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Threonine fuels glioblastoma through YRDC-mediated codon-biased translational reprogramming

Nature Cancer (2024)Cite this article

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Abstract

Cancers commonly reprogram translation and metabolism, but little is known about how these two features coordinate in cancer stem cells. Here we show that glioblastoma stem cells (GSCs) display elevated protein translation. To dissect underlying mechanisms, we performed a CRISPR screen and identified YRDC as the top essential transfer RNA (tRNA) modification enzyme in GSCs. YRDC catalyzes the formation of N6-threonylcarbamoyladenosine (t6A) on ANN-decoding tRNA species (A denotes adenosine, and N denotes any nucleotide). Targeting YRDC reduced t6A formation, suppressed global translation and inhibited tumor growth both in vitro and in vivo. Threonine is an essential substrate of YRDC. Threonine accumulated in GSCs, which facilitated t6A formation through YRDC and shifted the proteome to support mitosis-related genes with ANN codon bias. Dietary threonine restriction (TR) reduced tumor t6A formation, slowed xenograft growth and augmented anti-tumor efficacy of chemotherapy and anti-mitotic therapy, providing a molecular basis for a dietary intervention in cancer treatment.

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Fig. 1: GSCs exhibit high translation rates.
Fig. 2: CRISPR screening of tRNA modifiers in GSCs.
Fig. 3: YRDC is essential for GSC maintenance and translation.
Fig. 4: Threonine dynamically regulates t6A and translation.
Fig. 5: Threonine functions mainly through YRDC in GSCs.
Fig. 6: Threonine and YRDC fuel mitosis with ANN codon bias.
Fig. 7: Dietary TR inhibits tumor growth.
Fig. 8: Dietary intervention potentiates standard therapeutics.

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Data availability

RNA-seq, tRNA-seq and ribosome profiling data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE229673. The MS proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD049966. sgRNA counts for screening and processed MS data are provided in the Supplementary Tables. Human glioma transcriptomic data were derived from the TCGA Research Network (http://cancergenome.nih.gov/) and the CGGA database (http://www.cgga.org.cn/). All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

No custom code was generated for this study.

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Acknowledgements

We thank the Metabolomics and Lipidomics Core for MS analysis, the Biospecimen Core for histology analysis and the Flow Cytometry Core Facility at the University of Pittsburgh. We thank the Proteomics Resource Centre at the Rockefeller University for MS analysis of t6A and TMT-labeled quantitative proteomics. This work was supported by the National Natural Science Distinguished Youth Foundation of China (82125024 to N.Z.), Special Funds of the National Natural Science Foundation of China (82341007 to N.Z.), the Guangdong Province Regional Joint Fund—Key Project (2022B1515120023 to N.Z.), the Major Program of the National Natural Science Foundation of China (82192894 to N.Z.), the Science and Technology Planning Project of Guangzhou (202103000019 to N.Z.), start-up funds from the University of Pittsburgh (to J.N.R.), NIH grants (R35CA197718, R01CA238662, R01CA268634 and R01NS103434 to J.N.R.), the Defense Health Agency (HT9425-23-1-0689 to J.N.R.), and the American Cancer Society Lisa Dean Mosley Cancer Stem Cell Consortium (CSCC-LEAD-22-186-01-CSCC to J.N.R.).

Author information

Authors and Affiliations

  1. Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Xujia Wu, Huairui Yuan, Qiulian Wu, Tingting Duan, Tengfei Huang, Shuai Wang, Fanen Yuan, Derrick Lee, Suchet Taori, Dennis J. Hsu & Jeremy N. Rich

  2. Department of Neurosurgery, the First Affiliated Hospital of Sun Yat-sen University, Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangdong Translational Medicine Innovation Platform, Guangzhou, China

    Xujia Wu, Yixin Gao & Nu Zhang

  3. Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA

    Kailin Yang

  4. Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Tritan Plute & Sameer Agnihotri

  5. John G. Rangos Sr. Research Center, Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    Tritan Plute & Sameer Agnihotri

  6. Proteomics Resource Center, the Rockefeller University, New York, NY, USA

    Søren Heissel, Hanan Alwaseem, Michael Isay-Del Viscio & Henrik Molina

  7. Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Dennis J. Hsu

  8. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA

    Jeremy N. Rich

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  1. Xujia Wu
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Contributions

X.W. and J.N.R. conceived the project, designed the overall experiments, analyzed data and wrote the paper. X.W., H.Y., Q.W., Y.G., T.H., S.W., F.Y., D.L., S.T. and T.P. performed the experiments. S.H., H.A., M.I.-D.V. and H.M. performed the MS analysis. T.D., K.Y., S.A., D.J.H. and N.Z. provided scientific input and helped edit the paper.

Corresponding authors

Correspondence to Nu Zhang or Jeremy N. Rich.

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The authors declare no competing interests.

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Nature Cancer thanks Stephane Angers, Daniel Wahl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 GSCs display high translation rates.

a-e. Gating strategy (a), representative histogram plot (b, d) and statistical quantification (c, e, n = 5 mice per group) of OPP flow cytometric analysis of indicated cell populations in GSC456 derived intracranial tumour. f-i. Distribution of all GSCs and all tumour cells (f), visualization of stemness marker PPP1R14B and differentiation marker GFAP (g), distribution of GSCs and tumour-like GSCs split from all GSCs (h) and differentiated tumour cells and GSC-like tumour cells split from all tumour cells (i) on PCA plot in scRNA-seq analysis of 28 early-passage GSC cultures derived from 24 patients and 14,207 malignant cells from seven GBM patients. In g, PCA plots binned into hexbins that represent median AUC score of all overlapping cells within a given coordinate. Contour lines represent the outline of all GSC (red) and all tumour cells (black). j, k. Quantification of puromycin signal from immunoblot in Fig. 1n (j, n = 1 per group) and Fig. 1o (k, n = 1 per group). Puromycin signal was normalized to tubulin. l, m. Cell viability of indicated GSCs vs DGCs (n = 5 independent experiments). n, o. Cell viability of indicated cells with or without Alvocidib treatment (n = 5 independent experiments). p. Immunoblots showing puromycin incorporation in indicated cells with or without Alvocidib treatment for 72 h. q. Immunoblots showing p-mTORS2448, mTOR, p-AMPKαT172, AMPKα, p-eIF2αS51, eIF2α in matched GSCs and DGCs. In c and e, boxes represent data within the 25th to 75th percentiles, whiskers depict the range of all data points and horizontal lines within boxes represent median values. In l-o, data are presented as mean ± s.d. In p and q, immunoblots are representative from three independent experiments with similar results. Two-tailed paired t test for c and e. Two-way ANOVA followed by multiple comparison for l-o.

Source data

Extended Data Fig. 2 YRDC expression is driven by OLIG2.

a. Gene rank of positive selection results in CRISPR knockout screens of GSC456 and GSC23. The higher of the y axis indicates lesser gene essentiality. The top 3 ranked genes are labelled. b. ChIP-seq analysis of H3K27ac signal on YRDC promoter in indicated cells. c. ChIP-seq analysis of H3K27ac signal on YRDC promoter in DGC_MGG8 reprogramming (GSE54047). d. Comparison of the expression of TFs potentially associated with YRDC in RNA-seq data of GSC vs. DGC (GSE54791) and GSC vs. NSC (GSE119834). Dash lines show the cut-off (|log2FC|>1). Red dots indicate TFs enriched in GSCs. e. Box plot showing the expression of YRDC, OLIG1 and OLIG2 in RNA-seq data of GSC vs. DGC (GSE54791) (upper, n = 9 transcriptomics per group) and GSC vs. NSC (GSE119834) (lower, n = 44 GSC transcriptomics and 9 NSC transcriptomics). f-i. QPCR analysis (f, h, n = 3 independent experiments) and immunoblots (g, i) showing YRDC, OLIG1 and OLIG2 expression in GSCs with or without indicated knockdown. j. OLIG2 ChIP-seq analysis on YRDC promoter region in MGG8_GSC (GSE54047). k. Graphic illustration of predicted OLIG2 binding sites (BSs) on YRDC promoter. l. OLIG2 ChIP-qPCR analysis in GSCs detecting indicated BS in k (n = 3 independent experiments). m, n. Pearson correlations between YRDC and OLIG2 expression in RNA-seq data of CCGA_GBM (primary, IDH_WT, n = 183 patients) (m) and Mack_GBM (n = 45 patients) (n) datasets. Red lines show linear regression. In e, boxes represent data within the 25th to 75th percentiles, whiskers depict the range of all data points and horizontal lines within boxes represent median values. In f, h, and l, data are presented as mean ± s.d. In g and i, immunoblots are representative from three independent experiments with similar results. Two-tailed unpaired t test for e. One-way ANOVA followed by multiple comparison for f and h. Two-way ANOVA followed by multiple comparison for l. Two-tailed Pearson correlation for m and n.

Source data

Extended Data Fig. 3 Specific requirement of YRDC in GSCs than NSCs.

a. Immunoblots showing YRDC expression in GSCs with or without YRDC knockdown. b, c. Representative images of EdU incorporation assay (b) (scale bar, 50 μm) and sphere formation assay (c) (scale bar, 100 μm) in GSCs with or without YRDC knockdown. d. Immunoblots showing YRDC expression in NSCs with or without YRDC knockdown. e. Cell viability of NSCs with or without YRDC knockdown (n = 5 independent experiments). f. Immunoblots showing p-mTORS2448, mTOR, p-eIF2αS51, eIF2α expression in GSCs with or without YRDC knockdown. In e, data are presented as mean ± s.d. In a-d and f, immunoblots and images are representative from three independent experiments with similar results. Two-way ANOVA followed by multiple comparison for e.

Source data

Extended Data Fig. 4 Threonine metabolism in GSC supports t6A formation.

a, b. Immunoblots showing puromycin incorporation, p-mTORS2448, mTOR, p-eIF2αS51, eIF2α expression in GSC456 cultured in indicated media for 72 h. Ctrl, 800 μM threonine and 400 μM arginine. 5xThr, 4000 μM threonine. 5xArg, 2000 μM arginine. TR, 8 μM threonine. AR, 4 μM arginine. c. MS analysis of intracellular threonine levels in indicated cells. Left, n = 5 biologically independent samples per group; right, n = 3 biologically independent samples per group. d, e. QPCR analysis of SLC1A4 (d, n = 3 independent experiments) and SLC1A5 (e, n = 3 independent experiments) in matched GSCs and DGCs. f. Graphic illustration of threonine metabolism in human. Red indicates enriched and green indicates depleted in GSCs. Crosses indicate nonfunctional catabolic pathways in humans. g. QPCR analysis of SDS in matched GSCs and DGCs (n = 3 independent experiments). In c-e, and g, data are presented as mean ± s.d. In a and b, immunoblots are representative from three independent experiments with similar results. One-way ANOVA followed by multiple comparison for c. Two-way ANOVA followed by multiple comparison for d, e and g.

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Extended Data Fig. 5 YRDC and threonine shift the proteome supporting mitosis with ANN codon-bias.

a, b. Volcano plot showing the differentially expressed gene analysis of ANN and non-ANN decoding tRNA isodecoders in tRNA-seq data of GSC456 with indicated treatments. Cut-off: |log2FC|>1, adjusted P < 0.05. c. Expression of ANN decoding and non-ANN decoding tRNA isodecoders in tRNA-seq of GSC456 upon TR. d. GO enrichment analysis of downregulated proteins under YRDC knockdown in proteomics of GSC456. e. ANN codon frequencies of differentially expressed proteins upon TR in proteomics of GSC456. f. GO enrichment analysis of proteins downregulated only in YRDC knockdown (left), and proteins downregulated only in TR (right) in proteomics of GSC456. g, h. Flowcytometric analysis of cell cycle in GSC456 with indicated treatments (n = 3 independent experiments). i, j. QPCR analysis of indicated t6A targets in GSCs with indicated treatments (n = 3 independent experiments). In c and e, boxes represent data within the 25th to 75th percentiles, whiskers depict the range of all data points and horizontal lines within boxes represent median values. In g-j, data are presented as mean ± s.d. In a-c, tRNA-seq data are from 3 biologically independent samples per group. In e, proteomic data are from 4 biologically independent samples per group. Two-tailed unpaired t test for c. Overrepresentation test corrected by FDR for d and f. Two-tailed Mann-Whitney test for e. Two-way ANOVA followed by multiple comparison for g-j.

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Extended Data Fig. 6 Data mining of t6A downstream targets in clinical datasets.

a. BF scores of SPC25, MASTL, RACGAP1, CIP2A, CEP55 and NCAPG in genome wide CRISPR knockout screens of GSCs and GBM cell lines (n = 24 independent screens per group). The greater positive BF score indicates higher confidence of essentiality. Data are presented as mean ± s.d. b. Correlations between YRDC and SPC25, RACGAP1, CIP2A, CEP55, NCAPG protein abundance in proteomics of PDC_GBM (n = 99). Red lines show linear regression of the data. MASTL was not detected in this dataset. c. Correlation heatmap of targets from b. The size of circle indicates the value of correlation coefficient. Red colour represents positive correlation and blue colour represents negative correlation. d, e. Violin plot of SPC25, MASTL, RACGAP1, CIP2A, CEP55, NCAPG expression (log2(count+0.5)) in RNA-seq data of TCGA (Grade II, n = 226; Grade III, n = 244; Grade IV, n = 150) (d) and CGGA (Grade II, n = 232; Grade III, n = 194; Grade IV, n = 225) (e) datasets. Violin plots represent the overall distribution of data points, horizontal lines show median, upper, and lower quartiles. In b, d and e, the number of n indicates patients. Two-tailed Pearson correlation for b and c. One-way ANOVA followed by multiple comparison for d and e.

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Extended Data Fig. 7 YRDC loss reduces translation of cell cycle-related transcripts.

a. Distribution of read length of ribosome footprints in ribosome profiling of GSC456. Data are presented as mean ± s.e.m. b. Heatmap showing the 3-nt periodicity of ribosome footprints with different read lengths (25-35 nt) on CDS. Data are plotted as the signal of the first nucleotide of ribosome P site on three-nucleotide (0, 1, 2) codon frames. The small table on the right provides detailed experimental setup for each group. c. Volcano plot showing the differential translation efficiency (TE) of GSC456 upon YRDC knockdown (cut-off: |log2FC|>1, adjust P < 0.05). Red indicates genes with upregulated TE and blue indicates genes with downregulated TE. d. GO enrichment analysis of genes with downregulated TE upon YRDC knockdown. e. Non-ANN codon occupancy alteration upon YRDC knockdown and TR at ribosome A-site. Black dots and labels show overlapped stalled codons under both conditions. f-h. Overlapped-non-ANN codon frequencies of differentially expressed proteins in proteomics of GSC456 with indicated treatments. i. Distribution of overlapped-non-ANN codon frequencies in CDS. Yellow dots indicate 6 t6A targets. j, k. QPCR analysis of transcript expression of indicated reporters in 293 T with indicated treatments (n = 3 independent experiments). In f-h, boxes represent data within the 25th to 75th percentiles, whiskers depict the range of all data points and horizontal lines within boxes represent median values. In j and k, data are presented as mean ± s.d. In a-e, ribosome profiling data are from 2 biologically independent samples per group. In f-h, proteomics data are from 4 biologically independent samples per group. Overrepresentation test corrected by FDR for d. Two-tailed Mann-Whitney test for f-h. Two-tailed unpaired t test for j and k.

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Extended Data Fig. 8 Targeting YRDC in vivo suppresses tumour growth.

a, b. QPCR analysis (a, n = 3 independent experiments) and immunoblots (b) showing YRDC expression in two GSCs with or without YRDC knockdown for in vivo tumorigenesis. c. Representative in vivo bioluminescence images of tumour-bearing mice derived from GSC468 with or without YRDC knockdown at indicated timepoint. Scale bar, 1 cm. d, e. Representative images of H&E-stained brain sections of tumour-bearing mice derived from GSC456 (d) and GSC468 (e) at indicated timepoint. Scale bar, 1 mm. f-i. Representative images (f, h) and quantification (g, i, n = 15 randomly selected fields examined over 3 mice per group) of phosphorylated histone H3 (p-H3, red) in brain sections derived from indicated xenografts. α-tubulin (green) shows the cytoplasm and DAPI (blue) shows the nucleus. Scale bar, 20 μm. In a, g and i, data are presented as mean ± s.d. In b, immunoblots are representative from three independent experiments with similar results. In c-e, images are representative from 5 mice. One-way ANOVA followed by multiple comparison for a, g and i.

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Extended Data Fig. 9 Dietary threonine restriction is safe and effective.

a, b. Body weight (a, n = 5 mice per group) and serum threonine levels (b, n = 3 mice in control group at Day 14, n = 4 mice per group in others) of tumour-free mice fed with indicated diets at indicated time points. c, d. Weight (c, n = 5 mice per group) and representative H&E-stained images (d) of indicated organs from tumour-free mice fed with indicated diets for 30 days. Scale bar, 100 μm. e. Representative H&E-stained brain section of tumour-free mice fed with indicated diets. Rectangles show the region of interest for NSC analysis. Scale bar, 2 mm. f-i. Representative images (f, h) and quantification (g, i, n = 10 randomly selected fields examined over 3 mice per group) of V-SVZ GFAP+ cells (NSCs) and DCX+ cells (neuroblasts) in tumour-free mice fed with indicated diets for 30 days. Rectangles show the enlarged region. V, ventricle. V–SVZ, ventricular–subventricular zone. Scale bar, 100 μm. j-m. Representative images (j, l) and quantification (k, m, n = 20 randomly selected fields examined over 4 mice per group) of phosphorylated histone H3 (p-H3, red) in brain sections of indicated xenografts fed with indicated diets. α-tubulin (green) shows the cytoplasm and DAPI (blue) shows the nucleus. Scale bar, 20 μm. n. Immunoblots showing p-mTORS2448, mTOR, p-eIF2αS51, eIF2α expression in tumour tissues from indicated xenografts fed with indicated diets. Each lane represents a sample from one mouse. o-q. Representative histogram plot (o), quantification of the percentage of OPP-hi tumour cells (p, n = 4 mice per group), and quantification of the median fluorescence intensity (MFI) (q, n = 4 mice per group) of OPP flow cytometric analysis in GSC456 derived xenograft with indicated treatments. In a-c, g, i, k, m, p and q, data are presented as mean ± s.d. In n, immunoblots are from three independent experiments with similar results. Two-way ANOVA followed by multiple comparison for a and b. Two-tailed unpaired t test for c, g, i, k and m. One-way ANOVA followed by multiple comparison for p and q.

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Extended Data Fig. 10 Data mining of YRDC expression in clinical datasets.

a. Representative in vivo bioluminescence images of GSC468-bearing mice with indicated treatments at indicated timepoints. Scale bar, 1 cm. b-e. Violin plot of YRDC expression in RNA-seq data of TCGA_LGG, GBM (Grade II, n = 226; Grade III, n = 244; Grade IV, n = 150) (b), CGGA (Grade II, n = 232; Grade III, n = 194; Grade IV, n = 225) (c), Rembrandt (Grade I-II, n = 100; Grade III, n = 85; Grade IV, n = 130) (d) and Gravendeel (Grade I-II, n = 32; Grade III, n = 85; Grade IV, n = 159) (e) datasets. f. Violin plot of YRDC expression in different subtypes in RNA-seq data of TCGA_GBM. PN, proneural, n = 46; CL, classical, n = 59; MES, mesenchymal, n = 51. g. Correlations between YRDC and PDGFRA expressions in RNA-seq data of TCGA_GBM (n = 160). Red line shows linear regression. h-j. Violin plot of YRDC expression in different PDGFRA status (diploid, n = 106; gain or amplification, n = 30) (h), IDH status (WT, wildtype, n = 142; mutant, n = 8) (i) and MGMT methylation (methylated, n = 56; unmethylated, n = 66) (j) in RNA-seq data of TCGA_GBM. k. Violin plot of YRDC expression (log2(FPKM)) in different subtypes in RNA-seq data of GSCs (GSE119834). PN, proneural, n = 10; CL, classical, n = 16; MES, mesenchymal, n = 18. The number of n indicates biologically independent cell lines. In b-f and h-k, violin plots represent the overall distribution of data points. In a, images are representative of 5 mice. In b-j, the number of n indicates patients, and the expressions are presented as log2(count+0.5). One-way ANOVA followed by multiple comparison for b-f and k. Two-tailed Pearson correlation for g. Two-tailed unpaired t test for h-j.

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Wu, X., Yuan, H., Wu, Q. et al. Threonine fuels glioblastoma through YRDC-mediated codon-biased translational reprogramming. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00748-7

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