1. Cancer Biology
  2. Cell Biology

Mitochondrial ETF insufficiency drives neoplastic growth by selectively optimizing cancer bioenergetics

  1. David Papadopoli  Is a corresponding author
  2. Ranveer Palia
  3. Predrag Jovanovic
  4. Sébastien Tabariès
  5. Emma Ciccolini
  6. Valerie Sabourin
  7. Sebastian Igelmann
  8. Shannon McLaughlan
  9. Lesley Zhan
  10. HaEun Kim
  11. Nabila Chekkal
  12. Krzysztof J Szkop
  13. Thierry Bertomeu
  14. Jibin Zeng
  15. Julia Vassalakis
  16. Farzaneh Afzali
  17. Slim Mzoughi
  18. Ernesto Guccione
  19. Mike Tyers
  20. Daina Avizonis
  21. Ola Larsson
  22. Lynne-Marie Postovit
  23. Sergej Djuranovic
  24. Josie Ursini-Siegel
  25. Peter M Siegel
  26. Michael Pollak  Is a corresponding author
  27. Ivan Topisirovic  Is a corresponding author
  1. Lady Davis Institute, SMBD JGH, McGill University, Canada
  2. Gerald Bronfman Department of Oncology, McGill University, Canada
  3. Department of Experimental Medicine, McGill University, Canada
  4. Rosalind and Morris Goodman Cancer Institute, McGill University, Canada
  5. VIB Center for Cancer Biology, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Belgium
  6. Department of Biochemistry, McGill University, Canada
  7. Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Sweden
  8. Institute for Research in Immunology and Cancer, Université de Montréal, Canada
  9. Department of Biomedical and Molecular Sciences, Queen's University, Canada
  10. Center of OncoGenomics and Innovative Therapeutics (COGIT), Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, United States
  11. Program in Molecular Medicine, The Hospital for Sick Children, Canada
  12. Department of Molecular Genetics, University of Toronto, Canada
  13. Metabolomics Innovation Resource, McGill University, Canada
  14. Department of Cell Biology and Physiology, Washington University School of Medicine, United States
  15. Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, United States
https://doi.org/10.7554/eLife.106587.3
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Cite this article
  1. David Papadopoli
  2. Ranveer Palia
  3. Predrag Jovanovic
  4. Sébastien Tabariès
  5. Emma Ciccolini
  6. Valerie Sabourin
  7. Sebastian Igelmann
  8. Shannon McLaughlan
  9. Lesley Zhan
  10. HaEun Kim
  11. Nabila Chekkal
  12. Krzysztof J Szkop
  13. Thierry Bertomeu
  14. Jibin Zeng
  15. Julia Vassalakis
  16. Farzaneh Afzali
  17. Slim Mzoughi
  18. Ernesto Guccione
  19. Mike Tyers
  20. Daina Avizonis
  21. Ola Larsson
  22. Lynne-Marie Postovit
  23. Sergej Djuranovic
  24. Josie Ursini-Siegel
  25. Peter M Siegel
  26. Michael Pollak
  27. Ivan Topisirovic
(2026)
Mitochondrial ETF insufficiency drives neoplastic growth by selectively optimizing cancer bioenergetics
eLife 14:RP106587.
https://doi.org/10.7554/eLife.106587.3
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eLife Assessment

The authors present an important set of data implicating ETFDH as an epigenetically suppressed gene in cancer with tumor suppressive functions. The evidence is convincing, with the authors demonstrating that suppression of ETFDH activity results in accumulation of amino acids that impact metabolism via hyperactive mTORC1.

https://doi.org/10.7554/eLife.106587.3.sa0

Abstract

Mitochondrial electron transport flavoprotein (ETF) insufficiency causes metabolic diseases known as a multiple acyl-CoA dehydrogenase deficiency (MADD). In contrast to muscle, ETFDH is a non-essential gene in acute lymphoblastic leukemia NALM6 cells, and its expression is reduced across human cancers. In various human cancer cell lines and mouse models, ETF insufficiency caused by decreased ETFDH expression limits flexibility of OXPHOS fuel utilisation but paradoxically increases bioenergetics and accelerates neoplastic growth via activation of the mTORC1/BCL-6/4E-BP1 axis. Collectively, these findings reveal that while ETF insufficiency is rare and has detrimental effects in non-malignant tissues, it is common in neoplasia, where ETFDH downregulation leads to bioenergetic and signaling reprogramming that accelerates neoplastic growth.

Introduction

The mitochondrial electron transport chain (ETC) transfers electrons from reducing equivalents (NADH and FADH2) towards complexes I/II, or from peripheral metabolic enzymes that reduce coenzyme Q (ubiquinone, Q) to ubiquinol (QH2) (Banerjee et al., 2022). The electron transfer flavoproteins (ETFA and ETFB) accept electrons generated by the catabolism of branched-chain amino acids and fatty acids (Mereis et al., 2021; Ghisla and Thorpe, 2004; Zhang et al., 2019). ETFs interact with ETF dehydrogenase (ETFDH or ETF-ubiquinone oxidoreductase) (El-Gharbawy and Vockley, 2018), which controls electron flow towards complex III (Figure 1—figure supplement 1A). Loss-of-function mutations in ETF/ETFDH occur in metabolic diseases known as multiple acyl-CoA dehydrogenase deficiency (MADD) (Olsen et al., 2007; Missaglia et al., 2021). ETFDH is also essential for complex III activity in skeletal muscle (Herrero Martín et al., 2024). Surprisingly, we observed that in contrast to muscle, ETFDH is one of the most non-essential metabolic genes in cancer cells. Paradoxically, ETFDH downregulation leads to enhanced mitochondrial biogenesis and increased bioenergetic capacity of cancer cells. ETF insufficiency resulting from reduction in ETFDH levels triggers a trade-off favored by cancer cells, whereby reduced flexibility in fuel utilization for oxidative phosphorylation is offset by signaling from mitochondria to mTORC1 that remodels cancer cell bioenergetics to augment neoplastic growth.

Results

ETFDH abrogation accelerates tumor growth

ETFDH is essential in skeletal muscle cells (Herrero Martín et al., 2024). Intriguingly, a CRISPR-Cas9 knockout (KO) screen performed in a human acute lymphoblastic leukemia cell line (NALM6) (Bertomeu et al., 2018) ranked ETFDH as a top non-essential metabolic gene that clustered with well-established tumor suppressors TP53 and RB1 (Figure 1—figure supplement 1B). Mining DepMap also revealed that ETFDH is essential in only 1 out of 1150 cancer cell lines (Tsherniak et al., 2017). This motivated us to examine the role of ETFDH in cancer. ETFDH mRNA levels are reduced in patient samples across different cancer types, including colon (Figure 1—figure supplement 1C) and breast cancer (Figure 1—figure supplement 1D). Immunohistochemistry (IHC) staining confirmed that ETFDH protein levels are significantly reduced in colorectal cancer vs. non-adjacent patient-derived tissues (NAT) (Figure 1A).

Figure 1 with 1 supplement see all
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Electron transfer flavoprotein dehydrogenase (ETFDH) loss promotes tumor growth.

(A) ETFDH protein levels in non-adjacent tissue (NAT) or grade 3 cancerous tissue (Tumor) were determined by immunocytochemistry (IHC) of colorectal cancer tissue microarray. Staining intensity of DAB in arbitrary units (A.U.) is shown. Data are presented as means +/- SEM (NAT n=14, Tumor n=13), **p<0.01, unpaired Student’s t test. (B–C) Tumor growth of WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells following intra-caecal injection (B) and endpoint tumor volumes (C). Growth was assessed by luminescence (photons). Data are presented as means +/- SEM (WT EV n=6, ETFDH KO n=8, ETFDH Rescue n=8), *p<0.05, one-way ANOVA, Tukey’s post-hoc test. (D–E) Tumor growth of WT and ETFDH KO NT2197 cells following mammary fat-pad injection (D) and endpoint tumor volumes (E). Growth was assessed using a caliper. Data are presented as means +/- SEM (WT n=17, ETFDH KO n=17), *p<0.05, unpaired Student’s t test.

We next abrogated the ETFDH gene using a CRISPR-Cas9 approach in a variety of cancer cell lines, including HCT-116 (human colorectal cancer), NT2197 (murine HER2 +breast cancer), 4T1 (murine triple-negative breast cancer cells), and NALM6 cells (Figure 1—figure supplement 1E–H). We also investigated the effects of ETFDH loss in non-transformed normal murine mammary gland (NMuMG) cells (Figure 1—figure supplement 1I), a parental immortalized mammary epithelial cell line that was transformed with an oncogenic ErbB2 variant to generate NT2197 cells (Ursini-Siegel et al., 2008). Notwithstanding their high baseline proliferation rates, ETFDH ablation significantly increased proliferation across all tested cancer cell lines (Figure 1—figure supplement 1E–H). This was not caused by the inadvertent effects of the CRISPR-Cas9, as rescuing ETFDH expression in HCT-116 and NT2197 cells reverted proliferation rates to levels comparable to ETFDH-proficient controls (Figure 1—figure supplement 1E–F). In contrast, ETFDH abrogation did not affect proliferation of non-transformed NMuMG cells (Figure 1—figure supplement 1I). ETFDH KO HCT-116 cells also formed more colonies in soft agar (surrogate measurement of cellular neoplastic potential Borowicz et al., 2014) as compared to empty vector infected controls (WT EV), and ETFDH KO cells in which ETFDH was re-expressed (ETFDH Rescue) (Figure 1—figure supplement 1J).

Based on these findings, we tested the impact of ETFDH loss on neoplastic growth in vivo. WT EV, ETFDH KO, or ETFDH Rescue HCT-116 cells were injected orthotopically in the caeca of SCID-BEIGE mice. Loss of ETFDH significantly increased the tumor growth rate and volume at endpoint relative to control and ETFDH Rescue cells (Figure 1B–C). ETFDH loss also increased NT2197 tumor growth and the final tumor volume as compared to ETFDH-proficient tumors following orthotopic mammary fat pad injections in SCID-BEIGE mice (Figure 1D–E). Altogether, these results show that ETFDH is a non-essential gene in cancer that is under-expressed across different cancer types and that its disruption enhances neoplastic growth, while not affecting proliferation of non-transformed cells.

ETFDH abrogation results in ETF insufficiency that paradoxically increases bioenergetic capacity of cancer cells

In non-cancerous cells, ETFDH transfers electrons from catabolism of fatty acids and amino acids to the ETC (Figure 2—figure supplement 1A). Palmitate oxidation (monitored by oxygen consumption) and 13C leucine labeling into citrate (citrate m+2) were strongly attenuated by ETFDH abrogation in HCT-116 cells (Figure 2—figure supplement 1B–C), which confirmed that ETF plays a role in fatty and amino acid catabolism in malignant cells. Thus, ETFDH loss induces ETF insufficiency in cancer cells that mirrors defects observed in non-malignant tissues. Surprisingly, ETFDH loss in HCT-116 cells increased oxygen consumption and extracellular acidification, relative to control WT EV and ETFDH Rescues (Figure 2A–B). ETFDH KO HCT-116 cells produced higher levels of ATP from oxidative phosphorylation (J ATP ox) as compared to ETFDH-proficient cells (Figure 2C–D). Consequently, ETFDH loss increased the bioenergetic capacity of HCT-116 cells (Figure 2E). Comparable results were observed in NT2197 cells (Figure 2—figure supplement 1D–H). Collectively, these findings suggest that although ETF insufficiency limits the ability of cancer cells to utilize lipids and amino acids, it paradoxically upregulates their mitochondrial metabolism and bioenergetic capacity.

Figure 2 with 1 supplement see all
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Absence of electron transfer flavoprotein dehydrogenase (ETFDH) promotes mitochondrial metabolism.

(A–B) Oxygen consumption (A) and extracellular acidification (B) in WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells were determined using a Seahorse bioanalyzer. Data are normalized to cell count and presented as means +/- SD (n=4), *p<0.05, **p<0.01, one-way ANOVA, Tukey’s post-hoc test.(C) Basal J ATP calculations from WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells. J ATP ox represents ATP production from oxidative phosphorylation, while J ATP glyc is ATP production from glycolysis. Comparison between J ATP ox (gray bars; top) and J ATP glyc (white bars; bottom) is shown. Data are presented as means +/- SD (n=4), *p<0.05, **p<0.01, one-way ANOVA, Tukey’s post-hoc test. (D–E) Bioenergetic plot for Basal, FCCP, and Monensin J ATP fluxes (D) and bioenergetic capacity (E) of WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells. Data are presented as means +/- SD (n=4), *p<0.05, one-way ANOVA, Tukey’s post-hoc test. (F) Glutamine uptake and glutamate production in WT and ETFDH KO HCT-116 cells. Results are shown as fold changes of ETFDH KO cells relative to WT cells. Data are presented as means +/- SD (n=3), *p<0.05, paired Student’s t test. (G) Schematic of 13C-glutamine tracing throughout the citric acid cycle (CAC). Isotopomers labeled in green depict 13C-glutamine tracing in the forward direction of the CAC, while those in purple represent reverse tracing. (H) Relative abundance of 13C-labeled metabolites in the forward (green) and reverse (purple) directions from WT (blue) and ETFDH KO (red) NT2197 cells. Data are presented as means +/- SD (n=3), *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, paired Student’s t test. (I) Proliferation of WT and ETFDH KO HCT-116 cells grown in the absence or presence of glutamine for 48 hr. Results are shown as fold changes of cell counts under glutamine deprivation relative to those under glutamine repleted conditions. Data are presented as means +/- SD (n=3), *p<0.05, paired Student’s t test.

Glutamine plays an important role in fueling mitochondrial metabolism and de novo nucleotide biosynthesis to support cancer cell proliferation (DeBerardinis and Cheng, 2010). ETFDH abrogation increased glutamine uptake and glutamate production in both HCT-116 (Figure 2F) and NT2197 (Figure 2—figure supplement 1I) cells. ETFDH loss increased 13C-glutamine incorporation in the forward direction (green) and the reverse direction (purple) of the citric acid cycle (CAC; Figure 2G-H, Figure 2—figure supplement 1J), suggesting higher glutamine tracing into the CAC, as well as increased reductive glutamine metabolism. Indeed, the steady state levels of several nucleotides were also increased following ETFDH loss (Figure 2—figure supplement 1K). To highlight the dependence of glutamine in contributing to the proliferation of cells upon ETFDH abrogation, HCT-116 WT EV, and ETFDH KO cells were grown in the absence or presence of glutamine. Accordingly, ETFDH KO HCT-116 cells exhibited greater sensitivity to glutamine deprivation than control WT EV cells (Figure 2I). Collectively, these data demonstrate that ETF insufficiency in cancer cells remodels mitochondrial metabolism and increases glutamine consumption and anaplerosis.

ETFDH loss induces intracellular accumulation of amino acids, mTOR signaling, and protein synthesis

Consistent with the established role of ETFDH in amino acid catabolism (Mereis et al., 2021), and reduction of leucine consumption upon ETFDH abrogation (Figure 2—figure supplement 1C), steady-state levels of most intracellular amino acids were increased in ETFDH KO vs. WT HCT-116 and NT2197 cells (Figure 3A). Moreover, ETFDH-deficient HCT-116 cells exhibited higher rates of protein synthesis relative to ETFDH-proficient cells (Figure 3B). Amino acids activate the mechanistic target of rapamycin (mTOR) which stimulates protein synthesis (Saxton and Sabatini, 2017). Indeed, mTORC1 signaling was higher in ETFDH-deficient than ETFDH-proficient HCT-116 and NT2197 cells as illustrated by increased phosphorylation of ribosomal protein S6 kinases 1 and 2 (S6K1; T389) and their substrate ribosomal protein S6 (S6; S240/244) (Figure 3C). Re-expression of ETFDH in ETFDH KO cells resulted in normalization of mTORC1 signaling to the levels observed in WT EV cells (Figure 3D). mTORC2 signaling was also augmented upon ETFDH abrogation in HCT-116 and NALM6 cells, as evidenced by increased AKT phosphorylation (S473) (Figure 3—figure supplement 1A–B). Despite bioenergetic rewiring, ETFDH loss did not exert a major effect on the activity of adenosine monophosphate-activated protein kinase (AMPK) as monitored by its phosphorylation (T172) or phosphorylation of its substrate acetyl-CoA carboxylase (ACC; S79) (Figure 3—figure supplement 1A–B).

Figure 3 with 1 supplement see all
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Electron transfer flavoprotein dehydrogenase (ETFDH) loss bolsters mTORC1 signaling and downregulates 4E-BP1 protein levels.

(A) Amino acid profile in WT and ETFDH KO HCT-116 and NT2197 cells. Data are presented as fold change in amino acid levels in ETFDH KO compared to WT cells (n=3). (B) Puromycin incorporation assays in WT and ETFDH KO HCT-116 cells. Levels and phosphorylation status of indicated proteins were determined via western blotting using indicated antibodies. β-Actin was used as a loading control (representative blots of n=3). Quantification of puromycin incorporation is presented as fold change in ETFDH KO vs. WT cells. (C) Levels and phosphorylation status of indicated proteins in WT or ETFDH KO HCT-116 and NT2197 cells were determined by western blotting. β-Actin served as a loading control (representative blots of n=3). (D) Levels and phosphorylation status of indicated proteins in WT EV, ETFDH KO, or ETFDH Rescue HCT-116 and NT2197 cells were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (E) Total and phosphoprotein levels in WT or ETFDH KO HCT-116 cells were determined by western blotting using indicated proteins. Cells were serum starved overnight, then depleted of amino acids for indicated time points (0, 10, 30, 60 min), or stimulated with FBS for 4 hr (Stim). β-Actin was used as loading control (representative blots of n=3). (F) Quantification of pS6(S240/244)/S6 ratio from data presented in panel E. Data are presented as means +/- SD (n=3), *p<0.05, one-way ANOVA, Tukey’s post-hoc test. (G) Quantification of p4E-BP1(S65)/4E-BP1 ratio from data presented in panel E. Data are presented as means +/- SD (n=3), *p<0.05, one-way ANOVA, Tukey’s post-hoc test.

Figure 3—source data 1

PDF files containing original western blots for Figure 3B and C, Figure 3D , and E, indicating the relevant bands.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig3-data1-v1.zip
Figure 3—source data 2

Original files for western blot analysis displayed in Figure 3B and C, Figure 3D , and E.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig3-data2-v1.zip

Consistent with mTOR hyperactivation, ETFDH KO HCT-116 cells were more sensitive to the active site mTOR inhibitor Torin1 (Thoreen et al., 2009) as compared to their ETFDH-proficient counterparts (Figure 3—figure supplement 1C). To further determine which mTOR complex is responsible for mediating the hyper-proliferative phenotype triggered by ETFDH loss, we selectively inactivated mTORC1 or mTORC2 by depleting RAPTOR or RICTOR, respectively, using small hairpin RNA (shRNA) in WT and ETFDH KO HCT-116 cells (Figure 3—figure supplement 1D). Depletion of RAPTOR, but not RICTOR, strongly attenuated the proliferation of ETFDH KO cells (Figure 3—figure supplement 1E–F). Moreover, the anti-proliferative effects of bisteric mTORC1-specific inhibitor BiS-35x (Lee et al., 2021) were stronger in ETFDH KO vs. ETFDH Rescue HCT-116 cells (Figure 3—figure supplement 1G). Altogether, these data suggest that mTORC1, but not mTORC2, acts as a major mediator of hyper-proliferative phenotype triggered by ETFDH loss in cancer cells. Accordingly, the effects of amino acid deprivation on phospho-S6 (S240/244) and phospho-4E-BP1 (S65) were attenuated in ETFDH KO vs. WT control cells, which is consistent with the intracellular accumulation of amino acids upon ETFDH disruption (Figure 3E–G). In turn, serum stimulation (Stim) resulted in comparable induction in phosphorylation of S6 (S240/244) and 4E-BP1 (S65) in ETFDH-deficient and proficient cells (Figure 3E). Collectively, these findings suggest that ETFDH loss induces mTORC1 signaling at least in part by increasing intracellular amino acid levels.

Amino acid deprivation leads to general control nonderepressible 2 (GCN2)-dependent phosphorylation of the alpha subunit of eIF2 and subsequent induction of activating transcription factor 4 (ATF4) synthesis during the integrated stress response (ISR) (Harding et al., 2000). Consistent with an increase in intracellular amino acid, phospho-eIF2⍺ (S51) levels appeared to be modestly reduced in ETFDH-deficient vs. proficient HCT-116 cells under both serum starvation and amino acid depletion (Figure 3—figure supplement 1H). However, the ETFDH status in the cells did not affect ATF4 levels at baseline or upon amino acid depletion (Figure 3—figure supplement 1H). Altogether, these data demonstrate that ETFDH loss induces mTORC1 signaling and protein synthesis, which appears to be at least in part driven by intracellular accumulation of amino acids.

ETFDH loss diminishes 4E-BP1, but not 4E-BP2 protein levels

mTORC1 positively controls cell proliferation through the eukaryotic translation initiation factor 4E-binding proteins (4E-BPs; 4E-BP1-3 in mammals) (Dowling et al., 2010). Of note, 4E-BP3 is expressed in a tissue-restricted manner and does not appear to play a major role in regulating proliferation (Dowling et al., 2010). mTORC1 phosphorylates 4E-BPs to promote eukaryotic translation initiation factor 4 F (eIF4F) complex assembly by dissociating 4E-BPs from eIF4E, thereby stimulating cap-dependent translation initiation (Gingras et al., 1999). Consistent with the increased mTORC1 activity, ETFDH-deficient cells exhibited increased phosphorylation of 4E-BP1 (S65), which was rescued upon re-expression of ETFDH in HCT-116 and NT2197 cells (Figure 4A). Unexpectedly, we observed that ETFDH loss coincided with a dramatic reduction in total 4E-BP1 protein levels in HCT-116 (Figure 4A), NT2197 (Figure 4A), NALM6 (Figure 4—figure supplement 1A), and 4T1 cells (Figure 4—figure supplement 1B). We also treated WT and ETFDH KO HCT-116 cell lysates with λ-phosphatase to exclude potential confounding effects of different 4E-BP1 phosphorylation states, which confirmed that disruption of ETFDH is paralleled by reduction in 4E-BP1 levels (Figure 4B). Notably, loss of ETFDH did not exert a major effect on 4E-BP2 protein abundance in HCT-116 (Figure 4A), NT2197 (Figure 4A), or 4T1 cells (Figure 4—figure supplement 1B). In addition, 4E-BP1, but not 4E-BP2 protein levels were reduced in ETFDH-deficient as compared to WT HCT-116 tumors (Figure 4C–E). Strikingly, loss of ETFDH in non-transformed NMuMG cells did not exert a major effect on mTORC1 signaling (Figure 4—figure supplement 1C) and failed to alter 4E-BP1 protein levels (Figure 4—figure supplement 1C). Collectively, these findings indicate that ETF insufficiency is paralleled by a selective decrease in 4E-BP1 protein levels in cancer, but not non-transformed cells.

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Repression of EIF4EBP1 transcription mediates the effects of electron transfer flavoprotein dehydrogenase (ETFDH) loss on cancer bioenergetics and tumor growth.

(A) Levels and phosphorylation status of indicated proteins in WT EV, ETFDH KO, or ETFDH Rescue HCT-116 and NT2197 cells were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (B) Levels of indicated protein in λ-phosphatase treated WT or ETFDH KO HCT-116 and NT2197 cell lysates were monitored by western blotting. β-Actin was used as a loading control (representative blots of n=3). (C) Indicated protein levels in WT or ETFDH KO HCT-116 tumors were determined by western blotting. AKT was used as a loading control (WT n=8, ETFDH KO n=11). (D–E) Quantification of 4E-BP1 (D) and 4E-BP2 (E) from tumors described in panel C. Data are presented as means +/- SEM (WT n=8, ETFDH KO n=11), ***p<0.001, unpaired Student’s t test. (F) m7GDP pulldown assay in ETFDH KO and ETFDH KO overexpressing 4E-BP1 (ETFDH KO 4E-BP1) NT2197 cells. Specified protein levels in m7GDP pulldown or input were determined by western blot. β-Actin was used as loading control (input) and to exclude contamination in the pulldown material (m7GTP bound) (representative blots of n=3). (G) Oxygen consumption of ETFDH KO and ETFDH KO 4E-BP1 NT2197 cells. Data are normalized to cell count and presented as means +/- SD (n=5), **p<0.01, Student’s t test. (H–I) Tumor growth of ETFDH KO and ETFDH KO 4E-BP1 NT2197 cells following mammary fat-pad injection (H) and endpoint tumor volumes (I). Growth was assessed using caliper measurements. ETFDH KO measurements are the same as in Figure 1D, as these growth curves were obtained in the same experiment. Data are presented as means +/- SEM (ETFDH KO n=17, ETFDH KO 4E-BP1 n=18), **p<0.01, unpaired Student’s t test. (J) Mitochondrial DNA in WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells was quantified by qPCR. Mitochondrial DNA (mtDNA) content was normalized to genomic DNA (gDNA). Data are presented as fold change relative to WT EV cells +/- SD (n=3), *p<0.05, **p<0.01, one-way ANOVA, Tukey’s post-hoc test. (K) Levels of indicated proteins in WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells were assessed by western blotting. β-Actin was used as a loading control (representative blots of n=3). (L) EIF4EBP1 and EIF4EBP2 mRNA levels in WT EV, ETFDH KO, and ETFDH Rescue HCT-116 cells were determined by RT-qPCR. PP1A was used as a housekeeping gene. Data are presented as fold change of EIF4EBP1/PP1A and EIF4EBP2/PP1A ratios relative to WT EV cells (set to 1)+/-SD (n=3), *p<0.05, one-way ANOVA, Tukey’s post-hoc test. (M) Eif4ebp1 and Eif4ebp2 mRNA abundance in WT and ETFDH NT2197 KO cells (murine cell line) was determined by RT-qPCR. Actb was used as a housekeeping gene. Data are presented as fold change in Eif4ebp1/Actb and Eif4ebp2/Actb ratios relative to WT cells +/- SD (n=3), *p<0.05, paired Student’s t test.

Figure 4—source data 1

PDF files containing original western blots for Figure 4A and B, Figure 4C and F, and Figure 4K, indicating the relevant bands.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig4-data1-v1.zip
Figure 4—source data 2

Original files for western blot analysis displayed in Figure 4A and B, Figure 4C and F, and Figure 4K.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig4-data2-v1.zip

Decrease in 4E-BP1 protein levels underpins the bioenergetic rewiring induced by ETF insufficiency in cancer cells

Considering that in mammals 4E-BPs play a major role in mediating the effects of mTORC1 on proliferation, neoplastic growth, mitochondrial functions, and protein synthesis (Dowling et al., 2010; Hulea et al., 2018; Morita et al., 2013), we next investigated whether the observed decrease in 4E-BP1 levels contributes to increased proliferation and tumor growth caused by ETFDH abrogation. To this end, we overexpressed 4E-BP1 protein in ETFDH-deficient NT2197 cells by infecting them with a retrovirus wherein EIF4EBP1 expression was driven by an exogenous promoter (Dowling et al., 2010). Importantly, 4E-BP1 overexpression in ETFDH KO NT2197 cells decreased eIF4F complex assembly, as evidenced by m7-GTP pulldown (Figure 4F). 4E-BP1 overexpression also reduced oxygen consumption in ETFDH KO NT2197 cells, relative to empty vector-infected cells (Figure 4G). Moreover, restoring 4E-BP1 protein levels decreased cell proliferation (Figure 4—figure supplement 1D) and anchorage-independent growth (Figure 4—figure supplement 1E) of ETFDH KO HCT-116 cells. Lastly, 4E-BP1 overexpression decreased growth and the final volume of tumors formed by NT2197 ETFDH KO cells in SCID-BEIGE mice (Figure 4H–I). Collectively, these data indicate that decreased 4E-BP1 protein levels mediate the pro-neoplastic effects of ETF insufficiency.

The mTORC1/4E-BP axis stimulates mitochondrial biogenesis and metabolism by altering the synthesis of nuclear-encoded proteins with mitochondrial functions, including mitochondrial transcription factor A (TFAM) (Morita et al., 2013; Morita et al., 2015). ETFDH loss increased mitochondrial DNA levels (Figure 4J) and mitochondrial mass (Figure 4—figure supplement 1F), which was paralleled by TFAM induction (Figure 4K). Collectively, these data show that the loss of 4E-BP1 plays a prominent role in mediating the effects of ETF insufficiency on bioenergetic reprogramming of cancer cells.

ETFDH loss decreases EIF4EBP1 transcription via BCL-6

We next set out to determine the level(s) at which 4E-BP1 protein levels are downregulated in ETF-insufficient cancer cells. ETFDH loss did not have a major effect on 4E-BP1 protein synthesis or turnover (Figure 4—figure supplement 1G–H), but it decreased the EIF4EBP1 mRNA levels in ETFDH-deficient vs. ETFDH-proficient HCT-116 (Figure 4L) and NT2197 cells (Figure 4M). EIF4EBP1 and ETFDH mRNA levels were also positively correlated in tumors isolated from colorectal adenocarcinoma patients (Figure 4—figure supplement 1I). In turn, consistent with no apparent differences in 4E-BP2 protein levels, the levels of EIF4EBP2 mRNA were unaffected by the ETFDH status in both cell lines (Figure 4L–M). Notably, the observed decrease in EIF4EBP1 mRNA levels was not caused by the effects of ETFDH loss on mRNA stability (Figure 4—figure supplement 1J). Based on these data, we concluded that ETFDH abrogation is paralleled by suppression of EIF4EBP1 transcription and sought to identify transcription factors that may mediate the effects of ETFDH abrogation on suppression of EIF4EBP1 expression. ATF4, Snail, and Slug have been shown to stimulate transcription of EIF4EBP1, but not EIF4EBP2 (Yamaguchi et al., 2008; Wang et al., 2017). However, ATF4, Snail, and Slug proteins remain unchanged upon ETFDH disruption (Figure 3—figure supplement 1H, Figure 5—figure supplement 1A). We also re-analyzed publicly available RNA-seq datasets, which revealed that EIF4EBP1, but not EIF4EBP2 expression was increased following STAT1 abrogation in MT4788 breast cancer cells (Totten et al., 2021). Indeed, we observed that MT4788 STAT1 KO cells have increased 4E-BP1 protein levels relative to parental cells (Figure 5—figure supplement 1B). Accordingly, ETFDH KO cells displayed increased STAT1 and downregulated 4E-BP1 protein levels (Figure 5—figure supplement 1C). To determine the impact of STAT1 on EIF4EBP1 or EIF4EBP2 promoter occupancy, we performed chromatin immunoprecipitation qPCR (ChIP-qPCR) assay in control (WT EV), ETFDH KO, or ETFDH rescue HCT-116 cells (Figure 5—figure supplement 1D–E). Binding of STAT1 was, however, comparable to IgG across all tested cell lines, thereby suggesting that it is unlikely that STAT1 directly suppresses EIF4EBP1 transcription in the context of ETFDH loss (Figure 5—figure supplement 1D–E). Furthermore, reactive oxygen species (ROS) can also influence redox-responsive transcription (Hayes et al., 2020). Although loss of ETFDH induced ROS levels (Figure 5—figure supplement 1F), 4E-BP1 protein abundance was not altered following treatment with the antioxidant N-acetyl-cysteine (NAC) (Figure 5—figure supplement 1G).

We next investigated the potential role of B-cell lymphoma 6 (BCL-6) (Nakayamada et al., 2014; Liu et al., 2022; Madapura et al., 2017), which was previously suggested to suppress EIF4EBP1 transcription (Ramachandran et al., 2024). Notably, ETFDH abrogation in HCT-116 and NT2197 cells resulted in a marked increase in BCL-6 protein levels, compared to ETFDH-proficient control cells (Figure 5A). These changes in BCL-6 protein levels were mTOR-sensitive (Figure 5B) and not accompanied by the alterations in BCL6 mRNA abundance (Figure 5C), which is consistent with recently reported mTOR-dependent translational activation of BCL-6 (Yi et al., 2017; Xu et al., 2017; Raybuck et al., 2018). Indeed, BCL6 mRNA translation was increased in HCT-116 ETFDH KO relative to WT cells (Figure 5D). Taken together, these data point out that ETFDH disruption induces BCL-6 protein synthesis in an mTOR-dependent manner. ETF insufficiency increased BCL-6 binding to the EIF4EBP1, but not the EIF4EBP2 promoter (Figure 5E–F). BCL-6 silencing in NT2197 and 4T1 ETFDH KO cells increased 4E-BP1 protein levels (Figure 5G–H). Depletion of BCL-6 also attenuated proliferation of 4T1 ETFDH KO cells (Figure 5I). Collectively, these findings show that BCL-6 mediates the effects of ETF insufficiency on reduction in EIF4EBP1 expression and increased proliferation of cancer cells.

Figure 5 with 1 supplement see all
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Loss of electron transfer flavoprotein dehydrogenase (ETFDH) increases BCL-6 levels leading to BCL-6-dependent inhibition of EIF4EBP1 transcription.

(A) Levels of indicated proteins in WT or ETFDH KO HCT-116 and NT2197 cells were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (B) Levels of indicated proteins in ETFDH KO NT2197 cells treated with DMSO or Torin1 (250 nM) for 4 hr were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (C) BCL6/Bcl6 mRNA abundance in WT and ETFDH KO HCT-116 and NT2197 cells was determined by RT-qPCR. PP1A mRNA was used as a control for HCT-116 experiments, while Actb mRNA was used as a control for NT2197 experiments. Data are presented as fold change in EIF4EBP1/PP1A and EIF4EBP2/PP1A (HCT-116) and Eif4ebp1/Actb and Eif4ebp2/Actb (NT2197) ratios relative to WT cells +/- SD (n=3), paired Student’s t test. (D) Polysome profiling of WT and ETFDH HCT-116 KO cells. The absorbance at 254 nm (AU 254 nm) was used to monitor distribution of the 40S-, 60S- ribosomal subunits, monosomes (80 S), and polysomes across the gradient. BCL6 mRNA abundance from polysome gradient fractions was obtained using RT-qPCR. Data are shown as a mean percentage of mRNA in each fraction relative to cumulative corresponding mRNA amount across the whole gradient +/- SD (n=3). (E–F) Binding events of BCL-6 to the promoters of EIF4EBP1 and EIF4EBP2 were determined by ChIP-qPCR. IgG was used as a negative control. Data are presented as % of input (n=3), *p<0.05, two-way ANOVA, Dunnett’s post-hoc test. (G) The levels of indicated proteins in ETFDH KO NT2197 cells transfected with siRNA targeting BCL-6 (siBCL-6) or control, scrambled siRNA (siCTRL) were determined by western blotting. β-Actin was used as loading control (representative blots of n=2). (H) Levels of indicated proteins in ETFDH KO 4T1 cells infected with shRNAs targeting BCL-6 (shBCL-6 #1 or shBCL-6#2) or control, scrambled shRNA (shCTRL) were monitored by western blotting. β-Actin served as loading control (representative blots of n=3). (I) Proliferation of ETFDH KO shCTRL, shBCL-6 #1, or shBCL-6#2 4T1 cells. Data are presented as cell count means (n=3), *p<0.05, one-way ANOVA, Dunnett’s post-hoc test.

Figure 5—source data 1

PDF files containing original western blots for Figure 5A and B, Figure 5G , and H, indicating the relevant bands.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig5-data1-v1.zip
Figure 5—source data 2

Original files for western blot analysis displayed in Figure 5A and B, Figure 5G , and H.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig5-data2-v1.zip

ETFDH acts as a haploinsufficient tumor suppressor

Data available in cBioPortal from 2683 samples across cancer types (Aaltonen et al., 2020) indicate that missense or truncating mutations in ETFDH are rare (0.3%) (Figure 6A). This suggests that ETFDH is not lost, but rather downregulated in human cancers. We observed that DNA methylation inhibitors, 5-azacytidine (5-aza) and 5-aza-2′-deoxycytidine (5-aza-2-deoxyC) increased ETFDH protein levels in HCT-116 cells (Figure 6B), thus suggesting that one of the potential mechanisms of downregulated ETFDH expression in neoplasia may stem from increased DNA methylation. Based on these observations, we developed a model in murine NT2197 cells that more closely reflects ETFDH downregulation, but not a complete loss, in human tumors using a method based on targeted poly(A) track insertion within endogenous genes (Powell et al., 2021). The length of the poly(A) track directly correlates with the reduction in amount of protein produced, allowing for the investigation of gene-dosage effects on ensuing phenotypes (Powell et al., 2021; Arthur et al., 2015). Accordingly, mutants were designed by inserting poly(A) tracks of 12 and 18 adenosines [12 A, equivalent to four consecutive AAA (Lys) codons (AAA)4; 18 A, equivalent to six consecutive AAA (Lys) codons (AAA)6], as well as a control track (CTRL) [six consecutive lysine AAG codons (AAG)6] (Figure 6—figure supplement 1A, see methods). Based on an Alphafold (version 2) rendered structure (Jumper et al., 2021; Varadi et al., 2022) of murine ETFDH, we chose residue 38 in an unstructured loop as an insertion site (Figure 6—figure supplement 1B). Of note, (AAA)6 and (AAG)6 stretches are translated in the equal number of Lys residues, whereby (AAA), but not (AAG), stretches decrease ETFDH protein levels in NT2197 cells (Figure 6C). (AAG)6 construct was thus used as a control to mitigate potential inadvertent effects of the insertion of poly-Lys on the ETFDH function. (AAA)4 and (AAA)6 stretches progressively reduced ETFDH protein abundance as compared to constructs harboring the (AAG)6 track (Figure 6C). The reduction in ETFDH protein levels was mirrored by a gene-dose-dependent increase in mTORC1 signaling, BCL-6 expression, and reduction in 4E-BP1, but not 4E-BP2 protein levels (Figure 6C). Accordingly, Eif4ebp1 mRNA levels were reduced in an ETFDH-dose-dependent manner, while Eif4ebp2 mRNA levels remained unaffected (Figure 6D). Moreover, reduction in ETFDH protein levels correlated with increased mtDNA content (Figure 6E), oxygen consumption (Figure 6F), ATP production from OXPHOS (JATPox) (Figure 6G), bioenergetic capacity (Figure 6H), and cell proliferation (Figure 6I). Finally, ETFDH KO NT2197 tumors carrying (AAA)6 insertions exhibit increased tumor growth and larger final tumor volumes relative to control tumors expressing (AAG)6 insertions in SCID-BEIGE mice (Figure 6J–K). Altogether, these findings show that ETFDH gene dosage influences neoplastic growth, thus suggesting that ETFDH may act as a haploinsufficient tumor suppressor.

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Reduced expression of ETFDH increases mitochondrial metabolism and tumor growth.

(A) ETFDH mutations from 2683 samples across multiple cancer types. The number of missense and truncating mutations are shown relative to samples with no mutations. Data are acquired from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium (Aaltonen et al., 2020), accessed from the cBioPortal server (https://www.cbioportal.org). (B) Levels of indicated proteins in WT HCT-116 cells treated with 2.5 µM 5-azacytidine (5-aza), 10 µM 5-aza-2' -deoxycytidine (5-aza-2-deoxyC), or a vehicle (DMSO) for 72 hr were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (C) Levels and the phosphorylation status of indicated proteins in ETFDH KO NT2197 cells expressing ETFDH harboring (AAG)6, (AAA)4, (AAA)6 tracks were determined by western blotting. β-Actin was used as a loading control (representative blots of n=3). (D) Eif4ebp1 and Eif4ebp2 mRNA abundance in (AAG)6, (AAA)4, and (AAA)6 NT2197 cells were determined by RT-qPCR. Actb was used as a housekeeping gene. Data are presented as fold change in Eif4ebp1/Actb and Eif4ebp2/Actb ratios relative to (AAG)6 NT2197 cells (n=3), *P<0.05, **P<0.01, one-way ANOVA, Dunnett’s post-hoc test. (E) Mitochondrial DNA content in (AAG)6, (AAA)4, and (AAA)6 NT2197 cells were monitored by qPCR. Mitochondrial DNA (mtDNA) content was normalized to genomic DNA (gDNA) content. Data are presented as mean fold change relative to (AAG)6 NT2197 cells -/+ SD (n=3), *P<0.05, **P<0.01, one-way ANOVA, Dunnett’s post-hoc test. (F) Oxygen consumption of (AAG)6, (AAA)4, and (AAA)6 NT2197 cells was determined using Seahorse bioanalyzer. Data are normalized to cell count and presented as means +/- SD (n=4), one-way ANOVA, Dunnett’s post-hoc test. (G–H) Bioenergetic Plot for Basal, FCCP, and Monensin J ATP fluxes (G) and Bioenergetic Capacity (H) derived from (AAG)6, (AAA)4, and (AAA)6 NT2197 cells. Data are presented as means +/- SD (n=4), one-way ANOVA, Dunnett’s post-hoc test. (I) Proliferation of (AAG)6, (AAA)4, and (AAA)6 NT2197 cells. ETFDH rescue measurements are the same as in Figure 1—figure supplement 1F. Data are presented as cell count means +/- SD (n=4), **P<0.01, one-way ANOVA, Dunnett’s post-hoc test. (J–K) Tumor growth of (AAG)6 and (AAA)6 NT2197 cells following mammary fat-pad injection (J) and endpoint tumor volumes (K). Growth was assessed using calipers. Data are presented as means +/- SEM ((AAG)6 n=6–12, (AAA)6 n=11–13), **P<0.01, unpaired Student’s t test. (L) Schematic representation of the model whereby reduction in ETFDH levels accelerates tumor growth. Reduction of ETFDH triggers accumulation of intracellular amino acids, thus increasing mTORC1 signaling. This is orchestrated with BCL-6-dependent reduction in 4E-BP1 levels to drive mitochondrial biogenesis and increase bioenergetic capacity of cancer cells, ultimately leading to more aggressive tumor growth.

Figure 6—source data 1

PDF files containing original western blots for Figure 6B and C, indicating the relevant bands.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig6-data1-v1.zip
Figure 6—source data 2

Original files for western blot analysis displayed in Figure 6B and C.

https://cdn.elifesciences.org/articles/106587/elife-106587-fig6-data2-v1.zip

Lastly, we determined whether the catalytic activity of ETFDH is required for its tumor suppressive activity. To achieve this, we re-expressed WT or ETFDH mutant (Y304A, G306E) with disrupted catalytic activity (Herrero Martín et al., 2024) in HCT-116 ETFDH KO cells. Notwithstanding that both ETFDH variants were expressed to comparable levels, in contrast to WT, ETFDH (Y304A, G306E) mutant failed to suppress mTORC1 signaling and decrease 4E-BP1 levels (Figure 6—figure supplement 1C), reduce proliferation (Figure 6—figure supplement 1D) or perturb the bioenergetic profiles of HCT-116 ETFDH KO cells (Figure 6—figure supplement 1E–F). Taken together, these data suggest the catalytic activity of ETFDH is required for its tumor suppressive actions.

Discussion

Notwithstanding that ETFDH mutations play a detrimental role in MADD metabolic disorders, we show that ETF insufficiency caused by reduced ETFDH expression plays a major role in rewiring cancer metabolism and signaling to stimulate neoplastic growth. Reduction of ETFDH levels appears to be a common feature of cancer cells and is likely a consequence of DNA hypermethylation (Figure 6B), an event that is frequently observed among tumor suppressor genes (Jones and Baylin, 2002). Indeed, ETFDH was found among the most under-expressed metabolic enzymes in a study that compiled cancer microarray datasets (Nilsson et al., 2014). Moreover, recent studies highlighted crosstalk between aberrant mTOR signaling and epigenetic perturbations (Smith et al., 2019; Harachi et al., 2020; Chen et al., 2023; Kim et al., 2024), which further reinforces the plausibility that epigenetic reprogramming in cancer cells may underpin the pro-neoplastic role of ETFDH downregulation and/or explain resulting phenotypes.

Intriguingly, although ETF insufficiency limits the flexibility of cancer cells in using fuels (e.g. lipids and amino acids) for OXPHOS, it triggers signaling events that establish a feed-forward loop via mTORC1, leading to increased BCL-6 levels and reduced EIF4EBP1 transcription (Figure 6L). ETF insufficiency thus orchestrates enhanced mTORC1 signaling with downregulation of 4E-BP1 protein levels to reprogram protein synthesis and ultimately increase mitochondrial biogenesis and functions, thereby providing a selective proliferative advantage to cancer cells (Figure 6L). To this end, ETF insufficiency allows cancer cells to trade their metabolic flexibility for increased bioenergetic capacity and a rewired signaling state that favors neoplastic growth. Importantly, while ETFDH plays an essential role in muscle, we did not observe a major effect of ETFDH disruption on proliferation, mTOR signaling or 4E-BP1 levels in non-transformed murine breast epithelial NMuMG cells. These observations suggest that ETF insufficiency causes remodeling of signaling and metabolic networks that are favourable to cancer cells.

Our findings are consistent with previously attributed tumor-suppressive effects of 4E-BP1 (Petroulakis et al., 2009; Hsieh et al., 2010; Polunovsky et al., 2000; Lynch et al., 2004; Avdulov et al., 2004; Rousseau et al., 1996). More recently, it has been demonstrated that under certain contexts, 4E-BPs may play a beneficial role for tumor survival. For instance, 4E-BPs may facilitate tumor growth by regulating global and selective mRNA translation under periods of cellular stress (Musa et al., 2016; Jewer et al., 2020), including suppression of fatty acid biosynthesis under glucose deprivation (Teleman et al., 2005; Levy et al., 2024). Collectively, these findings demonstrate that 4E-BPs are likely to play more complex, context-dependent roles during cancer progression than previously anticipated. Moreover, the role of different 4E-BP proteoforms in cancer was, in general, thought to be redundant. Our findings, however, demonstrate that in the context of ETF insufficiency, selective decrease in 4E-BP1, but not 4E-BP2 levels, plays a role in driving neoplastic growth, thereby suggesting that 4E-BP1 and 4E-BP2 may play hitherto unappreciated non-overlapping roles in neoplasia.

Several metabolites can be sensed via signaling partners upstream of mTORC1, including leucine, arginine, methionine/SAM, and threonine (Valenstein et al., 2025). Branched-chain amino acids (leucine, isoleucine, and valine), which are among the highest upregulated metabolites in ETFDH-deficient cells (Figure 3A), serve as ETFDH substrates and have been described to display strong activation capabilities towards mTORC1 in the literature (Appuhamy et al., 2012; Herningtyas et al., 2008). Glutamine can also activate mTORC1 through the Arf family of GTPases (Jewell et al., 2015). Indeed, glutamine can supplement the non-essential amino acid (NEAA) pool through transamination (Tan et al., 2017) and amino acid uptake (Chen et al., 2014). Accordingly, the maintenance of NEAA that are non-ETFDH substrates may be supported by the global metabolic rewiring fueled by enhanced glutamine metabolism in ETFDH-deficient cells. Deciphering the mechanisms leading to accumulation of specific amino acids and their role in ETFDH-dependent mTORC1 modulation is warranted.

In conclusion, we show that ETF insufficiency caused by reduced ETFDH expression remodels signaling and mitochondrial metabolism in a manner that drives neoplastic growth (Figure 6L), while either being neutral (NMuMG) or deleterious (muscle cells) in non-malignant cells. These findings explain how disruption of a mitochondrial enzyme, rather than reducing cellular fitness, is common in cancer because it increases mitochondrial energy flux to drive neoplastic growth.

Materials and methods

Cell lines and cell culture

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HCT-116 (RRID:CVCL_0291), NALM6 (RRID:CVCL_0092), and HEK293T (RRID:CVCL_0063) cells were obtained from American Type Culture Collection (ATCC) and verified using ATCC Cell line authentication service (STR profiling). NMuMG, NT2197, and MT4788 Parental and STAT1 KO cells were obtained by Dr. J. Ursini-Siegel’s lab, while 4T1 (4T1-526) cells were obtained from Dr. P.M. Siegel’s group. HEK293T and HCT-116 cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS), 1% penicillin/streptomycin and supplemented with 2 mM L-Glutamine to obtain a final concentration of 6 mM L-Glutamine. NALM6 and 4T1 cells were cultured in RPMI-1640 supplemented with 10% FBS, 1% penicillin/streptomycin, and 2 mM L-Glutamine. NMuMG and NT2197 cells were cultured in DMEM supplemented with 10% FBS, 1% penicillin/streptomycin, 2 mM L-Glutamine, 10 mg/mL insulin, 20 mM HEPES, pH 7.5. NT2197 culture media was supplemented with puromycin (2 μg/mL) as previously described (Ursini-Siegel et al., 2008). MT4788 were grown in DMEM, 2.5% FBS, mammary epithelial growth supplement (MEGS), 1% penicillin/streptomycin, and gentamycin as previously described (Totten et al., 2021). All cell lines were maintained at 37 °C and 5% CO2 in a humidified environment and tested periodically for mycoplasma according to the manufacturer’s instructions.

Animals

6–8 week-old SCID-BEIGE mice were purchased from Charles River Laboratories (Quebec, Canada, RRID:IMSR_CRL:250). Male SCID-BEIGE mice were used for intracaecal experiments, while female mice were used for mammary fat pad injections. All animal experiments were performed in accordance with the guidelines of the McGill University Animal Ethics Committee and the Canadian Council on Animal Care as approved by the facility animal care committee (protocol numbers MCGL-10212 and AUP#5129).

Xenograft experiments

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Mice were randomized prior to cell line injections. Luciferase-expressing HCT-116 cells (250,000 cells) were injected intracaecally in male (8–10 weeks old) SCID-BEIGE mice as previously described (Tabariès et al., 2021). Tumor growth was monitored and assessed by luminescence (IVIS spectrum, PerkinElmer). Animals were sacrificed after 50 days. Quantification of signal intensity was performed with Living Image software (PerkinElmer, RRID:SCR_014247). NT2197 mammary tumor cells (50,000 cells) were injected in mammary fat pads of 6–8 week female SCID-BEIGE mice. Tumor growth was monitored using calipers. Measurements were shown as once tumor volume reached 100∼200 mm3 and animals were sacrificed before tumors reached 1 cm3. Tumors were excised, placed in 10% formalin, and embedded for immunohistochemistry. No animals were excluded from experiments due to attrition or failure to complete the tumor volume measurements. Pilot experiments were carried out to estimate size effects pertinent to differences between tested groups/conditions. The number of animals/group that was used in the experiments was determined by calculating ~90% power to detect a ~40% difference in means between groups assuming a standard deviation of ~40% (alpha = 0.05).

Cell proliferation assay

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Cells were counted using a Countess automated cell counter (Invitrogen) and trypan blue exclusion was used for determining cell viability. Cells (HCT-116, NT2197, NMuMG, 4T1, NALM6) were seeded in 6-well dishes and counted after 3–5 days. HCT-116 cells were seeded on day 0 at a density of 100,000 cells for Figure 1—figure supplement 1E, Figure 3—figure supplement 1C, Figure 3—figure supplement 1E–G, Figure 4—figure supplements 1D00 and 50,000 cells for Figure 6—figure supplement 1D. NT2197 cells were seeded on day 0 at a density of 60,000 cells in Figure 6I, Figure 1—figure supplement 1F. NMuMG cells were seeded on day 0 at a density of 60,000 cells in Figure 1—figure supplement 1I. 4T1 cells were seeded on day 0 at a density of 60,000 cells in Figure 5I, Figure 1—figure supplement 1G. NALM6 cells were seeded on day 0 at a density of 20,000 cells in Figure 1—figure supplement 1H. For glutamine deprivation experiments, HCT-116 cells were seeded in full culture for 24 hr. The next day, cells were grown in media in the presence or absence of glutamine for 48 hr and then counted. For Torin1/BiS-35x treatment experiments (Figure 3—figure supplement 1C; Figure 3—figure supplement 1G), HCT-116 cells were seeded overnight. The next day, media was replaced with treatment media containing Torin1 (50 nM, 100 nM, 250 nM, 500 nM) or BiS-35X (1 nM, 10 nM, 100 nM, 1000 nM) or DMSO for 72 hr and then counted. Counting was done as a single-blind study.

Immunohistochemistry

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Immunohistochemical staining of colon human paraffin-embedded tissue array (https://tissuearray.com/, CO992b) was performed as previously described (Igelmann et al., 2021). Briefly, the tissue slide was baked at 55 °C for 30 min followed by incubation in xylene (2×5  min), 100% ethanol (2×5  min), 95% ethanol (2×5  min), 75% ethanol (1×5 min), and ddH2O (2×5 min). The slide was submerged in 10 mM sodium citrate buffer (pH 6.0), 0.05% Tween-20, and heat-induced epitope retrieval was carried out in a pressure cooker for 30 min. The slide was cooled for 20 min at room temperature, rinsed in ddH2O for 5 min, and washed with 1xTBS +0.3% Triton X-100 for 10 min, then 1xTBS for 5 min at room temperature. The slide was incubated in 3% H2O2 for 10 min at room temperature and washed with TBST (1xTBS+0.1% Tween-20) for 5 min to inactivate peroxidases. A liquid blocker pen (PAP Pen, DAKO) was used to circle tissues on the slide. The TMA was blocked for 1 hr at room temperature in the presence of DAKO Protein Block-Serum Free (DAKO, Cat#X0909). The slide was incubated overnight at 4 °C with the ETFDH antibody (1:500, ProteinTech 11109–1-AP). The next day, the slide was washed in TBST (3×5 min), and secondary incubation was performed for 30 min at room temperature with secondary antibody (Antibody background reducing diluent and HRP rabbit substrate (Cell Signaling, Cat#7074)). The slide was washed in TBST (3×5 min) and DAB (Di-amine-benzidine) peroxidase substrate (Vector, Cat# SK-4100) was applied to the slide for 5 min. The reaction was stopped by washing in ddH2O for 5 min. The slide was counterstained with filtered hematoxylin for 20 s and rinsed with ddH2O for 5 min. The slide was placed in 75% ethanol (1 min), 95% ethanol (1 min), 100% ethanol (1 min), and xylene (2×5 min). The slide was mounted with a cytoseal at 60 °C and dried in a fume hood overnight. Quantitation of sections was performed using Imagescope software (Aperio; RRID:SCR_014247).

Western blotting

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Western blotting was performed as previously described (Kim et al., 2024). Briefly, cells were washed twice with PBS and lysed with RIPA buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.5% sodium deoxycholate, 1 mM PMSF, 1 mM DTT, 1% NP40, 0.1% SDS), supplemented with 1 X protease inhibitor (Roche) and 1 x PhoSTOP (Roche). Lysates were cleared at 4 °C (10 min, 13,000 rpm). Protein concentrations were determined using BCA kit (Thermo Fisher). Lysates were mixed with 5 x Laemmli buffer (60 mM Tris-HCl (pH 6.8), 10% glycerol, 2% SDS, 5% 2-mercaptoethanol, 0.05% bromophenol blue) and boiled (95 °C for 5 min). Proteins were resolved by SDS-PAGE (Bio-Rad) and transferred via wet transfer apparatus (Bio-Rad) to nitrocellulose membranes (Amersham). Membranes were blocked in 5% BSA (dissolved in TBST (0.1% Tween 20 in 1xTBS)) and incubated in primary antibodies overnight at 4 °C. The next day, membranes were washed in TBST and incubated with HRP-conjugated secondary antibodies for 1 hr. Membranes were washed and applied with ECL (Bio-Rad) for 1 min. Exposure was carried out on an Azure c300 (Azure Biosystems). Images were quantified by ImageJ and analysed using GraphPad Prism 10; RRID:SCR_014247. The list of primary antibodies is described in Supplementary file 1.

For λ-phosphatase experiments, cells were lysed with RIPA and supplemented with 1 X protease inhibitor, without 1 x PhoSTOP. Protein samples were combined with NEBuffer for protein metallophosphatases (PMP), MnCl2, and λ-protein phosphatase for 30 min at 30 °C according to the manufacturer’s instructions (New England Biolabs).

Cap-binding pull-down assay

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The assay was carried out as previously described (Dowling et al., 2010). Briefly, NT2197 cells were seeded overnight in 150 mm plates. The next day, media was changed, and cells were washed with PBS and dissolved in lysis buffer containing 50 mM MOPS/KOH (7.4), 100 mM NaCl, 50 mM NaF, 2 mM EDTA, 2 mM EGTA, 1% NP40, 1% sodium deoxycholate, 7 mM β-mercaptoethanol, supplemented with 1 X protease inhibitor (Roche), and 1 x PhoSTOP (Roche). Lysates were cleared at 4 °C (10 min, 16,000 g). Protein concentrations in samples were elucidated by BCA (Thermo Fisher). Samples were equilibrated for 20 min rotating at 4 °C with m7-GDP-agarose beads (Jena Bioscience), then washed in buffer containing 50 mM MOPS/KOH (7.4), 100 mM NaCl, 50 mM NaF, 0.5 mM EDTA, 0.5 mM EGTA, 7 mM β-mercaptoethanol, 0.5 mM PMSF, 1 mM Na3VO4, and 0.1 mM GTP, by centrifugation (500 g for 1 min). Bound proteins were eluted from beads via boiling in loading buffer. Western blotting was performed on eluted proteins and input samples to assess for eIF4F complex formation using 4E-BP1, eIF4G1, eIF4E, and β-actin antibodies.

Flow cytometry

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NT2197 cells were seeded in 6-well dishes for 24 hr. The next day, cells were stained with 50 nM MitoTracker Deep Red (Thermo Fisher) for 15 min at 37 °C in the dark. Cells were washed, counted, and 100,000 cells were dissolved in 500 μl HBSS (Gibco). Samples were analyzed with the LSR Fortessa cytometer (Becton Dickinson, Mountain View, CA). Fluorescence intensity was detected by excitation at 644 nm and acquisition on the 665 /-A channel for MitoTracker Deep Red. ROS measurements were performed as previously described (Igelmann et al., 2021). Briefly, cells were incubated with H2DCFDA (Molecular Probes) for 30 min at 37 °C. Cells were trypsinized, washed, and resuspended in 500 μl HBSS. Samples were sorted on a BD FACS Canto II system. Fluorescence intensities were calculated by FlowJo (Tree Star, Inc; RRID:SCR_008520).

Mitochondrial DNA quantitation

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Quantitation of mitochondrial DNA was carried out as previously described (Morita et al., 2013). HCT-116 or NT2197 cells were seeded in 6-well dishes for 24 hr. Genomic and mitochondrial DNA were extracted using PureLink Genomic DNA Mini Kit (Thermo Fisher) and quantified by qPCR using SensiFAST SYBR Lo-ROX kit (Bioline).

Polysome-profiling assay

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Experiments were carried out as previously described (Gandin et al., 2014). Briefly, HCT-116 cells were seeded overnight in 150 mm plates. The next day, cells were washed in PBS containing 100 μg/ml cycloheximide and lysed in hypotonic buffer (5 mM Tris-HCl [pH 7.5], 2.5 mM MgCl2, 1.5 mM KCl, 100 μg/ml cycloheximide, 2 mM DTT, 0.5% Triton X-100, and 0.5% sodium deoxycholate). Samples were loaded onto 10–50% wt/vol sucrose density gradients (20 mM HEPES-KOH pH 7.6, 100 mM KCl, 5 mM MgCl2) and spun on a SW40Ti rotor (Beckman Coulter) at 4 °C (36,000 rpm for 2 hr). Samples were fractionated and recorded at OD 254 nm using an ISCO fractionator (Teledyne ISCO). RNA from fractions and input was extracted using TRIzol (Thermo Fisher) according to the manufacturer’s instructions. RNA from each fraction and input was isolated using TRIzol (Invitrogen) according to the manufacturer’s instructions. RT-qPCR was performed on fractions and input RNA (refer to section ‘RNA extraction and RT-qPCR’ for more information). Primers are listed in Supplementary file 2.

Puromycilation assay

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HCT-116 cells were grown in 10 cm dishes for 24 hr. The next day, cells were treated with puromycin (10 μg/ml) for 20 min. Cells treated with DMSO were used as negative controls. Cells were lysed and protein samples were quantified. Western blotting was performed (refer to ‘Western Blotting’ section for more information) and membranes were incubated in anti-puromycin antibody (Millipore, Cat#MABE343). Quantification of bands was performed by ImageJ (RRID:SCR_003070).

Stable isotope tracing

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Metabolite detection by gas chromatography-mass spectrometry (GC-MS) and stable isotope tracing analysis (SITA) was performed as previously described (Papadopoli et al., 2021). Briefly, NT2197 cells were seeded in 6-well dishes. Prior to 13C-glutamine SITA experiments, culture media was replaced with one whose composition contains 6 mM unlabeled glutamine for 2 hr. Afterwards, media was replaced with media containing 6 mM labeled ([U-13C])-glutamine media (Cambridge Isotope Laboratories, MA, USA; CLM-1822; L-glutamine ([U-13C5]), 99%) for 5, 15, 30, and 60 min. For 13C-leucine SITA experiments, culture media was replaced with one containing unlabeled leucine (0.105 g/L) for 2 hr, then incubated in media containing labeled (0.105 g/L) ([U-13C])-leucine media (Cambridge Isotope Laboratories, MA, USA; CLM-2262-H-0.1; L-leucine ([U-13C6]), 99%) for 24 hr. Both steady state and tracing samples were washed in cold saline solution (9 g/L NaCl) and scraped off with 80% methanol. Samples were sonicated at 4 °C for 10 min (high setting, 30 s on/30 s off cycles) to rupture cells. Lysates were centrifuged at 4 °C (14,000 g, 10 min), and supernatants were collected with addition of an internal standard (750 ng myristic acid-D27). Samples were dried overnight at 4 °C by speed-vac (Labconco). Dried pellets are resuspended in methoxyamine hydrochloride (10 mg/ml), sonicated, and cleared for 10 min. Samples are heated to 70 °C for 30 min then applied with N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) at 70 °C for 1 hr. Derivatized samples are injected into an Agilent 5975 C GC/MS (Agilent Technologies, CA, USA) using methods described previously (Papadopoli et al., 2021). 13C-tracer samples were run in parallel with unlabeled samples. MassHunter software (Agilent) was used to carry out mass isotopomer distribution analyses, with metabolites normalized to myristic acid-D27 and cell number. Isotopic distributions were corrected for naturally occurring isotopes using an in-house algorithm (McGuirk et al., 2013).

Steady-state analysis of nucleotides

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Steady-state nucleotide abundances were determined using liquid chromatography-mass spectrometry (LC-MS/MS). Cells were washed with 150 mM ammonium formate solution (4 °C, pH 7.4), scraped, and extracted with 600 μl of methanol/acetonitrile solution (31.6% MeOH/36.3% ACN). Cell lysis and homogenization was conducted by bead-beating with four ceramic beads (2 mm) for 30 s at 50 Hz using a TissueLyser II (Qiagen). Dichloromethane was added to samples, which were subsequently centrifuged to separate extracts into aqueous and organic layers. Supernatants from aqueous layers were dried by vacuum centrifugation at –4 °C overnight (Labconco). Dried samples were re-suspended in 50 μl of water and centrifuged at 1 °C. 5 μl of sample was injected onto an Agilent 6470 Triple Quadrupole (QQQ)–LC–MS/MS (Agilent Technologies). Separation of metabolites was conducted using a 1290 Infinity ultra-performance quaternary pump liquid chromatography system, with a mass spectrometer equipped with a Jet Stream electrospray ionization source operating in negative mode (Agilent Technologies), with the following settings: source-gas temperature (150 °C), flow (13  L min−1), nebulizer pressure (45  psi), and capillary voltage (2000  V). Chromatographic resolution of metabolites was conducted using a Zorbax Extend C18 column 1.8 μm, 2.1×150 mm2 with guard column 1.8 μm, 2.1×5 mm2 (Agilent Technologies). The gradient commenced at 100% mobile phase A (97% water, 3% methanol, 10 mM tributylamine, 15 mM acetic acid, 5 µM medronic acid) for 2.5 min, followed by a 5 min gradient to 20% mobile phase C (methanol, 10 mM tributylamine, 15 mM acetic acid, 5 µM medronic acid), a 5.5 min gradient to 45% C and a 7 min gradient to 99% C at a flow rate of 0.25  mL min−1. This was followed by a 4 min hold time at 100% mobile phase C. The column was restored by washing with 99% mobile phase D (90% ACN) for 3 min at 0.25 mL min−1, followed by an increase of the flow rate to 0.8 mL min−1 over 0.5 min and a 3.85 min hold, after which the flow rate was decreased to 0.6 mL min−1 over 0.15 min. The column was subsequently re-equilibrated at 100% A over 0.75 min, during which the flow rate was decreased to 0.4 mL min−1 and held for 7.65 min. The flow was brought back to forward flow at 0.25 mL min−1 1 min before the next injection. The column temperature was maintained at 35 °C. Data were analyzed by using MassHunter Quantitative Data Analysis B.10.00 (Agilent Technologies). Data presented are peak area normalized by cell number. Authentic metabolite standards (Sigma Aldrich) were used to obtain reaction monitoring parameters (qualifier/quantifier ions and retention times). All LC/MS grade solvents and salts are purchased from Fisher.

RNA extraction and RT-qPCR

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HCT-116 and NT2197 cells were plated in 6-well dishes and extracted using TRIzol (Ambion) or Aurum Total RNA Mini Kit (Bio-Rad) following the manufacturer’s instructions. cDNA was synthesized from purified RNA using SensiFAST cDNA Synthesis Kit (Bioline), as per the manufacturer’s protocol. RT-qPCR was performed using SensiFAST SYBR Lo-ROX kit (Bioline). Primer sequences are found in Supplementary file 2.

Chromatin immunoprecipitation (ChIP)

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Experiments were performed as previously described (Kim et al., 2024). Briefly, HCT-116 cells were grown in 150 mm dishes and fixed in 4% formaldehyde for 10 min, followed by centrifugation. Pellets were resuspended in ChIP buffer (50 nM Tris (pH 8), 100 mM NaCl, 5 mM EDTA, 1 X PMSF, 2 mM NaF, 0.25% NP-40, 0.25% Triton X-100, 0.25% Sodium Deoxycholate, 0.005% SDS) supplemented with 1 x cOmplete protease inhibitor (Roche). Samples are sonicated using Sonic Dismembrator Model 500 (Thermo Fisher) at 5 cycles at 20% power, 5 cycles at 25% power, five cycles at 30% power; each cycle is 10 s. Lysates are spun at 4 °C (14,000 rpm, 10 min), and protein concentration was measured from supernatants using the BCA kit (Thermo Fisher). Samples were loaded onto Protein G Plus-Agarose Suspension Beads (Millipore) and pre-cleared at 4 °C for 3 hours. Input samples were collected, and immunoprecipitation was performed with anti-BCL6 (Cell Signaling, #5650) or anti-IgG (Cell Signaling, #2729) on the remainder of the sample overnight at 4 °C. The following day, samples were washed in three wash buffers (20 mM Tris (pH 8), 2 mM EDTA, 0.10% SDS, 1% Triton X-100 with 150/200/599 mM for Wash1/2/3 buffers, respectively), then a wash with LiCl buffer (10 mM Tris (pH 8), 1 mM EDTA, 0.25 M LiCl, 1% NP-40, 1% Sodium Deoxycholate), and two washes with TE buffer (10 mM Tris (pH 8), 1 mM EDTA). Samples were eluted in elution buffer (0.1 M NaHCO3, 1% SDS) and de-crosslinked at 65 °C overnight. The following day, proteinase K was applied to samples and heated at 42 °C for 1 hr. DNA was purified and collected using a DNA collection column (BioBasic). ChIP-qPCR was performed for EIF4EBP1 and EIF4EBP2 using sequences found in Supplementary file 3.

Respirometry assay

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Oxygen consumption (OCR) and extracellular acidification rate (ECAR) were measured using Seahorse XFe24 and XFe96 analyzers (Agilent) with the Mito Stress Test (Agilent) and Palmitate Oxidation Assay (Agilent), according to the manufacturer’s instructions. Briefly, HCT-116 and NT2197 cells were seeded in Seahorse culture plates (XFe24: 50,000 cells or XFe96: 20,000 cells) and incubated at 37 °C. Cells were washed twice and incubated in assay media. For mito stress test experiments, assay media is composed of 10 mM glucose, 2 mM glutamine, and 1 mM sodium pyruvate. Injections include oligomycin (1 µM), FCCP (1 µM), rotenone/antimycin A (1 µM), and monensin (20 µM). ATP production from OXPHOS (ATPox) or glycolysis (ATPglyc) were quantified using algorithms presented previously (Mookerjee et al., 2017). For the palmitate oxidation assay, cells were seeded in substrate-limited media (DMEM, 1% FBS, 0.5 mM glucose, 1 mM glutamine, 0.5 mM carnitine). The next day, cells are washed with FAO Buffer (includes 0.5 mM glucose, 0.5 mM carnitine, and 5 mM HEPES). Immediately prior to the run, palmitate-BSA or BSA control is added to the cell media. Basal respiration was recorded and values were normalized to cell counts.

Soft agar/colony formation assay

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Experiments were conducted as previously shown (Borowicz et al., 2014). Briefly, 6-well dishes are filled with a bottom layer solution (1:1; 1% noble agar and 2 X culture media (2 X DMEM, 20% FBS, 2% Pen/Strep/Glutamine)) per well. Dishes are incubated at room temperature for 30 min to allow the solution to solidify. 5000–10,000 HCT-116 and NT2197 cells are mixed 1:1 with 0.6% noble agar and 2 X culture media. 1.5 ml of this mixture is transferred on top of the solidified agar wells. Dishes are incubated at room temperature to allow the top layer to solidify. Cells are grown at 37 °C until colony formation is present. Dishes were stained with nitroblue tetrazolium (Sigma), and colonies were counted. Counting was done as a single-blind study.

Protein stability assay

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HCT-116 cells were seeded in 10 cm dishes. The next day, cells were treated with cycloheximide (CHX) (50 µg/ml) or DMSO for 2 hr. Furthermore, cells were treated with the proteasomal inhibitor, MG-132 (10 µM) or DMSO for 2 hr. Final time point for all conditions was 4 hr. Protein was extracted, quantified, and western blotting was performed for ETFDH, p21, 4E-BP1, or β-actin (refer to ‘Western Blotting’ section for more information).

RNA stability assay

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The assay was performed as previously described (Topisirovic et al., 2009). Briefly, HCT-116 cells were seeded in 6-well dishes. The next day, cells were treated with actinomycin D (ActD) for 2, 4, 8, and 24 hr. RNA was extracted, cDNA was synthesized, and RT-qPCR was performed (refer to ‘RNA extraction and RT-qPCR’ for more information).

Generation of ETFDH knockout cell lines

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To deplete endogenous ETFDH expression in HCT-116 and NALM6 cells, two guide RNAs (gRNAs) targeting exon 6 and exon 9 of human ETFDH (against ETFDH sequences AGGTTGGCCGAATGCTAGGATGG and GATGTAGGGATACAAAAGGATGG) (refer to Supplementary file 4 for gRNA sequences) were designed using CHOPCHOP (https://chopchop.cbu.uib.no/) (Labun et al., 2019) and purchased with the appropriate overhangs to be cloned into LentiCRISPRv2(GFP) (RRID:Addgene_82416). Cloning was carried out as previously described (Sanjana et al., 2014; Shalem et al., 2014), with the following modifications: BsmBI-v2 (New England Biolabs) was used to digest the lentiCRISPRv2 plasmid; plasmid DNA was recovered using Zymoclean Gel DNA Recovery Kit (Zymo Research), and ligation was performed overnight at 4 °C. Transformation of the ligated plasmid was carried out at 42 °C in chemically competent Stbl3 E. coli (Thermo Fisher Scientific, C737303). Bacteria were plated on Lysogeny Broth (LB)-agar plates containing ampicillin (1 µg/mL) (BioBasic). Extraction of plasmid DNA was conducted using the QIAprep Spin Miniprep Kit (Qiagen), and samples were sequenced using primer for human U6 promoter (refer to Supplementary file 5 for sequence). Nucleofection was used to deliver the validated lentiCRISPRv2 plasmids into HCT-116 and NALM6 cells. Transfections of the plasmids were carried out in Nucleofector cuvettes, according to the optimized protocols for NALM6 and HCT-116 cells (Lonza Bioscience), with 1×106 cells used and 1 X PBS used instead of the commercial 4-D Nucleofector solution. Post-nucleofection, cells were allowed to grow for 72 hr at 37 °C, 5% CO2, prior to cell sorting (BD FACSAria Fusion Flow Cytometer (BD Biosciences)). Cells expressing GFP were selected and grown in 96-well plates containing conditioned media (45% filtered culture media, 45% fresh media, and 10% FBS). After two weeks, sorted clones were transferred into 24-well plates and later into 6-well plates to be collected and validated. Depletion of endogenous ETFDH protein levels in both HCT-116 and NALM6 cells was initially confirmed by immunoblotting. Candidate clones with loss of ETFDH expression were kept for further validation. Moreover, the genomic DNA loci of candidate clones were sequenced and analyzed for insertion–deletion mutations (INDELs). Two sets of PCR primers were designed in regions flanking the CRISPR target sites (refer to Supplementary file 5 for validation PCR sequences). Extraction of genomic DNA for each clone was carried out using the PureLink Genomic DNA Mini Kit (Fisher Scientific). Two rounds of polymerase chain reaction (PCR) on the extracted genomic DNA (35 PCR cycles with Set 1 followed by 45 PCR cycles with Set 2) were carried out and gel electrophoresis was utilized to separate the PCR products. The smaller PCR product for each clone was excised and purified using the Zymoclean Gel DNA Recovery Kit (Zymo Research). The products were sequenced, and chromatograms were analyzed for the presence of mutations causing a premature stop codon or a frameshift. Insertion–deletion mutations (INDELs) in ETFDH were verified by PCR followed by sequencing, while the absence of ETFDH protein was confirmed by western blotting for HCT-116 cells and NALM6 cells.

To deplete ETFDH expression in NMuMG, NT2197, and 4T1 cells, two guides (gRNAs) targeting exon 2 of mouse Etfdh (against mEtfdh sequences 5’-ATTTTTATGCAGCGTATCACTGG-3’ and 5’-GAACATCTTGGAGCACACAGAGG-3’) were designed using CHOPCHOP and cloned in LentiCRISPRv2(GFP) (Addgene #82416) and sequenced as shown above. For lentiviral production, 2.5×105 HEK293T cells were seeded overnight in a 6 cm dish (Sarstedt). The next day, cells were co-transfected with 4 µg of respective lentiCRISPRV2 GFP plasmid containing either one of the gRNA inserts or empty-lentiCRISPRV2 GFP plasmid, 2.66 µg of psPAX2 packaging plasmid (RRID:Addgene_35002), and 1.66 µg of pMD2.G plasmid (RRID:Addgene_12259) using the jetPRIME transfection reagent as described by the manufacturer’s protocol (Polyplus transfection). Growth media was changed after 24 hr. After 48 hr, viral supernatant was filtered (0.45 µm filter; (Frogga Bio)), mixed 1:1 with fresh culture media, and applied to target cells grown in 6-well plates along with 8 µg/mL polybrene (Sigma-Aldrich). Cells were re-transduced the following two days with viral supernatant. Cells were allowed to recover for 48 hr post-transduction. Cells were sorted into 96-well plates (Sarstedt) and grown to generate stable cell lines, as shown above. Depletion of mouse Etfdh protein levels was initially confirmed via immunoblotting for NT2197, 4T1, and NMuMG. The presence of INDELs leading to a premature stop codon or frameshift was determined by sequencing as shown above. Two sets of PCR primers were designed in the regions flanking the CRISPR target sites (refer to Supplementary file 5).

Generation of ETFDH rescue cell lines

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To rescue human ETFDH expression in HCT-116 cells, ETFDH(NM_004453) Human Tagged ORF was cloned into pLenti-ETFDH-C-Myc-DDK-P2A-Puro plasmid according to the manufacturer’s instructions (Origene) to generate the resulting plasmid, pLenti-ETFDH-C-Myc-DDK-P2A-Puro. To re-express mutant ETFDH (Y304A, G306E), WT ETFDH, or ORF-Stuffer lentiviral plasmids conferring hygromycin B resistance were designed using VectorBuilder (VB250613-1532mdm, VB250613-1530fzr, and VB900172-2887sfj, respectively). Plasmids were transfected with psPAX2, pMD2.G into HEK293T cells with JetPrime reagent (Polyplus) according to the manufacturer’s instructions. Viral supernatants were filtered (0.45 µM) and applied to HCT-116 ETFDH KO cells with polybrene (6 µg/mL). After 6 hr, cells were re-infected overnight. After 2 days, selection with puromycin (4 µg/ml) was performed for 72 hr. Uninfected cells were selected with puromycin to serve as negative controls. The reintroduction of ETFDH expression or control plasmid in the knockout cells was confirmed via western blotting.

To rescue ETFDH expression in NT2197 ETFDH KO cells, a lentiviral plasmid conferring hygromycin B resistance and expressing mEtfdh with PAM site mutations or expressing an ORF-Stuffer was created using VectorBuilder (VB221206-1257xet and VB900123-2599cba, respectively). Lentivirus production and transduction were carried out as previously mentioned for three consecutive days. After 48 hr of recovery, cells were selected with 1 mg/mL hygromycin B for 3 days. Cells were confirmed by Western blotting.

Generation of Poly(A) track ETFDH variants

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Mutants were designed by inserting poly(A) tracks of multiple adenosines: control track (CTRL) [six consecutive lysine AAG codons (AAG)6], 12 adenosines [12 A, equivalent to four consecutive AAA (Lys) codons (AAA)4]; 18 adenosines [18 A, equivalent to six consecutive AAA (Lys) codons (AAA)6] within the rescue plasmid pLV[Exp]-Hygro-EF1A>mEtfdh. The polyA track insertion site was determined using AlphaFold (version 2) (Jumper et al., 2021; Varadi et al., 2022) to minimize unintended effects caused by the poly-lysine stretches on the possible mitochondrial import sequence. The polyA tracks were inserted following residue 38, in an unstructured loop of the Etfdh protein. Site-directed mutagenesis was performed using the QuikChange II site-directed mutagenesis kit, according to the manufacturer’s instructions (Agilent). Briefly, cycling parameters are followed as: Cycle 1: 95 °C for 1 min; Cycle 2: 95 °C for 30 s, 60 °C for 30 s, 68 °C for 10 min; Cycle 3: 68 °C for 7 min. The resulting plasmids were generated: pLV[Exp]-Hygro-EF1A>mEtfdh-AAG6, pLV[Exp]-Hygro-EF1A>mEtfdh-AAA4, pLV[Exp]-Hygro-EF1A>mEtfdh-AAA6. To generate the poly(A) track Etfdh mutant lines in NT2197 cells (termed AAG6, AAA4, AAA6), lentivirus production and transduction into NT2197 ETFDH KO cells was carried out as previously mentioned for three consecutive days. After 48 hr of recovery, cells were selected with 1 mg/mL hygromycin B for 3–4 days. ETFDH expression was confirmed by western blotting.

Generation of cells expressing EIF4EBP1

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pBABE-puro-EV and pBABE-puro-4E-BP1 plasmids were used to overexpress 4E-BP1 in HCT-116 ETFDH KO cells. Retrovirus production and transduction was carried out similarly to lentivirus production, except 2.66 μg of pUMVC packaging plasmid (RRID:Addgene_8449) was utilized instead of psPAX2 (RRID:Addgene_12260). Following three consecutive days of viral transduction, cells were allowed to recover for 48 hr prior to selection with 2 μg/mL puromycin (Sigma-Aldrich) for 3 days. To overexpress 4e-bp1 in NT2197 ETFDH KO cells, plasmids containing m4e-bp1 or ORF-Stuffer were generated by VectorBuilder (VB221206-1246sbx and VB900123-2599cba). Lentivirus production and transduction was carried out as previously mentioned for three consecutive days prior to a 48 hr recovery period. After this period, selection with hygromycin B (1 mg/mL) was conducted for 5–7 days. Validation was observed by western blotting.

Generation of knockdown models by shRNA/siRNA

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Knockdown using shRNA was performed as previously described (Hulea et al., 2018). For BCL6 knockdown experiments, the following shRNA vectors were used: pLKO.1 Non-Target shRNA Control (Sigma, SHC002), BCL6 shRNA #1 (Sigma, TRCN0000084654), or BCL6 shRNA #2 (TRCN0000084655). For RAPTOR/RICTOR knockdown experiments, the following shRNA vectors were used: scramble shRNA (Addgene, Plasmid #1864), RAPTOR_1 shRNA (Addgene, Plasmid #1857), and RICTOR_1 shRNA (Addgene, Plasmid #1853). Briefly, HEK293T cells were transfected with psPAX2, pMD2.G, or target pLKO.1 lentiviral shRNA vectors with JetPrime reagent (Polyplus) according to the manufacturer’s instructions. Viral supernatants were filtered (0.45 µM) and transferred to target cells with polybrene (6 µg/mL). After 6 hr, cells were re-infected overnight. After 2 days, selection with puromycin (4 µg/ml) was performed for 72 hr. Uninfected cells were selected with puromycin to serve as negative controls. Cells were seeded and processed for protein extraction or cell proliferation. Validation was confirmed by western blotting for BCL6 knockdown and RAPTOR or RICTOR knockdown.

Knockdown using siRNA was performed as previously described (Papadopoli et al., 2021). Briefly, target cells were transfected for 24 hr with 50 nM Silencer Negative Control No.1 (Thermo Fisher, AM4611) or BCL6 siRNA (Thermo Fisher, Cat#4390771) using JetPrime reagent (Polyplus) according to the manufacturer’s instructions. Cells were re-transfected the next day for 24 hr, then processed for protein extraction. Validation was confirmed by western blotting.

Expression profiles from TCGA/GTEx databases

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Expression of ETFDH and EIF4EBP1 mRNA across both normal and tumor samples were obtained from The Cancer Genome Atlas (TCGA) and GTEx repositories. The expression data were normalized using the TMM (trimmed mean of M values) method and presented as log2 counts per million. Correlations between the genes were assessed using Pearson’s correlation coefficient.

Quantification and statistical analysis

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Statistical analysis was performed using GraphPad Prism 10 (RRID:SCR_002798). Data are presented as mean +/- SD of independent experiments, unless stated otherwise. Technical replicates were averaged from two to three independent experiments. Details on data quantification, presentation, and statistical analysis are included in figure legends.

Material and correspondence

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Further information and requests for reagents may be directed upon reasonable request by the lead contact, Ivan Topisirovic (ivan.topisirovic@mcgill.ca).

Data availability

Plasmids generated in this study are deposited on Addgene. The original data are available at Mendeley Data (doi: 10.17632/p4wxkr3vdm.1). Metabolomics data are available at MetaboLights (MTBLS11048).

The following data sets were generated
    1. Papadopoli D
    (2026) Mendeley Data
    Mitochondrial ETF insufficiency drives neoplastic growth by selectively optimizing cancer bioenergetics.
    https://doi.org/10.17632/p4wxkr3vdm.1
    1. Papadopoli D
    (2026) Metabolights
    ID MTBLS11048. Mitochondrial ETF insufficiency drives neoplastic growth by selectively optimizing cancer bioenergetics.
    https://www.ebi.ac.uk/metabolights/MTBLS11048

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    216. Covington K
    217. Cowin PA
    218. Craft B
    219. Craft D
    220. Creighton CJ
    221. Cun Y
    222. Curley E
    223. Cutcutache I
    224. Czajka K
    225. Czerniak B
    226. Dagg RA
    227. Danilova L
    228. Davi MV
    229. Davidson NR
    230. Davies H
    231. Davis IJ
    232. Davis-Dusenbery BN
    233. Dawson KJ
    234. De La Vega FM
    235. De Paoli-Iseppi R
    236. Defreitas T
    237. Tos APD
    238. Delaneau O
    239. Demchok JA
    240. Demeulemeester J
    241. Demidov GM
    242. Demircioğlu D
    243. Dennis NM
    244. Denroche RE
    245. Dentro SC
    246. Desai N
    247. Deshpande V
    248. Deshwar AG
    249. Desmedt C
    250. Deu-Pons J
    251. Dhalla N
    252. Dhani NC
    253. Dhingra P
    254. Dhir R
    255. DiBiase A
    256. Diamanti K
    257. Ding L
    258. Ding S
    259. Dinh HQ
    260. Dirix L
    261. Doddapaneni H
    262. Donmez N
    263. Dow MT
    264. Drapkin R
    265. Drechsel O
    266. Drews RM
    267. Serge S
    268. Dudderidge T
    269. Dueso-Barroso A
    270. Dunford AJ
    271. Dunn M
    272. Dursi LJ
    273. Duthie FR
    274. Dutton-Regester K
    275. Eagles J
    276. Easton DF
    277. Edmonds S
    278. Edwards PA
    279. Edwards SE
    280. Eeles RA
    281. Ehinger A
    282. Eils J
    283. Eils R
    284. El-Naggar A
    285. Eldridge M
    286. Ellrott K
    287. Erkek S
    288. Escaramis G
    289. Espiritu SMG
    290. Estivill X
    291. Etemadmoghadam D
    292. Eyfjord JE
    293. Faltas BM
    294. Fan D
    295. Fan Y
    296. Faquin WC
    297. Farcas C
    298. Fassan M
    299. Fatima A
    300. Favero F
    301. Fayzullaev N
    302. Felau I
    303. Fereday S
    304. Ferguson ML
    305. Ferretti V
    306. Feuerbach L
    307. Field MA
    308. Fink JL
    309. Finocchiaro G
    310. Fisher C
    311. Fittall MW
    312. Fitzgerald A
    313. Fitzgerald RC
    314. Flanagan AM
    315. Fleshner NE
    316. Flicek P
    317. Foekens JA
    318. Fong KM
    319. Fonseca NA
    320. Foster CS
    321. Fox NS
    322. Fraser M
    323. Frazer S
    324. Frenkel-Morgenstern M
    325. Friedman W
    326. Frigola J
    327. Fronick CC
    328. Fujimoto A
    329. Fujita M
    330. Fukayama M
    331. Fulton LA
    332. Fulton RS
    333. Furuta M
    334. Futreal PA
    335. Füllgrabe A
    336. Gabriel SB
    337. Gallinger S
    338. Gambacorti-Passerini C
    339. Gao J
    340. Gao S
    341. Garraway L
    342. Garred Ø
    343. Garrison E
    344. Garsed DW
    345. Gehlenborg N
    346. Gelpi JLL
    347. George J
    348. Gerhard DS
    349. Gerhauser C
    350. Gershenwald JE
    351. Gerstein M
    352. Gerstung M
    353. Getz G
    354. Ghori M
    355. Ghossein R
    356. Giama NH
    357. Gibbs RA
    358. Gibson B
    359. Gill AJ
    360. Gill P
    361. Giri DD
    362. Glodzik D
    363. Gnanapragasam VJ
    364. Goebler ME
    365. Goldman MJ
    366. Gomez C
    367. Gonzalez S
    368. Gonzalez-Perez A
    369. Gordenin DA
    370. Gossage J
    371. Gotoh K
    372. Govindan R
    373. Grabau D
    374. Graham JS
    375. Grant RC
    376. Green AR
    377. Green E
    378. Greger L
    379. Grehan N
    380. Grimaldi S
    381. Grimmond SM
    382. Grossman RL
    383. Grundhoff A
    384. Gundem G
    385. Guo Q
    386. Gupta M
    387. Gupta S
    388. Gut IG
    389. Gut M
    390. Göke J
    391. Ha G
    392. Haake A
    393. Haan D
    394. Haas S
    395. Haase K
    396. Haber JE
    397. Habermann N
    398. Hach F
    399. Haider S
    400. Hama N
    401. Hamdy FC
    402. Hamilton A
    403. Hamilton MP
    404. Han L
    405. Hanna GB
    406. Hansmann M
    407. Haradhvala NJ
    408. Harismendy O
    409. Harliwong I
    410. Harmanci AO
    411. Harrington E
    412. Hasegawa T
    413. Haussler D
    414. Hawkins S
    415. Hayami S
    416. Hayashi S
    417. Hayes DN
    418. Hayes SJ
    419. Hayward NK
    420. Hazell S
    421. He Y
    422. Heath AP
    423. Heath SC
    424. Hedley D
    425. Hegde AM
    426. Heiman DI
    427. Heinold MC
    428. Heins Z
    429. Heisler LE
    430. Hellstrom-Lindberg E
    431. Helmy M
    432. Heo SG
    433. Hepperla AJ
    434. Heredia-Genestar JM
    435. Herrmann C
    436. Hersey P
    437. Hess JM
    438. Hilmarsdottir H
    439. Hinton J
    440. Hirano S
    441. Hiraoka N
    442. Hoadley KA
    443. Hobolth A
    444. Hodzic E
    445. Hoell JI
    446. Hoffmann S
    447. Hofmann O
    448. Holbrook A
    449. Holik AZ
    450. Hollingsworth MA
    451. Holmes O
    452. Holt RA
    453. Hong C
    454. Hong EP
    455. Hong JH
    456. Hooijer GK
    457. Hornshøj H
    458. Hosoda F
    459. Hou Y
    460. Hovestadt V
    461. Howat W
    462. Hoyle AP
    463. Hruban RH
    464. Hu J
    465. Hu T
    466. Hua X
    467. Huang K
    468. Huang M
    469. Huang MN
    470. Huang V
    471. Huang Y
    472. Huber W
    473. Hudson TJ
    474. Hummel M
    475. Hung JA
    476. Huntsman D
    477. Hupp TR
    478. Huse J
    479. Huska MR
    480. Hutter B
    481. Hutter CM
    482. Hübschmann D
    483. Iacobuzio-Donahue CA
    484. Imbusch CD
    485. Imielinski M
    486. Imoto S
    487. Isaacs WB
    488. Isaev K
    489. Ishikawa S
    490. Iskar M
    491. Islam SMA
    492. Ittmann M
    493. Ivkovic S
    494. Izarzugaza JMG
    495. Jacquemier J
    496. Jakrot V
    497. Jamieson NB
    498. Jang GH
    499. Jang SJ
    500. Jayaseelan JC
    501. Jayasinghe R
    502. Jefferys SR
    503. Jegalian K
    504. Jennings JL
    505. Jeon S-H
    506. Jerman L
    507. Ji Y
    508. Jiao W
    509. Johansson PA
    510. Johns AL
    511. Johns J
    512. Johnson R
    513. Johnson TA
    514. Jolly C
    515. Joly Y
    516. Jonasson JG
    517. Jones CD
    518. Jones DR
    519. Jones DTW
    520. Jones N
    521. Jones SJM
    522. Jonkers J
    523. Ju YS
    524. Juhl H
    525. Jung J
    526. Juul M
    527. Juul RI
    528. Juul S
    529. Jäger N
    530. Kabbe R
    531. Kahles A
    532. Kahraman A
    533. Kaiser VB
    534. Kakavand H
    535. Kalimuthu S
    536. von Kalle C
    537. Kang KJ
    538. Karaszi K
    539. Karlan B
    540. Karlić R
    541. Karsch D
    542. Kasaian K
    543. Kassahn KS
    544. Katai H
    545. Kato M
    546. Katoh H
    547. Kawakami Y
    548. Kay JD
    549. Kazakoff SH
    550. Kazanov MD
    551. Keays M
    552. Kebebew E
    553. Kefford RF
    554. Kellis M
    555. Kench JG
    556. Kennedy CJ
    557. Kerssemakers JNA
    558. Khoo D
    559. Khoo V
    560. Khuntikeo N
    561. Khurana E
    562. Kilpinen H
    563. Kim HK
    564. Kim H-L
    565. Kim H-Y
    566. Kim H
    567. Kim J
    568. Kim J
    569. Kim JK
    570. Kim Y
    571. King TA
    572. Klapper W
    573. Kleinheinz K
    574. Klimczak LJ
    575. Knappskog S
    576. Kneba M
    577. Knoppers BM
    578. Koh Y
    579. Komorowski J
    580. Komura D
    581. Komura M
    582. Kong G
    583. Kool M
    584. Korbel JO
    585. Korchina V
    586. Korshunov A
    587. Koscher M
    588. Koster R
    589. Kote-Jarai Z
    590. Koures A
    591. Kovacevic M
    592. Kremeyer B
    593. Kretzmer H
    594. Kreuz M
    595. Krishnamurthy S
    596. Kube D
    597. Kumar K
    598. Kumar P
    599. Kumar S
    600. Kumar Y
    601. Kundra R
    602. Kübler K
    603. Küppers R
    604. Lagergren J
    605. Lai PH
    606. Laird PW
    607. Lakhani SR
    608. Lalansingh CM
    609. Lalonde E
    610. Lamaze FC
    611. Lambert A
    612. Lander E
    613. Landgraf P
    614. Landoni L
    615. Langerød A
    616. Lanzós A
    617. Larsimont D
    618. Larsson E
    619. Lathrop M
    620. Lau LMS
    621. Lawerenz C
    622. Lawlor RT
    623. Lawrence MS
    624. Lazar AJ
    625. Lazic AM
    626. Le X
    627. Lee D
    628. Lee D
    629. Lee EA
    630. Lee HJ
    631. Lee JJ-K
    632. Lee J-Y
    633. Lee J
    634. Lee MTM
    635. Lee-Six H
    636. Lehmann K-V
    637. Lehrach H
    638. Lenze D
    639. Leonard CR
    640. Leongamornlert DA
    641. Leshchiner I
    642. Letourneau L
    643. Letunic I
    644. Levine DA
    645. Lewis L
    646. Ley T
    647. Li C
    648. Li CH
    649. Li HI
    650. Li J
    651. Li L
    652. Li S
    653. Li S
    654. Li X
    655. Li X
    656. Li X
    657. Li Y
    658. Liang H
    659. Liang S-B
    660. Lichter P
    661. Lin P
    662. Lin Z
    663. Linehan WM
    664. Lingjærde OC
    665. Liu D
    666. Liu EM
    667. Liu F-FF
    668. Liu F
    669. Liu J
    670. Liu X
    671. Livingstone J
    672. Livitz D
    673. Livni N
    674. Lochovsky L
    675. Loeffler M
    676. Long GV
    677. Lopez-Guillermo A
    678. Lou S
    679. Louis DN
    680. Lovat LB
    681. Lu Y
    682. Lu Y-J
    683. Lu Y
    684. Luchini C
    685. Lungu I
    686. Luo X
    687. Luxton HJ
    688. Lynch AG
    689. Lype L
    690. López C
    691. López-Otín C
    692. Ma EZ
    693. Ma Y
    694. MacGrogan G
    695. MacRae S
    696. Macintyre G
    697. Madsen T
    698. Maejima K
    699. Mafficini A
    700. Maglinte DT
    701. Maitra A
    702. Majumder PP
    703. Malcovati L
    704. Malikic S
    705. Malleo G
    706. Mann GJ
    707. Mantovani-Löffler L
    708. Marchal K
    709. Marchegiani G
    710. Mardis ER
    711. Margolin AA
    712. Marin MG
    713. Markowetz F
    714. Markowski J
    715. Marks J
    716. Marques-Bonet T
    717. Marra MA
    718. Marsden L
    719. Martens JWM
    720. Martin S
    721. Martin-Subero JI
    722. Martincorena I
    723. Martinez-Fundichely A
    724. Maruvka YE
    725. Mashl RJ
    726. Massie CE
    727. Matthew TJ
    728. Matthews L
    729. Mayer E
    730. Mayes S
    731. Mayo M
    732. Mbabaali F
    733. McCune K
    734. McDermott U
    735. McGillivray PD
    736. McLellan MD
    737. McPherson JD
    738. McPherson JR
    739. McPherson TA
    740. Meier SR
    741. Meng A
    742. Meng S
    743. Menzies A
    744. Merrett ND
    745. Merson S
    746. Meyerson M
    747. Meyerson W
    748. Mieczkowski PA
    749. Mihaiescu GL
    750. Mijalkovic S
    751. Mikkelsen T
    752. Milella M
    753. Mileshkin L
    754. Miller CA
    755. Miller DK
    756. Miller JK
    757. Mills GB
    758. Milovanovic A
    759. Minner S
    760. Miotto M
    761. Arnau GM
    762. Mirabello L
    763. Mitchell C
    764. Mitchell TJ
    765. Miyano S
    766. Miyoshi N
    767. Mizuno S
    768. Molnár-Gábor F
    769. Moore MJ
    770. Moore RA
    771. Morganella S
    772. Morris QD
    773. Morrison C
    774. Mose LE
    775. Moser CD
    776. Muiños F
    777. Mularoni L
    778. Mungall AJ
    779. Mungall K
    780. Musgrove EA
    781. Mustonen V
    782. Mutch D
    783. Muyas F
    784. Muzny DM
    785. Muñoz A
    786. Myers J
    787. Myklebost O
    788. Möller P
    789. Nagae G
    790. Nagrial AM
    791. Nahal-Bose HK
    792. Nakagama H
    793. Nakagawa H
    794. Nakamura H
    795. Nakamura T
    796. Nakano K
    797. Nandi T
    798. Nangalia J
    799. Nastic M
    800. Navarro A
    801. Navarro FCP
    802. Neal DE
    803. Nettekoven G
    804. Newell F
    805. Newhouse SJ
    806. Newton Y
    807. Ng AWT
    808. Ng A
    809. Nicholson J
    810. Nicol D
    811. Nie Y
    812. Nielsen GP
    813. Nielsen MM
    814. Nik-Zainal S
    815. Noble MS
    816. Nones K
    817. Northcott PA
    818. Notta F
    819. O’Connor BD
    820. O’Donnell P
    821. O’Donovan M
    822. O’Meara S
    823. O’Neill BP
    824. O’Neill JR
    825. Ocana D
    826. Ochoa A
    827. Oesper L
    828. Ogden C
    829. Ohdan H
    830. Ohi K
    831. Ohno-Machado L
    832. Oien KA
    833. Ojesina AI
    834. Ojima H
    835. Okusaka T
    836. Omberg L
    837. Ong CK
    838. Ossowski S
    839. Ott G
    840. Ouellette BFF
    841. P’ng C
    842. Paczkowska M
    843. Paiella S
    844. Pairojkul C
    845. Pajic M
    846. Pan-Hammarström Q
    847. Papaemmanuil E
    848. Papatheodorou I
    849. Paramasivam N
    850. Park JW
    851. Park J-W
    852. Park K
    853. Park K
    854. Park PJ
    855. Parker JS
    856. Parsons SL
    857. Pass H
    858. Pasternack D
    859. Pastore A
    860. Patch A-M
    861. Pauporté I
    862. Pea A
    863. Pearson JV
    864. Pedamallu CS
    865. Pedersen JS
    866. Pederzoli P
    867. Peifer M
    868. Pennell NA
    869. Perou CM
    870. Perry MD
    871. Petersen GM
    872. Peto M
    873. Petrelli N
    874. Petryszak R
    875. Pfister SM
    876. Phillips M
    877. Pich O
    878. Pickett HA
    879. Pihl TD
    880. Pillay N
    881. Pinder S
    882. Pinese M
    883. Pinho AV
    884. Pitkänen E
    885. Pivot X
    886. Piñeiro-Yáñez E
    887. Planko L
    888. Plass C
    889. Polak P
    890. Pons T
    891. Popescu I
    892. Potapova O
    893. Prasad A
    894. Preston SR
    895. Prinz M
    896. Pritchard AL
    897. Prokopec SD
    898. Provenzano E
    899. Puente XS
    900. Puig S
    901. Puiggròs M
    902. Pulido-Tamayo S
    903. Pupo GM
    904. Purdie CA
    905. Quinn MC
    906. Rabionet R
    907. Rader JS
    908. Radlwimmer B
    909. Radovic P
    910. Raeder B
    911. Raine KM
    912. Ramakrishna M
    913. Ramakrishnan K
    914. Ramalingam S
    915. Raphael BJ
    916. Rathmell WK
    917. Rausch T
    918. Reifenberger G
    919. Reimand J
    920. Reis-Filho J
    921. Reuter V
    922. Reyes-Salazar I
    923. Reyna MA
    924. Reynolds SM
    925. Rheinbay E
    926. Riazalhosseini Y
    927. Richardson AL
    928. Richter J
    929. Ringel M
    930. Ringnér M
    931. Rino Y
    932. Rippe K
    933. Roach J
    934. Roberts LR
    935. Roberts ND
    936. Roberts SA
    937. Robertson AG
    938. Robertson AJ
    939. Rodriguez JB
    940. Rodriguez-Martin B
    941. Rodríguez-González FG
    942. Roehrl MHA
    943. Rohde M
    944. Rokutan H
    945. Romieu G
    946. Rooman I
    947. Roques T
    948. Rosebrock D
    949. Rosenberg M
    950. Rosenstiel PC
    951. Rosenwald A
    952. Rowe EW
    953. Royo R
    954. Rozen SG
    955. Rubanova Y
    956. Rubin MA
    957. Rubio-Perez C
    958. Rudneva VA
    959. Rusev BC
    960. Ruzzenente A
    961. Rätsch G
    962. Sabarinathan R
    963. Sabelnykova VY
    964. Sadeghi S
    965. Sahinalp SC
    966. Saini N
    967. Saito-Adachi M
    968. Saksena G
    969. Salcedo A
    970. Salgado R
    971. Salichos L
    972. Sallari R
    973. Saller C
    974. Salvia R
    975. Sam M
    976. Samra JS
    977. Sanchez-Vega F
    978. Sander C
    979. Sanders G
    980. Sarin R
    981. Sarrafi I
    982. Sasaki-Oku A
    983. Sauer T
    984. Sauter G
    985. Saw RPM
    986. Scardoni M
    987. Scarlett CJ
    988. Scarpa A
    989. Scelo G
    990. Schadendorf D
    991. Schein JE
    992. Schilhabel MB
    993. Schlesner M
    994. Schlomm T
    995. Schmidt HK
    996. Schramm S-J
    997. Schreiber S
    998. Schultz N
    999. Schumacher SE
    1000. Schwarz RF
    1001. Scolyer RA
    1002. Scott D
    1003. Scully R
    1004. Seethala R
    1005. Segre AV
    1006. Selander I
    1007. Semple CA
    1008. Senbabaoglu Y
    1009. Sengupta S
    1010. Sereni E
    1011. Serra S
    1012. Sgroi DC
    1013. Shackleton M
    1014. Shah NC
    1015. Shahabi S
    1016. Shang CA
    1017. Shang P
    1018. Shapira O
    1019. Shelton T
    1020. Shen C
    1021. Shen H
    1022. Shepherd R
    1023. Shi R
    1024. Shi Y
    1025. Shiah Y-J
    1026. Shibata T
    1027. Shih J
    1028. Shimizu E
    1029. Shimizu K
    1030. Shin SJ
    1031. Shiraishi Y
    1032. Shmaya T
    1033. Shmulevich I
    1034. Shorser SI
    1035. Short C
    1036. Shrestha R
    1037. Shringarpure SS
    1038. Shriver C
    1039. Shuai S
    1040. Sidiropoulos N
    1041. Siebert R
    1042. Sieuwerts AM
    1043. Sieverling L
    1044. Signoretti S
    1045. Sikora KO
    1046. Simbolo M
    1047. Simon R
    1048. Simons JV
    1049. Simpson JT
    1050. Simpson PT
    1051. Singer S
    1052. Sinnott-Armstrong N
    1053. Sipahimalani P
    1054. Skelly TJ
    1055. Smid M
    1056. Smith J
    1057. Smith-McCune K
    1058. Socci ND
    1059. Sofia HJ
    1060. Soloway MG
    1061. Song L
    1062. Sood AK
    1063. Sothi S
    1064. Sotiriou C
    1065. Soulette CM
    1066. Span PN
    1067. Spellman PT
    1068. Sperandio N
    1069. Spillane AJ
    1070. Spiro O
    1071. Spring J
    1072. Staaf J
    1073. Stadler PF
    1074. Staib P
    1075. Stark SG
    1076. Stebbings L
    1077. Stefánsson ÓA
    1078. Stegle O
    1079. Stein LD
    1080. Stenhouse A
    1081. Stewart C
    1082. Stilgenbauer S
    1083. Stobbe MD
    1084. Stratton MR
    1085. Stretch JR
    1086. Struck AJ
    1087. Stuart JM
    1088. Stunnenberg HG
    1089. Su H
    1090. Su X
    1091. Sun RX
    1092. Sungalee S
    1093. Susak H
    1094. Suzuki A
    1095. Sweep F
    1096. Szczepanowski M
    1097. Sültmann H
    1098. Yugawa T
    1099. Tam A
    1100. Tamborero D
    1101. Tan BKT
    1102. Tan D
    1103. Tan P
    1104. Tanaka H
    1105. Taniguchi H
    1106. Tanskanen TJ
    1107. Tarabichi M
    1108. Tarnuzzer R
    1109. Tarpey P
    1110. Taschuk ML
    1111. Tatsuno K
    1112. Tavaré S
    1113. Taylor DF
    1114. Taylor-Weiner A
    1115. Teague JW
    1116. Teh BT
    1117. Tembe V
    1118. Temes J
    1119. Thai K
    1120. Thayer SP
    1121. Thiessen N
    1122. Thomas G
    1123. Thomas S
    1124. Thompson A
    1125. Thompson AM
    1126. Thompson JFF
    1127. Thompson RH
    1128. Thorne H
    1129. Thorne LB
    1130. Thorogood A
    1131. Tiao G
    1132. Tijanic N
    1133. Timms LE
    1134. Tirabosco R
    1135. Tojo M
    1136. Tommasi S
    1137. Toon CW
    1138. Toprak UH
    1139. Torrents D
    1140. Tortora G
    1141. Tost J
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Article and author information

Author details

  1. David Papadopoli

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    david.papadopoli@mail.mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1248-1839

  2. Ranveer Palia

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Department of Experimental Medicine, McGill University, Montréal, Canada
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4265-243X

  3. Predrag Jovanovic

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Department of Experimental Medicine, McGill University, Montréal, Canada
    Contribution
    Formal analysis, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Sébastien Tabariès

    Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Emma Ciccolini

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Department of Experimental Medicine, McGill University, Montréal, Canada
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Valerie Sabourin

    Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  7. Sebastian Igelmann

    VIB Center for Cancer Biology, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
    Contribution
    Formal analysis, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  8. Shannon McLaughlan

    Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  9. Lesley Zhan

    Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  10. HaEun Kim

    Department of Experimental Medicine, McGill University, Montréal, Canada
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  11. Nabila Chekkal

    Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  12. Krzysztof J Szkop

    Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared

  13. Thierry Bertomeu

    Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Canada
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5313-7057

  14. Jibin Zeng

    Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  15. Julia Vassalakis

    Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0000-1261-3638

  16. Farzaneh Afzali

    Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  17. Slim Mzoughi

    Center of OncoGenomics and Innovative Therapeutics (COGIT), Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  18. Ernesto Guccione

    Center of OncoGenomics and Innovative Therapeutics (COGIT), Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7764-5307

  19. Mike Tyers

    1. Program in Molecular Medicine, The Hospital for Sick Children, Toronto, Canada
    2. Department of Molecular Genetics, University of Toronto, Toronto, Canada
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared

  20. Daina Avizonis

    1. Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada
    2. Metabolomics Innovation Resource, McGill University, Montréal, Canada
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared

  21. Ola Larsson

    Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
    Contribution
    Resources, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared

  22. Lynne-Marie Postovit

    Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada
    Contribution
    Resources, Writing – review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8088-4197

  23. Sergej Djuranovic

    1. Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, United States
    2. Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9417-0822

  24. Josie Ursini-Siegel

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
    3. Department of Experimental Medicine, McGill University, Montréal, Canada
    4. Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Supervision, Writing – review and editing
    Competing interests
    No competing interests declared

  25. Peter M Siegel

    1. Department of Experimental Medicine, McGill University, Montréal, Canada
    2. Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada
    3. Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Resources, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5568-6586

  26. Michael Pollak

    1. Lady Davis Institute, SMBD JGH, McGill University, Montréal, Canada
    2. Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
    3. Department of Experimental Medicine, McGill University, Montréal, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    michael.pollak@mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3047-0604

  27. Ivan Topisirovic

    1. Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
    2. Department of Experimental Medicine, McGill University, Montréal, Canada
    3. Department of Biochemistry, McGill University, Montréal, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    ivan.topisirovic@mcgill.ca
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5510-9762

Funding

Canadian Institutes of Health Research (PJT-183843)

  • Ivan Topisirovic

Canadian Institutes of Health Research (PJT-479494)

  • Ivan Topisirovic

Terry Fox Research Institute (TFF-242122)

  • Peter M Siegel
  • Michael Pollak
  • Ivan Topisirovic

Cancer Research Society (NGS)

  • David Papadopoli

Canada Research Chairs (Tier 1)

  • Ivan Topisirovic

Canadian Institutes of Health Research (MFE-171312)

  • David Papadopoli

Cancerfonden (22 2186)

  • Ola Larsson

Swedish Research Council (2020-01665)

  • Ola Larsson

the Cancer Research Funds Radiumhemmet (231263)

  • Ola Larsson

Queen's University

  • Lynne-Marie Postovit

Fonds de Recherche du Québec – Santé (FRQS) Fellowships

  • HaEun Kim
  • Predrag Jovanovic

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are thankful to the members of all involved laboratories and Stephane Richard for helpful discussions. We thank Luc Choinière and Mariana Russo for assistance with metabolomic experiments, Christian Young for assistance with flow cytometry, and Christopher Rudd for assistance with respirometry. This research was funded by the Terry Fox Foundation (TFF) Oncometabolism Team Grant (TFF-242122) to IT, PS, and MP, and Canadian Institutes of Health Research (CIHR) (PJT-183843, PJT-479494) to IT. Research in OL’s lab is supported by grants from the Swedish Research Council (2020-01665), Swedish Cancer Society (22 2186), the Cancer Research Funds of Radiumhemmet (231263) and the Wallenberg Academy Fellow Program. L-MP acknowledges the funds from Queen's University. DP is supported by the CIHR Postdoctoral Fellowship (MFE-171312), Cancer Research Society (CRS) The Next Generation of Scientists Award (NGS), and LDI Desjardins Fellowship Program. PJ and HK are supported by Fonds de Recherche du Québec – Santé (FRQS) Fellowships. IT is supported by the Canada Research Chair in Regulation of mRNA Translation and Metabolism. Metabolic analysis was performed at the Rosalind and Morris Goodman Cancer Research Centre’s Metabolomics Core Facility, which is supported by the Canada Foundation for Innovation, the Dr. John R and Clara M Fraser Memorial Trust, the Terry Fox Foundation (TFF Oncometabolism Team Grant; TFF-242122), and McGill University.

Ethics

All animal experiments were performed in accordance with the guidelines of the McGill University Animal Ethics Committee and the Canadian Council on Animal Care as approved by the facility animal care committee. Protocol number MCGL-10212 and AUP-5129.

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© 2025, Papadopoli et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. David Papadopoli
  2. Ranveer Palia
  3. Predrag Jovanovic
  4. Sébastien Tabariès
  5. Emma Ciccolini
  6. Valerie Sabourin
  7. Sebastian Igelmann
  8. Shannon McLaughlan
  9. Lesley Zhan
  10. HaEun Kim
  11. Nabila Chekkal
  12. Krzysztof J Szkop
  13. Thierry Bertomeu
  14. Jibin Zeng
  15. Julia Vassalakis
  16. Farzaneh Afzali
  17. Slim Mzoughi
  18. Ernesto Guccione
  19. Mike Tyers
  20. Daina Avizonis
  21. Ola Larsson
  22. Lynne-Marie Postovit
  23. Sergej Djuranovic
  24. Josie Ursini-Siegel
  25. Peter M Siegel
  26. Michael Pollak
  27. Ivan Topisirovic
(2026)
Mitochondrial ETF insufficiency drives neoplastic growth by selectively optimizing cancer bioenergetics
eLife 14:RP106587.
https://doi.org/10.7554/eLife.106587.3

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