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Ribosome stalling during selenoprotein translation exposes a ferroptosis vulnerability

Nature Chemical Biology volume 18, pages 751–761 (2022)Cite this article

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Abstract

The selenoprotein glutathione peroxidase 4 (GPX4) prevents ferroptosis by converting lipid peroxides into nontoxic lipid alcohols. GPX4 has emerged as a promising therapeutic target for cancer treatment, but some cancer cells are resistant to ferroptosis triggered by GPX4 inhibition. Using a chemical-genetic screen, we identify LRP8 (also known as ApoER2) as a ferroptosis resistance factor that is upregulated in cancer. Loss of LRP8 decreases cellular selenium levels and the expression of a subset of selenoproteins. Counter to the canonical hierarchical selenoprotein regulatory program, GPX4 levels are strongly reduced due to impaired translation. Mechanistically, low selenium levels result in ribosome stalling at the inefficiently decoded GPX4 selenocysteine UGA codon, leading to ribosome collisions, early translation termination and proteasomal clearance of the N-terminal GPX4 fragment. These findings reveal rewiring of the selenoprotein hierarchy in cancer cells and identify ribosome stalling and collisions during GPX4 translation as ferroptosis vulnerabilities in cancer.

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Fig. 1: Genome-wide CRISPR screen identifies LRP8 as a genetic modifier of ferroptosis resistance in TNBC cells.
Fig. 2: LRP8 promotes ferroptosis resistance in breast cancer cells.
Fig. 3: LRP8 regulation of selenium levels affects ferroptosis resistance.
Fig. 4: Altered SEC protein expression in LRP8KO cells confers a ferroptosis vulnerability.
Fig. 5: GPX4 translation is disrupted under selenium-limiting conditions.
Fig. 6: GPX4 is susceptible to ribosome stalling and early translation termination under selenium-limiting conditions.

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Data availability

All data that support the conclusions in this manuscript are available from the corresponding author upon reasonable request. Raw data for Fig. 1d can be accessed in Supplementary Dataset 2. Raw data for Figs. 5 and 6 can be accessed in Supplementary Datasets 4 and 6. Raw data for Extended Data Fig. 1e can be accessed in Supplementary Dataset 1. Raw data for Figs. 1a and 2a and Extended Data Fig. 1a,b are publicly available from the CTRP and CCLE databases (portals.broadinstitute.org). Raw data for Extended Data Fig. 2a are publicly available from TCGA database (portal.gdc.cancer.gov). Raw data for Fig. 3a are publicly available from FIREWORKS (mendillolab.shinyapps.io/fireworks/). Raw data for Extended Data Fig. 5a are publicly available from the Coessentiality browser (coessentiality.net). Source data are provided with this paper.

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Acknowledgements

This research was supported by grants from the National Institutes of Health (NIH) (grant nos. R01GM112948 to J.A.O., T32GM007232 to L.F., F31DK121477 to M.A.R., 1R01GM122923 to S.J.D., R01CA223817 to A.G. and 2T32CA108462 to S.K.), American Cancer Society (Research Scholar award no. RSG-19-192-01 to J.A.O.), Melanoma Research Alliance (award no. 620458 to J.A.O.), CDMRP (grant no. W81XWH-18-1-0713 to A.G.) and the Mark Foundation (to A.G.). J.A.O. is a Chan Zuckerberg Biohub investigator and Miller Institute Professor. We thank M. Lange and K. Bersuker for critical reading of the manuscript. ICP–MS measurements were performed in the OHSU Elemental Analysis Core with partial support from the NIH (grant no. S10RR025512). We thank S. Eyles (UMass Amherst, RRID: SCR_019063) for assistance with high-resolution mass spectrometry acquired on an Orbitrap Fusion mass spectrometer funded by NIH grant no. 1S10OD010645-01A1.

Author information

Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Zhipeng Li, Lucas Ferguson, Kirandeep K. Deol, Melissa A. Roberts, Joseph M. Hendricks, Nicholas T. Ingolia & James A. Olzmann

  2. Department of Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, CA, USA

    Zhipeng Li, Kirandeep K. Deol, Melissa A. Roberts, Joseph M. Hendricks & James A. Olzmann

  3. Department of Biology, Stanford University, Stanford, CA, USA

    Leslie Magtanong & Scott J. Dixon

  4. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Gergey Alzaem Mousa

  5. Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, USA

    Seda Kilinc & Andrei Goga

  6. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA

    Seda Kilinc & Andrei Goga

  7. Department of Medicine, University of California, San Francisco, CA, USA

    Seda Kilinc & Andrei Goga

  8. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA

    Kaitlin Schaefer & James A. Wells

  9. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA

    James A. Wells

  10. Chan Zuckerberg Biohub, San Francisco, CA, USA

    James A. Wells & James A. Olzmann

  11. Department of Genetics, Stanford University, Stanford, CA, USA

    Michael C. Bassik

  12. Miller Institute for Basic Research in Science, University of California, Berkeley, Berkeley, CA, USA

    James A. Olzmann

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  1. Zhipeng Li
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Contributions

Z.L. and J.A.O. conceived the project and designed the experiments. J.A.O. and Z.L. wrote most of the manuscript. All authors read, edited and contributed to the manuscript. Z.L. performed most of the experiments. Z.L. and L.F. prepared samples for ribosome profiling. L.F. and N.T.I. analyzed the sequencing data. Z.L. and K.K.D. performed and analyzed protein translation using AHA-labeling and proteomics. Z.L. performed and analyzed the CRISPR screen with assistance from M.A.R., J.M.H. and M.C.B. S.J.D. and L.M. performed the glutathione measurements. Z.L. and G.A.M. performed the siZNF598 knockdown experiment. S.K., K.S., J.A.W. and A.G. generated MCF10A cells stably expressing MYC, HRasG12V, KRasG12V and HER2.

Corresponding author

Correspondence to James A. Olzmann.

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Competing interests

S.J.D. is a cofounder of Prothegen Inc. and a member of the advisory board for Ferro Therapeutics and Hillstream Biopharma. J.A.O. is a member of the advisory board for Vicinitas Therapeutics. S.J.D. and J.A.O. have patent applications related to ferroptosis.

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Extended data

Extended Data Fig. 1 Cell-specific role for FSP1 in ferroptosis.

a-b, Summary of FSP1 gene expression and ML162 (a) and ML210 (b) sensitivity in various cancer cells. Data were mined from the CTRP database. c, Schematics of CRISPR-Cas9 screen strategy. d, Dose response of RSL3-induced cell death of control and FSP1KO in U-2 OS and MDA-MB-453. Shading indicates 95% confidence intervals for the fitted curve, and each data point is the average of three replicates. e, Gene effects and gene scores calculated for individual genes analyzed in the genome-wide CRISPR screen of U-2 OS cells. f, Histogram of FSP1 from casTLE analysis showing the negative enrichment of FSP1 compared to controls. g, Schematic of cell competitive growth assay. h-j, Quantification of the ratio between mCherry+ : mCherry- cells after the indicated treatment. Data represent mean ± S.E.M. of three biological replicates, which were compared using a two-tailed, unpaired t-test. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

Source data

Extended Data Fig. 2 Analysis of LRP8 in cancers.

a, Relative expression of LRP8 in normal tissue and tumors from various primary sites. Plotted data were mined from the TCGA database. LRP8 is highly expressed in tumors across many cancer types. n=BRCA: N(112), T(1093); BLCA: N(19), T(408); CESC: N(3), T(304); CHOL: N(9), T(36); COAD: N(41), T(458); COADREAD :N(51), T(623); ESCA:N(11), T(184); HNSC: N(44), T(520); LICH: N(50), T(371); LUAD: N(59), T(515); LUCS: N(51), T(501); PAAD: N(4), T(178); PCPG: N(3), T(179); READ: N(10), T(166); SARC: N(2), T(259); STAD: N(35), T(415); STES: N(46), T(599); UCEC: N(35), T(545). b, Relative expression of LRP8 in various subtypes of breast cancer, n= Normal(473), LA(379), LB(244), HER2+(230), TNBC(251). c, Relative expression of LRP8 in breast cancer of different grades. n= I(7), II(58), III(46). The box and whiskers plot in a-c were generated by Tukey method. d, Kaplan-Meier survival curve showing that patients with higher expression of LRP8 have poorer survival rate. Plotted data were mined from the TCGA database.

Source data

Extended Data Fig. 3 Analysis of LRP8 as a ferroptosis resistance factor.

Western blot to validate LRP8KO in MDA-MB-453 cells. Two single-clonal LRP8KO lines generated from different sgRNAs were selected and compared with parental cells stably expressing Cas9. b, Western blot to validate LRP8KO in HCC1937 cells. Two single-clonal LRP8KO lines generated from the same sgRNA were selected and compared with control cells expressing sgSAFE. c, Dose response of RSL3-induced cell death of control and LRP8KO MDA-MB-453 cells. d, Dose response of RSL3-induced cell death of control and LRP8KO HCC1937 cells. e, Dose response of RSL3-induced cell death of control and LRP8KO HCC1143 cells by CellTiter Glo assay. f, Dose response of erastin2-induced cell death of control and LRP8KO HCC1143 cells. g, Time-lapse cell death analysis of control and LRP8KO cells treated with 3 µM auranofin alone or in the presence of 2 µM Fer1 over 43hr. h, Time-lapse cell death analysis of control and LRP8KO cells treated with 500 µM sulfasalazine alone or in the presence of 2 µM Fer1 over 48hr. i, Dose response of RSL3-induced cell death in control, LRP8KO, and LRP8KO expressing LRP8-GFP by CellTiter Glo assay. j, Dose response of IKE-induced cell death of control, LRP8KO, and LRP8KO expressing LRP8-GFP. k, Validation of LRP8 overexpression in LRP8KO MDA-MB-453 cells. l, Cell viability of MDA-MB-453 control and LRP8KO cells stably expressing GFP or LRP8-GFP subjected to 100 nM RSL3 for 24 hr. l, Validation of LRP8 overexpression in LRP8KO MDA-MB-453 cells. m, Live-cell imaging of control and LRP8KO cells incubated with SYTOX Green and treated with 100 nM RSL3 alone or co-treated with 2 µM Fer1. Scale Bar, 100 µm. n, Cell viability of control and LRP8KO cells following treatment with RSL3, etoposide, and H2O2 for 24 hr (RSL3, 200 nM; etoposide, 100 µM; H2O2, 30 µM). In c,d,e,f, and i,j shading indicates 95% confidence intervals for the fitted curve and each data point is the average of three biological replicates. Data in g and h represent mean ± S.E.M. of three biological replicates. Data in l and n represent mean ± S.E.M. of three biological replicates by two-tailed, unpaired t-test. ****P < 0.0001, ***P < 0.001, *P < 0.05.

Source data

Extended Data Fig. 4 LRP8 promotes ferroptosis resistance in multiple cancer cell lines.

a, Validation of LRP8KO in multiple cancer cell lines. Westen blot of pools of LRP8KO cells generated from two different sgRNAs and control Cas9-expressing cells. b-d, Time-lapse cell death analysis of U87-MG (b), A375 (c), and SK-MEL28 (d) cells treated with RSL3 over 24 hr or 72 hr. Data represent mean ± S.E.M. of three replicates.

Source data

Extended Data Fig. 5 Role of selenium metabolism and lipoprotein receptors in ferroptosis.

a, Overview of genome-wide gene coessentiality, highlighting LRP8 in a selenocysteine metabolism cluster. b, Schematic of selenocysteine synthesis. c, Time-lapse cell death analysis of control and CRISPR KO of genes from the selenoprotein synthesis pathway [SEPHS2 (left), SPESECS (mid), PSTK (right)]. Cells were treated with 100 nM RSL3 for 24 hr (n =1). d, Quantification of the percentage of SYTOX green positive cells (dead cells) of control and single or double KO of indicated genes related to selenocysteine metabolism. e, Time-lapse cell death analysis of control and CRISPR knockout of genes from lipoprotein receptor superfamily [LDLR (left), VLDLR (mid), LRP2 (right)]. Cells were treated with 100 nM RSL3 for 24 hr (Data represent mean ± S.E.M. of four independent experiments). f, Quantification of the percentage of SYTOX green positive cells (dead cells) of control and single or double KO of indicated genes related to lipoprotein receptor superfamily. Data in d, f represent mean ± S.E.M. of three biological replicates by two-tailed, unpaired t-test. ****P < 0.0001, **P < 0.01.

Source data

Extended Data Fig. 6 Role of LRP1 and SELENOP in ferroptosis.

a, Histograms of LRP1 results from casTLE analysis, including the average for the entire population (red line) and for the LRP1 sgRNAs (blue line). b, Representative immunoblotting of GPX4 in HCC1143 control and LRP1KO cells. c, Dose response of RSL3-induced cell death of HCC1143 control, LRP8KO, LRP1KO and LRP1/LRP8 double KO cells. d, Time-lapse cell death analysis of HCC1143 control, LRP8KO, LRP1KO and LRP1/LRP8 double KO cells. Cells were treated with 111 nM RSL3 for 24 hr. e, Quantification of the percentage of SYTOX green positive cells (dead cells) of control and single or double KO of indicated genes after 111 nM RSL3 for 24 hr. f, Histograms of SELENOP results from casTLE analysis, including the average for the entire population (red line) and for the SELENOP sgRNAs (blue line). g, Dose response of RSL3-induced cell death of HCC1143 control and single or double KO cells of indicated genes. h, Dose response of RSL3-induced cell death of HCC1143 control and LRP8KO cells subjected to non-targeting or SELENOP siRNA. In c,g-h, Shading indicates 95% confidence intervals for the fitted curves and each data point is the average of three biological replicates. All data represent mean ± S.E.M. of three biological replicates. Data in e represent mean ± S.E.M. of three biological replicates by one-way ANOVA. ****P < 0.0001, *P < 0.05.

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Extended Data Fig. 7 Measurement of metals in LRP8 knockout cells.

a-d, Total levels of iron (a), copper (b), manganese (c), and zinc (d) in control and LRP8KO cells alone or the presence of 200 nM Se by ICP-MS. e, Measure of the glutathione level (DNTB) in HCC1143 control and LRP8KO cells. f, Quantification of SYTOX green positive cells in KO cells of indicated genes subjected to 3 µM erastin2. g) Immunoblot showing the expression of GFP tagged LRP8 wildtype or truncation mutants in LRP8KO HCC1143 cells. h, Cell viability of control cells or LRP8KO cells stably expressing indicated LRP8 mutants in the presence of 3 µM erastin2 for 24 hr. All data represent mean ± S.E.M. of three biological replicates by one-way ANOVA (a-d,h) or paired t-test (e,f). ****P < 0.0001, **P < 0.01, *P < 0.05.

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Extended Data Fig. 8 LRP8 is essential for maintenance of GPX4 and suppression of ferroptosis in cancer cells.

a. Representative western blot of HCC1143 cell lysates subjected to various antioxidants for seven days. b, Representative western blot of GPX4 in MDA-MB-453 cell lysates from multiple clonal LRP8KO lines. c, Quantification of GPX4 levels from b (three independent replicates). d-f, Representative western blot of GPX4 in HCC1937 (d), U87-MG (e), SKMEL38 (f) LRP8KO cells. g,h, Western blot of GPX4 in MDA-MB-453 (g) and HCC1143 (h) LRP8KO cells stably expressing LRP8 wildtype or various truncation mutants. i, Representative western blot of cell lysates from MCF10A and HCC1143 cultured in Human Plasma-Like Medium (n = 3 independent experiments). j, Representative western blot of lysates from MCF10A control and LRP8KO cells stably expressing MYC, HRasG12V and KRasG12V (n = 3 independent experiments). k, Quantification of protein levels from panel h. The level of GPX4 was normalized to the levels of ACTB from the same lysate sample and subsequently normalized to that of control expressing the same oncogene. l, Representative Western blot analysis of cell lysates from MCF10A-HRasG12V subjected to siNT or siLRP8. m, Quantification of protein levels from l (n = 3 independent experiments). n, Representative Western blot analysis of cell lysates from MCF10A-HER2 treated with siNT or siLRP8 for 7 days. o, Quantification of protein levels from l (n = 3 independent experiments).p, Representative immunoblotting of GPX4 in MCF10A-MYC control and LRP8KO cells. Cell lysates were collected under basal conditions or pre-treated with 200 nM Se for 48 hr. q, Cell viability of MCF10A-MYC control and LRP8KO cells upon treatment with 9 µM RSL3 24 hr. Data in c,k,m,o,q represent mean ± S.E.M. of three biological replicates by two-tailed, unpaired t-test. ****P < 0.0001, **P < 0.01, *P < 0.05.

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Extended Data Fig. 9 LRP8KO impacts GPX4 translation but not transcription or protein turnover.

a, Validation of GPX4 wildtype and mutants expression in HCC1143 LRP8KO cells. b, Immunofluorescence of GPX4 (green) in LRP8KO cells stably expressing cytoGPX4 or mitoGPX4. Mitochondria were labeled by MitoTracker Deep Red. Scale bars, 10 μm. c, Relative GPX4 mRNA levels in control and LRP8KO cells as measured by quantitative PCR. Data represent mean ± S.E.M. of three biological replicates by two-tailed, unpaired t-test. d, Gene expression profiles of 23 human selenocysteine genes detected in control and LRP8KO HCC1143 cells with or without pre-treatment with 200 nM Se for 24 hr. Gene expression in LRP8KO cells is represented as log2 fold change relative to that of control cells from the same condition. e, Raw sequence counts of the 23 selenocysteine genes from RNA-seq. f, western blot of GPX4 in control and LRP8KO cells following 10 μM MG132 or 20 μM Chloroquine treatment for 24 hr. g, Representative western blots of AHA labeled, newly translated GPX4,GPX1 and SELENOH in control and LRP8KO cells. h, Quantification of GPX4 protein levels from g. Data represent mean ± S.E.M of five biological replicates by one-way ANOVA, and adjusted using Bonferroni correction for multiple comparisons. ****P < 0.0001.

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Extended Data Fig. 10 GPX4 SEICS impacts on ribosome collision.

a, Western blotting of control and LRP8KO cell lysates subjected to different dose of non-targeting or GPX4 siRNA. b, Immunoblotting analysis of SECISBP2 for EEFSEC from HCC1143 Control and LRP8 cells stably expressing V5-GPX4-Stag. Cells were incubated with vehicle or 200nM Se for 72hr before the harvest for co-immunoprecipitation. c, Representative western blot showing the protein level of different GPX4 SECIS-Swap mutants in HCC1143 control and LRP8KO cells (n = 3 independent experiments). d, Quantification of the percentage of SG+ cells (dead cells) in control and LRP8KO cell lines stably expressing the indicated GPX4-SECIS-Swap mutants over 24hr in the present of 100nM RSL3. Heatmap data represent mean ± S.E.M. of three biological replicates. e, Relative V5-GPX4 mRNA levels in control and LRP8KO cells stably expressing the indicated GPX4-SECIS-Swap mutants as measured by quantitative PCR. Data represent mean ± S.E.M. of two biological replicates by two-tailed, unpaired t-test.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Table 1.

Reporting Summary

Supplementary Dataset 1

U2OS CRISPR screen results.

Supplementary Dataset 2

MDAMB453 CRISPR screen results.

Supplementary Dataset 3

LRP8 expression correlates with resistance to GPX4 inhibitors.

Supplementary Dataset 4

RNA-seq results.

Supplementary Dataset 5

Proteomic data for newly translated proteins.

Supplementary Dataset 6

Ribosome profiling data.

Supplementary Dataset 7

Statistical source data for supplementary figures.

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Li, Z., Ferguson, L., Deol, K.K. et al. Ribosome stalling during selenoprotein translation exposes a ferroptosis vulnerability. Nat Chem Biol 18, 751–761 (2022). https://doi.org/10.1038/s41589-022-01033-3

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