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.

Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer

Nature Metabolism volume 4, pages 1119–1137 (2022)Cite this article

Subjects

Abstract

Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11–MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM’s machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: CLIM: a systems, multiomics and machine learning integrated-precision oncology platform that identifies collateral lethal metabolic targets.
Fig. 2: Multi-objective metabolic flux analysis identifies MTHFD2 as a collateral lethal target in UQCR11-null ovarian cancers with in vitro, clinical and pan-cancer implications.
Fig. 3: Metabolic flux analysis with isotope tracing using deuterium-labelled substrates demonstrates metabolic flux compensation through a paralogue pathway.
Fig. 4: Dynamic flux analysis with parallel tracers to quantify oxidative MTHFD2 flux.
Fig. 5: Metabolic profiling and pathway analysis reveal metabolic compensation and systemic metabolic changes in UQCR11-intact and -null ovarian cancers, respectively, following inhibition of MTHFD2.
Fig. 6: MTHFD2 is a potential collateral lethal target across varied genetic backgrounds and microenvironment pressures.
Fig. 7: Remission of UQCR11-null ovarian tumours on collateral lethal targeting of MTHFD2 in vivo.

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Source data for all figures are provided with this paper. RNA-seq data were obtained from GSE146026 (ref. 80), GSE118828 (ref. 85) and GSE115635 (ref. 88). TCGA sample data were obtained from the CBioPortal for datasets ov_tcga_pan_can_atlas_2018 (ovarian cancer), ucec_tcga_pan_can_atlas_2018 (uterine endometrial carcinoma) and ucs_tcga_pan_can_atlas_2018 (uterine serous carcinoma). Cell line genomics data were obtained from the CBioPortal using datasets cellline_nci60 and ccle_broad_2019 datasets. CCLE metabolomics data were obtained from https://doi.org/10.1038/s41591-019-0404-8. Cancer DepMap data were obtained from the DepMap Portal (CRISPR Public 20Q1). Metabolic pathways and genes were obtained from KEGG for Homo sapiens (kegg.jp). All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

Input data files used for all scripts and the generated output files are available on GitHub at https://github.com/DNagrathLab/CLIM/. Model reconstruction was performed using scripts from Quek and Turner89. MOMFA scripts were developed using the COBRA toolbox (v.3.0)72. The scripts for tumour stratification, multilayer machine learning, random survival forests and bulk and single-cell RNA-seq analysis developed for this study are available on GitHub at https://github.com/DNagrathLab/CLIM/.

References

  1. Pertesi, M. et al. Essential genes shape cancer genomes through linear limitation of homozygous deletions. Commun. Biol. 2, 262 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dey, P. et al. Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature 542, 119–123 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Muller, F. L., Aquilanti, E. A. & DePinho, R. A. Collateral lethality: a new therapeutic strategy in oncology. Trends Cancer 1, 161–173 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Muller, F. L. et al. Passenger deletions generate therapeutic vulnerabilities in cancer. Nature 488, 337–342 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sanderson, S. M., Mikhael, P. G., Ramesh, V., Dai, Z. & Locasale, J. W. Nutrient availability shapes methionine metabolism in p16/MTAP-deleted cells. Sci. Adv. 5, eaav7769 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ryland, G. L. et al. Loss of heterozygosity: what is it good for? BMC Med. Genet. 8, 45 (2015).

    Google Scholar 

  7. Liu, Y. et al. TP53 loss creates therapeutic vulnerability in colorectal cancer. Nature 520, 697–701 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hart, T., Koh, C. & Moffat, J. Coessentiality and cofunctionality: a network approach to learning genetic vulnerabilities from cancer cell line fitness screens. Preprint at bioRxiv https://doi.org/10.1101/134346 (2017).

  9. Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wainberg, M. et al. A genome-wide atlas of co-essential modules assigns function to uncharacterized genes. Nat. Genet. 53, 638–649 (2021).

  11. Ku, A. A. et al. Integration of multiple biological contexts reveals principles of synthetic lethality that affect reproducibility. Nat. Commun. 11, 2375 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bayraktar, E. C. et al. Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism. Nat. Metab. 2, 487–498 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Molina, J. R. et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat. Med. 24, 1036–1046 (2018).

    Article  CAS  PubMed  Google Scholar 

  14. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lee, J. Y. et al. A distinct region of chromosome 19p13.3 associated with the sporadic form of adenoma malignum of the uterine cervix. Cancer Res. 58, 1140–1143 (1998).

    CAS  PubMed  Google Scholar 

  16. Wang, Z. J. et al. Allele loss and mutation screen at the Peutz–Jeghers (LKB1) locus (19p13.3) in sporadic ovarian tumours. Br. J. Cancer 80, 70–72 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chang, K. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  CAS  Google Scholar 

  18. Fernández-Vizarra, E. & Zeviani, M. Nuclear gene mutations as the cause of mitochondrial complex III deficiency. Front. Genet. 6, 134 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Owens, K. M., Kulawiec, M., Desouki, M. M., Vanniarajan, A. & Singh, K. K. Impaired OXPHOS complex III in breast cancer. PLoS ONE 6, e23846 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hsu, C.-C., Tseng, L.-M. & Lee, H.-C. Role of mitochondrial dysfunction in cancer progression. Exp. Biol. Med. 241, 1281–1295 (2016).

    Article  CAS  Google Scholar 

  21. Acı́n-Pérez, R. et al. Respiratory complex III is required to maintain complex I in mammalian mitochondria. Mol. Cell 13, 805–815 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  22. DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci. Adv. 2, e1600200 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Weinberg, S. E. & Chandel, N. S. Targeting mitochondria metabolism for cancer therapy. Nat. Chem. Biol. 11, 9–15 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kawai, J. et al. Discovery of a potent, selective, and orally available MTHFD2 inhibitor (DS18561882) with in vivo antitumor activity. J. Med. Chem. 62, 10204–10220 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Martinez-Reyes, I. et al. Mitochondrial ubiquinol oxidation is necessary for tumour growth. Nature 585, 288–292 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Martínez-Reyes, I. et al. TCA cycle and mitochondrial membrane potential are necessary for diverse biological functions. Mol. Cell 61, 199–209 (2016).

    Article  PubMed  Google Scholar 

  27. Sommer, N. et al. Bypassing mitochondrial complex III using alternative oxidase inhibits acute pulmonary oxygen sensing. Sci. Adv. https://doi.org/10.1126/sciadv.aba0694 (2020).

  28. Zhu, Z. & Leung, G. K. K. More than a metabolic enzyme: MTHFD2 as a novel target for anticancer therapy? Front. Oncol. https://doi.org/10.3389/fonc.2020.00658 (2020).

  29. Liu, M. et al. The identification of key genes and pathways in glioma by bioinformatics analysis. J. Immunol. Res. 2017, 1278081 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Contreras, C. M. et al. Lkb1 inactivation is sufficient to drive endometrial cancers that are aggressive yet highly responsive to mTOR inhibitor monotherapy. Dis. Model. Mech. 3, 181–193 (2010).

    Article  PubMed Central  Google Scholar 

  31. Peña, C. G. & Castrillón, D. H. In Molecular Genetics of Endometrial Carcinoma (ed. Ellenson, L. H.) 211–241 (Springer International Publishing, 2017).

  32. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lin, Y. H. et al. An enolase inhibitor for the targeted treatment of ENO1-deleted cancers. Nat. Metab. 2, 1413–1426 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Leonard, P. G. et al. SF2312 is a natural phosphonate inhibitor of enolase. Nat. Chem. Biol. 12, 1053–1058 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fox, J. T. & Stover, P. J. In Vitamins & Hormones Vol. 79 (ed. Litwack, G.) 1–44 (Academic Press, 2008).

  36. Lewis, CarolineA. et al. Tracing compartmentalized NADPH metabolism in the cytosol and mitochondria of mammalian cells. Mol. Cell 55, 253–263 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gustafsson Sheppard, N. et al. The folate-coupled enzyme MTHFD2 is a nuclear protein and promotes cell proliferation. Sci. Rep. 5, 15029 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ducker, G. S. et al. Reversal of cytosolic one-carbon flux compensates for loss of the mitochondrial folate pathway. Cell Metab. 23, 1140–1153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Morscher, R. J. et al. Mitochondrial translation requires folate-dependent tRNA methylation. Nature 554, 128–132 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang, L. et al. Serine catabolism feeds NADH when respiration is impaired. Cell Metab. 31, 809–821 (2020).

  41. Fan, J. et al. Quantitative flux analysis reveals folate-dependent NADPH production. Nature 510, 298–302 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lim, E. W., Parker, S. J. & Metallo, C. M. In Metabolic Flux Analysis in Eukaryotic Cells: Methods and Protocols (ed. Nagrath, D.) 51–71 (Springer US, 2020).

  43. Ma, Y., Wang, L. & Jia, R. The role of mitochondrial dynamics in human cancers. Am. J. Cancer Res. 10, 1278–1293 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Macleod, K. F. Mitophagy and mitochondrial dysfunction in cancer. Annu. Rev. Cancer Biol. 4, 41–60 (2020).

    Article  Google Scholar 

  45. Li, H. et al. The landscape of cancer cell line metabolism. Nat. Med. 25, 850–860 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Birsoy, K. et al. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Khutornenko Anastasia, A. et al. Pyrimidine biosynthesis links mitochondrial respiration to the p53 pathway. Proc. Natl Acad. Sci. USA 107, 12828–12833 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Spinelli, J. B. et al. Fumarate is a terminal electron acceptor in the mammalian electron transport chain. Science 374, 1227–1237 (2021).

  49. Lee, W. D., Mukha, D., Aizenshtein, E. & Shlomi, T. Spatial-fluxomics provides a subcellular-compartmentalized view of reductive glutamine metabolism in cancer cells. Nat. Commun. 10, 1351 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yang, L. et al. Targeting stromal glutamine synthetase in tumors disrupts tumor microenvironment-regulated cancer cell growth. Cell Metab. 24, 685–700 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Viswanathan, S. R. et al. Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer. Nat. Genet. 50, 937–943 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kryukov, G. V. et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351, 1214–1218 (2016).

    Article  PubMed Central  Google Scholar 

  53. Neggers, J. E. et al. Synthetic lethal interaction between the ESCRT paralog enzymes VPS4A and VPS4B in cancers harboring loss of chromosome 18q or 16q. Cell Rep. 33, 108493 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nilsson, R. et al. Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Nat. Commun. 5, 3128 (2014).

    Article  PubMed  Google Scholar 

  55. Wei, Y. et al. The effect of MTHFD2 on the proliferation and migration of colorectal cancer cell lines. Onco Targets Ther. 12, 6361–6370 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wan, X. et al. Cisplatin inhibits SIRT3-deacetylation MTHFD2 to disturb cellular redox balance in colorectal cancer cell. Cell Death Dis. 11, 649 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Shukla, K. et al. MTHFD2 blockade enhances the efficacy of β-lapachone chemotherapy with ionizing radiation in head and neck squamous cell cancer. Front. Oncol. 10, 536377 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ben-Sahra, I., Hoxhaj, G., Ricoult, S. J. H., Asara, J. M. & Manning, B. D. mTORC1 induces purine synthesis through control of the mitochondrial tetrahydrofolate cycle. Science 351, 728–733 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bindra, S. et al. Mitochondria in epithelial ovarian carcinoma exhibit abnormal phenotypes and blunted associations with biobehavioral factors. Sci. Rep. 11, 11595 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Judge, M. T. et al. Continuous in vivo metabolism by NMR. Front. Mol. Biosci. https://doi.org/10.3389/fmolb.2019.00026 (2019).

  61. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  Google Scholar 

  62. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  63. AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

  64. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. Krzywinski, M. I. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

  66. Creed, J. H., Gerke, T. A. & Berglund, A. E. MatSurv: survival analysis and visualization in MATLAB. J. Open Source Softw. https://doi.org/10.21105/joss.01830 (2020).

  67. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2018).

    Article  PubMed Central  Google Scholar 

  69. Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Nagrath, D. et al. Soft constraints-based multiobjective framework for flux balance analysis. Metab. Eng. 12, 429–445 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Nagrath, D. et al. Integrated energy and flux balance based multiobjective framework for large-scale metabolic networks. Ann. Biomed. Eng. 35, 863–885 (2007).

    Article  PubMed  Google Scholar 

  72. Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639–702 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Messac, A., Ismail-Yahaya, A. & Mattson, C. A. The normalized normal constraint method for generating the Pareto frontier. Struct. Multidiscip. Optim. 25, 86–98 (2003).

    Article  Google Scholar 

  74. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 785–794 (ACM Digital, 2016).

  75. Achreja, A., Meurs, N. & Nagrath, D. Quantifying metabolic transfer mediated by extracellular vesicles using Exo-MFA: an integrated empirical and computational platform. Methods Mol. Biol. 2088, 205–221 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Achreja, A. et al. Exo-MFA – a 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism. Metab. Eng. 43, 156–172 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Yang, L. et al. Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancer. Mol. Syst. Biol. 10, 728 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zhao, H. et al. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. eLife 5, e10250 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhu, Z. et al. Tumour-reprogrammed stromal BCAT1 fuels branched-chain ketoacid dependency in stromal-rich PDAC tumours. Nat. Metab. https://doi.org/10.1038/s42255-020-0226-5 (2020).

  80. Izar, B. et al. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 26, 1271–1279 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Wickham, H. et al. ggplot2: Elegant Graphics for Data Analysis 2nd edn. Use R! (Springer-Verlag, 2016).

  84. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012); correction 2, 960 (2012).

  85. Shih, A. J. et al. Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq. PLoS ONE 13, e0206785 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Yeung, T. L. et al. Systematic identification of druggable epithelial-stromal crosstalk signaling networks in ovarian cancer. J. Natl Cancer Inst. 111, 272–282 (2019).

    Article  PubMed  Google Scholar 

  89. Quek, L. E. & Turner, N. Using the human genome-scale metabolic model Recon 2 for steady-state flux analysis of cancer cell metabolism. Methods Mol. Biol. 1928, 479–489 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

D.N. is supported by NCI grant nos. R01CA227622 and R01CA204969, and D.N. and X.L. by R01CA222251. D.N. is also supported by grants from the Rogel Cancer Center and the Forbes Institute for Cancer Discovery. The Orbitrap Fusion Tribrid Lumos mass spectrometer used in this work is supported by grant no. S10OD021619 and operated by the Chemistry Mass Spectrometry Facility, University of Michigan. A.A. is partially supported by the University of Michigan Precision Health Scholars award. S.D.M., A.A. and J.H.J. are funded by the Breast Cancer Research Foundation.

Author information

Author notes

  1. These authors contributed equally: Abhinav Achreja, Tao Yu.

  2. These authors jointly supervised this work: Xiongbin Lu, Deepak Nagrath.

Authors and Affiliations

  1. Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA

    Abhinav Achreja, Anjali Mittal, Srinadh Choppara, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman, Reva Kulkarni & Deepak Nagrath

  2. Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    Abhinav Achreja, Srinadh Choppara, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman & Deepak Nagrath

  3. Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA

    Abhinav Achreja, Anjali Mittal, Srinadh Choppara, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman, Sarah Owen, Chitra Subramanian, Sunitha Nagrath & Deepak Nagrath

  4. Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    Abhinav Achreja, Michele Cusato, Chitra Subramanian, Max S. Wicha, Sofia D. Merajver, Sunitha Nagrath, Kathleen R. Cho, Analisa DiFeo & Deepak Nagrath

  5. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Tao Yu & Xiongbin Lu

  6. Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Anjali Mittal, Olamide Animasahun, Sarah Owen, Sunitha Nagrath & Deepak Nagrath

  7. Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA

    Reva Kulkarni

  8. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Michele Cusato, Kathleen R. Cho & Analisa DiFeo

  9. Hematology and Oncology, University of Illinois, Chicago, IL, USA

    Frank Weinberg

  10. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA

    Hye Kyong Kweon

  11. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Max S. Wicha & Sofia D. Merajver

  12. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA

    Kathleen R. Cho & Analisa DiFeo

  13. Melvin & Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA

    Xiongbin Lu

  14. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA

    Xiongbin Lu

Authors
  1. Abhinav Achreja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anjali Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Srinadh Choppara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olamide Animasahun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Minal Nenwani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fulei Wuchu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Noah Meurs
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aradhana Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jin Heon Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Itisam Sarangi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Anusha Jayaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sarah Owen
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Reva Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michele Cusato
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Frank Weinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Hye Kyong Kweon
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Chitra Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Max S. Wicha
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Sofia D. Merajver
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Sunitha Nagrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Kathleen R. Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Analisa DiFeo
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Xiongbin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Deepak Nagrath
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.A. and D.N. designed the scientific approach, developed the CLIM platform and its source code and wrote the manuscript. T.Y. and X.L. developed the in vitro and in vivo models for validation and performed the corresponding functional experiments. A.Mittal and A.A. designed the machine learning approach. O.A. and A.A. developed the bioinformatics approach for bulk and single-cell RNA-seq analyses. S.C., M.N. and F.Wuchu performed tracer experiments. S.C., M.N. and C.S. performed OCR assays. O.A., M.N., F.Wuchu and A.A. developed the metabolomics techniques with GC–MS and liquid chromatography–tandem mass spectrometry for analysis of metabolic and tracer profiles. N.M., A.Mohan, J.H.J., I.S., A.J. and S.O. maintained cell lines and performed metabolite extractions for tracer experiments. T.Y., N.M., A.Mittal and O.A. helped in writing of the manuscript. R.K. contributed to CLIM source code development. M.C. and F.Weinberg contributed to clinical and survival analysis of ovarian and endometrial cancers. H.K.K. and A.A. developed techniques and analysed high-resolution mass spectrometry data for combinatorial deuterium tracer experiments. A.D., S.N., M.S.W., K.R.C. and S.D.M. provided technical expertise in designing and executing the experiments and helped in writing of the manuscript.

Corresponding authors

Correspondence to Xiongbin Lu or Deepak Nagrath.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Navdeep Chandel, Rune Linding and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Isabella Samuelson and George Caputa, in collaboration with the Nature Metabolism team.

Additional information

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

Extended data

Extended Data Fig. 1 Collateral lethal target identification in paralogous metabolic pathways.

(a) Most significant focal deletions in chromosomes 1-22, identified by GISTIC2.0, within deleted regions for breast, pancreatic, prostate, hepatocellular cancer, and acute myeloid leukemia (b) Number of metabolic genes found in frequently deleted chromosomal regions that have known paralogs for cancers shown in (a). (c) Number of metabolic gene deletions identified in various cancers that may or may not have known genetic paralog. (d) Identification of paralogous metabolic pathways: Several metabolic enzymes have isoforms encoded by genetic paralogs, for example, Gene X1 and X2. When Gene X2 does not exist or its inhibition is ineffective, redundancy via pathway encoded by Gene Z1, can compensate for loss of Gene X1. To obtain a collateral lethal effect, Gene Z1 encoding the paralogous metabolic pathway must be identified and targeted. (e) Correlation of UQCR11 copy-number with UQCR11 mRNA expression in ovarian cancer tumors from the TCGA HGSOC dataset. Data analyzed using Spearman’s rank correlation test.

Source data

Extended Data Fig. 2 CLIM workflow integrates multiobjective metabolic flux analysis and machine learning to identify and validate collateral lethal metabolic targets.

(a) Definition of Super Pareto, Infeasible Pareto and Blocked Pareto in context of metabolic deletions. (b) Schematic describing the reconstruction of genome-scale metabolic networks for cancers based on metabolic gene expressions and empirically observed extracellular fluxes. Recon 2.2 is used as the input model with all gene-protein-reaction (GPR) relationships. Average expression of cancer cell-lines and tumor samples along with extracellular fluxes are used to reconstruct a contextual model using the iMAT algorithm. Further, automated and manual curation is performed to edit reactions to fix gaps in the model. Final reconstruction is performed by iMAT’s extension rxnMILP algorithm. (c) Derivation of Pareto area under the curve (PAUC) to quantify overall metabolic changes across the entire Pareto frontier between deleted- and non-deleted conditions. (d) Derivation of therapeutic window index (TWI) using sensitivity analysis to quantify influence of metabolic fluxes on metabolic objective functions. (e) Decision-making heuristic for selection of collateral lethal candidate targets by analyzing PAUC and TWI scores.

Extended Data Fig. 3 Discovery of top-ranking collateral lethal candidate pathways by MOMFA within CLIM.

(a) Schematic of highlighted metabolic pathways identified as the top-ranking collateral lethal candidate pathways: PSAT, cytosolic MDH, PC, SHMT1, MTHFD1, and PGI. (b) Metabolic fluxes of ETC reactions encoded by UQCR11 (Complex III), NADH reductase (Complex I), and ATP synthase (Complex V) in UQCR11-null and -intact models. Radar plots represent all the Pareto-optimal flux distributions from maximal biomass to maximal ATP maintenance in the counter-clockwise direction. (c) Metabolic fluxes of top-ranking collateral lethal candidate pathways, PSAT, cytosolic MDH, PC, SHMT1, and PGI in UQCR11-null and -intact models. Radar plots represent all the Pareto-optimal flux distributions from maximal biomass to maximal ATP maintenance in the counter-clockwise direction. (d) TWI and log-transformed PAUC of top-25 ranking collateral lethal candidate pathways. High TWI reflects the selective in silico targeting of UQCR11-null cancer cells compared to UQCR11-intact cells upon inhibition of the metabolic pathway. High log[PAUC] values reflect the change in amplitude of fluxes in the metabolic pathway in UQCR11-null cells compared to UQCR11-intact cells, across the Pareto-optimal metabolic objectives. (e) Expanded results of sensitivity analysis of top-ranking collateral lethal candidate pathways used to calculate TWI. Gradual inhibition of flux leads to a detrimental effect to one or both of the metabolic objectives. The differences in the detrimental effect of flux inhibition on UQCR11-null and -intact models are used to estimate the TWI scores. PSAT, phosphoserine aminotransferase; MDH, malate dehydrogenase; PC, pyruvate carboxylase; SHMT1, serine hydroxymethyltransferase; MTHFD1, methylenetetrahydrofolate dehydrogenase; PGI, phosphoglucoisomerase.

Source data

Extended Data Fig. 4 Exploration of collateral lethal targeting of MTHFD2 in parental and doxycycline-inducible knockdown ovarian cancer lines.

(a) Copy-number alterations of UQCR11 and MTHFD2 estimated using GISTIC2.0 with the DNA segmentation data of the entire CCLE panel, shown for four ovarian cancer cell-lines selected for in vitro studies. (b) Cell viability assays comparing effect of MTHFD2 knockdown using two shRNAs in UQCR11-intact (SKOV3) and UQCR11-null (OVCAR8, CAOV3, and HeyA8) cell-lines. Data are representative of two independent experiments and presented as mean ± s.d, (n=3), and analyzed using ordinary two-way ANOVA with Dunnett’s correction for multiple comparisons. (c) mRNA expression of MTHFD2 measured in inducible-knockdown models of OVCAR8 and SKOV3 with and without doxycycline (5 µg/mL). Data is presented as mean ± s.d. and combined from N=5 independent experiments for OVCAR8, N=3 independent experiments for SKOV3, and analyzed using ordinary two-way ANOVA with Dunnett’s correction for multiple comparisons. (d) Protein expression of MTHFD2 measured in inducible-knockdown model of OVCAR8 and SKOV3 with and without treatment of doxycycline (5 µg/mL). Representative blots from two independent experiments with β-actin used as loading control. (e) Cell viability of dox-inducible shMTHFD2 OVCAR8 and shMTHFD2 SKOV3 lines measured with varying concentrations of doxycycline treatment (0, 1, 2, 5 µg/mL). Data are representative of three independent experiments and presented as mean ± s.d. (n=3), and analyzed using ordinary two-way ANOVA with Dunnett’s correction for multiple comparisons. (f) mRNA expression of UQCR11 to evaluate knockdown efficiency of stably expressed shUQCR11 in SKOV3 compared to SKOV3 expressing shScramble. Data are representative of three independent experiments and presented as mean ± s.d. (n=6). Data analyzed using one-way ANOVA with Welch’s test for multiple comparisons. (g) Relative viability of SKOV3 cells expressing shScramble or shUQCR11. Data are representative of three independent experiments and presented as mean ± s.d. (n=5). Data analyzed using unpaired two-tailed Student’s t-test. (h) Live-cell OCR measured using Resipher for isogenic SKOV3 cell-lines expressing shScramble (UQCR11-intact) and two clones of shUQCR11 (UQCR11-null). Data are representative of two independent experiments. Data are presented as mean ± s.e.m. (n=11) and analyzed using ordinary one-way ANOVA with Dunnett’s correction for multiple comparisons. (i) Basal OCR measured via Seahorse Mito Stress assay for parental ovarian cancer lines, CAOV3, HEYA8 and OVCAR8 (UQCR11-null), relative to basal OCR in SKOV3 (UQCR11-intact). Data are shown as means from N=5 independent experiments, relative to SKOV3. Data presented as mean ± s.d. and analyzed using Welch’s two-tailed t-test. (j) Total cellular NAD+ and NADH concentrations in parental ovarian cancer cells, SKOV3 (UQCR11-intact), CAOV3, HEYA8 and OVCAR8 (UQCR11-null). Data are representative of two independent experiments and presented as mean ± s.d. (n=3) and analyzed using one-way ANOVA with Dunnett’s correction for multiple comparisons. (k) Total cellular NAD+ and NADH concentrations in stable knockdown SKOV3 cells expressing shScramble or shUQCR11. Data are representative of two independent experiments and presented as mean ± s.d. (n=3) and analyzed using Welch’s two-tailed t-test.

Source data

Extended Data Fig. 5 Investigating alternative metabolic pathways to rescue UQCR11-null cells from collateral lethal targeting of MTHFD2.

(a) Correlation between NDUFS7 mRNA expression and copy-number alterations of the 19p13.3 locus, represented by UQCR11 copy-number in TCGA OVCA dataset (n=303). Correlations estimated using Spearman’s rank correlation test. (b) Gene expression of NDUFS7 measured via qRT-PCR in UQCR11-null lines (OVCAR8, CAOV3, HeyA8) relative to NDUFS7 expression in UQCR11-intact line (SKOV3). Data representative of two independent experiments presented as mean ± s.d. (n=5), analyzed using ordinary one-way ANOVA with Dunnett’s correction for multiple comparisons. (c) Dose-response curve for single-agent inhibition in SKOV3 cells by either Complex I inhibitor, rotenone, or MTHFD2 inhibitor. Curve fit using the SynergyFinder tool implementing a LL4 (four-parameter logistic regression) model. Calculation and visualization of two-drug synergy scores and HSA (highest single agent) models to identify combinations with highest synergy using SynergyFinder. Data are representative of two independent experiments. (d) Ectopic expression of AOX (relative to ACTIN mRNA) in HeyA8 cells verified using two primers. Data are representative of two independent experiments and presented as mean ± s.e.m (n=3) and analyzed using two-tailed Welch’s t-test. (e) Resistance to MTHFD2 inhibition upon introduction of AOX. AOX or vector controls were ectopically expressed in UQCR11-null HeyA8 cell-lines. Viability decrease was measured 72 h after inhibitor treatment relative to vehicle control. Data are representative of two independent experiments and presented as mean ± s.e.m. (n=6) and analyzed using ordinary 2-way ANOVA with Sidak’s correction for multiple comparisons. (f) Overexpression of UQCR11 in HeyA8 cells. Data are representative of two independent experiments and presented as mean ± s.d. (n=4) and analyzed using two-tailed Welch’s t-test. (g) Resistance to MTHFD2 inhibition upon overexpression of UQCR11 in UQCR11-null HeyA8 cells. Viability decrease was measured 72 h after inhibitor treatment relative to vehicle control. Data are representative of two independent experiments and presented as mean ± s.d. (n=5) and analyzed using two-tailed Welch’s t-test.

Source data

Extended Data Fig. 6 Expansion of UQCR11-MTHFD2 collateral lethality and machine-learning based evaluation of targeting MTHFD2 in endometrial cancer.

(a) Correlation between MTHFD2 and UQCR11 mRNA expression only for the TCGA uterine corpus endometrial carcinoma (UCEC) (Pan Cancer, 2018) dataset corresponding to the pan-cancer analysis results in Fig. 2i. Correlations estimated using Spearman’s rank correlation test. (b) Most significant focal deletions in chromosomes 1-22, identified by GISTIC2.0 within deleted regions for endometrial cancer samples from the TCGA UCEC and uterine carcinosarcoma (UCS) datasets combined (n=584). Correlation of mRNA expression and copy-number alteration of UQCR11 in the same set of samples for which data was available (n=568). Correlations estimated using Spearman’s rank correlation test. (c) Two-layer machine learning model trained using 54 mutations observed in endometrial cancers, select gene-expression panel of 112 mitochondrial genes, and fraction genome altered as input. Inputs for both layers are chosen a priori using linear support vector classification (SVC) for recursive feature elimination, and predictive output of 19p13.3 copy-loss from Layer #1 is used as input to Layer #2. Layer #1 is trained with data available for samples from TCGA and GENIE datasets. Layer #2 is trained using samples from the TCGA dataset. 20% is reserved as test data and not used for training either of the two ML layers. (d) Precision-recall curves for test and training data for Layer #1 of the multi-layer machine learning model that predicts 19p13.3 copy-loss in endometrial cancers. (e) Summary performance metrics and precision-recall curves for training and test data for layer #2 for the multi-layer model that predicts response to MTHFD2 inhibition in endometrial cancers.

Source data

Extended Data Fig. 7 Deuterium tracing to interrogate paralog pathway-mediated metabolic compensations.

(a) Stable-isotope tracing map elucidating the transfer of [2H] and enrichment of downstream metabolites from stable-isotope substrates 3-[2H]-glucose (blue), to trace [2H]-NADPH and cytosolic MTHFD1 activity, and 4-[2H]-glucose (orange) to trace [2H]-NADH and mitochondrial MTHFD2 activity. All four ovarian cancer cell-lines were cultured separately in each of the two tracers, and intracellular metabolites were analyzed after 3, 6, and 24 hours of culture with labeled media. Data are presented as mean ± s.d. (n=3). (b) Stable-isotope tracing map elucidating the transfer of 2H and enrichment of downstream metabolites from stable-isotope substrate 2,3,3-[2H]-serine (green) to dissect bidirectionality of SHMT2 and MTHFD2 fluxes in mitochondria and SHMT1 and MTHFD1 in cytosol. Intracellular metabolites were analyzed after 3, 6, and 24 hours of culture with labeled media. Data are presented as mean ± s.d. (n=3). Data analyzed using ordinary 2-way ANOVA with Dunnett’s correction for multiple comparisons.

Source data

Extended Data Fig. 8 Analysis of global metabolomics and nucleotide pathways of ovarian cancer lines with and without UQCR11 deletion.

(a) Pathway enrichment analysis using metabolic pathways in the KEGG database comparing UQCR11-intact and -null ovarian cancer lines from the CCLE panel (n=50). Pathway coverage represents fraction of metabolites in the KEGG pathway that have available data. Data analyzed using weighted z-test for pathway analysis built into MetaboAnalyst 5.0. (b) Pathway enrichment analysis on all metabolite concentrations measured in SKOV3 (UQCR11-intact) and OVCAR8, HeyA8 and CAOV3 (UQCR11-null) cells for metabolites involved in nucleotide metabolism. Pathway coverage, that is, percentage of metabolites measured from each pathway are represented by a gradient color-scale. Statistical significance and impact (effect size) of pathway-level differences estimated by MetaboAnalyst Pathway Analysis tool are represented by circle size and the x-axis, respectively. Data analyzed using weighted z-test for pathway analysis built into MetaboAnalyst 5.0. (c) Differential metabolite levels represented using a volcano plot show statistically significant (-log10P > 1.3) and biologically relevant (fold-change > 1.5 or <1.5-1). Data points are colored according to the pathway (purine metabolism, pyrimidine metabolism, or other nucleotides/nucleosides). Data are combined from two independent experiments and analyzed using two-sample t-tests corrected for multiple hypothesis testing, built into MetaboAnalyst 5.0. (n=6). (d) Metabolites involved in pyrimidine synthesis and salvage are measured in SKOV3 (UQCR11-intact) and OVCAR8, HeyA8 and CAOV3 (UQCR11-null) cells and overlaid onto the pyrimidine metabolic pathway map. Metabolite levels are scaled to a range [10, 100] to improve visualization. Data are combined from two independent experiments (n=6). (e) Heatmap of purines, pyrimidines, and pathway intermediate concentrations measured in SKOV3 (UQCR11-intact) and OVCAR8 (UQCR11-null) with dox induction of shScramble or shMTHFD2. (f) Differential purines, pyrimidines and pathway intermediates levels represented using a volcano plot in cells treated with MTHFD2 inhibitor vs. vehicle control. Top panel displays fold-change in SKOV3 cells expressing a shScramble and bottom panel displays fold-change in SKOV3 cells expressing shUQCR11. Data are combined from two independent experiments and analyzed using two-sample t-tests corrected for multiple hypothesis testing, built into MetaboAnalyst 5.0 (n=6). (g) K-means clustering (k=4 clusters) of nucleotides measured in samples shown in (e) and (f). K-means clustering performed using MetaboAnalyst 5.0 and visualized with the first two principal components. (h) De novo pyrimidine synthesis demonstrated by extent of [13C3]-UMP enrichment from [13C4]-aspartate in parental ovarian cancer lines, SKOV3 and OVCAR8, cultured with and without MTHFD2 inhibitor. Data shown is [13C3]-UMP enrichment in MTHFD2 inhibitor-treated cells relative to the respective control cells. Data are representative of two independent experiments and presented as mean ± s.d. (n=3) and analyzed using two-tailed Welch’s t-test.

Source data

Extended Data Fig. 9 Investigating metabolic consequence of Complex III impairment and activity of paralogous pathways other than oxidative MTHFD2.

(a) Effect of MTHFD2 inhibitor (10 µM) and uridine (50 µM) supplementation on viability of parental ovarian cancer lines that are UQCR11-intact (SKOV3) or UQCR11-null (HeyA8, OVCAR8, CAOV3). Data are representative of two independent experiments and presented as mean ± s.d. (n=6). Data analyzed with one-way ANOVA with Tukey’s correction for multiple comparisons. (b) Effect of MTHFD2 inhibitor and uridine supplementation on viability of isogenic SKOV3 lines expressing shScramble or shUQCR11. Data are representative of two independent experiments and presented as mean ± s.d. (n=6). Data analyzed with one-way ANOVA with Tukey’s correction for multiple comparisons. (c) Viability of SKOV3 and OVCAR8 cells expressing dox-inducible shScramble or shMTHFD2 supplemented with pyruvate. Data are representative of two independent experiments and presented as mean ± s.e.m. (n=6) and analyzed using two-tailed Student’s t-test. (d) mRNA expression of NNT, IDH2, IDH3A measured via qRT-PCR after knocking down with shNNT, shIDH2, and shIDH3A, relative to scramble shRNA (shScr), verifying knockdown efficiency in SKOV3 and OVCAR8 cell-lines. Data are representative of two independent experiments and presented as mean ± s.d. (n=4). Data analyzed using unpaired two-tailed Student’s t-test. (e) Viability of SKOV3 and OVCAR8 cells after transfection of shNNT, shIDH2, and shIDH3A, relative to respective SKOV3 and OVCAR8 controls transfected with scramble shRNA (shScr). Data are representative of two independent experiments and presented as mean ± s.d. (n=5). Data are analyzed using ordinary one-way ANOVA with multiple comparisons. (f) SKOV3 and OVCAR8 cells transfected with shScramble, shNNT, shIDH2, and shIDH3A treated with MTHFD2 inhibitor (10 µM) for 72 h. Loss of viability calculated by comparing respective viability in vehicle control-treated cells to MTHFD2 inhibitor-treated cells. Data are representative of two independent experiments and presented as mean ± s.e.m. (n=6) and analyzed using ordinary one-way ANOVA with Dunnett’s correction for multiple comparisons. Error bars are calculated using propagation of error principles.

Source data

Extended Data Fig. 10 Targeting MTHFD2 across co-occurring genetic mutations and heterogenous stromal microenvironment in tumors.

(a) Multidimensional scaling-based 2D projection of complete transcriptional profiles of the four HGSOC clusters with 19p13.3 deletion. (b) Venn diagram representing the overlap of reactions in reconstruction of metabolic models illustrating the average expression of four ovarian tumor clusters with 19p13.3 deletion and the overall original ovarian cancer model with 19p13.3 deletion used in Fig. 2. (c) Unsupervised PHATE analysis on scRNASeq data from ascites of ovarian cancer patients to assign cell-types. (d) UQCR11 and MTHFD2 mRNA expression across cell types projected on 2D PHATE embedding of scRNASeq data. (e-g) Average expression (pathway score) of genes in the one carbon metabolic signature #1 (e, Supplementary Information S5), serine glycine threonine metabolism signature #1 (f, Supplementary Information S5), and the NAD+ synthesis metabolism (g, Supplementary Information S5) in ovarian tumors from TCGA with 19p13.3 intact or 19p13.3 deletion. Data are represented as mean ± s.d, analyzed using Student’s two-tailed t-test with Welch’s correction (e-g). (h) Average expression (pathway score) of genes remaining signatures related to the serine-glycine one carbon metabolism (Supplementary Information S5) and mitochondrial metabolism (Supplementary Information S5) across the quadrants of the stromal 2D PHATE embedding. (i) UQCR11 mRNA expression and one carbon metabolism (signature #1) pathway score in microdissected malignant and stromal ovarian tumor compartments (GSE115635). Pairs that have reduced UQCR11 expression in malignant compartments compared to stromal compartments (lower than median of expression difference) are colored red, while those with higher UQCR11 expression in malignant compartments are marked in grey. Data analyzed using paired two-tailed Student’s t-test (n=17 red pairs, n=17 grey pairs).

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–5, Methods, Figs. 1–9 and Tables 1–5.

Reporting Summary.

Source data

Source Data Fig. 1

Statistical source data for Fig. 1.

Source Data Fig. 2

Statistical source data for Fig. 2

Source Data Fig. 2

Unprocessed immunoblots for Fig. 2d.

Source Data Fig. 3

Statistical source data for Fig. 3.

Source Data Fig. 4

Statistical source data for Fig. 4.

Source Data Fig. 5

Statistical source data for Fig. 5.

Source Data Fig. 6

Statistical source data for Fig. 6.

Source Data Fig. 7

Statistical source data for Fig. 7.

Source Data Fig. 7

Unprocessed immunoblots for Fig. 7e.

Source Data Extended Data Fig. 1

Statistical source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 4

Unprocessed immunoblots for Extended Data Fig. 4d.

Source Data Extended Data Fig. 5

Statistical source data for extended Data Fig. 5.

Source Data Extended Data Fig. 6

Statistical source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Statistical source data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Statistical source data for Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Statistical source data for Extended Data Fig. 9.

Source Data Extended Data Fig. 10

Statistical source data for Extended Data Fig. 10.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Achreja, A., Yu, T., Mittal, A. et al. Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer. Nat Metab 4, 1119–1137 (2022). https://doi.org/10.1038/s42255-022-00636-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-022-00636-3

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