivirus production in HEK293T cells using Liposomal Transfection Reagent for transduction into EL4 cell line. For transient expression experiments in HEK293T cells, the vector plasmid pcDNA4\/TO or pFLAG-CMV-4 was used according to the experimental need.<\/p>\n<p>Primary mouse T cell isolation, viral transduction and culture<\/p>\n<p>Naive OT-I T lymphocytes were isolated from the spleens and peripheral lymph nodes of male and female OT-I mice (6\u20138\u2009weeks). Spleens and peripheral lymph nodes were collected, and mashed through a 70-\u03bcm filter, and red blood cells were lysed using red blood cell lysis buffer (BioLegend, 420301) followed by washing with 1\u00d7 phosphate-buffered saline (PBS). CD8+ OT-I T cells were purified using a CD8+ Naive T cell isolation kit (BioLegend, 480043) according to the manufacturer\u2019s instructions. Primary mouse T cells were counted and then resuspended in RPMI-1640 supplemented with 10% FBS, 1% sodium pyruvate, 1% non-essential amino acids, 10\u2009mM HEPES, 1% GlutaMAX, 1% P\/S, 50\u2009\u03bcM \u03b2-mercaptoethanol and mouse IL-2 (20\u2009U\u2009ml\u22121, Peprotech, 212-12). Then, the resuspended CD8+ OT-I T cells were seeded at a concentration of 1\u2009million cells per ml on 24-well plates with overnight-bound anti-mouse CD3 (2\u2009\u03bcg\u2009ml\u22121, BioLegend, 100359) and anti-mouse CD28 (1\u2009\u03bcg\u2009ml\u22121, BioLegend, 102121) antibodies. Cells were activated in 24-well plates for 48\u2009h and then transferred out of the activation plates and passaged to new plates every 2\u2009days with a concentration of 1\u2009million cells per ml. For drug treatment experiments, DMSO (Sigma, D2650), 2\u2009\u03bcM LFHP-1c (MCE, HY-139598) or 10\u2009\u03bcM Mdivi-1 (Selleck, S7162) and 20\u2009\u03bcM M1 (Selleck, S3375) were added to cultures daily starting on day 3 after T cell activation. In viral transduction, 7.5\u2009\u00d7\u2009105 OT-I cells were transduced with unconcentrated retroviral supernatant after 24\u2009h of activation in 24-well plates coated with RetroNectin reagent (15\u2009\u03bcg\u2009ml\u22121, Takara, T100B). Following centrifugation at 2,500\u2009rpm for 90\u2009min at 30\u2009\u00b0C, T cells were cultured in the incubator for 24\u2009h. The transduction was repeated 24\u2009h later and then returned to fresh medium for culture. Drug-treated or retrovirus-transduced OT-I cells were sorted by flow cytometry and then adoptively transferred into recipient mice that were inoculated with B16-OVA tumour cells before transfer.<\/p>\n<p>Human T cell isolation, viral transduction and culture<\/p>\n<p>Human PBMCs from healthy donors were purchased from Sailybio and isolated using Lymphoprep (Cytiva, 17144003) according to the manufacturer\u2019s protocol. Isolated PBMCs were cultured in RPMI-1640 medium supplemented with 5% Human Serum AB (Gemini, 100-512), 1% GlutaMAX, 1% non-essential amino acids, 1% P\/S, 1\u2009mM sodium pyruvate, 10\u2009mM HEPES and 50\u2009\u03bcM \u03b2-mercaptoethanol in the presence of human IL-2 (100\u2009U\u2009ml\u22121, Peprotech, 200-02). PBMCs were activated by anti-human CD3 (1\u2009\u03bcg\u2009ml\u22121, BioLegend, 317347) and anti-human CD28 (1\u2009\u03bcg\u2009ml\u22121, BioLegend, 302943) monoclonal antibodies for 2\u2009days and then underwent viral transduction. In brief, 1\u2009\u00d7\u2009106 PBMCs were transferred to a new 24-well plate and dually transduced by 1G4 TCR-specific and KLHL6-specific retroviral supernatant in the presence of 10\u2009\u03bcg\u2009ml\u22121 polybrene (Sigma, TR-1003-G). Following centrifugation at 2,500\u2009rpm for 90\u2009min at 30\u2009\u00b0C, PBMCs were cultured in the incubator for 24\u2009h with fresh medium and then underwent repeated transduction. The transduced PBMCs were adoptively transferred into female NCG mice that were inoculated with HepG2-ESO tumour cells before transfer.<\/p>\n<p>B16 tumour model and ACT immunotherapy<\/p>\n<p>To investigate the anti-tumour activity of T cells in vivo, 2\u2009\u00d7\u2009105 B16-OVA melanoma cells were subcutaneously injected into female C57BL\/6N mice. Nine days after tumour implantation, each tumour-bearing mouse was intravenously injected with the required number of CD8+ OT-I T cells from female OT-I mice, which had been expanded for 6\u2009days according to different experimental designs. Tumour-bearing mice received 5\u2009Gy of sublethal irradiation for lymphodepletion 1\u2009day before ACT. For the analysis of tumour growth and mice survival, tumour volume was measured every 2\u2009days and calculated as length (mm)\u2009\u00d7\u2009width (mm)\u2009\u00d7\u2009width (mm)\u2009\u00d7\u20090.5. Mice with tumour volumes greater than 1,500\u2009mm3 were euthanized and defined as dead for survival analysis. For the analysis of functional phenotype, mice were euthanized and tissues from tumours, spleens and lymph nodes were collected at days 7, 14, 21 or 28 post-ACT, depending on different experimental designs. For the CellTrace Violet labelling assay, equal numbers of CellTrace Violet-labelled control and KLHL6-OE OT-I T cells were cotransferred into recipient tumour-bearing mice, and TILs were analysed by flow cytometry on day 4 post-ACT. The tumours were digested by Type II collagenase (Worthington Biochemical, LS004176) and processed with Percoll (Cytiva, 17089109). Adoptively transferred OT-I T cells were isolated from tumours, spleens, and lymph nodes, and cell numbers were counted. Isolated T cells were washed and resuspended in ice-cold PBS with 2% FBS in the presence of specific antibodies for the determination of their proportion and functional phenotype through flow cytometry.<\/p>\n<p>In vivo Tpex transfer assay<\/p>\n<p>Female C57BL\/6N (CD45.2+) mice were subcutaneously implanted with 2\u2009\u00d7\u2009105 B16-OVA cells on day 0. On day 9, each tumour-bearing mouse was intravenously injected with 3\u2009\u00d7\u2009106 control or KLHL6-OE CD45.1+ OT-I T cells. Then 14\u2009days after ACT, Tpex (Ly108+TIM-3\u2212) TILs were sorted from tumours by flow cytometry. After sorting, the cells were centrifuged and resuspended in PBS. A total of 5\u2009\u00d7\u2009104 Tpex cells were transferred through tail vein injection into female C57BL\/6N (CD45.2+) mice that had been subcutaneously implanted with 3\u2009\u00d7\u2009105 B16-OVA cells 2\u2009days before. Tumour sizes were measured on day 8 after ACT and every 2\u2009days thereafter. TILs were isolated at days 8 and 16 for phenotypic analysis as previously described in ref. <a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 33\" title=\"Miller, B. C. et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326&#x2013;336 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR33\" id=\"ref-link-section-d94805179e3294\" rel=\"nofollow noopener\" target=\"_blank\">33<\/a>.<\/p>\n<p>NCG mouse model and 1G4 TCR-T cell therapy<\/p>\n<p>Female NCG mice were subcutaneously implanted with 4\u2009\u00d7\u2009106 HepG2-ESO cells. Subsequently, 1G4 TCR-T cells transduced with or without KLHL6, respectively, were expanded for 12\u2009days in vitro and adoptively transferred into the tumour-bearing mice (6\u2009million cells per mouse) when tumour volumes reached 80\u2009mm3. Mice were euthanized on day 16 after ACT, and the tumours were collected for weighing. For the in vivo phenotyping, the blood, tumours and spleens were collected. The spleens and blood were mashed and\/or lysed with red blood cell lysis buffer for 5\u2009min on ice. To isolate T cells from the tumour, the tumours were digested by Type II collagenase and processed with Percoll. Then, the isolated T cells were stained with antibody cocktails and analysed by flow cytometry.<\/p>\n<p>LCMV infection and adoptive T cell transfer<\/p>\n<p>CD45.2+ C57BL\/6 recipient mice were intraperitoneally infected with 2\u2009\u00d7\u2009105 plaque-forming units (PFU) of LCMV-Armstrong or intravenously injected through the tail vein with 2\u2009\u00d7\u2009106 PFU of LCMV-Clone 13. One day before infection, mice received adoptive transfers of 5\u2009\u00d7\u2009104 (for Armstrong) or 5\u2009\u00d7\u2009103 (for Clone 13) P14 CD8+ T cells. Phenotypic analyses were performed at various time points p.i. according to the experimental design<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 20\" title=\"McManus, D. T. et al. An early precursor CD8(+) T cell that adapts to acute or chronic viral infection. Nature 640, 772&#x2013;781 (2025).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR20\" id=\"ref-link-section-d94805179e3331\" rel=\"nofollow noopener\" target=\"_blank\">20<\/a>. Naive WT CD8+ T cells and Klhl6\u2212\/\u2212 (KO) CD8+ T cells for transfer were isolated from P14 mice using a naive CD8+ T cell isolation kit and adoptively transferred into recipient mice. For retroviral transduction, naive P14 CD8+ T cells were activated for 24\u2009h and then transduced with MSGV-Thy1.1-Klhl6 (KLHL6-OE) or MSGV-Thy1.1-Vector (Control) retrovirus. The following day, transduced CD8+ T cells were sorted, resuspended in cold 1\u00d7 PBS and adoptively transferred into recipient mice, followed by LCMV infection 1\u2009day later.<\/p>\n<p>LCMV viral RNA quantification<\/p>\n<p>CD45.2+ C57BL\/6 recipient mice were intravenously injected with 2\u2009\u00d7\u2009106\u2009PFU of LCMV-Clone 13. One day before infection, mice were adoptively transferred with 1\u2009\u00d7\u2009104 P14 CD8+ T cells. Liver and lung samples were collected on day 15 p.i., and viral load was quantified using a quantitative PCR (qPCR)-based assay, as previously described<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 60\" title=\"Zhao, X. et al. The transcriptional cofactor Tle3 reciprocally controls effector and central memory CD8(+&#x2009;) T cell fates. Nat. Immunol. 25, 294&#x2013;306 (2024).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR60\" id=\"ref-link-section-d94805179e3369\" rel=\"nofollow noopener\" target=\"_blank\">60<\/a>. In brief, total RNA was extracted using the Qiagen RNA isolation kit and subsequently subjected to reverse transcription with the Reverse Transcription Kit (Vazyme, R323-01). cDNA was then used as template for qPCR with 2\u00d7 SYBR Green qPCR Master Mix (Bimake, b21203). Primers for LCMV GP and hypoxanthine-guanine phosphoribosyltransferase\u00a0(HPRT) are listed in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">6<\/a>.<\/p>\n<p>Surprisal analysis<\/p>\n<p>We analysed harmonized bulk RNA-seq datasets comprising 136 samples from 8 previously published studies<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 8\" title=\"Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452&#x2013;456 (2017).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR8\" id=\"ref-link-section-d94805179e3386\" rel=\"nofollow noopener\" target=\"_blank\">8<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 18\" title=\"Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160&#x2013;1165 (2016).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR18\" id=\"ref-link-section-d94805179e3389\" rel=\"nofollow noopener\" target=\"_blank\">18<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 33\" title=\"Miller, B. C. et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326&#x2013;336 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR33\" id=\"ref-link-section-d94805179e3392\" rel=\"nofollow noopener\" target=\"_blank\">33<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" title=\"Scott-Browne, J. P. et al. Dynamic changes in chromatin accessibility occur in CD8(+) T cells responding to viral infection. Immunity 45, 1327&#x2013;1340 (2016).\" href=\"#ref-CR61\" id=\"ref-link-section-d94805179e3395\">61<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" title=\"Man, K. et al. Transcription factor IRF4 promotes CD8(+) T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129&#x2013;1141 (2017).\" href=\"#ref-CR62\" id=\"ref-link-section-d94805179e3395_1\">62<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" title=\"Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8(+) T cells sustain the immune response to chronic viral infections. Immunity 45, 415&#x2013;427 (2016).\" href=\"#ref-CR63\" id=\"ref-link-section-d94805179e3395_2\">63<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" title=\"Mognol, G. P. et al. Exhaustion-associated regulatory regions in CD8(+) tumor-infiltrating T cells. Proc. Natl Acad. Sci. USA 114, E2776&#x2013;E2785 (2017).\" href=\"#ref-CR64\" id=\"ref-link-section-d94805179e3395_3\">64<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 65\" title=\"Chen, J. et al. NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530&#x2013;534 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR65\" id=\"ref-link-section-d94805179e3398\" rel=\"nofollow noopener\" target=\"_blank\">65<\/a> including gene expression profiles from CD8+ memory and effector T cells, TILs and chimeric antigen receptor T cells, as well as endogenous Tex cells exposed to chronic antigen stimulation, with or without immune checkpoint inhibition. A complete list of datasets is provided in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">1<\/a>. Despite thousands of genes that could all be changing across various studies and conditions, we proposed that many genes are coordinately changing together as a group (or gene module), which reflects the fundamental biology of T cell exhaustion programs. Surprisal analysis has been well-documented in deconvoluting the change of thousands of genes into the change of only a couple of gene modules and one unchanged gene expression baseline<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 66\" title=\"Zadran, S., Arumugam, R., Herschman, H., Phelps, M. E. &amp; Levine, R. D. Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition. Proc. Natl Acad. Sci. USA 111, 13235&#x2013;13240 (2014).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR66\" id=\"ref-link-section-d94805179e3407\" rel=\"nofollow noopener\" target=\"_blank\">66<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 67\" title=\"Remacle, F., Kravchenko-Balasha, N., Levitzki, A. &amp; Levine, R. D. Information-theoretic analysis of phenotype changes in early stages of carcinogenesis. Proc. Natl Acad. Sci. USA 107, 10324&#x2013;10329 (2010).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR67\" id=\"ref-link-section-d94805179e3410\" rel=\"nofollow noopener\" target=\"_blank\">67<\/a>. The unchanged gene expression baseline reflected the biological processes that are conserved across conditions and time points. The gene module reflected the deviation from the global stable state.<\/p>\n<p>We used surprisal analysis<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 66\" title=\"Zadran, S., Arumugam, R., Herschman, H., Phelps, M. E. &amp; Levine, R. D. Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition. Proc. Natl Acad. Sci. USA 111, 13235&#x2013;13240 (2014).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR66\" id=\"ref-link-section-d94805179e3417\" rel=\"nofollow noopener\" target=\"_blank\">66<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 67\" title=\"Remacle, F., Kravchenko-Balasha, N., Levitzki, A. &amp; Levine, R. D. Information-theoretic analysis of phenotype changes in early stages of carcinogenesis. Proc. Natl Acad. Sci. USA 107, 10324&#x2013;10329 (2010).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR67\" id=\"ref-link-section-d94805179e3420\" rel=\"nofollow noopener\" target=\"_blank\">67<\/a>, an information-theoretical analysis technique that integrates principles of thermodynamics and maximal entropy, here to simplify the transcriptome changes into two main gene modules and one unchanged gene expression baseline, which when added together, accurately capture the global transcriptomic profiles of the raw data. Briefly, the logarithm of the measured level of a transcript i at a specific study a sample b, \\(\\mathrm{ln}{X}_{i}(a\\_b)\\), is expressed as a sum of a log-transformed gene expression baseline, term \\(\\mathrm{ln}{X}_{i}^{0}\\), and several gene modules \\({\\lambda }_{j}(a\\_b)\\times {G}_{{ij}}\\), representing deviations from the common expression baseline. Each deviation term is a product of a study-sample-dependent module score\\({\\lambda }_{j}(a\\_b)\\), and the study-sample-independent module-specific contribution score Gij of the gene i. Gene i that shows large positive or negative contribution to a module j (high positive or negative Gij value) represents a gene that is functionally positively or negatively correlated with the module j. In other words, the biological function of module j could be inferred by functional enrichment analysis of genes with positive and negative Gij values. The study-sample-dependent module scores of the top modules (in this case, modules 1 and 2) should be able to illustrate the global transcriptome similarities or dissimilarities. To calculate these gene modules, we first computed the singular value decomposition of the matrix \\({\\rm{ln}}X(a\\_b)\\). As described previously<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 67\" title=\"Remacle, F., Kravchenko-Balasha, N., Levitzki, A. &amp; Levine, R. D. Information-theoretic analysis of phenotype changes in early stages of carcinogenesis. Proc. Natl Acad. Sci. USA 107, 10324&#x2013;10329 (2010).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR67\" id=\"ref-link-section-d94805179e3673\" rel=\"nofollow noopener\" target=\"_blank\">67<\/a>, the singular value decomposition factored this matrix in a way that determined the two sets of parameters that are required in the surprisal analysis: the Lagrange multipliers (\\({\\lambda }_{j}(a\\_b)\\)) for all gene modules at a given sample and for all samples in all studies, as well as the module-specific contribution scores (Gij for all transcripts i at each gene module j. Further enrichment analysis of the functions associated with each module was performed based on the module-specific contribution scores of the genes associated with that module. These two dominant gene modules (modules 1 and 2) each consist of two gene sets showing opposite expression trends across all samples (M+\/\u2212) (Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">2<\/a>). M1+ genes were low in naive and memory T cells but elevated in both early and late exhausted T cells from tumours, as well as in exhausted T cells from chronic infections, whereas M1\u2212 genes showed the inverse trend. M2+ genes were selectively enriched in late exhausted cells across the tumour and chronic infection settings, and M2\u2212 genes were more highly expressed in naive and early exhausted states.<\/p>\n<p>GSEA of the gene modules<\/p>\n<p>GSEA was performed using MSigDB (v.7.5.1) pathways and custom gene sets derived from the existing literature<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 33\" title=\"Miller, B. C. et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326&#x2013;336 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR33\" id=\"ref-link-section-d94805179e3748\" rel=\"nofollow noopener\" target=\"_blank\">33<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 68\" title=\"Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545&#x2013;15550 (2005).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR68\" id=\"ref-link-section-d94805179e3751\" rel=\"nofollow noopener\" target=\"_blank\">68<\/a>. Genes were ranked by surprisal analysis scores and analysed separately for association with modules 1 and 2 using the R package clusterProfiler (v.4.12.0)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 69\" title=\"Yu, G., Wang, L. G., Han, Y. &amp; He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284&#x2013;287 (2012).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR69\" id=\"ref-link-section-d94805179e3755\" rel=\"nofollow noopener\" target=\"_blank\">69<\/a>. Ties (zero scores) were excluded. Enrichment scores were normalized by use of permutation tests, and P values were derived accordingly. Custom gene sets consisted of the top 400 differentially expressed genes (Mann\u2013Whitney U-test) after removing housekeeping, ribosomal and mitochondrial genes. The full GSEA results are provided in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">3<\/a>.<\/p>\n<p>Mitochondrial function analysis from public databases<\/p>\n<p>To investigate genes associated with mitochondrial function in T cells, we analysed roughly 400,000 TILs from 316 patients across 21 cancer types<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 22\" title=\"Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR22\" id=\"ref-link-section-d94805179e3776\" rel=\"nofollow noopener\" target=\"_blank\">22<\/a>, correlating gene expression with pathway activity for hallmark_oxidative_phosphorylation in the Molecular Signature Database. This analysis identified 286 E3 ligases positively associated with mitochondrial function. To account for patient variability in the TIL atlas, we also analysed a \u2018cleaner\u2019 mouse RNA-seq dataset within the context of adoptive T cell therapy, in which tumour-specific T cells were sorted into two subsets: (MTDR\/MTG)hi functional mitochondria and (MTDR\/MTG)lo dysfunctional mitochondria<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 6\" title=\"Yu, Y. R. et al. Disturbed mitochondrial dynamics in CD8(+) TILs reinforce T cell exhaustion. Nat. Immunol. 21, 1540&#x2013;1551 (2020).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR6\" id=\"ref-link-section-d94805179e3784\" rel=\"nofollow noopener\" target=\"_blank\">6<\/a>. Differential pathway enrichment analysis confirmed that ubiquitin-related pathways are significantly associated with mitochondrial function. Through this, we identified 191 E3 ligases positively linked to mitochondrial function. The 133 E3 ligases identified as overlapping between human and mouse analyses (Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">4<\/a>) were selected for in vivo CRISPR screening.<\/p>\n<p>CRISPR\u2013Cas9 screens using the retroviral E3-related libraryRetroviral sgRNA vector and sgRNA cloning<\/p>\n<p>In this study, CRISPR\u2013Cas9 sgRNA was expressed using pSL21-Thy1.1 or pSL21-mCherry (Addgene, 164410)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 23\" title=\"Chen, Z. et al. In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262&#x2013;1280 (2021).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR23\" id=\"ref-link-section-d94805179e3803\" rel=\"nofollow noopener\" target=\"_blank\">23<\/a>. sgRNAs were generated by annealing two DNA oligos and then ligated into the pSL21-Thy1.1 or pSL21-mCherry vector after digestion with BbsI.<\/p>\n<p>E3-related library construction<\/p>\n<p>The pSL21-mCherry vector was used for the construction of sgRNA library. A computational-guided sgRNA library targeting 78 exhaustion-related E3 ligase genes and 133 mitochondrial-related E3 ligase genes were selected and synthesized. The guide RNA (gRNA) sequences were designed according to previously published data and using the gRNA-design tool (GenScript)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 70\" title=\"Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246&#x2013;1260 (2015).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR70\" id=\"ref-link-section-d94805179e3815\" rel=\"nofollow noopener\" target=\"_blank\">70<\/a>. The library associated with exhaustion differentiation comprised a total of 400 gRNAs, including 10 non-targeting controls and 390 unique sgRNAs, with 5 gRNAs designed for each targeting gene. Another library related to mitochondrial function included a total of 671 gRNAs, including 17 non-targeting controls and 654 unique sgRNAs, with 3\u20135 gRNAs designed for each targeting gene. All sgRNA oligos, including both positive and negative control sgRNAs, were synthesized by SYNBIO Technologies and pooled in equal molarity. The pooled sgRNA oligos were subsequently amplified through PCR and cloned into BbsI-digested pSL21-mCherry vector using Gibson Assembly Kit (NEB, E5510S). The product of Gibson Assembly reaction was then introduced into TG1 Electrocompetent Cells (Biosearch Technologies, 60502) by means of electroporation and cultured overnight on solid Luria-Bertani agar plates (24\u2009\u00d7\u200924-cm culture plate). The total number of colonies across all plates was counted, exceeding 50\u00d7 representation, and the plasmids were purified using the EndoFree Plasmid Maxi Kit (CWBIO, CW2104M). To verify the identity and relative representation of sgRNAs in the pooled plasmids, a deep-sequencing analysis was performed by a NovaSeq 6000 (PE150) instrument. We confirmed that 100% of the designed sgRNAs were cloned into the vector and the final library is diverse with a Gini index of 0.05.<\/p>\n<p>In vivo screening<\/p>\n<p>The in vivo screening approach was conducted following established protocols from previous studies<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 23\" title=\"Chen, Z. et al. In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262&#x2013;1280 (2021).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR23\" id=\"ref-link-section-d94805179e3827\" rel=\"nofollow noopener\" target=\"_blank\">23<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 71\" title=\"Wei, J. et al. Targeting REGNASE-1 programs long-lived effector T cells for cancer therapy. Nature 576, 471&#x2013;476 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR71\" id=\"ref-link-section-d94805179e3830\" rel=\"nofollow noopener\" target=\"_blank\">71<\/a>. Briefly, a retrovirus pool containing sgRNAs was generated by cotransfecting the specific library plasmids and a packaging vector (pCL-Eco) in HEK293T cells. After 48\u2009h of transfection, the viral supernatant was collected and stored at \u221280\u2009\u00b0C. Naive Cas9+ OT-I T cells were isolated from spleens and activated using anti-CD3 and anti-CD28 antibodies. At 24\u2009h after activation, Cas9+ OT-I T cells were transduced with the retrovirus library, and the transduction process was repeated after 24\u2009h. The transduction efficiency was assessed based on the fluorescence intensity of mCherry, and it reached roughly 40% by the end of transduction. Following viral transduction, the cells were washed and cultured in the medium supplemented with mouse IL-2 for 4\u2009days to allow for expansion and gene editing. mCherry-positive cells were sorted by flow cytometry. Roughly 2\u2009\u00d7\u2009105 (400 gRNAs library) or 3.5\u2009\u00d7\u2009105 (671 gRNAs library) transduced Cas9+ OT-I T cells were saved as \u2018day 0 input\u2019 (around 500\u00d7 cell coverage per sgRNA). Subsequently, transduced Cas9+ OT-I T cells (3\u2009\u00d7\u2009106) were transferred into Cas9+ hosts bearing B16-OVA melanoma tumours. At day 7 after ACT, non-exhausted T cells (PD-1\u2212TIM-3\u2212) and exhausted T cells (PD-1+TIM-3+) or (MTDR\/MTG)hi and (MTDR\/MTG)lo cells were sorted using flow cytometry and frozen at \u221280\u2009\u00b0C until genomic DNA extraction. A minimum of 2\u2009\u00d7\u2009105 or 3.5\u2009\u00d7\u2009105 Cas9+ OT-I T cells per sample were collected for further analysis.<\/p>\n<p>sgRNA library sequencing<\/p>\n<p>Genomic DNA was extracted by using the PureLink Genomic DNA Mini Kit (Invitrogen, K182001) according to the manufacturer\u2019s instructions. Two rounds of PCR were performed by using the PrimeSTAR HS DNA Polymerase (Takara, R045B) to amplify the sgRNAs and attach Illumina adaptors and indexes to barcode each sample. The primer sequences used to amplify sgRNAs for the PCR are as follows: next-generation sequencing forward (F), AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGTATTTCGATTTCTTGGCTTTATATATCTTGT; next-generation sequencing reverse (R), CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGG AGTTCAGACGTGTGCTCTTCCGATCTGACTAGCCTTATTTAAACTTGCTATGC. Different index sequences were added to distinguish between experimental groups. After each PCR reaction, the PCR products were purified using the AMPure XP beads (Beckman A63881). The library sequencing was performed using the Illumina NovaSeq 6000 (PE150) platform (Novogene).<\/p>\n<p>CRISPR screen data processing and analysis<\/p>\n<p>For data analysis, single-end reads were trimmed and quality filtered using the MAGeCK-VISPR package (v.0.5.5)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 72\" title=\"Li, W. et al. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 16, 281 (2015).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR72\" id=\"ref-link-section-d94805179e3888\" rel=\"nofollow noopener\" target=\"_blank\">72<\/a> and run using Python (v.3.7.4) and matched against sgRNA sequences from the sgRNA library. Read counts for sgRNAs were normalized by control guides when possible, and when not through median counts values. log2 fold changes were calculated with a 1\u2009\u00d7\u200910\u22122 pseudo-count to account for zero-count genes and avoid infinite values; these fold changes were used as enrichment differences between DP (PD-1+TIM-3+) cell samples and those of DN (PD-1\u2212TIM-3\u2212) cell samples. The same analyses were also performed between (MTDR\/MTG)lo versus (MTDR\/MTG)hi cells. Gene-targeting sgRNAs consistently showed enrichment or depletion, whereas non-targeting controls were tightly centred around zero, indicating minimal selection bias. Gene retrieval was 100% across all targets in both screens; sgRNA retrieval was 100% in the exhaustion screen and 99.1% in the mitochondrial fitness screen. The log2 fold-change values for each gene and sgRNA from the CRISPR screens are compiled in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">5<\/a>.<\/p>\n<p>Experimental workflow in RNA-seq<\/p>\n<p>For the transcriptional profiling of Tex cells, we established a B16-OVA melanoma model to analyse tumour antigen-specific CD8+ T cell exhaustion. Briefly, OT-I CD8+ T cells were activated in vitro using anti-CD3 and anti-CD28 antibodies. On day 9 following the implantation of B16-OVA tumours, 3\u2009\u00d7\u2009106 OT-I CD8+ T cells were adoptively transferred to each tumour-bearing mouse. On day 14 after ACT, cells were sorted from tumours and spleens using flow cytometry. PD-1 and TIM-3 were used to label different subsets of exhausted TILs: the PD-1\u2212TIM-3\u2212 population, PD-1+TIM-3\u2212 population and PD-1+TIM-3+ population. For the transcriptional analysis of adoptively transferred WT and Klhl6\u2212\/\u2212 CD8+ OT-I T cells in the tumours, Klhl6\u2212\/\u2212 CD8+ OT-I T cells (CD45.1+) and WT CD8+ OT-I T cells (CD45.1\/2+) were mixed in a 1:1 ratio and adoptively transferred into the same B16-OVA tumour-bearing mice. After 14\u2009days, the transferred CD45.1+ and CD45.1\/2+ CD8+ T cells were sorted from tumours using flow cytometry and prepared for RNA extraction. Total RNA from the isolated transferred OT-I T cells was extracted using the RNeasy Micro Kit (Qiagen, 74004) following the manufacturer\u2019s instructions and stored at \u221280\u2009\u00b0C for RNA-seq. RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent). Subsequently, the libraries were prepared using the TruSeq RNA sample prep kit (Illumina, FC-122-1001). These libraries were then subjected to sequencing on an Illumina NovaSeq 6000 (PE150) platform, generating roughly 40\u2009million paired-end reads (Novogene).<\/p>\n<p>RNA-seq data processing and analysis<\/p>\n<p>The raw read counts were extracted and then normalized by their library size factors and read and gene lengths using edgeR (v.3.36.0)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 73\" title=\"Robinson, M. D., McCarthy, D. J. &amp; Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139&#x2013;140 (2010).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR73\" id=\"ref-link-section-d94805179e3977\" rel=\"nofollow noopener\" target=\"_blank\">73<\/a>, which was then used to calculate differential genes. Detailed information on trimming, alignment and quantification can be found as previously reported<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 74\" title=\"Su, Y. et al. Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line. Nat. Commun. 11, 2345 (2020).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR74\" id=\"ref-link-section-d94805179e3981\" rel=\"nofollow noopener\" target=\"_blank\">74<\/a> and further details are available at <a href=\"https:\/\/github.com\/danielgchen\/FH_bulk-RNA-seq_pipeline\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/github.com\/danielgchen\/FH_bulk-RNA-seq_pipeline<\/a>. In brief, data were trimmed using cutadapt (v.2.9)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 75\" title=\"Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10&#x2013;12 (2011).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR75\" id=\"ref-link-section-d94805179e3992\" rel=\"nofollow noopener\" target=\"_blank\">75<\/a>, quality checked before and after trimming using FastQC (v.0.11.9), and then mapped and quantified using STAR (v.2.7.7a)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 76\" title=\"Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15&#x2013;21 (2013).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR76\" id=\"ref-link-section-d94805179e3996\" rel=\"nofollow noopener\" target=\"_blank\">76<\/a>. The pathway enrichment analysis of differentially expressed genes was conducted using clusterProfiler (v.4.12.0)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 77\" title=\"Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR77\" id=\"ref-link-section-d94805179e4001\" rel=\"nofollow noopener\" target=\"_blank\">77<\/a>. GSEA was performed with GSEA (v.4.1.0)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 68\" title=\"Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545&#x2013;15550 (2005).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR68\" id=\"ref-link-section-d94805179e4005\" rel=\"nofollow noopener\" target=\"_blank\">68<\/a>; log2 fold changes were calculated with a 1\u2009\u00d7\u200910\u22122 pseudo-count to account for zero-count genes and avoid infinite values.<\/p>\n<p>Experimental workflow in scRNA-seq<\/p>\n<p>Activated CD8+ OT-I T cells were transduced with either Vector (MSGV-Thy1.1-Vector) or Klhl6 (MSGV-Thy1.1-Klhl6). Then, these transduced cells were adoptively transferred into B16-OVA tumour-bearing mice at a concentration of 3\u2009\u00d7\u2009106 cells per mouse. At day 14 after ACT, OT-I T cells were sorted from tumour samples using flow cytometry. Subsequently, these sorted single cells were encapsulated into droplets, loaded into Chromium microfluidic chips with 30 (v.3) chemistry, and barcoded using a 10\u00d7 Chromium Controller (10X Genomics). The RNAs from these barcoded cells were subsequently reverse-transcribed, and sequencing libraries were prepared using reagents from a Chromium Single Cell 3\u2032 (v3) reagent kit (10X Genomics), according to the manufacturer\u2019s instructions. Library quantification was performed using the Qubit 3.0 Fluorometer (ThermoFisher Scientific), and library quality was assessed using the 2100 Bioanalyzer with the High Sensitivity DNA kit (Agilent). The NovaSeq 6000 platform (Illumina) was used for sequencing the libraries in 50-base pair paired-end mode.<\/p>\n<p>scRNA-seq data processing and analysis<\/p>\n<p>scRNA-seq analysis pipeline closely follows previously reported methods<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 78\" title=\"Su, Y. et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 185, 881&#x2013;895 (2022).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR78\" id=\"ref-link-section-d94805179e4039\" rel=\"nofollow noopener\" target=\"_blank\">78<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 79\" title=\"Su, Y. et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell 183, 1479&#x2013;1495 (2020).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR79\" id=\"ref-link-section-d94805179e4042\" rel=\"nofollow noopener\" target=\"_blank\">79<\/a>. Briefly, Droplet-based sequencing data were aligned and quantified by use of Cell Ranger Single-Cell Software Suite (v.7.1.0, 10X Genomics) using refdata-gex-mm10-2020-A as a reference. Cells from each sample were first filtered for cells with 500 or more genes and 1,000 or more counts, then filtered based on (1) fewer than 50,000 counts per cell (library size); (2) fewer than 7,000 detected genes per cell and (3) proportion of mitochondrial gene counts (mitochondrial gene unique molecular identifiers (UMIs)\/total UMIs) less than 5%. Doublets were identified through clustering; low-quality, low-count cells were also removed. After quality control-based filtering, Scanpy<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 80\" title=\"Wolf, F. A., Angerer, P. &amp; Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR80\" id=\"ref-link-section-d94805179e4046\" rel=\"nofollow noopener\" target=\"_blank\">80<\/a> was used to normalize cells by means of counts per million normalization (UMI count per cell was set to 106) and log1p transformation (natural log of counts per million plus one). Principal component analysis was performed using variable genes. Leiden clustering and UMAP plots were generated based on selected principal component analysis dimensions. Normalized data are shown in the form of UMAP colour-coding or violin plots. Embedding density was used for density plots and calculated using scanpy.tl.embedding_density, which is a wrapper for the gaussian density algorithm under scipy. TOX signature was defined by taking the genes differentially upregulated, defined as a false discovery rate less than 0.05 and log2 fold change greater than or equal to 1, on Tox-overexpressed T cells in a tumour model<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 38\" title=\"Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270&#x2013;274 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR38\" id=\"ref-link-section-d94805179e4060\" rel=\"nofollow noopener\" target=\"_blank\">38<\/a>. Stemness and terminal exhaustion signature were defined as the following Lef1, Tcf7, Aqp3, Ccr7, Sell, Il7r, Gzmk, Dusp1, Dusp2, Fos and Junb for stemness and Pdcd1, Ctla4, Cd200r1, Cd244a, Havcr2, Lag3 and Tigit for terminal exhaustion; genes were derived from literature. Published datasets on T cell exhaustion were obtained from studies related to chronic infection and human tumour-infiltrating CD8+ T cells<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 22\" title=\"Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR22\" id=\"ref-link-section-d94805179e4123\" rel=\"nofollow noopener\" target=\"_blank\">22<\/a>,<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 33\" title=\"Miller, B. C. et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326&#x2013;336 (2019).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR33\" id=\"ref-link-section-d94805179e4126\" rel=\"nofollow noopener\" target=\"_blank\">33<\/a>. The public T cell exhaustion data in chronic infection was processed from raw by filtering for n counts between 2,000 and 10,000, n genes between 1,000 and 3,000, and less than 5% mitochondrial reads; data were then normalized according to the aforementioned methods and projection algorithms.<\/p>\n<p>qPCR with reverse transcription<\/p>\n<p>Total RNAs from cells were extracted using Trizol reagent (Takara, 9109) or the RNeasy Micro Kit, according to the manufacturer\u2019s instructions. The extracted RNAs were reverse-transcribed into cDNA using HiScript Reverse Transcriptase (Vazyme, R323-01). Quantitative real-time PCR was performed using the ABI prism 7500 real-time PCR System (ThermoFisher) and 2\u00d7 SYBR Green qPCR Master Mix (Bimake, b21203), following the respective manufacturer\u2019s protocols. The data are presented as the fold change in gene expression normalized to an internal reference gene (B2M). The relative expression of mRNA was calculated using the 2\u2212\u0394\u0394CT method. Primer sequences used for qPCR can be found in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">6<\/a>.<\/p>\n<p>Flow cytometry and sorting<\/p>\n<p>T cells were stained using fluorescent antibodies and subsequently analysed by flow cytometry. To prepare for staining, T cells were collected and washed once with fluorescence-activated cell sorting (FACS) buffer (PBS with 2% FBS). For surface protein staining, cells were stained with fluorescently conjugated antibodies and Live\/Dead Fixable Dead Cell Stain Kit (Invitrogen, 65-0866-18) in FACS buffer, then fixed with 2% paraformaldehyde (Casmart) for 30\u2009min at 4\u2009\u00b0C. For transcription factor staining, cells were prestained with Live\/Dead Fixable Dead Cell Stain Kit and fluorescently conjugated antibodies in FACS buffer to detect surface markers. The cells were then fixed for 30\u2009min at 4\u2009\u00b0C using FOXP3\/transcription factor fixation buffer (Invitrogen, 00-5523-00) and stained with transcription factor antibodies in permeabilization buffer (Invitrogen, 00-8333-56). For detection of intracellular cytokines, cells were stimulated with phorbol myristate acetate in the presence of Brefeldin A (BFA) (BioLegend, 423304) for 4.5\u2009h. Then, the prestained cells were fixed and stained with cytokines antibodies in the permeabilization buffer. After staining, cells were resuspended in FACS buffer for flow cytometric analysis. Flow cytometry data were collected using BD LSR Fortessa and BD FACSDiva (v.8.0.2), and analysed with FlowJo (v.10.4) software. Cell sorting was performed using BD FACS Aria III and BD FACSDiva (v.8.0.2). A list of antibodies and their dilutions used can be found in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">8<\/a>.<\/p>\n<p>Transmission electron microscopy<\/p>\n<p>The indicated OT-I T cells were activated in each well of a 24-well plate with mouse anti-CD3 and anti-CD28 antibodies for 3\u2009days and cultured in RPMI-1640 medium for another 3\u2009days. Subsequently, 1\u2009\u00d7\u2009106 OT-I T cells were gathered and fixed in a precooled fixation buffer (2.5% glutaraldehyde, 0.1\u2009M phosphate buffer, pH\u20097.4) overnight at 4\u2009\u00b0C. After 3 washes with PBS, cells were postfixed in 1% osmium tetroxide in PBS for 2\u2009h, dehydrated and embedded in Spurr\u2019s resin following standard procedures. Ultrathin sections were stained with uranyl acetate and lead citrate. Mitochondrial morphology was visualized using Hitachi HT-7800 transmission electron microscopy (v.01.20) and an AMT-XR81DIR camera. For quantitation of mitochondrial cross-sectional area and total crista length, images of each cell profile containing four or five mitochondria from a single thin section for the indicated samples were analysed. Cross-sectional area and total crista length per mitochondrion were calculated using the lasso tool in Image J (v.1.8.0) software.<\/p>\n<p>Seahorse analysis<\/p>\n<p>To investigate metabolic characteristics, we used a Seahorse XF24 analyser (Agilent) to measure both OCR and glycoPER in in vitro-expanded T cells and TILs sorted from tumours at day 14 after ACT, according to different experimental designs. Before analysis, these cells were pretreated with the non-buffered XF medium (RPMI-1640 supplemented with 10\u2009mM glucose, 1\u2009mM sodium pyruvate and 2\u2009mM glutamine). Subsequently, the cells were seeded at a density of 1.3\u2009\u00d7\u2009105 cells per well in an XF24 cell culture microplate and incubated in a non-CO2 environment for 1\u2009h at 37\u2009\u00b0C. To optimize cell adhesion, the plates underwent a 5\u2009min spin at room temperature at 100g with zero brake. Measurements of OCR and glycoPER were conducted under both basal conditions and in response to specific compounds, such as 1.25\u2009\u03bcM oligomycin (Oligo), 50\u2009mM 2-deoxy-d-glucose, 1.5\u2009\u03bcM carbonyl cyanide-p-trifluoromethoxy-phenylhydrazone, 0.5\u2009\u03bcM rotenone and antimycin A (R&amp;A). The SRC was calculated by subtracting basal OCR from maximum OCR. The OCR coupled with mitochondrial ATP production (coupled OCR) is defined as the OCR reduction after the injection of oligomycin, which inhibits ATP synthase. OCR and glycoPER were analysed by Seahorse wave software (Seahorse, Agilent Technologies, v.2.6).<\/p>\n<p>Mitochondrial mass and membrane potential analysis<\/p>\n<p>Mitochondrial mass and membrane potential were assessed using MTG (Invitrogen, M7514) and either TMRE (Invitrogen, T669) or MTDR (Invitrogen, M46753). In brief, cells were stained with 250\u2009nM MTG and 50\u2009nM TMRE or MTDR, and incubated at 37\u2009\u00b0C (5% CO2) for 30\u2009min. Following incubation, cells were washed three times with FACS buffer and subsequently subjected to surface marker staining for further flow cytometric analysis.<\/p>\n<p>Western blotting<\/p>\n<p>For protein expression analysis, cells were gathered, washed with cold PBS, and then lysed in 1% SDS (Sangon, 151-21-3) for 30\u2009min on ice. The protein samples were denatured at 95\u2009\u00b0C for 15\u2009min and stored at \u221220\u2009\u00b0C. Protein samples were separated on SDS\u2013PAGE gels and then transferred onto methanol-activated polyvinylidene fluoride membranes (Millipore, IPVH00005). Membranes were blocked with 5% nonfat milk in PBS containing Tween-20 (0.1%) for 1\u2009h and then incubated overnight at 4\u2009\u00b0C with the respective primary antibodies. The following day, membranes were incubated with the corresponding HRP-coupled secondary antibodies for 2\u2009h at room temperature, followed by signal development using ECL Western Blotting substrate (Tanon, 180-5001) and the Chemidoc automated detection system (Bio-Rad). Data analysis was performed using Image J (v.1.8.0) software. The antibodies and their dilutions used can be found in Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">8<\/a>.<\/p>\n<p>E-STUB for mass spectrometry<\/p>\n<p>As previously described in ref. <a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 37\" title=\"Huang, H. T. et al. Ubiquitin-specific proximity labeling for the identification of E3 ligase substrates. Nat. Chem. Biol. 20, 1227&#x2013;1236 (2024).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR37\" id=\"ref-link-section-d94805179e4221\" rel=\"nofollow noopener\" target=\"_blank\">37<\/a>, Jurkat cells were cultured in DMEM supplemented with 10% dialysed FBS. Three biological replicates were prepared for each treatment condition. Jurkat cells were transduced with retroviruses packaged in HEK293T cells using MSGV-NGFR-KLHL6-BirA and MSGV-Thy1.1-BAP-Ub plasmids to co-express KLHL6-BirA and BAP-tagged ubiquitin. Cells transduced with retroviruses packaged from MSGV-NGFR-Empty-BirA and MSGV-Thy1.1-BAP-Ub plasmids served as controls. Then 72\u2009h post-transduction, cells were pretreated with proteasome inhibitor MG132 to promote accumulation of ubiquitylated substrates and then pulsed with 50\u2009\u03bcM biotin for 30\u2009min. Following biotin labelling, cells were washed once with ice-cold PBS and lysed on ice using E-STUB RIPA buffer (RIPA buffer supplemented with EDTA-free protease inhibitor cocktail, Pierce Universal Nuclease, 10\u2009mM N-ethylmaleimide, 1\u2009mM EGTA and 1.5\u2009mM MgCl2). Lysates were collected into microcentrifuge tubes, rotated at 4\u2009\u00b0C for at least 1\u2009h and clarified by centrifugation at 12,000g for 15\u2009min at 4\u2009\u00b0C. Total protein lysates were incubated with 50\u2009\u03bcl of resuspended and prewashed streptavidin beads overnight at 4\u2009\u00b0C with rotation. The following day, beads were washed 5 times with Wash Buffer (PBS containing 0.05% Tween-20), then incubated with 30\u2009\u03bcl of 0.1% SDS at 95\u2009\u00b0C for 5\u2009min in a metal bath. Samples were sent to Shanghai Omicsspace Biotech for mass spectrometry analysis. Significant changes between the relative protein abundance of the experimental samples to the control samples were assessed by two-sided moderated t-test as implemented in the limma package (v.3.54.2)<a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 81\" title=\"Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#ref-CR81\" id=\"ref-link-section-d94805179e4237\" rel=\"nofollow noopener\" target=\"_blank\">81<\/a> (Supplementary Table <a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM3\" rel=\"nofollow noopener\" target=\"_blank\">7<\/a>).<\/p>\n<p>Co-IP and ubiquitination assays<\/p>\n<p>For the Co-IP assay, cells were transfected with the plasmids according to the experimental designs outlined in the figures. After 36\u201348\u2009h, the cells were gathered and lysed in Co-IP lysis buffer containing 20\u2009mM Tris-HCl (pH\u20097.5), 150\u2009mM NaCl, 1\u2009mM EDTA, 1\u2009mM EGTA, 1% NP-40, 2.5\u2009mM sodium pyrophosphate and 1\u2009mM Na3VO4, for 1\u2009h at 4\u2009\u00b0C. The cell lysates were centrifuged at 12,000g for 10\u2009min at 4\u2009\u00b0C to remove cell debris. The supernatants were collected and incubated with anti-Flag or anti-Myc beads that had been precleaned with Co-IP buffer. These mixtures were then rotated overnight at 4\u2009\u00b0C. On the following day, the beads were subjected to five washes with Co-IP lysis buffer and then resuspended in 1\u00d7 loading buffer. Subsequently, the samples were denatured at 95\u2009\u00b0C for 15\u2009min and stored at \u221220\u2009\u00b0C. To assess the ubiquitination of TOX and PGAM5, TOX proteins (both endogenous and exogenous TOX proteins) and endogenous PGAM5 proteins were immunoprecipitated from cell lysates using anti-Flag beads or TOX\/PGAM5 antibody-coated beads, depending on the different experimental conditions. In brief, cells were collected and lysed using ultrasonic cracking, and then denatured at 95\u2009\u00b0C for 15\u2009min. Cell lysates were incubated in Co-IP lysis buffer with protease inhibitors (Roche) and rotated for 1\u2009h at 4\u2009\u00b0C. Subsequently, the cell lysates were centrifuged at 12,000g for 10\u2009min at 4\u2009\u00b0C to collect cell supernatant. The supernatant was incubated with the respective antibody\u2013beads complex and rotated overnight at 4\u2009\u00b0C. Afterwards, the beads were washed 5 times with Co-IP buffer and denatured by heating at 95\u2009\u00b0C for 15\u2009min. These samples were separated by SDS\u2013PAGE, transferred to polyvinylidene fluoride membranes and then subjected to western blotting using the designated primary and secondary antibodies.<\/p>\n<p>Statistical analysis<\/p>\n<p>Statistical analyses were performed using GraphPad Prism (v.8.0). No statistical methods were used to predetermine sample size.\u00a0Data collection and analysis were conducted without blinding to the experimental conditions.\u00a0A two-tailed Student\u2019s t-test was used to compare the two groups. For multiple comparisons, a two-way analysis of variance (ANOVA) with Tukey\u2019s or Sidak\u2019s multiple-comparisons test was applied. The log-rank (Mantel\u2013Cox) test was performed to compare mouse survival curves. Data are presented as mean\u2009\u00b1\u2009standard error of the mean (s.e.m.). The numbers of mice used per experiment and the number of experimental repeats are indicated in the figure legends. P\u2009&lt;\u20090.05 was considered statistically significant.<\/p>\n<p>Reporting summary<\/p>\n<p>Further information on research design is available in the\u00a0<a data-track=\"click\" data-track-label=\"link\" data-track-action=\"supplementary material anchor\" href=\"http:\/\/www.nature.com\/articles\/s41586-025-09926-8#MOESM2\" rel=\"nofollow noopener\" target=\"_blank\">Nature Portfolio Reporting Summary<\/a> linked to this article.<\/p>\n","protected":false},"excerpt":{"rendered":"Mice Male and female mice were used for the study. CD45.1+ OT-I or P14 TCR-transgenic mice were housed&hellip;\n","protected":false},"author":2,"featured_media":408032,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32],"tags":[87587,66026,1159,30319,1160,79,66028],"class_list":["post-408031","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-cancer-immunotherapy","tag-cancer-microenvironment","tag-humanities-and-social-sciences","tag-immunotherapy","tag-multidisciplinary","tag-science","tag-tumour-immunology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/408031","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=408031"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/408031\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/408032"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=408031"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=408031"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=408031"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}