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Loss of YTHDF2 enhances Th9 programming and CAR-Th9 cell antitumor efficacy

Nature Immunology volume 26, pages 1501–1515 (2025)Cite this article

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Abstract

CD4+ T cells differentiate into various subsets, including T helper 1 (Th1), Th2, Th9, Th17 and regulatory T (Treg) cells, which are essential for immune responses and cancer immunotherapy. However, the role of RNA N6-methyladenosine (m6A) modification in this differentiation is unclear. Here we show that YTHDF2, an important m6A reader protein known to destabilize m6A-modified mRNA, negatively regulates Th9 cell differentiation. Ablation of Ythdf2 in both mouse and human naive CD4+ T cells promotes Th9 differentiation by stabilizing Gata3 and Smad3 mRNA under interleukin-4 (IL-4) and transforming growth factor β (TGF-β) signaling, respectively. Ythdf2-deficient Th9 cells produce increased amounts of IL-9 and IL-21, leading to increased tumor infiltration and cytotoxicity by CD8+ T cells and natural killer (NK) cells, thereby improving antitumor activity compared with wild-type Th9 cells. Moreover, YTHDF2 depletion in CAR-Th9 cells enhances their immune activation, reduces their terminal differentiation and augments their antitumor efficacy. Targeting YTHDF2 is thereby a promising strategy to enhance Th9 and CAR-Th9 cell-based cancer immunotherapies.

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Fig. 1: YTHDF2 deficiency enhances Th9 cell differentiation in mice.
Fig. 2: Loss of YTHDF2 promotes Th9 cell differentiation via Gata3 and Smad3.
Fig. 3: Gata3 and Smad3 cooperatively regulate Il9 promoter activity in the presence of p65.
Fig. 4: Ythdf2-deficient Th9 cells have greater antitumor efficacy in vivo.
Fig. 5: Ythdf2-deficient Th9 cells enhance immune cell infiltration and effector function.
Fig. 6: Ythdf2-deficient Th9 cells exhibit enhanced antitumor efficacy that depends on NK cells, CD8+ T cells and DCs.
Fig. 7: YTHDF2 deficiency promotes the differentiation of human Th9 cells.
Fig. 8: YTHDF2 deficiency augments the therapeutic efficacy of CAR-Th9 cells.

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Data availability

The m6A-seq, RIP-seq and bulk RNA-seq data have been deposited in the Gene Expression Omnibus under accession code GSE279983. The m6A-seq and RIP-seq were mapped to the mouse (GRCm38) genomes. The bulk RNA-seq data were mapped to the human (GRCh39) or mouse (GRCm38) genomes. Source data are provided with this paper.

Code availability

Code is available at https://github.com/zerostwo/ythdf2-th9.

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Acknowledgements

We thank the City of Hope Center for Comparative Medicine, Pathology Core, Analytical Cytometry Core and Integrative Genomics Core for performing parts of the work. The research was supported by grants from the National Institutes of Health (CA210087, CA265095 and CA163205 to M.A.C.; NS106170, AI129582, CA247550, CA264512, CA266457 and CA223400 to J.Y.) and the California Institute for Regenerative Medicine (TRAN1-14716 to M.A.C.; TRAN1-14003 and DISC2-14190 to J.Y.).

Author information

Author notes

  1. These authors contributed equally: Sai Xiao, Songqi Duan, Yaqun Hong.

Authors and Affiliations

  1. Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA

    Sai Xiao, Songqi Duan, Yaqun Hong, Shoubao Ma & Michael A. Caligiuri

  2. Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA, USA

    Sai Xiao, Songqi Duan, Yaqun Hong, Shoubao Ma & Michael A. Caligiuri

  3. Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Los Angeles, CA, USA

    Jianying Zhang

  4. City of Hope Comprehensive Cancer Center, Los Angeles, CA, USA

    Jianying Zhang, Shoubao Ma & Michael A. Caligiuri

  5. Division of Hematology & Oncology, Department of Medicine, School of Medicine, University of California, Irvine, CA, USA

    Jianhua Yu

  6. Institute for Precision Cancer Therapeutics and Immuno-Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA

    Jianhua Yu

  7. The Clemons Family Center for Transformative Cancer Research, University of California, Irvine, CA, USA

    Jianhua Yu

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Contributions

S.X., S.M., M.A.C. and J.Y. conceived and designed the project. S.X. and Y.H. contributed to most of the in vitro and in vivo experiments. S.D. contributed to the RNA-seq, m6A-seq and RIP-seq analysis. S.X., S.D. and J.Z. analyzed the data. S.X., S.M., M.A.C. and J.Y. wrote, reviewed and/or revised the paper. J.Y. and M.A.C. acquired funding and supervised the study. All authors discussed and approved the paper.

Corresponding authors

Correspondence to Shoubao Ma, Michael A. Caligiuri or Jianhua Yu.

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Extended data

Extended Data Fig. 1 YTHDF2 deficiency does not affect Th1, Th2, Th17, and Treg cell differentiation.

(a) YTHDF2 and β-actin expression in mouse wild-type (WT) and Ythdf2-knockout (KO) naive CD4+ T cells, determined by immunoblotting. Images are representatives of at least three independent experiments. (b-e) Representative plots of IFN-γ, IL-13, IL-17A, and FOXP3 expression in WT or Ythdf2-KO Th1 (b), Th2 (c), Th17 (d), and Treg (e) cells on the 5th day after differentiation. (f, g) Representative plots and percentage of IL-2 (f) and IL-21 (g) production from WT (Ythdf2+/+) or Ythdf2−/− Th9 cells on the 5th day after differentiation (n = 3 mice). Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (f right and g right).

Source data

Extended Data Fig. 2 Ythdf2cKO mice have normal T cell development.

(a) YTHDF2 and β-actin expression in mouse naive CD4+ T cells isolated from Ythdf2f/f and Ythdf2cKO mice, determined by immunoblotting (n = 4 mice). (b-k) Flow cytometric analysis of the frequencies of the indicated lymphoid immune cells in the thymus (b, c), spleen (d-g), and peripheral lymph nodes (h-k) of Ythdf2f/f and Ythdf2cKO mice. Data are presented as representative plots (a, c, e, g, and i) and summary bar graphs (b, d, f, h, and j) (n = 4 mice). Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (D and F) or the one-way ANOVA model (b, h, and j). DP, double positive; DN, double negative. Tn, naive T cells; Tcm, central memory T cells; Teff, effector T cells.

Source data

Extended Data Fig. 3 YTHDF2 deficiency in mice does not affect Th1, Th2, Th17, and Treg differentiation.

(a-h) Flow cytometric analysis of the frequencies of the indicated protein expression in Ythdf2f/f and Ythdf2cKO Th1 (a, b), Th2 (c, d), Th17 (e, f), and Treg cells (g, h). Data are presented as representative plots (a, c, e, and g) and summary bar graphs (b, d, f, and h) (n = 3 mice). (i-l) Representative plots and percentage of IL-2 (i, j) and IL-21 (k, l) production from Ythdf2f/f and Ythdf2cKO Th9 cells on the 5th day after differentiation (n = 3 mice). (m, n) Representative plots (m) and percentage (n) of WT (Ythdf2+/+) or Ythdf2−/− Tc9 cells on the 5th day after differentiation (n = 5 mice). Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (b, d, f, h, j, l, and n).

Source data

Extended Data Fig. 4 Transcriptome-wide RNA-seq, m6A-seq, and RIP-seq assays in murine Th9 cells.

(a) The m6A motif was detected by the HOMER motif discovery tool with m6A-seq data. (b) Density distribution of the m6A peaks across the mRNA transcriptome from m6A-seq data. (c) The proportion of m6A peak distribution in Th9 cells from Ythdf2f/f and Ythdf2cKO mice. (d) GO analysis of transcripts with m6A peaks. (e) Density distribution of the YTHDF2-binding sites across the mRNA transcriptome from RIP-seq data. (f) The proportion of YTHDF2-binding site distribution from RIP-seq data. (g) Top 10 GO clusters from GO analysis of YTHDF2 target genes from RIP-seq data. (h) Venn plot showing an overlapping analysis of genes identified by RNA-seq (upregulated genes), m6A-seq, and RIP-seq. (i) qPCR analysis of mRNA expression of Gata3 in Ythdf2f/f and Ythdf2cKO Th9 cells after intrasample normalization to the reference gene 18S (n = 4 biological replicates). (j and k) Immunoblot analysis of GATA3 in whole-cell lysates of naive Ythdf2f/f and Ythdf2cKO CD4+ T cells cultured under Th9 conditions (n = 3 biological replicates). (l) qPCR analysis of mRNA expression of Smad3 in Ythdf2f/f and Ythdf2cKO Th9 cells after intrasample normalization to the reference gene 18S (n = 3 biological replicates). (m and n) Immunoblot analysis of SMAD3 in whole-cell lysates of naive Ythdf2f/f and Ythdf2cKO CD4+ T cells cultured under Th9 conditions (n = 3 biological replicates). (o and p) GATA3, SMAD3 and β-actin expression in Gata3 (o) or Smad3 (p) knockout Ythdf2f/f Th9 cells, as well as in Ythdf2cKO Th9 cells, determined by immunoblotting. Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (i, k, l, and n). P values in a, d, e, and g were determined by a hypergeometric test.

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Extended Data Fig. 5 Smad3 and Gata3 interaction promotes Th9 cell differentiation.

(a) Luciferase reporter assay showing that Gata3 alone, or Smad3 alone, or their combination did not activate Il9 gene transcription without p65 in 293T cells (n = 3 biological replicates). (b) Luciferase reporter assay showing that Irf4 alone, or Pu.1 alone, or their combination did not activate Il9 gene transcription without p65 in 293T cells (n = 3 biological replicates). (c) Luciferase reporter assay showing that Irf4 and/or Pu.1 activates Il9 gene transcription in the presence of p65 in 293T cells (n = 3 biological replicates). (d, e) Binding of Smad3 to the Il9 promoter in Smad3, Smad3 and Gata3 overexpressed 293T cells was determined by ChIP-qPCR (n = 3 biological replicates). Data are represented as the mean ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (d, e), or two-way ANOVA models with P values adjusted for multiple comparisons by Holm-Šídák method (a, b). For statistical analysis in (c), luciferase reporter assay results were log2-transformed after adding a pseudo count of 1 to each value to stabilize variance and account for zero values, and then two-way ANOVA models with P values adjusted for multiple comparisons by Holm-Šídák method.

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Extended Data Fig. 6 Ythdf2cKO Th9 cells don’t affect mast cells, macrophages, and MDSCs.

(a) qPCR analysis of mRNA expression of Gzmb in Ythdf2f/f and Ythdf2cKO Th9 cells after intrasample normalization to the reference gene 18S (n = 4 mice). (b, c) Tumor growth in NSG (b; n = 3 mice) and Rag2−/−Il2gc−/−(c; n = 4 mice) mice that were s.c. inoculated with B16-OVA tumor cells and then adoptively transferred with Ythdf2f/f and Ythdf2cKO Th9 cells. (d-p) Flow cytometric analysis of tumor-infiltrating mast cells (d-g, n = 5 mice), macrophages (i-k: n = 4 mice; l: n = 5 mice), and MDSCs (n-p; n = 4 mice), and IHC staining analysis of tumor-infiltrating mast cells (h; n = 3 biological replicates), macrophages (m; n = 5 independent replicates) in B16-OVA tumor-bearing mice that received adoptive transfer of either OT-II Ythdf2f/f or Ythdf2cKO Th9 cells. Scale bar, 100 μm (m, n). Data are presented as representative plots, summary graphs, and the absolute number of per gram tumors. Data are represented as the mean ± SD. Statistical analysis was performed using unpaired two-tailed t-tests (a, e, f, h, i, k, and l), or two-way ANOVA models with P values adjusted for multiple comparisons by Holm-Šídák method (b, c).

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Extended Data Fig. 7 Ythdf2cKO Th9 cells enhance cytotoxic CD8+ T cell infiltration.

(a) Representative plots (left), summary graphs (right) of tumor-infiltrating perforin+GZMB+ CD8+ T cells in B16-OVA tumor-bearing mice, by flow cytometric analysis (n = 5 mice). (b-g) Representative plots (b, e), summary graphs (c, n = 4 mice; f, n = 5 mice), and the absolute number of per gram tumors (d, n = 4 mice; g, n = 5 mice) of tumor-infiltrating CD8+ T cells, as indicated in different tumor-bearing mice, by flow cytometric analysis. (h-m) Representative plots (h, k), summary graphs (i, n = 4 mice; l, n = 5 mice), and the absolute number of per gram tumors (j, n = 4 mice; m, n = 5 mice) of tumor-infiltrating IFN-γ+CD8+ T cells, as indicated in different tumor-bearing mice, by flow cytometric analysis. Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests.

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Extended Data Fig. 8 YTHDF2-deficient Th9 cells enhance the infiltration of NK cells.

(a, b) Flow cytometric analysis of tumor-infiltrating NK cells, as indicated in LLC1-OVA (a; n = 4 mice) and E0771-OVA (b; n = 5 mice) tumor-bearing mice that received adoptive transfer of either Ythdf2f/f or Ythdf2cKO Th9 cells. Data are presented as representative plots (left panels), summary graphs (middle panels), and the absolute number of per gram tumors (right panels). (c, d) Flow cytometric analysis of tumor-infiltrating IFN-γ+ NK cells, as indicated in different tumor-bearing mice (c, n = 4 mice; d, n = 5 mice). Data are presented as representative plots (left panels), summary graphs (middle panels), and the absolute number of per gram tumors (right panels). Data are represented as means ± SD. Statistical analysis was performed using unpaired two-tailed t-tests.

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Extended Data Fig. 9 YTHDF2-deficient Th9 cells enhance the effector function of NK cells in Rag1−/− mice.

(a-d) (a, b) Flow cytometric analysis of tumor-infiltrating NK cells, as indicated in B16-OVA tumor-bearing Rag1−/− mice and (c, d) in EO771-OVA tumor-bearing mice. Data are presented as representative plots (left panels), summary graphs (middle panels), and the absolute number of per gram tumors (right panels) (a, c n = 4 mice; b, d, n = 5 mice). (e) The ability of NK cells to lyse B16F10 tumor cells in the presence of either Ythdf2f/f or Ythdf2cKO Th9 cells, measured by real-time cell analysis (n = 3 mice). Data are represented as the mean ± SD (a, b, c, and d) or the mean (e). Statistical analysis was performed using unpaired two-tailed t-tests (a, b, c, d).

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Extended Data Fig. 10 YTHDF2 deficiency promotes the differentiation of Th9 cells in humans.

(a) Representative plots and percentage of human YTHDF2+/+ and YTHDF2−/− Tc9 cells on the 7th day after differentiation (n = 5 independent donors). (b, c) Flow cytometric analysis of the anti-PSCA (b; n = 5 independent donors) or anti-EGFR (c; n = 4 independent donors) CAR transduction efficacy of human YTHDF2+/+ and YTHDF2−/− CAR-Th9 cells. Data are presented as representative plots (left panels) and summary graphs (right panels). (d, e) Flow cytometric analysis of the IL-9 production in YTHDF2+/+ or YTHDF2−/− CAR-Th9 cells. Data are presented as representative plots (left panels) and summary graphs (right panels) (d, n = 5 independent donors; e, n = 4 independent donors). (f, g) The ability of human YTHDF2+/+ or YTHDF2−/− anti-EGFR CAR-Th9 to lyse A549 or Capan-1 tumor cells, measured by real-time cell analysis (representative of 2 experiments, showing the mean cell index). (h) Schematic of primary tumor growth assay involving NSCLC cells. (i) Tumor growth of established tumor cells, as described in f. Tumor volumes were measured every 4 days (one experiment was performed with n = 4 mice). (j) Schematic of primary tumor growth assay involving PDAC cells. (k) Tumor growth of established tumor cells, as described in H. Tumor volumes were measured every 4 days (one experiment was performed with n = 4 mice). Data in (i,k) show the mean plus SD. Statistical analysis was performed using paired two-tailed t-tests (a-e) and two-way ANOVA with a mixed-effects model with P values adjusted for multiple comparisons by Holm-Šídák method (i, k).

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Supplementary information

Supplementary Information

Supplementary Fig. 1. Gating strategy of NK cells (CD45+CD3−NK1.1+) and CD8+ T cells (CD45+CD3+NK1.1−CD4−CD8+) in B16-OVA tumor-bearing mice that received adoptively transferred either OT-II-Ythdf2f/f or OT-II-Ythdf2cKO Th9 cells.

Reporting Summary

Supplementary Tables

Supplementary Table 1. A list of 117 genes that overlap among RNA-seq upregulated genes, m6A-seq data, and YTHDF2 RIP-seq data. Supplementary Table 2. List of immunoblotting antibodies used for this study. Supplementary Table 3. List of primers used for this study.

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Xiao, S., Duan, S., Hong, Y. et al. Loss of YTHDF2 enhances Th9 programming and CAR-Th9 cell antitumor efficacy. Nat Immunol 26, 1501–1515 (2025). https://doi.org/10.1038/s41590-025-02235-2

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