Cancer SLC43A2 alters T cell methionine metabolism and histone methylation

Abstract

Abnormal epigenetic patterns correlate with effector T cell malfunction in tumours1,2,3,4, but the cause of this link is unknown. Here we show that tumour cells disrupt methionine metabolism in CD8+ T cells, thereby lowering intracellular levels of methionine and the methyl donor S-adenosylmethionine (SAM) and resulting in loss of dimethylation at lysine 79 of histone H3 (H3K79me2). Loss of H3K79me2 led to low expression of STAT5 and impaired T cell immunity. Mechanistically, tumour cells avidly consumed methionine and outcompeted T cells for methionine by expressing high levels of the methionine transporter SLC43A2. Genetic and biochemical inhibition of tumour SLC43A2 restored H3K79me2 in T cells, thereby boosting spontaneous and checkpoint-induced tumour immunity. Moreover, methionine supplementation improved the expression of H3K79me2 and STAT5 in T cells, and this was accompanied by increased T cell immunity in tumour-bearing mice and patients with colon cancer. Clinically, tumour SLC43A2 correlated negatively with T cell histone methylation and functional gene signatures. Our results identify a mechanistic connection between methionine metabolism, histone patterns, and T cell immunity in the tumour microenvironment. Thus, cancer methionine consumption is an immune evasion mechanism, and targeting cancer methionine signalling may provide an immunotherapeutic approach.

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Fig. 1: Tumour cells outcompete T cells for methionine to impair T cell function.
Fig. 2: Tumour cells alter CD8+ T cell methionine metabolism to diminish H3K79me2.
Fig. 3: Loss of H3K79me2 impairs T cell anti-tumour immunity through STAT5.
Fig. 4: Methionine supplementation in tumours restores T cell immunity.
Fig. 5: Tumour cells outcompete T cells for methionine via SLC43A2.

Data availability

RNA sequencing data that support the findings of this study have been deposited in NCBI Gene Expression Omnibus (GEO) under accession number GSE150887. All other data that supported the findings of this study are available from the corresponding author upon request. Source data are provided with this paper.

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Acknowledgements

We thank P. King for scientific input. We acknowledge support from the Advanced Genomics Core and Bioinformatics Core of the University of Michigan Medical School’s Biomedical Research Core Facilities. This work was supported in part by research grants from the US NIH/NCI (W. Zou) (CA217648, CA123088, CA099985, CA193136 and CA152470) and the NCI Cooperative Human Tissue Network (CHTN). C.A.L. was supported by a 2017 AACR NextGen Grant for Transformative Cancer Research (17-20-01-LYSS) and an ACS Research Scholar Grant (RSG-18-186-01). Metabolomics studies performed at the University of Michigan were supported by NIH grant DK097153, the Charles Woodson Research Fund, and the University of Michigan Pediatric Brain Tumor Initiative. C.A.L. and W. Zou were supported by the NIH through the University of Michigan Rogel Cancer Center Grant (P30 CA046592).

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Authors

Contributions

Y.B., W.L., and W. Zou proposed the research concept. Y.B. performed the majority of the experiments and explored the concept for the SLC transporter. Y.B., W.L., and W. Zou designed the experiments. W.L. and J.C. performed some in vivo experiments with Dot1l/ mice. D.M.K., P.S., L.Z., Z.C.N. and C.A.L. designed, performed, and analysed the MS experiments for metabolite tests and analysis. S.L., J.L., M.C. and A.M.C. assisted with the RNA-seq and single cell RNA-seq data analysis. H.X., P.L., J.Y., L.V., W.S., and I.K. aided in mouse and human sample collection and FACS data analysis. S.W. and S.G performed mouse genotyping and breeding. J.R.L. and K.M. assisted in clinical study design and collection of specimens from patient with ovarian cancer. A.C., A.P., W. Zgodziński, G.W., I.W., and K.O. performed the clinical study on patients with colorectal cancer. Y.B., W.L., and W. Zou wrote the manuscript.

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Correspondence to Weiping Zou.

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The authors declare no competing interests.

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Peer review information Nature thanks Tak Mak, Stefani Spranger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Tumour cells outcompete T cells for methionine to impair T cell function.

ac, Effect of tumour cells on T cell apoptosis. Tumour supernatants were collected from MC38 (a), CT26 (b), and human melanoma A375 (c) tumour cells cultured for 48 h with media containing different concentrations of methionine (Met). Then, CD8+ T cells were cultured for 36 h with these tumour supernatants (Sup) or fresh medium. Apoptosis was determined by Annexin V staining. d, e, Effect of methionine on ID8 tumour infiltrating cells. T cells were cultured with fresh medium, ID8 supernatant (Sup), and supernatant plus methionine (Sup + Met). T cell apoptosis (d) and cytokine production (e) were determined by FACS. f, g, Amino acid levels in ovarian cancer patient plasma. Amino acids were detected in healthy donor and ovarian cancer patient plasma by liquid chromatography mass spectrometry (LC-MS). (f) Volcano showed plasma free amino acid changes. Red dot showed methionine (Met). (g) Plasma methionine in ovarian cancer patients vs healthy controls. h, Methionine concentration in pre- and post- tumour cultured medium. i, j, Effect of amino acid supplementation on human T cell function. CD8+ T cells were cultured with A375 supernatants (Sup) supplemented with different amino acids for 36 h. FACS analysis showed T cell apoptosis (i) and effector cytokines (j). k–m, Effect of glucose supplementation on the role of methionine-affected T cell apoptosis and function. n, Schematic figure showing tumour and T cell co-culture in the Transwell system. o, p, Effect of methionine on human CD8+ T cell (o) and tumour cell (p) viability, EC50 was determined by nonlinear regression (log(agonist) vs. response). Sup, tumour supernatant. Data are mean ± s.e.m. Information on sample sizes, experimental number, times, biological replicates, statistical tests, and P values is available in ‘Statistics and reproducibility’ (Methods). Source data

Extended Data Fig. 2 Tumour alters CD8+ T cell methionine metabolism to diminish H3K79me2.

a, Gene profile changes in CD8+ T cells. Mouse CD8+ T cells were cultured with fresh medium, B16F10 tumour supernatant (Sup), or tumour supernatants plus methionine (Sup + Met) for 36 h. Gene profile changes were analysed by RNA-seq. b, Gene signatures were compared between groups from fresh medium and Sup. Functionally grouped network of enriched categories was generated for the hub genes and their regulators using ClueGO. Visualization has been carried out using Cytoscape 3.7.1. c–e, GSEA plot showed recovery of TCR signalling pathway (c) and methionine metabolism signalling (d, e) in CD8+ T cells cultured with Sup + Met compared to Sup. f, Metabolites changes in CD8+ T cells cultured with fresh medium, Sup and Sup + Met. Upper panel: Metabolites induced upon methionine supplementation. Lower panel: Metabolites suppressed upon methionine supplementation. g, The diagram of methionine cycle is shown. h, i, CD8+ T cells were cultured with fresh medium, Sup, or Sup + Met for 36 h. Metabolites related to the methionine cycle, including intracellular serine (h) and l-cystathionine (i), were detected by MS. j, k, Effect of tumour supernatants on CD8+ T cell histone methylation. Mouse (j) or human (k) CD8+ T cells were cultured with or without methionine (Met) for 36 h with fresh medium, CT26 and MC38 tumour supernatants (j), or human A375 tumour supernatants (Sup) (k). T cell histone marks were determined by western blots. Data are mean ± s.e.m. Information on sample sizes, experimental number, times, biological replicates, statistical tests, and P values is available in ‘Statistics and reproducibility’ (Methods). Source data

Extended Data Fig. 3 Loss of H3K79me2 impairs T cell anti-tumour immunity through STAT5.

a, Genotyping for Dot1lf/f and Dot1l/ mice by PCR. b, Effect of Dot1l knockout on histone marks in T cells. c-e, Gene signature comparison between Dot1l/− and Dot1lf/f CD8+ T cells. Functionally grouped network of enriched categories was generated for the hub genes and their regulators using ClueGO. Visualization has been carried out using Cytoscape 3.7.1. (c). GSEA plot showed enriched apoptotic gene pathway (d) and impaired TCR signalling pathway (e) in Dot1l/ CD8+ T cells. f, Effect of DOT1L deficiency on T cell function in MC38 tumour. MC38 cells were inoculated into Dot1lf/f and Dot1l/ mice. Expression of TNFα, IFNγ, and granzyme B in tumour infiltrating CD8+ T cells was determined by FACS. g, Effect of anti-PD-L1 on tumour growth in Dot1l+/+ and Dot1l/ mice. h, i, B16F10 cells were inoculated into Dot1l/ and Dot1lf/f mice. Effect of T cell DOT1L deficiency on tumour growth (h) and T cell viability (i) were monitored. j, Real-time PCR showed Stat5a and Stat5b transcripts in fresh or anti-CD3/CD28 activated Dot1lf/f and Dot1l/ CD8+ T cells. k, Real-time PCR showed Stat5a and Stat5b transcripts in activated CD8+ T cells cultured with fresh medium, B16F10 tumour supernatants (Sup), or supernatants plus methionine (Sup + Met) for 24 h. l–n, RNA-seq showed the effect of DOT1L inhibitor (SGC0946) on human CD8+ T cells (Database: GSE108694). STAT5A and STAT5B (l) transcripts were quantified in human CD8+ T cells treated with DOT1L inhibitor SGC0946. GSEA enrichment plot showed enrichment of apoptotic gene pathway (m) and defects in T cell receptor related pathways (n) in human CD8+ T cells treated with DOT1L inhibitor. o, p, H3K79me2 ChIP-seq in ENCODE database showing Stat5b promoter in mice (o) and humans (p). Data are mean ± s.e.m. Information on sample sizes, experimental number, times, biological replicates, statistical tests, and P values is available in ‘Statistics and reproducibility’ (Methods). Source data

Extended Data Fig. 4 Methionine supplementation promotes T cell anti-tumour immunity.

a, b, H3K79me2 (a) and STAT5 (b) levels in CD8+ T cells from tumour draining lymph node and tumour in B16F10 bearing mice. c, d, H3K79me2 (c) and STAT5 (d) levels in CD8+ T cells from spleen and tumour ascites in ID8 bearing mice. e, H3K79me2 levels in CD8+ T cells from healthy peripheral blood and human ovarian cancers ascites. f, g, H3K79me2 (f) and STAT5 (g) levels in CD8+ T cells from healthy human blood and human ovarian cancer omentum tissues. h, i, FACS showed H3K79me2 and STAT5 levels in human tumour infiltrating CD8+ T cells. j–m, Effect of methionine on human tumour infiltrating CD8+ T cells. Human colorectal cancer infiltrating CD8+ T cells were cultured with or without methionine. T cell cytokine production (j, k), H3K79me2 (l), and STAT5 (m) were analysed by FACS. One representative of four is shown. n, Effect of methionine supplementation on apoptosis of tumour infiltrating CD8+ T cells and ID8 tumour cells in vivo. ID8 tumour bearing mice were treated with methionine or PBS. T cell and tumour cell apoptosis was determined by FACS. o, Methionine levels in ID8 tumour after methionine or PBS treatment. p–r, Effect of anti-PD-L1 on methionine-affected CT26 tumour progression. Mice bearing CT26 tumour were treated with anti-PD-L1, methionine, and their combination. Tumour volume (p), T cell tumour infiltration (q) and apoptosis (r) were assessed. Data are mean ± s.e.m. Information on sample sizes, experimental number, times, biological replicates, statistical tests, and P values is available in ‘Statistics and reproducibility’. Source data

Extended Data Fig. 5 Tumour SLC43A2 correlates to poor T cell immunity.

a, b, Effects of SLC inhibitors (BCH or MeAIB) on tumour cell affected CD8 T cell apoptosis (a) and cytokine production (b). c, Real-time PCR showed SLC transporter transcripts in activated CD8+ T cells and B16F10 tumour cells. d, Western Blot showed SLC43A2 and SLC7A5 proteins in activated CD8+ T cells and tumour cells. e, Western Blot showed SLC43A2 protein in human CD8+ T cells and human tumour cells. f, Western Blot showed SLC43A2 knockdown efficiency in B16F10 cells. g, Effect of tumour cell SLC43A2 knockdown on methionine consumption. WT (scramble) and sh-SLC43A2 tumour cells were cultured with fresh medium containing 30 μM methionine for 24 h. Methionine concentration was measured by MS in fresh medium and supernatants. h, Wild-type and sh-SLC43A2 B16F10 tumour growth in Dot1l/ mice. i, Wild-type and SLC43A2 knockdown B16F10 tumour growth in Rag1−/− mice. j, Effect of tumour SLC43A2 knockdown on T cell tumour infiltration in WT or sh-SLC43A2 B16F10 bearing mice. k, Effect of SLC43A2 knockdown and the combination of anti-PD-L1 on B16F10 bearing mice. l, Western Blot showed SLC43A2 knockdown efficiency in ID8-luc cells. m, Wild type and SLC43A2 knockdown ID8-luc tumour growth in Rag1−/− mice. n, o, Effect of tumour SLC43A2 knockdown on ID8 growth (n) and T cell tumour infiltration in WT or sh-SLC43A2 ID8 bearing mice. p, T cell tumour infiltration in B16F10 bearing mice treated with BCH, anti-PD-L1, or their combination. q–s, Kaplan–Meier survival curves showed the prognostic values of SLC43A2 expression in different types of tumour: Cholangiocarcinoma (CHOL, q), low grade glioma (LGG, r), and lung squamous cell carcinoma (LUSC, s). The raw data was from TCGA. ty, The analysis was based on single cell RNA-seq data (GSE72056). t, SLC43A2 transcripts were compared in tumour cells versus tumour infiltrating T cells from the same human melanoma tissues. u, GSEA plots showed methionine metabolic process genes in tumour cells expressing high versus low SLC43A2. v, Correlation was analysed between CD8A, CD8B, IFNG transcripts in T cells and SLC43A2 transcripts in tumour cells in the same human melanoma tissues. w-y, GSEA enrichment plot analysis showed defective pathways in tumour infiltrating T cells in melanoma patients with high tumour SLC43A2 compared to low tumour SLC43A2. The pathways included T cell methionine metabolic process (w), histone methylation (x), and IFNγ production (y). Data are mean ± s.e.m. Information on sample sizes, experimental number, times, biological replicates, statistical tests, and P values is available in ‘Statistics and reproducibility’ (Methods). Source data

Extended Data Fig. 6 Graphical model.

Model of how tumour cells outcompete T cells for methionine and disrupt T cell survival and function.

Extended Data Table 1 ChIP primers for mouse Stat5b
Extended Data Table 2 Characteristics of patients with colorectal cancer
Extended Data Table 3 Primers for RT–PCR and Dot1l mouse genotyping

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Bian, Y., Li, W., Kremer, D.M. et al. Cancer SLC43A2 alters T cell methionine metabolism and histone methylation. Nature 585, 277–282 (2020). https://doi.org/10.1038/s41586-020-2682-1

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