Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Valine tRNA levels and availability regulate complex I assembly in leukaemia

Nature volume 601, pages 428–433 (2022)Cite this article

Subjects

Abstract

Although deregulation of transfer RNA (tRNA) biogenesis promotes the translation of pro-tumorigenic mRNAs in cancers1,2, the mechanisms and consequences of tRNA deregulation in tumorigenesis are poorly understood. Here we use a CRISPR–Cas9 screen to focus on genes that have been implicated in tRNA biogenesis, and identify a mechanism by which altered valine tRNA biogenesis enhances mitochondrial bioenergetics in T cell acute lymphoblastic leukaemia (T-ALL). Expression of valine aminoacyl tRNA synthetase is transcriptionally upregulated by NOTCH1, a key oncogene in T-ALL, underlining a role for oncogenic transcriptional programs in coordinating tRNA supply and demand. Limiting valine bioavailability through restriction of dietary valine intake disrupted this balance in mice, resulting in decreased leukaemic burden and increased survival in vivo. Mechanistically, valine restriction reduced translation rates of mRNAs that encode subunits of mitochondrial complex I, leading to defective assembly of complex I and impaired oxidative phosphorylation. Finally, a genome-wide CRISPR–Cas9 loss-of-function screen in differential valine conditions identified several genes, including SLC7A5 and BCL2, whose genetic ablation or pharmacological inhibition synergized with valine restriction to reduce T-ALL growth. Our findings identify tRNA deregulation as a critical adaptation in the pathogenesis of T-ALL and provide a molecular basis for the use of dietary approaches to target tRNA biogenesis in blood malignancies.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Valine tRNA biogenesis is upregulated by NOTCH1 in T-ALL.
Fig. 2: Dietary valine restriction reduces leukaemic burden and increases survival.
Fig. 3: Dietary valine restriction reduces translation of mRNAs involved in the mitochondrial ETC.
Fig. 4: Valine tRNA biogenesis and bioavailability regulate mitochondrial complex I assembly.

Data availability

All sequencing data created within this study were uploaded to the NCBI Gene Expression Omnibus (GEO) and is available under the accession codes: GSE165736; GSE165661 for RNA-seq; GSE165734 for tRNA-seq; GSE167534 for the CRISPR screen; and GSE167535 for Ribo-seq. Source data are provided with this paper.

Code availability

All custom codes written within this study can be found in the Supplementary Information

References

  1. Rapino, F. et al. Codon-specific translation reprogramming promotes resistance to targeted therapy. Nature 558, 605–609 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Goodarzi, H. et al. Modulated expression of specific tRNAs drives gene expression and cancer progression. Cell 165, 1416–1427 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wolfe, A. L. et al. RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer. Nature 513, 65–70 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Truitt, M. L. et al. Differential requirements for eIF4E dose in normal development and cancer. Cell 162, 59–71 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Loayza-Puch, F. et al. Tumour-specific proline vulnerability uncovered by differential ribosome codon reading. Nature 530, 490–494 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Cordo’, V., van der Zwet, J. C. G., Canté-Barrett, K., Pieters, R. & Meijerink, J. P. P. T-cell acute lymphoblastic leukemia: a roadmap to targeted therapies. Blood Cancer Discov. 2, 19–31 (2021).

    Article  PubMed  Google Scholar 

  7. Van Vlierberghe, P. et al. ETV6 mutations in early immature human T cell leukemias. J. Exp. Med. 208, 2571–2579 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Gerstberger, S., Hafner, M. & Tuschl, T. A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Gingold, H. et al. A dual program for translation regulation in cellular proliferation and differentiation. Cell 158, 1281–1292 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Aharon-Hefetz, N. et al. Manipulation of the human tRNA pool reveals distinct tRNA sets that act in cellular proliferation or cell cycle arrest. eLife 9, e58461 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Weng, A. P. et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  12. Palomero, T. et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 18261–18266 (2006).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kloetgen, A. et al. Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia. Nat. Genet. 52, 388–400 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Weng, A. P. et al. c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes Dev. 20, 2096–2109 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Medyouf, H. et al. High-level IGF1R expression is required for leukemia-initiating cell activity in T-ALL and is supported by Notch signaling. J. Exp. Med. 208, 1809–1822 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. González-García, S. et al. CSL-MAML-dependent Notch1 signaling controls T lineage-specific IL-7Rα gene expression in early human thymopoiesis and leukemia. J. Exp. Med. 206, 779–791 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Harper, A. E., Miller, R. H. & Block, K. P. Branched-chain amino acid metabolism. Annu. Rev. Nutr. 4, 409–454 (1984).

    Article  CAS  PubMed  Google Scholar 

  18. Aster, J. C. et al. Oncogenic forms of NOTCH1 lacking either the primary binding site for RBP-Jκ or nuclear localization sequences retain the ability to associate with RBP-Jκ and activate transcription. J. Biol. Chem. 272, 11336–11343 (1997).

    Article  CAS  PubMed  Google Scholar 

  19. King, B. et al. The ubiquitin ligase FBXW7 modulates leukemia-initiating cell activity by regulating MYC stability. Cell 153, 1552–1566 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kamata, S. et al. Dietary deprivation of each essential amino acid induces differential systemic adaptive responses in mice. Mol. Nutr. Food Res. 58, 1309–1321 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Knott, S. R. V. et al. Asparagine bioavailability governs metastasis in a model of breast cancer. Nature 554, 378–381 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Krall, A. S. et al. Asparagine couples mitochondrial respiration to ATF4 activity and tumor growth. Cell Metab. 33, 1013–1026.e6 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Taya, Y. et al. Depleting dietary valine permits nonmyeloablative mouse hematopoietic stem cell transplantation. Science 354, 1152–1155 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  24. D’Antona, G. et al. Branched-chain amino acid supplementation promotes survival and supports cardiac and skeletal muscle mitochondrial biogenesis in middle-aged mice. Cell Metab. 12, 362–372 (2010).

    Article  PubMed  CAS  Google Scholar 

  25. Cummings, N. E. et al. Restoration of metabolic health by decreased consumption of branched-chain amino acids. J. Physiol. 596, 623–645 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Solon-Biet, S. M. et al. Branched chain amino acids impact health and lifespan indirectly via amino acid balance and appetite control. Nat. Metab. 1, 532–545 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang, W. & Zou, W. Amino acids and their transporters in T cell immunity and cancer therapy. Mol. Cell 80, 384–395 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. White, P. J. et al. The BCKDH kinase and phosphatase integrate BCAA and lipid metabolism via regulation of ATP-citrate lyase. Cell Metab. 27, 1281–1293.e7 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Altman, B. J., Stine, Z. E. & Dang, C. V. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat. Rev. Cancer 16, 619–634 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nicklin, P. et al. Bidirectional transport of amino acids regulates mTOR and autophagy. Cell 136, 521–534 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rosilio, C. et al. l-type amino-acid transporter 1 (LAT1): a therapeutic target supporting growth and survival of T-cell lymphoblastic lymphoma/T-cell acute lymphoblastic leukemia. Leukemia 29, 1253–1266 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Wempe, M. F. et al. Metabolism and pharmacokinetic studies of JPH203, an l-amino acid transporter 1 (LAT1) selective compound. Drug Metab. Pharmacokinet. 27, 155–161 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Souers, A. J. et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Costa-Mattioli, M. & Walter, P. The integrated stress response: from mechanism to disease. Science 368, eaat5314 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Giacomello, M., Pyakurel, A., Glytsou, C. & Scorrano, L. The cell biology of mitochondrial membrane dynamics. Nat. Rev. Mol. Cell Biol. 21, 204–224 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. To, T. L. et al. A compendium of genetic modifiers of mitochondrial dysfunction reveals intra-organelle buffering. Cell 179, 1222-1238.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Herranz, D. et al. Metabolic reprogramming induces resistance to anti-NOTCH1 therapies in T cell acute lymphoblastic leukemia. Nat. Med. 21, 1182–1189 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kishton, R. J. et al. AMPK is essential to balance glycolysis and mitochondrial metabolism to control T-ALL cell stress and survival. Cell Metab. 23, 649–662 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Garcia-Bermudez, J. et al. Aspartate is a limiting metabolite for cancer cell proliferation under hypoxia and in tumours. Nat. Cell Biol. 20, 775–781 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Manoli, I. & Venditti, C. P. Disorders of branched chain amino acid metabolism. Transl. Sci. Rare Dis. 1, 91–110 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Haijes, H. A., van Hasselt, P. M., Jans, J. J. M. & Verhoeven-Duif, N. M. Pathophysiology of propionic and methylmalonic acidemias. Part 2: treatment strategies. J. Inherit. Metab. Dis. 42, 745–761 (2019).

    Article  PubMed  Google Scholar 

  42. Sivanand, S. & Vander Heiden, M. G. Emerging roles for branched-chain amino acid metabolism in cancer. Cancer Cell 37, 147–156 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  44. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Breese, M. R. & Liu, Y. NGSUtils: a software suite for analyzing and manipulating next-generation sequencing datasets. Bioinformatics 29, 494–496 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  48. Pavlova, N. N. et al. Translation in amino-acid-poor environments is limited by tRNAGln charging. eLife 9, e62307 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10-12 (2011).

    Article  Google Scholar 

  52. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all members of the Aifantis laboratory for discussions throughout this project; S. Tavazoie (Rockefeller University) for sharing the tRNA-seq protocol along with the probe sequences; and N. Sanjana (NYU/NY Genome Center) for sharing the CRISPR–Cas13d tools. P.T. was supported by an AACR Incyte Corporation Leukemia Research Fellowship and by a Young Investigator Grant from Alex’s Lemonade Stand Cancer Research Foundation. M.T.W. is supported by the Leukemia & Lymphoma Society Career Development Program, American Society of Hematology Restart Award, and The Jeffrey Pride Foundation for Pediatric Cancer Research and the Children’s Oncology Group Foundation. C.G. is supported by the NIH/NCI 1K99CA252602-01 grant and is a Special Fellow of the Leukemia & Lymphoma Society. We thank NYU Langone Health Microscopy Laboratory for consultation and assistance with transmission electron microscopy (and light microscopy) work. This shared resource is partially supported by the Cancer Center support grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. T.P. is supported by NIH grants (R37CA222504 and R01CA227649) and an American Cancer Society research scholar grant (RSG-17-200-01–TBE). A.T. is supported by the grants NC/NIH P01CA229086 and NCI/NIH R01CA252239. We thank the Genome Technology Center (GTC) for sequencing, and the Applied Bioinformatics Laboratories (ABL) for providing bioinformatics support. GTC and ABL are shared resources partially supported by the Cancer Center support grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. This work used computing resources at the NYU School of Medicine High Performance Computing Facility. I.A. is supported by the NCI/NIH (1P01CA229086, 1R01CA228135, R01CA216421, R01CA242020, R01CA173636 and 1R01HL159175), the Alex’s Lemonade Stand Cancer Research Foundation, the St. Baldrick’s Foundation, the Leukemia and Lymphoma Society (TRP no. 6580-20), the Edward P Evans Foundation and the NYSTEM program of the New York State Health Department.

Author information

Author notes

  1. These authors contributed equally: Andreas Kloetgen, Matthew T. Witkowski, Christina Glytsou

Authors and Affiliations

  1. Department of Pathology and Laura & Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY, USA

    Palaniraja Thandapani, Andreas Kloetgen, Matthew T. Witkowski, Christina Glytsou, Anna K. Lee, Eric Wang, Jingjing Wang, Sarah E. LeBoeuf, Kleopatra Avrampou, Thales Papagiannakopoulos, Aristotelis Tsirigos & Iannis Aifantis

  2. Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany

    Andreas Kloetgen

  3. Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY, USA

    Aristotelis Tsirigos

  4. Institute for Computational Medicine, NYU School of Medicine, New York, NY, USA

    Aristotelis Tsirigos

Authors
  1. Palaniraja Thandapani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Kloetgen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew T. Witkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christina Glytsou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna K. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jingjing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sarah E. LeBoeuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kleopatra Avrampou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Thales Papagiannakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Aristotelis Tsirigos
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Iannis Aifantis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.T. conceived, planned and performed most experiments, and co-wrote the manuscript. A.K. performed all the computational analyses with input from A.T. M.T.W. contributed to the conception of using valine-restricted diets and helped with performing the in vivo experiments and characterizing overall haematopoiesis following dietary deprivation/restriction of valine. C.G. helped with the mitochondrial experiments and performed the electron micrography sample preparation and analysis. A.K.L. performed the tRNA aminoacylation assays. E.W. helped with the tRNA CRISPR screen. J.W. and K.A. helped with the xenograft experiments. S.E.L. performed the plasma quantification of valine with input from T.P. I.A. directed and coordinated the study, oversaw the results and co-wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Palaniraja Thandapani or Iannis Aifantis.

Ethics declarations

Competing interests

I.A. is a consultant for Foresite Labs. A.T. is a scientific advisor to Intelligencia AI. T.P. has received honoraria and/or consulting fees from Calithera Biosciences, Vividion Therapeutics and research support from Bristol Myers Squibb, Dracen Pharmaceuticals and Agios Pharmaceuticals. All other authors declare no competing interests.

Peer review information

Nature thanks Reuven Agami and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 VARS mRNA expression is upregulated in primary T-ALL and is essential in T-ALL.

a, Gene Set Enrichment Analysis (GSEA) of genes involved in tRNA biogenesis in primary T-ALL compared to thymocytes and mature T cells subsets from healthy individuals. b, Screen validation of VARS using a competition-based proliferation assay in Cas9 expressing cell lines CUTLL1, Jurkat and DND41. Plotted are GFP percentages measured during 20 days in culture and normalized to day 4. Negative control sgRosa and three independent sgRNAs targeting VARS are shown in the graphs (n=3; mean ± SD of biological replicates). c, Box plot showing the expression of VARS mRNA in T-ALL patients (n=57 individuals) and thymocyte subsets (7 thymocyte and mature T-cell subsets derived from (n=3) independent donors) (quantile-normalized microarray results downloaded from GSE33469 and GSE33470). Boxes represent first and third quartiles and the line represent the median. Statistical evaluation between groups was performed using unpaired two-sided t-test followed by multiple testing correction (FDR) (Box plot values for CD34+CD1a-: min=6.93, max=6.99, q25%=6.94, q50%=6.96, q75%=6.97; for CD34+CD1a+: min=7.05, max=7.46, q25%=7.07, q50%=7.10, q75%=7.23; for CD4ISP: min=7.79, max=7.92, q25%=7.82, q50%=7.86, q75%=7.89; for DPCD3-: min=7.07, max=7.13, q25%=7.09, q50%=7.10, q75%=7.12; for DPCD3+: min=6.70, max=7.29, q25%=6.84, q50%=6.97, q75%=7.13; for CD3+CD4+: min=7.02, max=7.46, q25%=7.14, q50%=7.26, q75%=7.36; for CD3+CD8+: min=6.91, max=7.22, q25%=6.94, q50%=6.96, q75%=7.09; for T-ALL: min=8.98, max=11.28, q25%=10.29, q50%=10.47, q75%=10.63). d, Heatmap representation of changes in gene expression of all the amino acyl tRNA synthetases (RNA-seq) between primary T-ALL and naïve CD4 T cells. Heatmap shows log2 fold-change (FC) of FPKM of T-ALL against average CD4 T cells.

Source data

Extended Data Fig. 2 tRNA-Val-TAC expression is upregulated in primary T-ALL and is strongly essential for T-ALL survival.

a, Scatter plot showing the total expression of tRNAs in primary T-ALL samples (n=5 biological replicates) in the x-axis with differential expression of each tRNA species relative to mature CD4 T cell subset (n=2 biological replicates) in y-axis. b, Scatter plot showing the total expression of tRNAs in T-ALL cell lines (n=4 independent cell lines) in the x-axis with differential expression of each tRNA species relative to mature CD4 T cell subset (n=2 biological replicates) in y-axis. c, Schematic depicting the CRISPR Cas9 targeting of the anti-codon loop of tRNA-Val-TAC family genes. d, Sanger sequencing trace file of tRNA-Val-TAC genomic region in sgROSA and sgVal-TAC transduced CUTLL1 Cas9 cells. e, Size of indels along with their frequencies observed as quantified by the Synthego ICE program. f, Competition based proliferation assay in Cas9 expressing T-ALL cell lines CUTLL1, Jurkat and DND41. Plotted are GFP+ percentages measured during 16 days in culture and normalized to day 4. Negative control sgRosa and sgRNA targeting Val-TAC tRNA family are shown in the graphs (mean ± SD of n=3 independent experiments).

Source data

Extended Data Fig. 3 Oncogenic NOTCH1 signaling upregulates valine tRNA biogenesis.

a, GRO-Seq cluster analysis was performed by applying dimensional reduction with a manifold approximation and projection (UMAP) followed by k-means clustering with k=8. Clusters 2 (n = 2105 genes) and 3 (n = 2177 genes) are highlighted in orange and red respectively. b, Heatmap representation of mRNA expression changes measured by GRO-Seq within k-means clusters 2 and 3. The clusters define γSI responding genes with fast re-establishment of expression post inhibitor wash-off (see Extended Data Fig. 3a for median expression levels of all k-means clusters). Heatmap shows log2 FC of FPKM of individual GRO-Seq replicates against average DMSO signal of the respective genes. c, Snapshots of H3K4me3 ChIP-Seq on tRNA-Val-TAC 1-1 promoter and d, IL7R promoter as fold-enrichment over input. Below GRO-Seq in CUTLL1 cells upon DMSO, γSI treatment and release after drug washout as counts-per-million (cpm). e, Volcano plot showing differential expression of tRNA pathway genes (153 genes) in CUTLL1 (left) and KOPTK1 (right) upon treatment with DMSO or γSI for 72h. (n=2; FDR < 0.05 and log2FC > 0.58 or < -0.58), cytoplasmic aminoacyl tRNA synthetases are highlighted in green. Statistical evaluation was performed using two-sided edgeR analysis (function glmQLFTest) followed by multiple testing correction (FDR). f, Relative expression of VARS mRNA by qPCR analysis in human T-ALL cell line TAL1 following treatment with γSI for 72h. (mean ± SD of n=3 independent replicates, two-sided unpaired t-test). g, Volcano plot of differential expression of tRNA genes in CUTLL1 upon treatment with DMSO or γSI for 4 days. Valine tRNA genes are highlighted in green (n=2; FDR < 0.1 and log2FC > 0.58 or < -0.58). Statistical evaluation was performed using two-sided edgeR analysis (function glmQLFTest) followed by multiple testing correction (FDR). h, Val-tRNA and control Leu-tRNA aminoacylation analysis of CUTLL1 cells treated with either DMSO or γSI (mean ± SD of n=3 independent replicates, two-sided unpaired t-test).

Source data

Extended Data Fig. 4 Dietary valine deprivation/restriction reduce leukemic burden and increases survival in mice.

a, Kaplan-Meier curve representing morbidity of recipient mice transplanted with ckit+ cells transduced with either pMIG (n=8) or NOTCH1-ΔE-IRES-GFP (n=9) (Log-rank test, two-sided). b, Representative image of spleen from mice secondary transplanted with NOTCH-ΔE-GFP+ tumors and fed either control diet or valine-deficient diet for 3 weeks. c, Absolute leukemic burden in peripheral blood from mice fed either control diet or valine deficient diet for 3 weeks (mean ± SD of n=5 animals; two-sided unpaired t-test). d, Percentage annexin V positive cells (mean ± SD of n=3 animals; two-sided unpaired t-test). e, Absolute leukemic burden in peripheral blood from the mice transplanted with NOTCH-ΔE-GFP+ tumors and fed complete amino acid diet (n=7) or diets deficient in either valine (n=7), lysine (n=6) or asparagine (n=6); (mean ± SD of individual animals; two-sided unpaired t-test). f, Kaplan–Meier survival graph of mice represented in (e) (Log-rank test, two-sided). g, Representative image of spleen from mice fed different diets in (e) and (f). h, Peripheral tumor burden in mice fed valine deficient diet for 2 weeks at late stages of tumor development (n=10; two-sided unpaired t-test). i, Percentage GFP+ cells in peripheral blood of mice transplanted with (BCR-ABL-GFP+) tumors and fed either control diet or valine-deficient diet for 2 weeks (mean ± SD of n = 4 animals; two-sided unpaired t-test). j, Kaplan–Meier survival graph of NSG mice transplanted with patient derived xenograft T-ALL PDX_1 and fed either control diet (n=5) or valine deficient diet (n=5) (Log-rank test, two-sided). Death owing to leukemia or toxicity of valine deprivation is highlighted. k, Body weights of NSG mice transplanted with T-ALL PDX_1 and fed either control valine proficient diet or valine deficient diet (mean ± SD of n=5 animals; two-sided unpaired t-test). l, Schematic representation of secondary transplant experiment to test sensitivity of T-ALL to decreasing levels of dietary valine (created with Biorender.com). m, Body weights of C57BL/6 mice secondary transplanted with NOTCH-ΔE GFP+ tumors and fed either control valine proficient diet (n=5) or valine deficient diet (n=5) or valine deficient diet substituted with 0.8g/l valine (n=4) or 0.4g/l in drinking water (n=5) (mean ± SD of individual animals; two-sided unpaired t-test).

Source data

Extended Data Fig. 5 Dietary valine restriction does not affect hematopoiesis.

a, Body weight, White blood cell (WBC), red blood cell (RBC), lymphocyte, hemoglobin (Hgb) and Platelet counts in mice fed either control valine proficient diet (8g/Kg valine), valine deficient diet (0g/Kg valine) or valine deficient diet (0g/Kg valine) substituted with 0.8g/l valine in drinking water (mean ± SD of n=5 animals each condition; two-sided unpaired t-test) for 4 weeks. b, Total cell numbers of bone marrow (tibia+femur), spleen and thymus from mice fed either a control valine proficient diet (8g/Kg valine), valine deficient diet (0g/Kg valine) or valine deficient diet (0g/Kg valine) substituted with 0.8g/l valine in drinking water for 4 weeks (mean ± SD of n=4 animals each condition; two-sided unpaired t-test). c, Plasma valine levels in peripheral blood serum of mice fed either control valine proficient diet (8g/Kg valine), valine deficient diet (0g/Kg valine) or valine deficient diet (0g/Kg valine) substituted with 0.8g/l valine (mean ± SD of n=5 animals each condition; two-sided unpaired t-test).

Source data

Extended Data Fig. 6 Dietary valine restriction does not affect LSK and thymocytes.

a, Frequency of LSK and different progenitors (GMP, CMP and MEP) from mice fed either a complete valine proficient diet (8g/Kg valine), valine deficient diet (0g/Kg valine) or valine deficient (0g/Kg valine) diet substituted with 0.8g/l valine in drinking water (right) (mean ± SD of n=4 animals each condition; two-sided unpaired t-test). b, Representative flow cytometry plots of the different hematopoietic compartments highlighted in (a). c, Frequency of intra-thymic T-cell population represented as percentage of CD45+ cells from mice fed either a valine proficient diet (8g/Kg valine), valine deficient diet (0g/Kg valine) or valine deficient diet (0g/Kg valine) substituted with 0.8g/l valine in drinking water for 4 weeks (mean ± SD of n=4 animals each condition; Two-way Anova analysis with Tukey multiple test correction). d, Representative flow cytometry plots of the different thymocyte subsets from (c).

Source data

Extended Data Fig. 7 Genome-wide CRISPR maps positive and negative genetic interactions with valine restriction.

a, Val-tRNA and control Leu-tRNA aminoacylation analysis of CUTLL1 cells cultured in either standard valine media (20mg/l) or low valine media (2mg/l and 1mg/l) (mean ± SD of n=3 independent replicates, two-sided unpaired t-test). b, Gene ranks plotted based on Δ CERES dependency score between low valine media and standard valine media conditions for 4 days. The top negatively and positively selected genes in low valine media are highlighted in blue and red respectively. Genes involved in Branched chain amino acid metabolism are highlighted in green. c, Validation of SLC7A5 dependency and d, BCL2 in low valine media relative to standard valine media using a competition-based survival assay in Cas9 expressing T-ALL cell lines. Plotted are GFP percentages measured during 20 days in culture and normalized to day 4. Negative control sgRosa and two independent sgRNAs targeting SLC7A5 and BCL2 are shown in the graphs (mean ± SD of n=3 independent experiments). e, IC50 curve of SLC7A5 inhibitor JPH 203 and f, BCL2 inhibitor venetoclax of T-ALL cell lines in either standard valine and low valine media. Representative of (n=2 of independent experiments).

Source data

Extended Data Fig. 8 Valine restriction reduces translation of mitochondrial electron transport chain proteins.

a, Ribosome protected fragment (RPF) length distribution of the Ribo-Seq datasets (n=2 biological replicates each condition). b, Relative CDS distribution of the RPF fragments (n=2 biological replicates each condition). c, Percentage valine (left) and lysine (right) codons between mRNAs with lowest translational efficiency (TE; bottom 5%) and mRNAs with no significant changes in translational efficiency (log2FC > -0.1375 and < 0.1375) between leukemic cells isolated from mice fed control diet and valine restricted diet (n=2 biological replicates each condition) (Box plot values for valine: for genome 0.8g/l: min=1.42, max=13.73, q25%=4.99, q50%=5.99, q75%=7.09; for bottom 5% 0.8g/l: min=0.00, max=11.84, q25%=5.03, q50%=6.10, q75%=7.42; for genome 0.4g/l: min=0.00, max=17.07, q25%=5.04, q50%=6.09, q75%=7.20; for bottom 5% 0.4g/l: min=0.00, max=11.75, q25%=5.21, q50%=6.18, q75%=7.39; for genome 0g/l: min=0.00, max=15.79, q25%=5.02, q50%=6.9, q75%=7.19; for bottom 5% 0g/l: min=0.00, max=17.07, q25%=5.08, q50%=6.29, q75%=7.36) (Box plot values for lysine: for genome 0.8g/l: min=0.00, max=29.02, q25%=4.28, q50%=5.75, q75%=7.41; for bottom 5% 0.8g/l: min=0.00, max=14.85, q25%=3.77, q50%=5.36, q75%=7.14; for genome 0.4g/l: min=0.34, max=27.93, q25%=4.36, q50%=5.87, q75%=7.43; for bottom 5% 0.4g/l: min=0.47, max=14.85, q25%=3.92, q50%=5.45, q75%=7.11; for genome 0g/l: min=0.47, max=27.92, q25%=4.43, q50%=6.05, q75%=7.67; for bottom 5% 0g/l: min=0.00, max=15.76, q25%=3.63, q50%=5.26, q75%=6.96). d, Volcano plots of Δ translational efficiency in NOTCH-ΔE-GFP+ cells isolated from mice fed different levels of dietary valine (n=2 of biological replicates each condition). (p-value < 0.05 and log2FC > 0.58 or < -0.58). e, STRING Network Pathway enrichment analysis of the 162 mRNAs with reduced translational efficiencies in mice fed valine restricted diet relative to mice fed control valine proficient diet. f, Snapshot of Ribosome protected fragment (RPF) and total RNA tracks for gene Ndufs7 and g, Ndufs3. h, RPKM values of total RNA and RPF read counts for Ndufs3 and Ndufs7 (n=2 of biological replicates each condition). i, Schematic of the reporter constructs expressing GFPd2 in fusion with either NDUFB1 (high valine content) or NDUFS5 (low valine content). mCherry serves as an internal control. j, CUTLL1 and Jurkat cells infected with the reporter constructs were cultured in either complete RPMI media or media lacking either valine or tryptophan for 8h. Quantification of GFPd2 fluorescence normalized to mCherry signal and cycloheximide treatment are plotted. (mean ± SD of n=4 independent replicates; two-sided unpaired t-test.). k, Gene signatures upregulated in control diet (8g/Kg valine) is shown in red whereas gene signatures upregulated in mice fed reduced valine is highlighted in green. l, Immunoblots for ATF4 and actin (same gel) from NOTCH-ΔE-GFP+ cellular lysates isolated from mice fed different levels of dietary valine (n=2 biological replicates). For gel source data, see Supplementary Fig. 1. RPKM, reads per kilobase transcript per million mapped reads.

Source data

Extended Data Fig. 9 Valine tRNA biogenesis and bioavailability regulates levels of mitochondrial complex I.

a, Blue Native (BN)-PAGE analysis (same gel, for gel source data, see Supplementary Fig. 1) and b, maximal respiration of KOPTK1 cells cultured in different levels of valine for 72 h. c, Oxygen consumption rate (OCR) and d, Maximal respiration of CUTLL1 cells cultured in different levels of valine for 72 h (mean ± SD of n=5 independent replicates; two-sided unpaired t-test). e, Relative expression of VARS mRNA by qPCR analysis following 4 days of doxycycline treatment to induce Cas13d expression. (n=2 independent replicates). f, Competition based proliferation assay to validate loss of fitness following VARS knockdown in CUTLL1 Cas13d cells. Plotted are GFP percentages measured during 20 days in culture and normalized to day 0 of doxycycline treatment. Negative control sgRosa and three independent sgRNAs targeting VARS are shown in the graphs (mean ± SD of n=3 independent replicates). g, BN-PAGE (top) analysis of CUTLL1 cells expressing inducible Cas13d and sgRNA_1 targeting VARS transcript. SDS-PAGE and immunoblots confirming VARS knockdown (same gel, for gel source data, see Supplementary Fig. 1). h, NAD+/NADH ratio of CUTLL1 cells cultured in RPMI with different levels of valine for 72 h. Representative of (n=2) independent experiments. i, MitoSox staining of CUTLL1, KOPTK1 cells cultured in RPMI with different levels of valine for 72 h and analyzed by flow cytometry (left, middle). CUTLL1 Cas13d cells transduced with three independent sgRNAs targeting VARS transcript was treated with doxycycline to induce Cas13 expression. Day 8 of doxycycline treatment, cells were stained with MitoSOX and analyzed by flow cytometry (right). Graphs show Mito-SOX mean fluorescence intensity (MFI). Representative of (n=3) independent experiments (mean ± SD of n=4 technical replicates). j, Electron micrograph from CUTLL1 cells illustrating the mitochondrial cristae structure. Arrow denotes the parameter of maximal Cristae Lumen Width (CLW) quantified in this study. k, Representative electron micrographs of CUTLL1 cells cultured in RPMI with different levels of valine for 72 h. l, Representative electron micrographs of CUTLL1-Cas13d cells transduced with either sgROSA or sgRNA targeting VARS and doxycycline treated for 8 days to induce Cas13d expression (left). Quantification of maximal cristae width in 10 randomly selected mitochondria from 15 cells (right) (n=110 cristae; mean ± SD; two-sided unpaired t-test).

Source data

Extended Data Fig. 10 Loss of pathway intrinsic ETC proteins promotes resistance to valine restriction.

a, Validation of NDUFB4 as a positively selected gene in low valine media relative to standard valine media using a competition-based proliferation assay in Cas9 expressing cell lines CUTLL1 and DND41. Plotted are GFP percentages measured during 20 days in culture and normalized to day 4. Negative control sgRosa and two independent sgRNAs targeting NDUFB4 are shown in the graphs (mean ± SD of n=3 independent replicates). b, Schematic depicting how upregulated valine tRNA biogenesis regulates mitochondrial bio-energetics in T-ALL (created with Biorender.com).

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Fig. 1 (uncropped gel images), Supplementary Figs. 2–9 (FACS plots gating strategy) and Supplementary Custom Codes.

Reporting Summary

Supplementary Table 1

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Thandapani, P., Kloetgen, A., Witkowski, M.T. et al. Valine tRNA levels and availability regulate complex I assembly in leukaemia. Nature 601, 428–433 (2022). https://doi.org/10.1038/s41586-021-04244-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04244-1

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer