Metabolic determinants of cancer cell sensitivity to canonical ferroptosis inducers

AbstractCancer cells rewire their metabolism and rely on endogenous antioxidants to mitigate lethal oxidative damage to lipids. However, the metabolic processes that modulate the response to lipid peroxidation are poorly defined. Using genetic screens, we compared metabolic genes essential for proliferation upon inhibition of cystine uptake or glutathione peroxidase-4 (GPX4). Interestingly, very few genes were commonly required under both conditions, suggesting that cystine limitation and GPX4 inhibition may impair proliferation via distinct mechanisms. Our screens also identify tetrahydrobiopterin (BH4) biosynthesis as an essential metabolic pathway upon GPX4 inhibition. Mechanistically, BH4 is a potent radical-trapping antioxidant that protects lipid membranes from autoxidation, alone and in synergy with vitamin E. Dihydrofolate reductase catalyzes the regeneration of BH4, and its inhibition by methotrexate synergizes with GPX4 inhibition. Altogether, our work identifies the mechanism by which BH4 acts as an endogenous antioxidant and provides a compendium of metabolic modifiers of lipid peroxidation.

Data availabilityAll data generated for and reported in this study are available from the corresponding authors upon request. Gene scores obtained from each screen are available in Supplementary Dataset 1. The UniProt human database used for proteomic analysis is publicly available at https://www.uniprot.org/proteomes/UP000005640. The REACTOME pathway database is publicly available at https://reactome.org/. The gene expression data in Extended Data Fig. 7a,b are publicly available at https://depmap.org/portal/. Data plotted in Extended Data Fig. 5d are publicly available at https://portals.broadinstitute.org/ctrp.v2.1/. Source data are provided with this paper.References1.Sies, H., Berndt, C. & Jones, D. P. Oxidative stress. Annu. Rev. Biochem. 86, 715–748 (2017).CAS 
PubMed 

Google Scholar 
2.Yin, H., Xu, L. & Porter, N. A. Free radical lipid peroxidation: mechanisms and analysis. Chem. Rev. 111, 5944–5972 (2011).CAS 
PubMed 

Google Scholar 
3.Bersuker, K. et al. The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis. Nature 575, 688–692 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Doll, S. et al. FSP1 is a glutathione-independent ferroptosis suppressor. Nature 575, 693–698 (2019).CAS 
PubMed 

Google Scholar 
5.Ingold, K. U. & Pratt, D. A. Advances in radical-trapping antioxidant chemistry in the 21st century: a kinetics and mechanisms perspective. Chem. Rev. 114, 9022–9046 (2014).CAS 
PubMed 

Google Scholar 
6.Yang, W. S. et al. Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317–331 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Dixon, S. J. et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 149, 1060–1072 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Skouta, R. et al. Ferrostatins inhibit oxidative lipid damage and cell death in diverse disease models. J. Am. Chem. Soc. 136, 4551–4556 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Cerutti, P. Prooxidant states and tumor promotion. Science 227, 375–381 (1985).CAS 
PubMed 

Google Scholar 
10.Lei, G. et al. The role of ferroptosis in ionizing radiation-induced cell death and tumor suppression. Cell Res. 30, 146–162 (2020).CAS 
PubMed 

Google Scholar 
11.Wang, W. et al. CD8+ T cells regulate tumour ferroptosis during cancer immunotherapy. Nature 569, 270–274 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Lu, B. et al. Identification of PRDX6 as a regulator of ferroptosis. Acta Pharmacol. Sin. 40, 1334–1342 (2019).CAS 
PubMed 

Google Scholar 
13.Kory, N. et al. SFXN1 is a mitochondrial serine transporter required for one-carbon metabolism. Science 362, eaat9528 (2018).PubMed 
PubMed Central 

Google Scholar 
14.Conrad, M. & Pratt, D. A. The chemical basis of ferroptosis. Nat. Chem. Biol. 15, 1137–1147 (2019).CAS 
PubMed 

Google Scholar 
15.Doll, S. et al. ACSL4 dictates ferroptosis sensitivity by shaping cellular lipid composition. Nat. Chem. Biol. 13, 91–98 (2017).CAS 
PubMed 

Google Scholar 
16.Zou, Y. et al. A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis. Nat. Commun. 10, 1617 (2019).PubMed 
PubMed Central 

Google Scholar 
17.Dixon, S. J. et al. Human haploid cell genetics reveals roles for lipid metabolism genes in nonapoptotic cell death. ACS Chem. Biol. 10, 1604–1609 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Yuki, K., Shindou, H., Hishikawa, D. & Shimizu, T. Characterization of mouse lysophosphatidic acid acyltransferase 3: an enzyme with dual functions in the testis. J. Lipid Res. 50, 860–869 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Aguado, B. & Campbell, R. D. Characterization of a human lysophosphatidic acid acyltransferase that is encoded by a gene located in the class III region of the human major histocompatibility complex. J. Biol. Chem. 273, 4096–4105 (1998).CAS 
PubMed 

Google Scholar 
20.Dorninger, F. et al. Homeostasis of phospholipids—the level of phosphatidylethanolamine tightly adapts to changes in ethanolamine plasmalogens. Biochim. Biophys. Acta 1851, 117–128 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Zhu, X. G. et al. CHP1 regulates compartmentalized glycerolipid synthesis by activating GPAT4. Mol. Cell 74, 45–58 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Jain, I. H. et al. Genetic screen for cell fitness in high or low oxygen highlights mitochondrial and lipid metabolism. Cell 181, 716–727 (2020).CAS 
PubMed 

Google Scholar 
23.Shen, R. & Zhang, Y. in Chemistry and Biology of Pteridines and Folates (eds Ayling, J. E., Nair, M. G. & Baugh, C. M.) 351–354 (Springer, 1993).24.Kojima, S. et al. Antioxidative activity of 5,6,7,8-tetrahydrobiopterin and its inhibitory effect on paraquat-induced cell toxicity in cultured rat hepatocytes. Free Radic. Res. 23, 419–430 (1995).CAS 
PubMed 

Google Scholar 
25.Kraft, V. A. N. et al. GTP cyclohydrolase 1/tetrahydrobiopterin counteract ferroptosis through lipid remodeling. ACS Cent. Sci. 6, 41–53 (2020).CAS 
PubMed 

Google Scholar 
26.Cronin, S. J. F. et al. The metabolite BH4 controls T cell proliferation in autoimmunity and cancer. Nature 563, 564–568 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Kagan, V. E. et al. Oxidized arachidonic and adrenic PEs navigate cells to ferroptosis. Nat. Chem. Biol. 13, 81–90 (2017).CAS 
PubMed 

Google Scholar 
28.Rosen, G. M. et al. The role of tetrahydrobiopterin in the regulation of neuronal nitric-oxide synthase-generated superoxide. J. Biol. Chem. 277, 40275–40280 (2002).CAS 
PubMed 

Google Scholar 
29.Kapralov, A. A. et al. Redox lipid reprogramming commands susceptibility of macrophages and microglia to ferroptotic death. Nat. Chem. Biol. 16, 278–290 (2020).CAS 
PubMed 

Google Scholar 
30.Watschinger, K. et al. Tetrahydrobiopterin and alkylglycerol monooxygenase substantially alter the murine macrophage lipidome. Proc. Natl Acad. Sci. USA 112, 2431–2436 (2015).CAS 
PubMed 

Google Scholar 
31.Shen, R. S. Inhibition of dopamine autoxidation by tetrahydrobiopterin and NADH in the presence of dihydropteridine reductase. Neurotoxicology 12, 201–208 (1991).CAS 
PubMed 

Google Scholar 
32.Shah, R., Farmer, L. A., Zilka, O., Van Kessel, A. T. M. & Pratt, D. A. Beyond DPPH: use of fluorescence-enabled inhibited autoxidation to predict oxidative cell death rescue. Cell Chem. Biol. 26, 1594–1607 (2019).CAS 
PubMed 

Google Scholar 
33.Zilka, O. et al. On the mechanism of cytoprotection by ferrostatin-1 and liproxstatin-1 and the role of lipid peroxidation in ferroptotic cell death. ACS Cent. Sci. 3, 232–243 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Shah, R., Shchepinov, M. S. & Pratt, D. A. Resolving the role of lipoxygenases in the initiation and execution of ferroptosis. ACS Cent. Sci. 4, 387–396 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Haidasz, E. A., Van Kessel, A. T. M. & Pratt, D. A. A continuous visible light spectrophotometric approach to accurately determine the reactivity of radical-trapping antioxidants. J. Org. Chem. 81, 737–744 (2016).CAS 
PubMed 

Google Scholar 
36.Chauvin, J.-P. R., Griesser, M. & Pratt, D. A. Hydropersulfides: H-atom transfer agents par excellence. J. Am. Chem. Soc. 139, 6484–6493 (2017).CAS 
PubMed 

Google Scholar 
37.Valgimigli, L. et al. The unusual reaction of semiquinone radicals with molecular oxygen. J. Org. Chem. 73, 1830–1841 (2008).CAS 
PubMed 

Google Scholar 
38.Zielinski, Z., Presseau, N., Amorati, R., Valgimigli, L. & Pratt, D. A. Redox chemistry of selenenic acids and the insight it brings on transition state geometry in the reactions of peroxyl radicals. J. Am. Chem. Soc. 136, 1570–1578 (2014).CAS 
PubMed 

Google Scholar 
39.Burton, G. W. & Ingold, K. U. Vitamin E: application of the principles of physical organic chemistry to the exploration of its structure and function. Acc. Chem. Res. 19, 194–201 (1986).CAS 

Google Scholar 
40.Kallen, R. G. & Jencks, W. P. The dissociation constants of tetrahydrofolic acid. J. Biol. Chem. 241, 5845–5850 (1966).CAS 
PubMed 

Google Scholar 
41.Davis, M. D. & Kaufman, S. Products of the tyrosine-dependent oxidation of tetrahydrobiopterin by rat liver phenylalanine hydroxylase. Arch. Biochem. Biophys. 304, 9–16 (1993).CAS 
PubMed 

Google Scholar 
42.Patel, K. B., Stratford, M. R. L., Wardman, P. & Everett, S. A. Oxidation of tetrahydrobiopterin by biological radicals and scavenging of the trihydrobiopterin radical by ascorbate. Free Radic. Biol. Med. 32, 203–211 (2002).CAS 
PubMed 

Google Scholar 
43.Armarego, W. L., Randles, D. & Waring, P. Dihydropteridine reductase (DHPR), its cofactors, and its mode of action. Med. Res. Rev. 4, 267–321 (1984).CAS 
PubMed 

Google Scholar 
44.Curtius, H. C. et al. Tetrahydrobiopterin biosynthesis. Studies with specifically labeled (2H)NAD(P)H and 2H2O and of the enzymes involved. Eur. J. Biochem. 148, 413–419 (1985).CAS 
PubMed 

Google Scholar 
45.Niki, E. Interaction of ascorbate and alpha-tocopherol. Ann. N. Y. Acad. Sci. 498, 186–199 (1987).CAS 
PubMed 

Google Scholar 
46.Garcia-Bermudez, J. et al. Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death. Nature 567, 118–122 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Novera, W. et al. Cysteine deprivation targets ovarian clear cell carcinoma via oxidative stress and iron-sulfur cluster biogenesis deficit. Antioxid. Redox Signal. https://doi.org/10.1089/ars.2019.7850 (2020).48.Cramer, S. L. et al. Systemic depletion of l-cyst(e)ine with cyst(e)inase increases reactive oxygen species and suppresses tumor growth. Nat. Med. 23, 120–127 (2017).CAS 
PubMed 

Google Scholar 
49.Aruoma, O. I., Halliwell, B., Hoey, B. M. & Butler, J. The antioxidant action of taurine, hypotaurine and their metabolic precursors. Biochem. J. 256, 251–255 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Sayin, V. I. et al. Activation of the NRF2 antioxidant program generates an imbalance in central carbon metabolism in cancer. eLife 6, e28083 (2017).PubMed 
PubMed Central 

Google Scholar 
51.Eaton, J. K. et al. Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles. Nat. Chem. Biol. 16, 497–506 (2020).CAS 
PubMed 

Google Scholar 
52.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).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Chong, J. et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nat. Methods 10, 730–736 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Chen, W. W., Freinkman, E., Wang, T., Birsoy, K. & Sabatini, D. M. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166, 1324–1337 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Pino, L. K. et al. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 39, 229–244 (2017).PubMed 

Google Scholar 
57.Hu, C. et al. RPLC-ion-trap-FTMS method for lipid profiling of plasma: method validation and application to p53 mutant mouse model. J. Proteome Res. 7, 4982–4991 (2008).CAS 
PubMed 

Google Scholar 
58.Taguchi, R. & Ishikawa, M. Precise and global identification of phospholipid molecular species by an Orbitrap mass spectrometer and automated search engine Lipid Search. J. Chromatogr. A 1217, 4229–4239 (2010).CAS 
PubMed 

Google Scholar 
59.Yamada, T. et al. Development of a lipid profiling system using reverse-phase liquid chromatography coupled to high-resolution mass spectrometry with rapid polarity switching and an automated lipid identification software. J. Chromatogr. A 1292, 211–218 (2013).CAS 
PubMed 

Google Scholar 
Download referencesAcknowledgementsWe thank all members of the Birsoy Lab for helpful suggestions and also members of the Rockefeller University Genomics Resource Center, Proteomics Resource Center and the Flow Cytometry Resource Center for their assistance. We also thank K. Johnsson (Max Planck Institute of Medical Research) and C. Woolf (Boston Children’s Hospital) for their generous gift of QM385. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health (T32GM066699; M.S.) and by the NIH Director’s New Innovator Award (DP2 CA228042–01; K.B.), Pershing Square Sohn Foundation (K.B.), AACR NextGen Grant (K.B.), the Natural Sciences and Engineering Research Council of Canada (D.A.P.) and Canada Foundation for Innovation (D.A.P.). J.G.-B. is a Special Fellow of the Leukemia & Lymphoma Society. K.B. is a Searle Scholar, Pew-Stewart Scholar and Sidney Kimmel Scholar.Author informationAuthor notesThese authors contributed equally: Ross A. Weber, Omkar Zilka.AffiliationsLaboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USAMariluz Soula, Ross A. Weber, Konnor La, Frederick Yen, Javier Garcia-Bermudez & Kıvanç BirsoyDepartment of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, CanadaOmkar Zilka & Derek A. PrattThe Proteomics Resource Center, The Rockefeller University, New York, NY, USAHanan Alwaseem & Henrik MolinaContributionsK.B., M.S. and J.G.-B. conceived the project and designed most of the experiments. M.S. performed most of the experiments with help from J.G.-B. R.A.W. performed the screens in Fig. 1 and Extended Data Fig. 2 and the follow-up work on SFXN1 with assistance from F.Y. O.Z. performed inhibited autoxidation experiments with input from D.A.P. H.A. performed metabolomics and lipidomics with H.M. K.L. performed CRISPR screen analyses. K.B. and M.S. wrote the manuscript with contributions from J.G.-B., D.A.P. and O.Z.Corresponding authorsCorrespondence to
Javier Garcia-Bermudez or Derek A. Pratt or Kıvanç Birsoy.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Cystine depletion and GPX4 inhibition may impact cell proliferation via distinct mechanisms.a, Fold change in cell number (log2) of wild type Jurkat cells cultured with increasing concentrations of cystine (left, black) or ML162 (right, black) and co-treated with ferrostatin-1 (blue, Ferr-1, 1 µM). Data shown as mean ± SD, n = 3 biological replicates. b, Fold change in cell number (log2) of wild type Jurkat cells treated with erastin (right, black) or RSL3 (left, black) and co-treated with necrostatin-1 (blue, Nec-1, 10 µM). Data shown as mean ± SD, n = 3 biological replicates. c, Fold change in cell number (log2) of wild type Jurkat cells treated with erastin (right, black) or RSL3 (left, black) and co-treated with Q-VD-OPh (blue, 20 µM). Data shown as mean ± SD, n = 3 biological replicates. d, Representative immunoblot analysis of procaspase-3 (top) and cleaved caspase-3 (middle) in wild type Jurkat cells left untreated or treated for 12 h with erastin (50 µM) alone or co-treated with ferrostatin-1 (Ferr-1, 1 µM) or Q-VD-OPh (20 µM). β-actin was used as a loading control (bottom). e, Fold change in cell number (log2) of wild type MIA PaCa-2 cells treated with erastin (5 µM) and co-treated with Q-VD-OPh (20 µM) or ferrostatin-1 (Ferr-1, 1 µM). Data shown as mean ± SD, n = 3 biological replicates.
Source data
Extended Data Fig. 2 CRISPR-Cas9 genetic screens identify metabolic regulators of the cellular response to cystine depletion and GPX4 inhibition.a, Gene scores of Jurkat cells left untreated (x-axis) or cultured in low cystine (10 µM, y-axis). b, Top-scoring genes under low cystine conditions. Negative scores represent genes whose loss potentiates low cystine toxicity while positive scores represent genes whose loss provides resistance to low cystine. c, Gene scores of untreated (x-axis) and erastin-treated (3 µM, y-axis) Jurkat cells. d, Significantly enriched pathways (REACTOME) represented within the top 50 most essential genes in the low dose erastin screen. e, Significantly enriched pathways (REACTOME) represented within the top 50 most essential genes in the RSL3 screen.Extended Data Fig. 3 SFXN1 loss enables cell proliferation under cystine depletion.a, Representative immunoblot analysis of SFXN1 in wild type, SFXN1 knockout, and FLAG-SFXN1 cDNA-expressing Jurkat cells (top). GAPDH was used as a loading control (bottom). b, Fold change in cell number (log2) of wild type, SFXN1 knockout, and FLAG-SFXN1 cDNA-expressing Jurkat cells left untreated (gray) or treated with erastin (left, blue, 3 µM) or cultured in low cystine (right, blue, 4 µM). Data shown as mean ± SD, n = 3 biological replicates. c, Relative fold change in SLC7A11 mRNA transcripts in HEK 293T cells left untreated (gray) or treated with erastin (top, blue, 3 µM for 24 h) or cultured in cystine-depleted media (bottom, blue, 5 µM for 24 h). Data shown as mean ± SD, n = 3, d, Representative immunoblot analysis of SLC7A11 in wild type or SFXN1-null HEK 293T cells left untreated or treated with erastin (top, 3 µM for 24 h) or cultured in cystine-depleted media (bottom, 5 µM for 24 h). CTH was used as a marker of cysteine depletion and GAPDH was used as a loading control. e, Fractional labeling of glutathione from 13C3-labeled cysteine in whole cell and mitochondrial lysates obtained from HEK 293T cells. Data shown as mean ± SD, n = 3 biological replicates.
Source data
Extended Data Fig. 4 Characterization of SFXN1 knockout cells.a, Phylogenic tree of the five human sideroflexin paralogs generated from BIONJ clustering using uncorrected pairwise differences from uncorrected pairwise differences of Clustal Omega alignments. From these analyses, SFXN3 is the closest paralog to SFXN1. Mass spectrometric analyses on 3xFLAG-SFXN1 immunoprecipitants, revealed protein-protein interactions with SFXN1 and SFXN3. b, Representative immunoblot analyses of various SFXN1 and SFXN3 constructs from input, FLAG, and HA immunoprecipitants of SFXN1 knockout HEK 293T cells. c, Fold change in cell number (log2) of wild type, SFXN1 knockout, and HA-SFXN3 cDNA-expressing Jurkat cells untreated (gray) or treated with erastin (blue, 3 µM). Data shown as mean ± SD, n = 3 biological replicates. d, Representative immunoblot analysis of SHMT2 in wild type and SHMT2 knockout Jurkat cells (top). Fold change in cell number (log2) of wild type (gray), SFXN1 knockout (blue), and SHMT2 knockout (red) Jurkat cells untreated or treated with erastin (2 µM). Data shown as mean ± SD, n = 3 biological replicates. e, Abundance of 85 polar metabolites in whole cell lysates of HEK 293T cells. Data shown as the ratio of metabolite abundance in SFXN1-null cells to wild type cells (log2, x-axis) versus significance of difference between the median abundances of each metabolite in both groups (-log10p-value, y-axis).
Source data
Extended Data Fig. 5 BH4 availability determines cancer cell sensitivity to ferroptosis.a, Relative abundance of BH4 and its oxidation product, BH2, in wild type Jurkat cells left untreated (gray) or treated with RSL3 (blue, 175 nM for 15 h). Data shown as mean ± SD, n = 3 biological replicates. b, Fold change in cell number (log2) of wild type, PTS knockout, and PTS/ACSL4 double-knockout Jurkat cells untreated (gray) or treated with 200 nM RSL3 (blue). Data shown as mean ± SD, n = 3 biological replicates. c, Fold change in cell number (log2) of wild type, GCH1, SPR, and PTS knockout Karpas-299 cells untreated (gray) or treated with 50 nM RSL3 (blue). Data shown as mean ± SD, n = 3 biological replicates. d, Fold change in cell number (log2) of wild type, GCH1 (left), SPR (middle), and PTS (right) knockout, and respective ACSL4 double-knockout Jurkat cells left untreated (gray) or treated with 200 nM ML162 (blue). Data shown as mean ± SD, n = 3 biological replicates.
Source data
Extended Data Fig. 6 GCH1 expression predicts dependence on BH4 upon ferroptosis induction.a, Fold change in cell number (log2) of wild type Jurkat cells left untreated (gray) or treated with RSL3 (300 nM, blue), co-treated with or without QM385 (3 µM) and supplemented with BH2 (50 µM). Data shown as mean ± SD, n = 3 biological replicates. b, Fold change in cell number (log2) of wild type Karpas-299 cells treated with ML210 only (black trace) or supplemented with BH2 (blue trace, 50 µM). Data shown as mean ± SD, n = 3 biological replicates. c, Fold change in cell number (log2) of wild type (gray) and GCH1-KO (blue) Jurkat cells treated with erastin (1.5 µM) and supplemented with BH2 (50 µM). Data shown as mean ± SD, n = 3 biological replicates. d, Z-scores of correlations between GCH1 mRNA levels and resistance to small molecule probes and drugs across cancer cell lines (CTRP v2, 2015). e, Representative immunoblot analysis of GPX4 in wild type and GPX4 knockout Karpas-299 cells. β-actin was used as a loading control. f, Fold change in cell number (log2) of wild type and GPX4 knockout Karpas-299 cells supplemented with BH2 (200 µM) or ferrostatin-1 (Ferr-1, 1 µM). Data shown as mean ± SD, n = 3 biological replicates. g, Fold change in cell number (log2) of a panel of cancer cell lines that are not sensitive to GPX4 inhibition upon BH4 depletion (blue traces, co-treated with 3 µM QM385). Data shown as mean ± SD, n = 3 biological replicates. h, BH4 abundance in wild type and GCH1 overexpressing A375 cells. Data shown as mean ± SD, n = 3 biological replicates.
Source data
Extended Data Fig. 7 BH4 protects cells from ferroptosis in an enzyme-independent manner.a, RNAseq gene expression data for BH4-associated enzymes in Jurkat cells. Data shown as log2transcript per million (TPM, DepMap). b, RNAseq gene expression data for BH4-associated enzymes in Karpas-299 cells. Data shown as log2transcript per million (TPM, DepMap). c, Representative immunoblot analysis of AGMO in Jurkat and Karpas-299 (K-299) cells (top) and of NOS3 in Jurkat cells (bottom). β-actin was used as a loading control and K562 cells were used as a positive control for AGMO expression. d, Fold change in cell number (log2) and GCH1 knockout Jurkat cells left untreated (gray) or treated with L-NIO (blue, 10 µM) co-treated with or without RSL3 (300 nM). Data shown as mean ± SD, n = 3 biological replicates. e, Fold change in cell number (log2) of wild type, SPR knockout, and SPR/NOS3 double-knockout Jurkat cells left untreated (gray) or treated RSL3 (blue, 300 nM). Data shown as mean ± SD, n = 3 biological replicates. f, Comparison of gene score ranks from BH2 and ferrostatin-1 (Ferr-1) rescue screens in Karpas-299 cells. Unique hits (p 
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