Abstract
Adoptive T cell therapies have therapeutic potential for treating solid tumors, but long-term efficacy is limited by reduced functional fitness and poor persistence within the tumor microenvironment. Here we show that intratumoral T cells undergo translatome remodeling, transitioning into a hypertranslational state as they acquire dysfunctional traits. The RNA-binding protein LARP4 is a translation regulator that drives hypertranslation and dysfunction by selectively enhancing the translation of nuclear-encoded oxidative phosphorylation (OXPHOS) mRNAs in exhausted T cells, disrupting OXPHOS subunit balance and causing mitochondrial dysfunction. Knockout of Larp4 in tumor-specific CD8+ T cells reduces hypertranslation, restores mitochondrial function, mitigates exhaustion and enhances effector persistence, resulting in enhanced anti-tumor responses. Additionally, LARP4 knockdown in chimeric antigen receptor T cells prevents terminal exhaustion and improves the response to liquid and solid tumors. This study highlights translation dysregulation as a determinant of T cell dysfunction in tumors.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
RPLace-seq, RNA-seq, LACE-seq, scRNA-seq and PRO-seq data have been deposited in the Genome Sequence Archive (GSA) repository under accession codes CRA019040 and CRA019813. RBP eCLIP-seq data were downloaded from the ENCODE database (https://www.encodeproject.org/eclip). The mapping relationship between human and mouse gene symbols was downloaded from BioMart (https://asia.ensembl.org/biomart/martview). Other public sequencing data used in this study are as follows: polysome profiling on CD8+ TN cells (GEO, GSE71643), ATAC-seq for TdLNs TPEX cells (GEO, GSE180084), ATAC-seq for tumor TEX cells (GEO, GSE122713), CUT&Tag for H3K27ac in tumor TEX cells (GEO, GSE175437), CHIP-seq for TOX (GEO, GSE93953), CHIP-seq for NR4A1 (GEO, GSE266286), CHIP-seq for NFAT (GEO, GSE64407), CHIP-seq for IRF4 (GEO, GSE54191), CHIP-seq for BATF (GEO, GSE149796), CHIP-seq for BCL-6 (GEO, GSE182034), RNA-seq for intratumor wild-type and Tox-KO T cells (GEO, GSE126973), scRNA for intratumor wild-type and Nr4a1/2-KO T cells (GEO, GSE247641), scRNA for intratumor OT-I T cells (GEO, GSE218372) and scRNA for intratumor T cells in patients with melanoma (GEO, GSE159251). Source data are provided with this paper.
Change history
04 August 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41590-025-02260-1
References
Albelda, S. M. CAR T cell therapy for patients with solid tumours: key lessons to learn and unlearn. Nat. Rev. Clin. Oncol. 21, 47–66 (2024).
Baulu, E., Gardet, C., Chuvin, N. & Depil, S. TCR-engineered T cell therapy in solid tumors: state of the art and perspectives. Sci. Adv. 9, eadf3700 (2023).
Zebley, C. C., Zehn, D., Gottschalk, S. & Chi, H. T cell dysfunction and therapeutic intervention in cancer. Nat. Immunol. 25, 1344–1354 (2024).
Chow, A., Perica, K., Klebanoff, C. A. & Wolchok, J. D. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat. Rev. Clin. Oncol. 19, 775–790 (2022).
Beltra, J. C. et al. Stat5 opposes the transcription factor Tox and rewires exhausted CD8+ T cells toward durable effector-like states during chronic antigen exposure. Immunity 56, 2699–2718.e11 (2023).
Zehn, D., Thimme, R., Lugli, E., de Almeida, G. P. & Oxenius, A. ‘Stem-like’ precursors are the fount to sustain persistent CD8+ T cell responses. Nat. Immunol. 23, 836–847 (2022).
Connolly, K. A. et al. A reservoir of stem-like CD8+ T cells in the tumor-draining lymph node preserves the ongoing antitumor immune response. Sci. Immunol. 6, eabg7836 (2021).
Yu, Y. R. et al. Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat. Immunol. 21, 1540–1551 (2020).
Baessler, A. & Vignali, D. A. A. T cell exhaustion. Annu. Rev. Immunol. 42, 179–206 (2024).
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
Belk, J. A. et al. Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence. Cancer Cell 40, 768–786.e7 (2022).
Signer, R. A., Magee, J. A., Salic, A. & Morrison, S. J. Haematopoietic stem cells require a highly regulated protein synthesis rate. Nature 509, 49–54 (2014).
Wick, M. J. & Pfeifer, J. D. Major histocompatibility complex class I presentation of ovalbumin peptide 257–264 from exogenous sources: protein context influences the degree of TAP-independent presentation. Eur. J. Immunol. 26, 2790–2799 (1996).
Schenkel, J. M. et al. Conventional type I dendritic cells maintain a reservoir of proliferative tumor-antigen specific TCF-1+ CD8+ T cells in tumor-draining lymph nodes. Immunity 54, 2338–2353.e6 (2021).
Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).
Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex tissues. Proc. Natl Acad. Sci. USA 106, 13939–13944 (2009).
Su, R. et al. Global profiling of RNA-binding protein target sites by LACE-seq. Nat. Cell Biol. 23, 664–675 (2021).
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
Araki, K. et al. Translation is actively regulated during the differentiation of CD8+ effector T cells. Nat. Immunol. 18, 1046–1057 (2017).
Wolf, T. et al. Dynamics in protein translation sustaining T cell preparedness. Nat. Immunol. 21, 927–937 (2020).
Castanza, A. S. et al. Extending support for mouse data in the Molecular Signatures Database (MSigDB). Nat. Methods 20, 1619–1620 (2023).
Ho, J. J. D. et al. A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism. Nat. Commun. 11, 2677 (2020).
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).
Zhou, P. et al. Single-cell CRISPR screens in vivo map T cell fate regulomes in cancer. Nature 624, 154–163 (2023).
Pauken, K. E. et al. Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment. J. Exp. Med. 218, e20200920 (2021).
Lewis, B. M. et al. LARP4 is an RNA-binding protein that binds nuclear-encoded mitochondrial mRNAs to promote mitochondrial function. RNA 30, 223–239 (2024).
Matsumoto, S. et al. Localization of mRNAs encoding human mitochondrial oxidative phosphorylation proteins. Mitochondrion 12, 391–398 (2012).
Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-seq. Cell 178, 473–490.e26 (2019).
Vardhana, S. A. et al. Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen. Nat. Immunol. 21, 1022–1033 (2020).
Pendergrass, W., Wolf, N. & Poot, M. Efficacy of MitoTracker green and CMXrosamine to measure changes in mitochondrial membrane potentials in living cells and tissues. Cytometry A 61, 162–169 (2004).
Lisci, M. & Griffiths, G. M. Arming a killer: mitochondrial regulation of CD8+ T cell cytotoxicity. Trends Cell Biol. 33, 138–147 (2023).
Scharping, N. E. et al. Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat. Immunol. 22, 205–215 (2021).
Yang, R. et al. La-related protein 4 binds poly(A), interacts with the poly(A)-binding protein MLLE domain via a variant PAM2w motif, and can promote mRNA stability. Mol. Cell. Biol. 31, 542–556 (2011).
Ranjan, A. et al. The short conserved region-2 of LARP4 interacts with ribosome-associated RACK1 and promotes translation. Nucleic Acids Res. 53, gkaf053 (2025).
Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211.e10 (2019).
Chu, Y. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat. Med. 29, 1550–1562 (2023).
Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).
Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334.e10 (2019).
Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).
Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).
Hashimoto, M. et al. PD-1 combination therapy with IL-2 modifies CD8+ T cell exhaustion program. Nature 610, 173–181 (2022).
Granhoj, J. S. et al. Tumor-infiltrating lymphocytes for adoptive cell therapy: recent advances, challenges, and future directions. Expert Opin. Biol. Ther. 22, 627–641 (2022).
Long, A. H. et al. 4-1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat. Med. 21, 581–590 (2015).
Sterner, R. C. & Sterner, R. M. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 11, 69 (2021).
Grosser, R., Cherkassky, L., Chintala, N. & Adusumilli, P. S. Combination Immunotherapy with CAR T cells and checkpoint blockade for the treatment of solid tumors. Cancer Cell 36, 471–482 (2019).
Zhang, H. et al. A chimeric antigen receptor with antigen-independent OX40 signaling mediates potent antitumor activity. Sci. Transl. Med. 13, eaba7308 (2021).
Marchingo, J. M. & Cantrell, D. A. Protein synthesis, degradation, and energy metabolism in T cell immunity. Cell Mol. Immunol. 19, 303–315 (2022).
Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).
Lesnik, C., Golani-Armon, A. & Arava, Y. Localized translation near the mitochondrial outer membrane: an update. RNA Biol. 12, 801–809 (2015).
Liu, Y. et al. Tumors exploit FTO-mediated regulation of glycolytic metabolism to evade immune surveillance. Cell Metab. 33, 1221–1233.e11 (2021).
Chen, Z. et al. In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262–1280.e22 (2021).
Zhao, H. et al. Genome-wide fitness gene identification reveals Roquin as a potent suppressor of CD8 T cell expansion and anti-tumor immunity. Cell Rep. 37, 110083 (2021).
Abdel-Hakeem, M. S. et al. Epigenetic scarring of exhausted T cells hinders memory differentiation upon eliminating chronic antigenic stimulation. Nat. Immunol. 22, 1008–1019 (2021).
Zhang, X. et al. Depletion of BATF in CAR-T cells enhances antitumor activity by inducing resistance against exhaustion and formation of central memory cells. Cancer Cell 40, 1407–1422.e7 (2022).
Liu, Y. et al. Chimeric STAR receptors using TCR machinery mediate robust responses against solid tumors. Sci. Transl. Med. 13, eabb5191 (2021).
Wu, L. et al. Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α. Cell Metab. 35, 1580–1596.e9 (2023).
Mattijssen, S. et al. LARP4 mRNA codon-tRNA match contributes to LARP4 activity for ribosomal protein mRNA poly(A) tail length protection. Elife 6, e28889 (2017).
van der Windt, G. J. W., Chang, C. H. & Pearce, E. L. Measuring bioenergetics in T cells using a Seahorse extracellular flux analyzer. Curr. Protoc. Immunol. 113, 3.16B.1–3.16B.14 (2016).
Buck, M. D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).
Han, D. et al. Anti-tumour immunity controlled through mRNA m6A methylation and YTHDF1 in dendritic cells. Nature 566, 270–274 (2019).
Judd, J. et al. A rapid, sensitive, scalable method for Precision Run-On sequencing (PRO-seq). Preprint at https://doi.org/10.1101/2020.05.18.102277 (2020).
Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
Hounkpe, B. W., Chenou, F., de Lima, F. & De Paula, E. V. HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res. 49, D947–D955 (2021).
Tang, K. et al. Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer. Nucleic Acids Res. 49, e99 (2021).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Krakau, S., Richard, H. & Marsico, A. PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data. Genome Biol. 18, 240 (2017).
Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb.) 2, 100141 (2021).
Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
Smedley, D. et al. BioMart—biological queries made easy. BMC Genomics 10, 22 (2009).
Huang, Q. et al. The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell 185, 4049–4066.e25 (2022).
Srirat, T. et al. NR4a1/2 deletion promotes accumulation of TCF1+ stem-like precursors of exhausted CD8+ T cells in the tumor microenvironment. Cell Rep. 43, 113898 (2024).
Ford, B. R. et al. Tumor microenvironmental signals reshape chromatin landscapes to limit the functional potential of exhausted T cells. Sci. Immunol. 7, eabj9123 (2022).
Page, N. et al. Expression of the DNA-binding factor TOX promotes the encephalitogenic potential of microbe-induced autoreactive CD8+ T cells. Immunity 48, 937–950.e8 (2018).
Hao, J. et al. NR4A1 transcriptionally regulates the differentiation of stem-like CD8+ T cells in the tumor microenvironment. Cell Rep. 43, 114301 (2024).
Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015).
Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).
Chen, Y. et al. BATF regulates progenitor to cytolytic effector CD8+ T cell transition during chronic viral infection. Nat. Immunol. 22, 996–1007 (2021).
Sun, Q. et al. BCL6 promotes a stem-like CD8+ T cell program in cancer via antagonizing BLIMP1. Sci. Immunol. 8, eadh1306 (2023).
Zou, Z., Ohta, T. & Oki, S. ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data. Nucleic Acids Res. 52, W45–W53 (2024).
Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).
Acknowledgements
We thank Y. Xue (Institute of Biophysics, Chinese Academy of Sciences) for assistance with the LACE-seq experiment. We are grateful to M. Peng (Tsinghua University) for the gift of the Cas9 transgenic mice. We thank D. Pan (Tsinghua University) for the gift of the pMYS-U6-GFP plasmid and M. Xu (National Institute of Biological Sciences) for the gift of the pMSCV plasmid. We are grateful to X. Lin (Tsinghua University) for providing the Raji-Luc cell line. We thank X. Yang (Shanghai Jiao Tong University) for assistance with CAR T cell-related experiments. We thank B. Liang and C. Jiao (Core Facility, Center of Biomedical Analysis, Tsinghua University) for technical support with confocal microscopy and Seahorse XF96. We thank Z. Chang in the Animal Facility, Tsinghua University, for animal care support. We thank J. Zhu for valuable advice and guidance on mitochondrial metabolism. This work was funded by the National Natural Science Foundation of China (NSFC) T2495272, Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0570101), NSFC 32121001, 22293052, 32370644, National Key R&D Program of China (2024YFA1802102, 2024YFC3405901), Beijing Natural Science Foundation (L244023), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (ZDBS-LY-SM013), Next-Generation Bioinformatics Algorithms (XDA0460302) and CAS Youth Interdisciplinary Team.
Author information
Authors and Affiliations
Contributions
M.M.X. and D.H. conceived the project and supervised the research. Y.L. performed in vitro and in vivo experiments and performed data analysis with the help of J.L. and J.Y. Library construction, in vitro T cell function assay and qPCR validation were performed by Y.L., J.Y., I.S., B.E.S., J.H. and S.L.; Z.Z. performed library construction of scRNA-seq. H.N. performed bioinformatics analysis. S.D.S., M.S.P. and M.K. provided support for T cell metabolism experiments. F.Z. analyzed the PRO-seq data. Y.Z., Z.H. and Y.X. proofread the paper. T.W.M. provided support and supervision during the paper revision process. M.M.X., D.H., H.N., Y.L. and J.L. wrote the paper with input from all authors. M.M.X. and D.H. acquired funding for the project. All authors discussed the results and commented on the paper. All authors approved the final draft and agreed to the submission for publication.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Immunology thanks Ping-Chih Ho and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Characterization of chronically stimulated T cell exhaustion phenotype.
(a) Bar plots showing the percentage of cells expressing IFN-γ, TNF, or IL-2 in acutely stimulated d8 TEFF cells, and chronically stimulated d4 and d8 TEX cells (n = 4). (b) Bar plots showing the levels of mitochondrial reactive oxygen species (mtROS, measured using MitoSox) in d8 TEFF, d4 TEX, and d8 TEX cells (n = 3). (c) Oxygen consumption rate (OCR) using the Seahorse XF bioanalyzer measuring the respiratory capacity of d8 TEFF cells, d4 TEX cells, and d8 TEX cells. Cells were seeded at 5 ×105/well in the XF96 plate (n = 5). (d) Representative flow cytometry analysis showing the expression of PD-1 and TIM-3 in d8 TEFF, d4 TEX, and d8 TEX cells. (e) Bar plots showing the MFI of PD-1 and TIM-3 in d0 TN, d8 TEFF, d4 TEX, and d8 TEX cells (n = 3). (f) Bar plots showing the percentage of PD-1hi TIM-3+ CD8+ T cells in d8 TEFF, d4 TEX, and d8 TEX cells (n = 4). (g) Flow cytometry gating strategy to identify PD-1hi TIM-3− early TEX and PD-1hi TIM-3+ terminal TEX in tumor-infiltrating OT-I T cells. n, number of technical replicates. For statistical analysis, figures (a-c and e-f) are presented as mean ± SEM and figures (a-b and e-f) were analyzed using unpaired two-sided Student’s t-tests. The data represent three independent experiments (a-c and e-f).
Extended Data Fig. 2 Differential analysis of RPF and TE between d8 Tex and d4 Tex cells.
(a) Boxplot showing the RNA level (left) and RPF abundance (right) of nuclear-encoded mRNAs (n = 1314, filtered lowly expressed mRNAs with RNA level < 10) and mitochondrial-encoded mRNAs (n = 13) in naïve CD8+ T cells, as measured by RNA-seq and RPLace-seq. P-value was calculated with the two-sided Student’s t-tests. (b) Representative IGV tracks showing read coverage of RPLace-seq and RNA-seq in the 3′UTR of Atp5d and Cd3d genes in naïve CD8+ T cells. 3′UTR of genes were highlighted with blue background color. (c) GSEA to assess the enrichment of translation-repressed mRNAs in high-translating (high TE) mRNAs or low-translating (low TE) mRNAs in naïve CD8+ T cells. Transcripts were ranked based on TE in naïve CD8+ T cells. NES, normalized enrichment score. P-value was calculated with the two-sided permutation test.
Extended Data Fig. 3 Translatome landscapes of tumor-specific and in vitro chronically stimulated CD8+ T cells profiled by RPLace-seq.
(a) Bar plot showing the gene count of mRNAs (TPM > 0.5) identified by RPLace-seq in TdLNs TPEX and Tumor TEX cells. (b and c) Scatter plot comparing normalized expression (rlog) of mRNAs between RPLace-seq replicates in TdLNs TPEX cells (b) and Tumor TEX cells (c). Pearson correlation was used to assess agreement, and the coefficient of determination(R2), P-values (two-sided), and the line of best fit are indicated. (d) Volcano plot showing the differential analysis of RPF abundance between TdLNs TPEX and Tumor TEX cells. Genes with significantly differential RPF abundance were defined by threshold |log2FC | > 1 and P-value < 0.05. P-values were calculated with the two-sided Wald test. Numbers of up- or down-regulated genes are indicated. (e and f) Volcano plot showing the differential analysis of RPF abundance between d4 TEX and d0 TN (e), and between d8 TEX and d4 TEX (f). Genes with significantly differential RPF abundance were defined by threshold |log2FC | > 1 and P-value < 0.05. P-values were calculated with the two-sided Wald test. Numbers of up- or down-regulated genes are indicated. (g) Scatter plot comparing log2FC of mRNA levels and RPF abundance between Tumor TEX cells and TdLNs TPEX cells. Pearson correlation was used to assess agreement, and the coefficient of determination (R2) and p-value (two-sided) are indicated. (h) Scatter plot comparing log2FC of mRNA levels and RPF abundance between Tumor TEX cells and TdLNs TPEX cells. The genes with significantly differential RPF abundance were highlighted and are further divided into three groups based on the log2FC of mRNA levels: (i) less than −0.5, (ii) between −0.5 and 0.5, or (iii) greater than 0.5. The mRNA numbers in each group are indicated. (i) Scatter plot showing the log2FC of TE and mean RPF abundance in Tumor TEX cells and TdLNs TPEX cells. OXPHOS mRNAs are highlighted in red (log2FC TE > 0) or blue (log2FC TE < 0). The mRNA numbers in each group are indicated. (j) Heatmap showing the relative levels of mRNA, RPF, and TE across indicated gene sets in Tumor TEX cells and TdLNs TPEX cells. (k) Box plots showing the mRNA levels, RPF abundance, and TE for indicated genes in Tumor TEX cells and TdLNs TPEX cells (n = 2).
Extended Data Fig. 4 LARP4 selectively targets hypertranslated mRNAs, particularly nuclear-encoded OXPHOS mRNAs, in exhausted CD8 + T cells.
(a) Schematic diagram of workflow for searching the potential regulatory RBPs for hypertranslated mRNAs in d8 TEX cells. (b-d) Immunoblotting of LARP4 in CD8+ TN cells, TEFF cells, and TEX cells under in vitro chronic or acute antigenic stimulation with indicated days. (e) Box plots showing the Larp4 expression in Tumor TEX cells and TdLNs TPEX cells (n = 2). (f) Dot plots showing the Larp4 expression in three subsets of tumor-infiltrating OT-I T cells in the B16-OVA mice model (GSE218372). (g) Dot plots showing the LARP4 expression in six subsets of tumor-infiltrating CD8+ T cells in patients with melanoma (GSE159251). (h) IGV tracks showing signal of translation factors without significant signal enrichment at Larp4 loci and the nearby enhancer region, including NFAT (GSE64407), IRF4 (GSE54191), BATF (GSE149796), and BCL-6 (GSE182034). The promoter and enhancer regions are highlighted with a colored background. (i) Boxplot showing the Larp4 expression in WT and Tox-KO tumor-infiltrating CD8+ T cells (n = 3) by RNA-seq (GSE126973). P-values were calculated using two-sided unpaired Student’s t-tests. (j) Violin plot showing the Larp4 expression in WT and Nr4a1/2-KO tumor-infiltrating PD-1+ TIM-3+ CD8+ T cells by scRNA-seq (GSE247641). Gene expression was normalized by sctransform algorithm and ten neighboring cells were merged into metacells. P-values were calculated using two-sided unpaired Student’s t-tests. (k) IGV tracks showing the LACE-seq signal intensity for LARP4-IP and IgG control across mitochondrial-encoded RNA regions. (l) Bar plot showing the LACE-seq signal intensity of LARP4-IP and IgG control at reads-accumulated sites in mitochondrial-encoded RNA regions (n = 39). The reads-accumulated sites were identified by peak calling using LARP4-IP data alone. The diffBind was used to quantify signal intensity in LARP4-IP and IgG control at these sites. P value is calculated by two-sided unpaired Student’s t-tests. (m) Profile plot showing the LACE-seq signal intensity for LARP4-IP and IgG control at reads-accumulated sites in mitochondrial-encoded mRNA regions. The deeptools was used to quantify coverage differences between LARP4-IP and IgG control at these sites. (n) Profile plot showing the LACE-seq signal intensity for LARP4-IP and IgG control at LARP4-targeted sites in nuclear-encoded mRNA regions. (o and p) Top 5 motifs identified by HOMER across total LARP4-targeted sites (o) and on LARP4-targeted sites within OXPHOS mRNAs (p) in TEX cells after 8 days of in vitro antigenic stimulation. The asterisk (*) indicates the non-significant motif as labeled by HOMER. (q) Venn diagram showing the overlap between LARP4-targeted mRNAs and mitochondria-proximal mRNAs identified by APEX-seq (Fazal et al). P-value is calculated by one-sided Fisher’s exact test. The data represent three independent experiments (b-d).
Extended Data Fig. 5 LARP4 is regulated by NR4A1/2 and modulate mRNA translation in exhausted CD8+ T cells.
(a) Immunoblotting is shown to validate changes in LARP4 expression in control and Larp4-KO TEX cells after 8 days of in vitro chronic antigenic stimulation. (b) Immunoblotting showing the change of LARP4 expression in control and Nr4a1/2-KO Cas9+ T cells after 6 days of in vitro chronic antigenic stimulation. (c and d) Representative flow cytometry histograms (c) and bar plots (d) showing OPP incorporation in control, Larp4-KO, Nr4a1-KO, Nr4a2-KO and Nr4a1/2-KO Cas9+ T cells after 6 days of chronic antigenic stimulation (n = 4). P-values were calculated using two-sided unpaired Student’s t-tests. Data are shown as mean ± SEM. (e) Volcano plot showing mRNAs with differential TE between control and Larp4-KO TEX cells. LARP4-targeted mRNAs with significant differential TE (|log2FC| > 0.5, p-value < 0.01) are highlighted. (f) MSigDB pathway analysis of LARP4-targeted mRNAs with significantly downregulated TE after Larp4 knockout. The systematic name of each pathway in MSigDB is labeled. (g) Heatmap showing the TE of indicated genes in control and Larp4-KO in exhausted T cells after 8 days of in vitro chronic antigenic stimulation. (h) Bar plot showing the polysome / non-polysome ratio of indicated LARP4-targeted mRNAs and non-LARP4-targeted mRNA revealed by sucrose polysome profiling and qPCR (n = 2). The data represent two independent experiments (b, c, h) and three independent experiments (a).
Extended Data Fig. 6 LARP4 drives mitochondrial dysfunction in tumor-specific CD8+ T cells.
(a) Bar plots showing the levels of mitochondrial mass (measured by Mitotracker) in chronically stimulated d8 TEX cells from control (n = 3) and Larp4-KO (n = 3) groups, represented as the MFI of Mitotracker staining. (b) Schematic diagram of the dual-genome-encoded mitochondrial electron transport complex showing nuclear-encoded COX4 (green) and mitochondrial-encoded COX1 (purple) components.CI–CV, Complexes I–V; IMS, intermembrane space. (c) Bar graphs showing the MFI of COX4 (left) and COX1 (right) proteins in the indicated group of T cells (n = 4). The expression levels of COX proteins were measured using flow cytometry and normalized to the levels in d0 TN cells. (d) Oxygen consumption rate (OCR) using the Seahorse XF bioanalyzer measuring the respiratory capacity of control and Larp4-KO TEFF cells after 6 days of in vitro acute antigenic stimulation. Cells were seeded at 5 × 105/well in the XF96 plate (n = 6). n, number of technical replicates. Data in plots of (a, c, d) are presented as mean ± SEM. Unpaired two-sided Student’s t-tests were performed. The data represent two independent experiments in (a, c, d).
Extended Data Fig. 7 The impact of Larp4-KO on T cell exhaustion and function in vitro and in vivo.
(a) Bar plots showing the MFI of exhaustion markers PD-1 and TIM-3 in control and Larp4-KO TEX cells after 8 days of in vitro chronic antigenic stimulation under hypoxia condition. The MFI values were normalized to control cells (n = 3). (b) Representative flow cytometry graphs and bar plots showing the percentage of PD-1hi TIM-3+ cells in vector control and Larp4-overexpression (OE) TEX cells after 8 days of in vitro chronic antigenic stimulation under hypoxia condition (n = 4). (c) Schematic diagram depicting the wild-type and mutant LARP4 constructs used in the experiment. The diagram illustrates the structural organization of the LARP4 protein, including the La motif (LaM), RNA recognition motif (RRM), conserved region-2 (CR2) and other relevant domains. Key mutations introduced in the LARP4-M3 and LARP4-ΔRIR constructs are highlighted, along with their positions relative to the conserved regions. (d) Representative flow cytometry histograms showing the expression of HA-tag in T cells overexpressing empty vector (Vector Ctrl), LARP4-WT, LARP4-M3 and LARP4-ΔRIR after 4 days of in vitro chronic antigenic stimulation. (e and f) Representative flow cytometry histograms (e) and bar plots (f) showing the percentage of PD-1hi TIM-3+ cells in T cells overexpressing empty vector (Vector Ctrl), LARP4-WT, LARP4-M3 and LARP4-ΔRIR after 4 days of in vitro chronic antigenic stimulation (n = 4). (g) Bar plots showing the level of mtROS in T cells overexpressing empty vector (Vector Ctrl), LARP4-WT, LARP4-M3 and LARP4-ΔRIR after 4 days of in vitro chronic antigenic stimulation (n = 4), represented as the MFI of MitoSox staining. (h) Representative flow cytometry histograms showing the expression of activation markers CD44, CD69, and CD25 in TN cells, control TEFF cells, and Larp4-KO TEFF cells after 8 days of in vitro acute antigenic stimulation. (i) Bar graphs showing the MFI of CD44, CD69, and CD25 activation markers in control and Larp4-KO TEFF cells after 8 days of in vitro acute antigenic stimulation. The MFI values were normalized to control cells (n = 6). (j) Bar graphs showing the percentage of TNF+ IFN-γ+ cells in control and Larp4-KO TEFF cells after 8 days of in vitro acute antigenic stimulation (n = 5). (k) Representative flow cytometry graphs showing the percentage of PD-1hi TIM-3+ cells in cells overexpressing empty vector (Ctrl), Larp4, indicated LARP4-targeted OXPHOS mRNAs and indicated non-LARP4-targeted mRNAs after 6 days of in vitro chronic antigenic stimulation. (l) Bubble heatmap depicting the expression of representative genes in each C9OT-I T cell subset. (m) Bubble heatmap depicting the gene set score of indicated signatures in each C9OT-I T cell subset. (n) Boxplot showing the gene set score of NK signatures in each C9OT-I T cell subset. (o) Pseudotime of trajectory in space of diffusion map. (p) Heatmap showing the transcriptional trends of indicated genes along the pseudotime in two differentiation trajectories. Color represents scaled expression levels. (q) Volcano plot showing mRNAs with differential expression between cells in control and Larp4-KO TPEX subsets. mRNAs with significant differential expression ( | log2FC | > 0.5, P-value < 0.01) are highlighted. n, number of technical replicates (a, b, f, g, h, i, j). For statistical analysis, data in bar plots (a, b, f, g, i, j) are presented as mean ± SEM and unpaired two-sided Student’s t-tests were performed for (a, b, f) and unpaired one-sided Student’s t-tests were performed for (g). Boxplots in (n) include the following elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5 IQR of the upper quartile and lower quartile. The data represent two independent experiments (a, b, f, g, i, j).
Extended Data Fig. 8 Flow cytometry uncovers the impact of Larp4-KO on differentiation and function of tumor-specific CD8+ T cell within TME.
(a) Flow cytometry gating strategy to identify PD-1hi TIM-3+ TEX cells and PD-1Int CX3CR1+ TEFF-like cells within TME. Endogenous cells were shown. (b) Representative flow cytometry graphs showing the percentage of PD-1int CX3CR1+ TEFF-like cells in control and Larp4-KO C9OT-I T cells within TME. PD-1int CX3CR1+ TEFF-like cells were gated from PD-1+ TIM-3− C9OT-I T cells. (c) Bar plots showing the MFI of CD25 in control and Larp4-KO C9OT-I T cells within TME. The MFI values were normalized to control cells (n = 6). (d) Representative flow cytometry graphs and bar plots showing the percentage of IFN-γ+ cells in control and Larp4-KO C9OT-I T cells within TME (n = 6). (e) Representative flow cytometry graphs and bar plots showing the percentage of TNF+ cells in control and Larp4-KO C9OT-I T cells within TME (n = 6). (f) Representative flow cytometry graphs and bar plots showing the percentage of IL-2+ cells in control and Larp4-KO C9OT-I T cells within TME (n = 6). (g) Flow cytometry graphs showing the percentage of IFN-γ+ IL-2+ (left) and TNF+ IL-2+ (right) cells in control and Larp4-KO C9OT-I T cells within TME. n, numbers of mice (c-f). For statistical analysis, data in bar plots are presented as mean ± SEM and unpaired two-sided Student’s t-tests were performed for (b-f).
Extended Data Fig. 9 Anti-tumor response of LARP4-KD CD20-CAR T cells.
(a) Flow cytometry graphs showing the expression of PD-1 and TIM-3 in control and Larp4-KO C9OT-I TPEX cells in TdLNs. Endogenous cells are shown in gray. (b) Flow cytometry graphs showing the expression of PD-1 and Ly108 in control and Larp4-KO C9OT-I TPEX cells in TdLNs. Endogenous cells are shown in gray. (c) Western blot showing the expression of LARP4 and GAPDH in control and LARP4-KD CD20-CAR T cells. (d) Representative bioluminescence imaging of tumor-bearing mice treated with PBS, control CD20-CAR T cells, and LARP4-KD CD20-CAR T cells on days 5, 10, 15, and 20 post-tumor inoculation. These data represent two independent experiments.
Supplementary information
Supplementary Table 1–6
Supplementary Table 1. Differential analysis of RPF and TE between tumor TEX and TdLNs TPEX cells. Supplementary Table 2. Differential analysis of RPF and TE between d8 TEX and d4 TEX cells. Supplementary Table 3. LACE-seq detected LARP4 binding sites. Supplementary Table 4. TE of LARP4-targeted mRNAs in ctrl and LARP4-KO cells. Supplementary Table 5. Signature genes used in scRNA analysis. Supplementary Table 6. Sequences of oligonucleotides.
Source data
Source Data Fig. 1
Statistical source data.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 3i
Unprocessed western blots.
Source Data Fig. 4
Statistical source data.
Source Data Fig. 4a
Unprocessed western blots.
Source Data Fig. 5
Statistical source data.
Source Data Fig. 6
Statistical source data.
Source Data Fig. 7
Statistical source data.
Source Data Fig. 7e
Unprocessed western blots.
Source Data Extended Data Fig. 1
Statistical source data.
Source Data Extended Data Fig. 4
Unprocessed western blots.
Source Data Extended Data Fig. 5
Statistical source data.
Source Data Extended Data Fig. 5a,b
Unprocessed western blots.
Source Data Extended Data Fig. 6
Statistical source data.
Source Data Extended Data Fig. 7
Statistical source data.
Source Data Extended Data Fig. 8
Statistical source data.
Source Data Extended Data Fig. 9
Unprocessed western blots.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, Y., Ni, H., Li, J. et al. LARP4-mediated hypertranslation drives T cell dysfunction in tumors. Nat Immunol 26, 1488–1500 (2025). https://doi.org/10.1038/s41590-025-02232-5
Received:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/s41590-025-02232-5
