Metabolic determinants of cancer cell sensitivity to canonical ferroptosis inducers

Abstract

Cancer 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.

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Fig. 1: Metabolism-focused CRISPR-Cas9 screens identify metabolic modifiers of lipid peroxidation upon cystine deprivation and GPX4 inhibition.
Fig. 2: Loss of SFXN1 enables cell proliferation under cystine depletion.
Fig. 3: BH4 biosynthesis is necessary for cell proliferation upon GPX4 inhibition.
Fig. 4: GCH1 expression predicts dependence on BH4 upon ferroptosis induction.
Fig. 5: BH4 is a potent radical-trapping antioxidant in lipid membranes.
Fig. 6: DHFR regenerates BH4, which synergizes with α-tocopherol to suppress ferroptosis.

Data availability

All 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.

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Acknowledgements

We 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.

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Authors

Contributions

K.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 authors

Correspondence to Javier Garcia-Bermudez or Derek A. Pratt or Kıvanç Birsoy.

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The authors declare no competing interests.

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Extended data

Extended 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 < 0.01) in the BH2 screen are highlighted (blue) in quadrant II, shared hits are highlighted (purple) in quadrant III, and unique hits in the ferrostatin-1 screen are highlighted (red) in quadrant IV. g, The initial rate and inhibited period of STY-BODIPY consumption is used to derive the rate constant and stoichiometry of added RTAs. h, Representative autoxidations of STY-BODIPY (1 µM)-embedded liposomes of egg phosphatidylcholine lipids (1 mM, ~100 nm particle size) suspended in phosphate-buffered saline pH 7.4 at 37 °C initiated by 0.2 mM DTUN in the presence of NADPH (60 µM) with BH4 (4 µM), BH2, α-tocopherol (5 µM), DHFR (50 nM), and 50 U/mL each of superoxide dismutase (SOD) and catalase (CAT). Source data

Extended Data Fig. 8 Possible mechanisms for lipid peroxyl radical trapping by BH4.

a, Hydrogen atom transfer from BH4 to yield a BH3 (aminyl) radical that has various fates. b, Sequential proton loss electron transfer to yield a BH3 (aminyl) radical that has various fates. c, Competing initiation and propagation of BH4 autoxidation.

Extended Data Fig. 9 DHFR regenerates BH4 efficiently.

a, Representative autoxidations of STY-BODIPY (1 µM)-embedded liposomes of egg phosphatidylcholine lipids (1 mM, ~100 nm particle size) suspended in phosphate-buffered saline pH 7.4 at 37 °C initiated by 0.2 mM DTUN containing 60 µM NADPH, BH2 (10 µM), CoQ10 (5 µM), and DHFR (50 nM) as indicated. b, Representative immunoblot analysis (top) of DHFR in wild type and DHFR knockout Karpas-299 cells. β-actin was used as a loading control. Fold change in cell number (bottom, log2) of wild type (gray) and DHFR knockout (blue) Karpas-299 cells left untreated or treated with RSL3 (50 nM), co-treated with or without methotrexate (MTX, 1.25 µM), and supplemented with BH2 (50 µM) or ferrostatin-1 (Ferr-1, 1 µM). Data shown as mean ± SD, n = 3 biological replicates. c, Representative immunoblot analysis (top) of QDPR in wild type and QDPR knockout Jurkat cells. β-actin was used as a loading control. Fold change in cell number (bottom, log2) of wild type (gray) and QDPR knockout (blue) Jurkat cells left untreated or treated with RSL3 (800 nM), co-treated with or without methotrexate (MTX, 1.5 µM), and supplemented with BH2 (50 µM). Data shown as mean ± SD, n = 3 biological replicates. Source data

Supplementary information

Supplementary Information

Supplementary Figure 1, Supplementary Table 1 and Supplementary Table 2.

Reporting Summary

Supplementary Dataset 1

Gene scores of all CRISPR screens.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

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Soula, M., Weber, R.A., Zilka, O. et al. Metabolic determinants of cancer cell sensitivity to canonical ferroptosis inducers. Nat Chem Biol (2020). https://doi.org/10.1038/s41589-020-0613-y

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