Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death

Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death View ORCID Profile Chesta Jain , Muqit Essani , Roshan Kumar , Nupur K. Das , Rashi Singhal , Nicholas J. Rossiter , Brandon Chen , Wesley Huang , Zheng Hong Lee , Sumeet Solanki , Yuezhong Zhang , Peter Sajjakulnukit , Li Zhang , Prarthana J. Dalal , View ORCID Profile David A Hanna , Shannon McCollum , Elena M. Stoffel , Joel K. Greenson , L James Maher III , View ORCID Profile Costas A. Lyssiotis , Ruma Banerjee , Yatrik M. Shah doi: https://doi.org/10.1101/2025.11.05.686813 Chesta Jain 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chesta Jain Muqit Essani 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roshan Kumar 2 University of Michigan, Department of Biological Chemistry , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nupur K. Das 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rashi Singhal 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicholas J. Rossiter 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 3 University of Michigan, Program in Cellular and Molecular Biology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brandon Chen 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 3 University of Michigan, Program in Cellular and Molecular Biology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wesley Huang 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 3 University of Michigan, Program in Cellular and Molecular Biology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zheng Hong Lee 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 4 University of Michigan, Program in Immunology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sumeet Solanki 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuezhong Zhang 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 5 The Second Xiangya Hospital, Central South University , Changsha, China 6 M.D./Ph.D. Dual-degree program, Xiangya Medical School-Central South University and University of Michigan Medical School-University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Sajjakulnukit 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Li Zhang 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Prarthana J. Dalal 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 7 Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A Hanna 2 University of Michigan, Department of Biological Chemistry , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A Hanna Shannon McCollum 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 3 University of Michigan, Program in Cellular and Molecular Biology , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elena M. Stoffel 8 Division of Gastroenterology and Hepatology, Department of Internal Medicine, Rogel Cancer Center, University of Michigan , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joel K. Greenson 9 Department of Pathology, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site L James Maher III 10 Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science , Rochester, MN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Costas A. Lyssiotis 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 8 Division of Gastroenterology and Hepatology, Department of Internal Medicine, Rogel Cancer Center, University of Michigan , Ann Arbor, Michigan 11 University of Michigan, Rogel Cancer Center , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Costas A. Lyssiotis Ruma Banerjee 2 University of Michigan, Department of Biological Chemistry , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yatrik M. Shah 1 University of Michigan, Department of Molecular & Integrative Physiology , Ann Arbor, Michigan 8 Division of Gastroenterology and Hepatology, Department of Internal Medicine, Rogel Cancer Center, University of Michigan , Ann Arbor, Michigan 11 University of Michigan, Rogel Cancer Center , Ann Arbor, Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: shahy{at}umich.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Colorectal cancer (CRC) cells are addicted to iron, which fuels nucleotide synthesis, mitochondrial respiration, and rapid proliferation. Yet paradoxically, high intracellular iron is cytotoxic to most other cells, raising the question of how CRC cells tolerate and exploit iron-rich environments. One pathway thought to mediate iron toxicity is ferroptosis, an iron-dependent form of cell death. However, most ferroptosis regulators were identified through synthetic chemical screens or small molecule activators, and it remains unclear whether these canonical pathways explain how iron itself triggers cell death, particularly in vivo . Here, using multi-omics profiling, CRISPR screening, and in vivo models, we uncover a heme–succinate dehydrogenase (SDH)–Coenzyme Q (CoQ) axis that enables CRC cells to buffer iron-induced oxidative stress. Heme-dependent SDH reduces CoQ, which redistributes to mitochondrial and plasma membranes to detoxify lipid ROS as a radical trapping antioxidant. This pathway functions alongside, and in some contexts independently of, canonical ferroptosis regulators. These findings reveal that CRCs co-opt metabolic cofactors not only for growth but also for survival under physiologically toxic iron levels, uncovering new vulnerabilities for therapy. INTRODUCTION Colorectal cancer (CRC) is the third most diagnosed cancer globally and the third leading cause of cancer related mortality 1 . CRC cells are remarkably resilient, displaying profound metabolic adaptations that enable survival and growth in nutrients and oxygen deficient microenvironments 2 . This metabolic rewiring is essential for optimal nutrient utilization and prioritizing cellular energetics for proliferation 2 . While such adaptations support tumor growth, they also create dependencies on specific metabolites and micronutrients. Although much attention has been focused on the role of altered metabolism in tumorigenesis, the contribution of micronutrients to cancer progression remains poorly understood. Iron is a critical micronutrient required by nearly all cells. Both systemic and cellular iron homeostasis are tightly regulated through transcriptional and translational control of iron uptake, utilization, and storage 3 . Dysregulation of these mechanisms can lead to iron deficiency or iron overload, manifesting clinically as anemia or hemochromatosis, respectively 4 . At the cellular level, labile iron supports proliferation, ATP production, and mitochondrial respiration through its redox activity. The role of iron and its regulated pathways in cancer initiation, progression, and metastasis is well established and continues to be a highly active area of investigation 5 . In CRC, epidemiological studies have linked elevated systemic iron, with increased CRC incidence 6 – 8 . CRC cells accumulate significantly higher levels of iron compared to adjacent normal tissue, largely by enhancing iron uptake and limiting export 9 – 12 . We and others have shown that restricting iron availability reduces proliferation, lowers tumor burden, and improves survival in mouse models 9 , 12 . However, therapeutic iron depletion remains clinically challenging. CRC cells can access iron from both dietary sources and systemic circulation, and lowering intracellular iron primarily slows proliferation rather than eliminating transformed cells 9 , 13 . Excess intracellular iron is toxic, driving a form of lipid peroxidation–dependent, non-apoptotic cell death known as ferroptosis 14 , 15 . This creates a striking paradox; CRC cells sequester and accumulate iron and resist its toxicity. Previous work has pointed to elevated antioxidant defenses in CRC models and patient tissues, but the specific metabolic mechanisms that allow cancer cells to tolerate such iron overload remain unknown 16 . This represents a fundamental gap in our understanding of CRC biology. How do CRC cells withstand and even thrive under conditions that should be lethal due to iron-induced oxidative stress? Addressing this question is critical, as uncovering these adaptive mechanisms could expose new therapeutic vulnerabilities. We show that the canonical anti-ferroptosis response mediated by GPX4 and SLC7A11 is dispensable for CRC growth and progression in both sporadic and colitis-associated cancer models. A metabolism focused CRISPR screen revealed a protective role of cellular heme against iron toxicity. As a critical cofactor for mitochondrial electron transport chain (ETC) enzymes, heme depletion caused profound mitochondrial defects, including loss of complex II (CII) activity. Through multi-omics profiling of in vitro and in vivo CRC models, we identify a previously unrecognized role of mitochondrial CII in buffering iron-induced cell death by regulating coenzyme Q (CoQ) biosynthesis. Strikingly, pharmacological, or genetic inhibition of CII sensitized CRC cells to iron-induced toxicity in vitro and in vivo . Collectively, these findings uncover a non-canonical antioxidant function of CII in protecting CRC cells from iron-induced toxicity. By defining how CRC cells adapt to survive under iron overload, our study addresses a major gap in the field and highlights metabolic strategies for development of new therapies. RESULTS GPX4 is dispensable for CRC initiation and progression A defining paradox in CRC biology is its ability to accumulate and tolerate exceptionally high levels of intracellular iron 9 . Iron is indispensable for DNA synthesis, mitochondrial metabolism, and rapid proliferation 13 , 17 . Yet in most cells, sustained iron loading is toxic, as it accelerates reactive oxygen species (ROS) production and triggers ferroptosis, an iron-dependent cell death program driven by lipid peroxidation 14 , 15 . The prevailing paradigm is that cancer cells buffer this stress primarily through the GPX4–SLC7A11 antioxidant axis, which detoxifies lipid peroxides at the expense of glutathione 18 , 19 . However, whether this canonical ferroptosis defense is required for CRC initiation or progression has not been directly addressed. We first assessed the expression of SLC7A11 and GPX4 in tumor tissues and matched normal mucosa from individual CRC patients, and in enteroids generated from these same tumors and matched normal tissues. In all cases, RNA transcripts for both were significantly elevated in the tumor-derived material compared to matched normal controls ( Fig. 1A–B ), indicating that upregulation of these pathways is intrinsic to the tumor epithelium and maintained ex vivo . These findings were consistent with TCGA datasets, where GPX4 and SLC7A11 transcripts are elevated in CRC tumors relative to normal colon tissue ( Fig. S1A ). Despite this consistent upregulation, Kaplan–Meier survival analysis revealed no correlation between high GPX4/SLC7A11 expression and overall patient survival ( Fig. 1C ), suggesting that these pathways may not be essential for CRC fitness. Download figure Open in new tab Figure S1 A) Gene expression of GPX4 and SLC7A11 in colon adenocarcinoma patients cancer and normal adjacent tissues from the TCGA cancer database. B) Schematic showing treatment protocol for colitis associated cancer model using AOM/DSS. C) % body weight change post AOM treatment in mice from (B) D) Tumor number from (B) E) Tumor burden from B) F) Representative H&E images of GPX4 deficient Single / Double-p53 / Double-KRAS G12D / Triple mutant mice post tamoxifen induction via 100 mg/kg body weight I.P. injection showing regions of dysplasia in the colon. All data are mean ± SEM. t test for (A). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Download figure Open in new tab Figure 1: GPX4 is Dispensable for CRC Initiation and Progression A) Schematic showing enteroid isolation from normal and tumor tissues from patient biopsies. B) qPCR showing mRNA expression of GPX4 and SLC7A11 from patient derived tumor and normal adjacent tissues and enteroids. C) Kaplan-Meier survival curves of colon adenocarcinoma cancer patients stratified by the GPX4 and SLC7A11 gene expression with data from TCGA cancer database. D) Schematic showing breeding strategy to generate tamoxifen inducible intestine specific GPX4 deficient mice. E) Western blot showing hepatic and intestinal GPX4 protein levels in GPX4 +/+ and GPX4 ΔIE mice 5 days post tamoxifen induction via 100 mg/kg body weight I.P. injection. F) qPCR of colonic GPX4 transcript levels in GPX4 +/+ and GPX4 ΔIE mice from (E). G) H&E of GPX4 +/+ and GPX4 ΔIE mice from (E) showing normal colonic histology. H) Schematic showing mouse model breeding strategy to generate Single/Double- Tp53 /Double- Kras G12D /Triple mutant mice with or without functional GPX4. I) Survival plot of GPX4 deficient Single J) Double- Tp53 , K) Double- Kras G12D L) Triple mutant mice post tamoxifen induction via 100 mg/kg body weight I.P. injection. All data are mean ± SEM. t tests for (B), (I), (J), (K), (L) or One-way ANOVA with Tukey’s multiple comparisons test (F). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To test this functionally, we generated intestine-specific Gpx4 knockout mice ( Fig. 1D–F ). Intestine specific Gpx4 deletion does not manifest as any significant histological or physiological changes and does not alter overall GI health or overall survival ( Fig. 1G ) 20 . In the colitis-associated cancer (CAC) model , Gpx4 loss did not significantly alter tumor number, size, or overall burden ( Fig. S1B–E ). While these findings suggest that CRC cells rely on metabolic adaptations beyond the canonical SLC7A11-GPX4 dependent anti-ferroptotic response, colitis associated cancer is rare in human population and majority of the sporadic CRC cases are APC , P53 and KRAS mutation driven. To examine the role of GPX4 in sporadic CRC, we crossed the Gpx4 F/F mice with a series of genetically engineered colon cancer models: Single mutant, ( Apc F/F ), Double mutant ( Apc F/F ; Tp53 F/F ) or Triple mutant ( Apc F /F ; Tp53 F/F ; Kras G12D ) 21 . All mutations were specifically targeted to the colon epithelium using CDX2-CreERT2 ( Fig. 1H ). If GPX4 were essential to buffer tumoral iron and resist iron-induced toxicity, its deletion would be expected to slow tumor progression and improve survival. Surprisingly , Gpx4 deficiency did not improve survival in any of these models ( Fig. 1I-L ). There were no histological differences observed in the colon between the Gpx4 F/F and Gpx4 ΔIE ( Fig. S1F ). Notably, in mice harboring Apc or Apc/Tp53 mutations, Gpx4 deficiency slightly shortened lifespan, which may reflect altered inflammatory responses in the absence of GPX4 22 . This indicates that CRC does not depend on the canonical SLC7A11-GPX4 ferroptosis defense to tolerate iron stress. Instead, CRC must exploit alternative metabolic adaptations that enable survival under high iron. CRC cells resist iron toxicity through metabolic adaptations To understand if the resistance to high iron levels is a cell autonomous mechanism, we first compared proliferation under millimolar concentrations of ferric ammonium citrate (FAC) in CRC cell lines versus normal intestinal epithelial cells and non-CRC cancer lines. CRC cells maintained robust proliferation, whereas non-CRC cells rapidly succumbed to iron toxicity ( Fig. 2A–C ). Sensitivity to iron varied across CRC lines, reflecting heterogeneity in intrinsic metabolic adaptations ( Fig. S2A ). Chronic high-iron exposure reduced CRC growth, sensitized cells to ferroptosis inducers (RSL3 and erastin) and induced cell death, which was partially rescued by ferroptosis inhibitors and antioxidants such as liproxstatin (Lip-1) and N-acetyl cysteine (NAC) ( Fig. S2B , Fig. 2D ;). By contrast, iron-induced death in non-CRC lines (fibroblasts CD1, NIH3T3, and fibrosarcoma HT1080) was fully reversed by ferroptosis inhibition ( Fig. S2C-D ). Download figure Open in new tab Figure S2 A) IC50, IC20 and IC80 values for human and mouse CRC cell lines treated with Ferric Ammonium Citrate (FAC) for 72h, calculated by measuring cell growth using bright field microscopy. B) Relative growth of HCT116 and SW480 cells 72h post treatment with FAC with or without ferroptosis inducers erastin (1μM) or RSL3 (1μM) using brightfield microscopy. C) Representative Sytox staining images from CD1, NIH3T3, HT1080 and MC38 cells 72h post FAC treatment with or without ferroptosis inhibitor Liproxstatin (Lip-1 2μM). D) % cell death quantification from (B). E) Relative growth of sh FTH1 cells 72h post dox treatment with or without ferroptosis inhibitor liproxstatin (Lip-1 1μM), ferrostatin (Fer-1 1μM) or FG4592 (100μM) F) Western blot images showing protein expression of FTH1 and beta actin in HCT116 or SW480 cells stable of doxycycline inducible FTH1 overexpression construct 48h post dox treatment. G) Representative crystal violet staining images for cells from (F) 14 days post dox treatment with or without FAC treatment. H) Differential CRISPR score (DMSO-FG4592) highlighting significantly depleted genes of interest in doubling 14 populations. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (B), (D) and (E). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Download figure Open in new tab Figure 2: CRC Cells Resist Iron Toxicity Through Metabolic Adaptations A) Relative growth of CRC or normal epithelial cells 72h post FAC treatment measured using bright field microscopy. B) Relative growth of CRC or non-CRC cancer cells 72h post FAC treatment measured using bright field microscopy. C) Representative crystal violet staining images of HCT116 and SW480 cells 14 days post FAC treatment. D) Representative sytox green staining images from HCT116 and SW480 cells showing dead cells in green 72h post FAC treatment with or without ferroptosis inhibitor Liproxstatin (Lip-1, 2μM) or N-acetyl cysteine (NAC, 5mM). E) Schematic showing role of ferritin in maintaining cellular iron homeostasis. F) Western blot images showing protein expression of FTH1 and beta Actin in HCT116 or SW480 cells stable for empty tet-pLKO-puro (Scr) or shRNA targeting FTH1 (shRNA 1 or shRNA 2) 48h post doxycycline (dox) treatment (250ng/mL). G) Representative crystal violet staining images of cells from (F) 14 days post dox treatment. H) Relative fold change quantified from (G) I) Western blot images showing protein expression of FTH1 and beta actin in CRC cell lines 24h post FAC treatment at different concentrations. J) Schematic showing workflow for the metabolism focused CRISPR library screen under FAC (2.5mM) or FG4592 (100μM) treatment. K) Differential CRISPR score (DMSO-FAC) highlighting significantly depleted genes of interest in doubling 14 populations. L) Scatter plot comparing CRISPR scores under FAC (x axis) or FG4592 (y axis) treatment. Genes of interest highlighted in red. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test (H). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. This raises the question: what metabolic adaptations allow CRC cells to survive under iron loads that are lethal for normal intestinal epithelial cells or non-CRC cancer cells? Intracellular iron homeostasis depends on coordinated regulation of iron import/export and ferritin-mediated storage 3 . CRC cells have increased expression of iron importers and hepcidin, an iron export repressor, via sustained HIF2α transcriptional activity 9 , 10 , 13 . Ferritin acts as both a bioavailable iron reservoir and a buffer against iron overload ( Fig. 2E ) 23 , 24 . To test its role in CRC iron tolerance, we generated CRC lines with doxycycline-inducible shRNA targeting ferritin heavy chain ( FTH1 ) ( Fig. 2F ). FTH1 knockdown was lethal, markedly reducing proliferation, and growth defects were not rescued by lipid ROS scavengers ( Fig. 2G–H , S2E). Interestingly, FTH1 protein levels saturated at low FAC concentrations and did not increase further under higher FAC, and overexpression did not improve iron tolerance ( Fig. 2I , S2F–G). These data indicate that while FTH1 maintains basal iron homeostasis, it is insufficient to buffer the extreme labile iron pools in CRC. To uncover additional mechanisms, we performed a metabolism-focused CRISPR-Cas9 screen 25 . Cells were maintained under chronic FAC exposure to mimic iron overload, or under HIF2α activation via FG4592 treatment, which we and others have shown increases intracellular iron levels and susceptibility to iron-induced toxicity ( Fig. 2J ) 26 . This approach allowed us to probe metabolic pathways critical for CRC survival under physiologically relevant iron stress conditions. The screen identified depletion of several mitochondrial metabolism-related genes, including those involved in purine/pyrimidine biosynthesis, cholesterol biosynthesis, and heme biosynthesis, under both FAC and HIF2α activation ( Fig. 2K–L , S2H). FTH1 was selectively depleted under FAC but not FG4592, consistent with HIF2α-mediated transcriptional regulation of ferritin. These results suggest that CRC survival under high iron depends on mitochondrial and heme-dependent metabolic pathways, beyond canonical ferritin-mediated iron storage. Heme biosynthesis buffers iron-induced toxicity The metabolism-focused CRISPR-Cas9 screen ( Fig. 2L ) identified uroporphyrinogen decarboxylase ( UROD ), a key enzyme in heme biosynthesis, as a top-scoring gene significantly depleted under both FAC and HIF2α activation (FG4592) treatments. Heme, an iron-containing cofactor, is essential for numerous cellular processes, including mitochondrial respiration, Fe–S cluster biogenesis, and nucleotide synthesis 27 . While dietary heme is a major source, all cells including CRC cells express the full complement of enzymes required for de novo heme synthesis. Increasing intracellular iron elevates cellular heme pools in CRC cells ( Fig. 3A–B ). To validate the screen, we generated HCT116 and SW480 cells with Tet-inducible knockdown (KD) of UROD using two independent shRNAs (shRNA1 and shRNA2), with empty vector (Scr) controls ( Fig. S3A ). Consistent with the previous findings, depleting intracellular heme genetically by knocking down UROD or pharmacologically using succinyl acetone (SA), a competitive inhibitor of delta-aminolevulinate dehydratase (ALAD) sensitizes CRC cells to iron induced toxicity ( Fig. S3B, C ; Fig. 3C ). Download figure Open in new tab Figure S3 A) Western blot images showing protein expression of UROD and Vinculin in HCT116 and SW480 cells stable for empty tet-pLKO-puro (Scr) or UROD targeting shRNA (shRNA1 and shRNA2) 24h, 48h and 72h post dox treatment (250ng/mL). B) Representative crystal violet staining image in cells from b) 14 days post FAC treatment (0.25, 0.5mM) with or without dox (250ng/mL). C) Measurement of relative growth by quantification of relative colony count from (B). D) Western blot images showing protein expression of FECH and Vinculin in HCT116 and SW480 cells stable for empty tet-pLKO-puro (Scr) or FECH targeting shRNA (shRNA1 and shRNA2) 24h, 48h and 72h post dox treatment (250ng/mL). E) Representative crystal violet staining image in cells from e) 14 days post FAC treatment (0.25, 0.5mM) with or without dox (250ng/mL) or heme (10μM). F) Measurement of relative growth by quantification of relative colony count from (E). G) Representative crystal violet staining image in human CRC cells from 14 days post FAC treatment with or without SA (250μM) and Heme (10μM). All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (C), and (F). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Download figure Open in new tab Figure 3: Heme Biosynthesis Buffers Iron-Induced Toxicity A) Schematic showing cellular heme uptake and biosynthesis pathway in mammalian cells. B) Intracellular heme levels in HCT166 and SW480 cells 72h post FAC (0.5, 5mM), hemin chloride (heme, 10μM) and Succinyl acetone (SA, 500μM) treatment. C) Representative crystal violet staining of cells 14 days post FAC treatment at indicated concentration with or without SA (250μM). D) Representative crystal violet staining of cells 14 days post FAC treatment at indicated concentration with or without SA (500μM) and or heme (10μM). E) Quantification of relative colony count from (D). F) Lipid ROS and Cell ROS measured by C11-BODIPY581/591 and free radical sensor carboxy-H2DCFDA respectively 72h post FAC (100μM) with or without SA (500μM) and heme (10μM). G) Relative growth of HCT116 and SW480 cells 72h post FAC (0.5mM) with or without SA (500μM), heme (10μM) liproxstatin (Lip-1, 2μM). H) Oxygen consumption rate of HCT116 and SW480 cells 72h post FAC treatment (100uM) with or without SA (500μM) or Heme (10μM) measured using the Resipher TM oxygen sensor normalized with cell count measured by brightfield microscopy. I) Western blot images showing expression of subunits of the mitochondrial electron transport chain (CI: NDUFB8, CII: SDHB, CIII: UQCRC2, CIV: MTCO1, CV: ATP5A) in HCT116 and SW480 cells 72h post SA treatment (500uM). J) Kaplan-Meier survival curves of colon adenocarcinoma cancer patients stratified by the FLVCR2, SLCO2B1, CD91, CD163 gene expression with data from TCGA cancer database. All data are mean ± SEM. t tests for (B) or One-way ANOVA with Tukey’s multiple comparisons test for (E), (F), (G), (H). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Heme depletion induced iron sensitivity is completely rescued by supplementing heme back in the culture media ( Fig. 3D-E ). Knockdown of ferrochelatase ( FECH ), which inserts Fe²⁺ into protoporphyrin IX in the mitochondrial matrix, similarly sensitized CRC cells to iron toxicity, with rescue upon heme supplementation ( Fig. S3D–F ). These results indicate that heme functions as an essential cofactor supporting antioxidant and metabolic responses, rather than acting as a passive iron store. This response is conserved in other CRC cell lines as well as other cancer lines ( Fig. S3G ). Moreover, depletion of intracellular heme does not sensitize CRC cells to toxicity associated with other metals or oxidizing agents such as copper, selenium and hydrogen peroxide or the chemotherapeutic agent 5-fluorouracil (5-FU) ( Fig. S4A ). Interestingly heme supplementation alone conferred resistance to oxidative stress from selenium and H₂O₂, but not to the chemotherapeutic 5-fluorouracil, demonstrating specificity for redox stress ( Fig. S4B, C ). Download figure Open in new tab Figure S4 A) Representative crystal violet staining of HCT116 and SW480 cells 14 days post SA (250μM), copper sulphate (100μM) + elesclomol (Cu-Es), Sodium selenite (Se), hydrogen peroxide (H 2 O 2 ) and 5-fluoroUracil (5FU) at indicated concentrations. B) Relative growth of HCT116 measured by bright field microscopy, 72h post SA (250μM), copper sulphate + elesclomol (Cu-Es), Sodium selenite (Se), hydrogen peroxide (H 2 O 2 ) and 5-fluoroUracil (5FU) at indicated concentrations. C) Relative growth of SW480 measured by bright field microscopy, 72h post SA (250μM) copper sulphate + elesclomol (Cu-Es), Sodium selenite (Se), hydrogen peroxide (H 2 O 2 ) and 5-fluoroUracil (5FU) at indicated concentrations. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (B), and (C). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Heme depletion increased both general cellular ROS and lipid ROS under FAC treatment, effects that were reversed by either heme supplementation or ferroptosis inhibitor liproxstatin (Lip-1) ( Fig. 3F ). Interestingly, only heme supplementation rescued SA-induced growth suppression, indicating a non-ferroptotic, heme-dependent oxidative vulnerability ( Fig. 3G ). This data shows that iron induced toxicity is aggravated by induction of general ROS and lipid ROS; however, quenching these species is not sufficient to reverse iron toxicity, suggesting a non ferroptotic iron-dependent oxidative cell death. Heme depletion also reduced oxygen consumption rates (OCR) and decreased protein levels of ETC complexes (CII, CIII, and CIV) in CRC cells; both parameters were restored by heme supplementation ( Fig. 3H, I ). These findings are consistent with the known role of heme in mitochondrial respiration and suggest a protective role of heme for buffering iron-induced oxidative stress. Overall our findings align with the epidemiological data that high expression of genes involved in heme uptake and absorption correlates with reduced overall survival in CRC patients, consistent with the role of heme in promoting tumor cell fitness under high iron conditions ( Fig. 3J ) 8 . Collectively, these data demonstrate that increase in cellular heme through biosynthesis is a key metabolic adaptation that enables CRC cells to tolerate high intracellular iron, bridging the gap between iron accumulation and oxidative stress resistance. Mitochondrial complex II protects CRC cells from iron-induced oxidative cell death Heme itself can be degraded by hemoxygenase (HO)-1 into the byproducts labile iron, bilirubin and biliverdin 28 . The latter two are hydrophobic molecules with potent antioxidant properties in membranes, where they can protect cells from iron-induced toxicity 29 . However, this process simultaneously releasees free iron, complicating the interpretation of their protective effects. We did not see enhanced iron sensitivity upon alteration of cellular bilirubin and biliverdin levels by inhibition of HO-1 or protection by directly supplementing with products of HO-1 ( Fig. S5A-C ). Download figure Open in new tab Figure S5 A) Schematic showing hemoxygenase (HO-1) dependent heme degradation into bilirubin and biliverdin. B) Representative crystal violet staining images 14 days post FAC (at indicated concentrations), SA (250μM) with or without HMOX1 inhibitor SnPPIX, or products of HMOX1 dependent heme degradation Biliverdin and Bilirubin. C) Relative growth of HCT116 and SW480 cells 72h post FAC (0.5, 5mM), SA (500μM) with or without SnPPIX, Biliverdin and Bilirubin. D) Schematic showing heme dependent BACH1 mediated antioxidant response. E) qPCR showing mRNA levels of various BACH1 targets - GPX4, Slc7a11, GCLC, GCLM, NQO1, NRF2, HMOX1 and FTH in HCT116 and SW480 cells 72h FAC (250μM), SA (500μM), and heme (10μM) treatment. F) Schematic showing heme dependent ATF4 stabilization and induction of integrated stress response (ISR). G) qPCR showing mRNA levels of various heme regulated inhibitor (HRI) targets CHAC1, CHOP, ATF3, ATF4 in HCT116 and SW480 cells 72h FAC (250), SA (500μM), and heme (10μM) treatment. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (C), (E) and (G). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Beyond providing structural and catalytic support to mitochondrial proteins, heme availability has been reported to regulate antioxidant responses through BACH1 (BTB and CNC homology 1) and cap-dependent translation via the heme-regulated kinase(HRI) 30 , 31 . Free heme can bind to BACH1, a translational repressor of other master antioxidant response transcription factors such as Nuclear factor erythroid 2-related factor 2 (NRF2) 32 . We did not see a significant reduction in the mRNA transcripts of NRF2 targets in SA treated cells (heme deficient) ( Fig. S5D, E ). Heme deficiency activates HRI kinase activity through autophosphorylation, triggering the ATF4-dependent integrated stress response and reduces translation of mitochondrial proteins including the ETC complexes 31 . We observed no changes in canonical ATF4 target gene expression across FAC, FAC + SA, and FAC + SA + heme treatments ( Fig. S5F, G ). Notably, mRNA levels of both CHOP and ATF4 were increased in FAC + SA treatment. Since CHOP is broadly induced during cell death, this increase is likely reflective of enhanced cell death rather than a specific heme-regulated signaling effect. Because ∼20% of total heme-containing proteins function in the ETC and ∼50% serve catalytic roles in diverse metabolic reactions, we next investigated whether heme deficiency disrupts CRC metabolic programs beyond respiration 33 . Our prior work demonstrated that CRC cells rely on iron-dependent nucleotide synthesis, but it was unclear whether this phenotype was strictly iron- or heme-dependent 13 . Metabolomics revealed that SA-induced heme depletion caused a broad metabolic reprogramming, which was only partially corrected by heme supplementation ( Fig. 4A ). TCA and glycolytic intermediates including lactate, fructose-1,6-bisphosphate (F-1,6BP), glucose-6-phosphate (G6P), and dihydroxyacetone phosphate (DHAP) were markedly reduced in SA-treated cells and restored upon heme repletion, while succinate as accumulated in SA treated cells ( Fig. 4B ). Under nutrient and oxygen limiting conditions, resembling the tumor microenvironment, amino acids such as glutamine can fuel the TCA cycle to maintain cell metabolism independent of oxygen driven mitochondrial respiration 34 , 35 . Consistent with this, glutamine, and α-ketoglutarate, as well as glutaminolysis-dependent intermediates including aspartate, orotate, and uridine, accumulated in both SA- and SA+heme–treated cells ( Fig. 4B – D). Download figure Open in new tab Figure 4: Mitochondrial complex II protects CRC cells from iron-induced oxidative cell death A) Principal component analysis (PCA) plots of targeted metabolomics performed on HCT116 cells treated with FAC (250μM), SA (500μM) and heme (10μM) for 72h. B) Heatmap representation of log2 fold change in metabolites involved in TCA cycle, C) amino acids, D) metabolites de novo nucleotide biosynthesis, (* and # indicate p<0.05 between Veh and SA or # Veh and SA + Heme respectively). E) Schematic showing 13 C 5 L glutamine labeling strategy to trace glutamine consumption via TCA cycle to generate intermediates for de novo nucleotide synthesis pathway. F) % labeled asparagine, aspartate, malate, and lactate, 8h post 4mM 13C5 L-glutamine treatment. G) Oxygen consumption rate of HCT116, SW480 48h post FAC (0.1 and 0.5mM) or heme (10μM) treatment measured using Resipher TM oxygen sensor, normalized with cell count measured by brightfield microscopy. H) Oxygen consumption rate of HT1080 and RKO under 48h post FAC (0.1 and 0.5mM) or heme (10μM) treatment measured using Resipher TM oxygen sensor, normalized with cell count measured by brightfield microscopy. I) Schematic showing TCA cycle and its contribution to mitochondrial respiration. J) Representative Sytox green staining images of HCT116 and SW480 cells treated with FAC (0.5, 5mM) with or without inhibitors of the mitochondrial electron transport chain complexes (CI: Piericidin, 10nM, CII: TTFA, 100μM, CIII: Antimycin A, 100μM, CIV: KCN, 2.5mM, CV: Oligomycin, 1μM) for 24h K) Heatmap representation of % cell death calculated from J) * indicate p<0.05 as compared to Vehicle control +/- FAC. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (B), (C), (D), (G) and (K). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To probe whether elevated iron enhances flux through these pathways, we performed 13 C₅-L-glutamine tracing ( Fig. 4E ). FAC treatment did not significantly increase incorporation of label into metabolites altered by heme deficiency, whereas iron chelation with deferoxamine reduced flux ( Fig. 4F ). Nevertheless, both FAC and heme supplementation increased OCR in iron resistant (HCT116 and SW480) but not in iron sensitive (HT1080 and RKO) cells, suggesting that the heme-dependent metabolic rewiring of the TCA cycle and nucleotide synthesis occurs primarily through respiratory chain–mediated processes ( Fig. 4G-I ). Given the observed ETC defects upon heme depletion, we hypothesized that disruption of specific complexes may sensitize CRC cells to iron stress. Indeed, pharmacologic inhibition of succinate dehydrogenase (SDH) selectively sensitized CRC cells to iron-induced toxicity, while inhibition of other ETC complexes produced modest effects ( Fig. 4J, K ). These findings point to the heme–CII axis as a central metabolic safeguard that enables CRC cells to tolerate high iron and sustain proliferation. Succinate dehydrogenase (SDH)–CoQ axis integrates mitochondrial metabolism with iron detoxification CII is composed of four nuclear-encoded SDH subunits that mediate electron transfer from the TCA cycle into the ETC through succinate oxidation to fumarate. Electrons released from this reaction pass sequentially through FAD, three iron–sulfur clusters, and a heme cofactor before ultimately reducing CoQ, which then serves as an electron carrier to CIII 36 ( Fig. 5A ). Similar to pharmacological inhibition of CII by (2-Thenoyltrifluoroacetone) TTFA, CRC cells deficient in the structural subunits SDHA or SDHB exhibited heightened sensitivity to iron toxicity but not to other stressors such as copper, H₂O₂, or 5-fluorouracil ( Fig. 5B-C ; Fig S6A-E). Interestingly, inhibition of succinate oxidation within the TCA cycle using competitive inhibitors (malonate, succinate) did not alter iron sensitivity, whereas inhibition of CoQ reduction (via TTFA) did ( Fig. S6F ), suggesting that the CoQ-reducing function of CII, rather than its dehydrogenase activity, is critical for iron homeostasis. Download figure Open in new tab Figure S6 A) Western blot images showing protein expression of SDHA, SDHB and beta actin in SDHA and SDHB knockdown HT29 cells via constitutive shRNA expression. B) Representative crystal violet staining of HT29 SDHA and SDHC knockdown cells from A) 14 days post copper sulphate (100μM) + elesclomol (Cu-Es), C) hydrogen peroxide (H 2 O 2 ) and D) 5-fluoroUracil (5FU) at indicated concentrations. E) Heatmap representation of relative colony count from B). F) Representative crystal violet staining images 14 days post FAC (at indicated concentrations), dimethyl succinate, dimethyl malonate, and diethyl malonate. G) Representative crystal violet staining image of NDUSF1 and 2 knockdown cells 14 days post FAC treatment (0.25, 0.5mM) with or without SA (250μM) or heme (10μM). Download figure Open in new tab Figure 5: Succinate dehydrogenase (SDH)–CoQ axis integrates mitochondrial metabolism with iron detoxification A) Schematic showing flow of electrons generated from succinate oxidation to through the different subunits of succinate dehydrogenase (CII) and ultimately reduce CoQ10 a terminal e-acceptor. B) Representative crystal violet staining images of HT29 cells stable for empty pLKO-puro (Scr) or shRNA targeting SDHA or SDHB (shRNA 1 or shRNA 2) 14 days post FAC treatment. C) Heatmap representation of relative colony count measured from B), * indicate p<0.05 compared to Vehicle treated control group. D) Total CoQ 10 levels in HCT116 and SW480 cells treated with FAC (500μM) for 72h, with or without TTFA (100μM) for 24h measured by HPLC. E) Lipid ROS measured by C11-BODIPY581/591 in HCT116 and SW480 cells 24h post TTFA (100μM) and Heme (10μM) treatment. F) General ROS measured by free radical sensor carboxy-H2DCFDA in HCT116 and SW480 cells 24h post TTFA (100μM) and Heme (10μM) treatment. G) Total CoQ 10 levels in HT1080 and RKO cells treated with FAC (500μM) for 72h, with or without TTFA (100μM) for 24h measured by HPLC. H) Schematic showing role of CoQ 10 in mitigating lipid ROS. I) Western blot showing expression of ZO-1 and TOM20 as plasma membrane and mitochondria specific proteins. J) Relative CoQ 10 levels in the plasma membrane (PM) in HCT116 cells treated with 0.5mM FAC for 72h using HPLC. K) % cell death measured by Sytox green staining in HCT116 and SW480 cells treated with FAC (0.5, 5mM) and TTFA (100μM) with or without heme (10μM) and CoQ analogs (Idebenone: IDB, 4μM, MitoQ: 20mM, Liproxstatin: Lip-1, 2μM) for 24h L) Heatmap representation of log2 fold change in % cell death in HT1080 (non-CRC) and RKO, DLD1, HT29 (CRC) cell lines treated as described in (K), * indicate p<0.05 compared to Vehicle treated control group. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (C), (D), (E), (F), (G), (J), (K) and (L). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. We next considered whether heme availability, which regulates multiple ETC complexes ( Fig. 4H-I ), could influence CII activity 37 . Heme depletion did not significantly alter cellular succinate levels; however, we found that CII-dependent CoQ reduction was essential for buffering iron toxicity ( Fig. 5D ). Indeed, both total and reduced CoQ levels increased in cells exposed to elevated iron in a CII-dependent manner. Inhibition of CII-dependent CoQ reduction increased both lipid ROS and total ROS, effects that could not be rescued by heme supplementation ( Fig. 5E-F ). Interestingly increasing intracellular iron via FAC treatment did not induce total CoQ 10 levels in iron sensitive HT1080 and RKO cells ( Fig 5G ). Given prior data that CoQ can translocate from mitochondria to other cellular membranes, where it contributes to detoxification of lipid ROS, we examined CoQ distribution under iron stress( Fig 5H ) 38 , 39 . Remarkably, we observed an iron-induced, CII-dependent increase in plasma membrane CoQ levels, further supporting its role as a lipid antioxidant defense system ( Fig. 5I-J ). In contrast, deletion of CI subunits (NDUFS1, NDUFS2) did not alter iron sensitivity, underscoring the specificity of the heme–SDH–CoQ axis in protecting against iron toxicity ( Fig. S6G ). Notably, CII inhibition–induced iron sensitivity was rescued by exogenous CoQ analogs and the ferroptosis inhibitor liproxstatin-1, but only the membrane-permeable CoQ analog idebenone—not the mitochondria-targeted MitoQ—restored viability ( Fig. 5J–L ). Together, these findings identify succinate dehydrogenase–dependent CoQ reduction as a central metabolic defense against iron-induced oxidative stress, linking TCA cycle electron flow, heme metabolism, and extramitochondrial antioxidant capacity across mitochondrial and plasma membrane compartments. Heme dependent SDHC expression is essential for protection against iron induced toxicity in CRC cells Heme depletion led to a loss of multiple ETC subunits, diminished respiratory output, and a profound shift in the cellular metabolome. In erythroid progenitors, heme availability is known to transcriptionally and translationally control essential processes such as proliferation, differentiation, and intercellular communication 40 . However, whether similar heme-dependent regulation operates in cancer cells has remained unclear. To address this, we performed quantitative proteomics using tandem mass tag labeling of mitochondrial-enriched fractions from HCT116 cells treated with FAC, SA, or their combination for 72 h ( Fig. S7A, B ; Fig. 6A, B ) 41 . Among the most striking changes, CII subunits and assembly factors emerged as iron-induced, heme-dependent proteins. SDHC (succinate dehydrogenase subunit C) was significantly enriched under FAC (heme sufficiency) but strongly depleted with FAC + SA ( Fig. 6C ). Heme-dependent SDHC stabilization was required to sustain CII activity and maintain cellular CoQ pools, thereby mitigating iron toxicity in CRC cells. Consistently, heme depletion and CII inhibition yielded highly concordant proteomic signatures, with ∼48% overlap in differentially altered proteins ( Fig. 6D, E ). Notably, heme biosynthetic enzymes were largely unaffected by SA treatment, with the exception of ALAS1, which is regulated by cellular iron via IREs ( Fig. S7C ) 42 . Beyond CII, protein abundance across CI–CV subunits and iron–sulfur cluster–containing proteins was increased under FAC and reduced with SA, whereas Fe–S biosynthetic enzymes remained unchanged ( Fig. S7D–F ). Importantly, other CoQ-reducing enzymes that have also been implicated in mediating mitochondrial redox balance (DHODH, SQOR, ETFDH) 43 , 44 were upregulated in FAC + SA cells, underscoring the central role of reduced CoQ as an antioxidant defense ( Fig. S7G ). Download figure Open in new tab Figure S7 A) Mean fluorescence intensity (MFI) of Tetramethylrhodamine, ethyl ester (TRME) and mitobrilliant 646 in HCT116 cells 72h post FAC (500μM), SA (500μM) and TTFA (100μM). B) Western blot image showing protein expression of TOM20 and vinculin in the whole cell, cytoplasm and mitochondrial fractions of HCT116 cells treated with 500μM FAC for 72h. C) Heat map representation of Log2 fold change abundances for proteins involved in heme biosynthesis, D) Mitochondrial electron transport chain, E) Iron-sulfur cluster containing proteins, F) Iron-sulfur cluster biogenesis proteins, G) and Co enzyme Q reducing enzymes. * Indicates p<0.05 as compared to Vehicle treated control group. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (A), and t test for (D), (E), (F) and (G). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Download figure Open in new tab Figure 6: Heme Dependent SDHC Expression is Essential for Protection Against Iron Induced Toxicity in CRC Cells A) Schematic showing the workflow for quantitative TMT labeling based proteomics in the mitochondrial enriched fractions of HCT116 cells 72h post FAC (500μM), SA (500μM) and Heme (10μM). B) Principal component analysis (PCA) plots of proteomics performed as described in A). C) Heatmap representation of log2 abundance fold change in cells treated with FAC and FAC + SA as described in A) D) Venn diagram showing the overlap between significantly altered proteins (p<0.05) upon treatment with SA, 500μM for 72h or TTFA, 100μM for 24h E) Heatmap representation of log2 abundance fold change in cells treated with SA and TTFA as described in D) F) Protein abundance of SDHC in Normal and tumor tissue of patients from CPTAC database. G) Representative Immunohistochemistry images showing expression of SDHC in normal and tumor tissue from patient tissue sections. The annotated images generated in Qupath categorized cells based on cell mean DAB OD and are shown as SDHC negative cells in blue and low, medium, high expression cells in yellow, orange, and red respectively). H) % low, medium, and high SDHC expressing cells represented as average of numbers computed for 3-5 different cross sections for each patient tissue section using Qupath as described in H) I) Western blot image showing SDHC expression and beta actin in HCT116 and SW480 cells stable for empty tet-pLKO-puro (Scr) or shRNA targeting SDHC (shRNA 1 or shRNA 2) 48h post Doxycycline treatment (250ng/mL). J) % cell death measured via Sytox green staining in HCT16 and SW480 cells (described in G), 48h post dox and FAC (0.5, 5mM) treatment. K) Total CoQ10 levels in HCT116 Scr or SDHC targeting shRNA1 cells 48h post dox or FAC (0.5mM) treatment measured by HPLC. All data are mean ± SEM. t test for (C), (E) and (F) or One-way ANOVA with Tukey’s multiple comparisons test for (J) and (K). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Interestingly, our data suggest that iron itself directly increases SDHC protein abundance in CRC cells, however, large-scale patient datasets demonstrate the opposite. Analysis of The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium revealed that all SDH subunits ( SDHA–D ) are decreased at the mRNA and proteins level in CRC tumors relative to matched normal tissue ( Fig. 6F ) 45 . Our immunohistochemistry analyses demonstrated a clear increase in SDHC protein specifically within tumor epithelial cells of COAD patients as compared to the normal adjacent tissue ( Fig. 6G, H ). These findings demonstrate that bulk tumor profiling underestimates tumor intrinsic SDH expression as invasive CRC lesions are intermixed with surrounding stromal tissue, which expresses negligible SDH. Functional validation confirmed the tumor-specific requirement for SDHC. Inducible knockdown of SDHC in HCT116 and SW480 cells enhanced iron toxicity, phenocopying pharmacological inhibition of CII with TTFA, and concomitantly reduced iron-induced CoQ 10 levels ( Fig. 6I–K ). Together, these findings define a heme-dependent regulatory mechanism that sustains SDHC expression and CoQ 10 homeostasis, positioning the heme–SDH–CoQ axis as a critical antioxidant pathway that enables CRC cells to thrive under iron-rich conditions. SDHC inhibition sensitizes CRC cells to iron induced toxicity and reduce tumor progression To evaluate the in vivo efficacy of SDHC inhibition in promoting iron-induced toxicity, we generated CT26 mouse CRC cells with a doxycycline-inducible shRNA targeting Sdhc and implanted them subcutaneously into Balb/c mice ( Fig. 7A ; Fig. S8A ). Once tumors were established, doxycycline-containing chow was administered to induce Sdhc knockdown. SDHC-deficient tumors were significantly smaller and exhibited elevated lipid ROS, although the magnitude of this effect was moderate ( Fig. 7B–C ; Fig. S8B-C ). Because subcutaneous tumor models do not fully recapitulate the colonic tumor microenvironment or the extent of iron accumulation observed in patients, we next crossed Sdhc F/F mice with a tamoxifen-inducible intestinal epithelial Cre line (Villin-ER T2 Cre mice) to generate intestine-specific Sdhc -deficient mice (Sdhc-IntKO) ( Fig. 7D ) 9 , 46 . Immunoblot analysis confirmed near-complete loss of SDHC in Sdhc -IntKO mice, whereas mice retaining one wild-type allele ( Sdhc -IntHet) showed an ∼50% reduction in Sdhc levels with no histological changes in the intestinal epithelium ( Fig. 7E ; Fig. S8D ). Similar to in vitro findings, Sdhc deficient mice showed signs of GI toxicity when fed high 1000ppm iron containing diet ( Fig. 7F-G ). Sdhc deficient mice on high iron diet for one week, lost up to 25% of total body weight with significant damage to the epithelial lining and accumulation of lipid peroxides as assessed by 4-hydroxynonenal staining ( Fig. S8E-G ; Fig. 7H ). Because complete Sdhc loss in the intestine is predicted to compromise epithelial viability and confound tumorigenesis studies, we focused on the heterozygous model to test whether partial loss of Sdhc is sufficient to sensitize tumors to iron-induced stress 46 . Download figure Open in new tab Figure S8 A) Western blot image showing SDHC expression and beta actin in CT26 cells stable for empty tet-pLKO-puro (Scr) or shRNA targeting SDHC (shRNA 1 or shRNA 2) 48h post Doxycycline treatment (250ng/mL). B) Gross tumor images from the xenograft tumor model with subcutaneous implantation. C) General ROS measured by free radical sensor carboxy-H2DCFDA in EPCAM positive cells from the tumors from xenograft tumor model. D) Representative H&E of small colon 5 days post tamoxifen induction via 100mg/kg body weight I.P. injection in WT ( Sdhc F/F ), Het ( Sdhc Het IE ) and KO ( Sdhc ΔIE ) mice. E) % body weight change in Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. F) Crypt damage score (0-4) in small intestine (SI) and colon from Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. G) % tissue involved score (0-4) in small intestine (SI) and colon from Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. H) % body weight change in Sdhc F/F and Sdhc Het IE mice post AOM treatment I) Representative H&E images of colon from Sdhc F/F and Sdhc Het IE mice on the AOM-DSS induced CAC model. J) General ROS measured by free radical sensor carboxy-H2DCFDA in EPCAM positive cells from the colonic epithelium from Sdhc F/F and Sdhc Het IE mice from AOM-DSS induced CAC model. All data are mean ± SEM. t test for (C), (E), (F), (G) and (J). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Download figure Open in new tab Figure 7: SDHC Inhibition Sensitizes CRC Cells To Iron Induced Toxicity and Reduce Tumor Progression A) Schematic showing the xenograft tumor model with subcutaneous implantation. B) Tumor weight from B) C) Lipid ROS are measured by C11-BODIPY581/591 in EPCAM positive cells from the tumors from B). D) Schematic showing breeding strategy to generate tamoxifen inducible intestine specific SDHC deficient mice. E) Western blot images showing cellular and mitochondrial protein expression of SDHC 5days post tamoxifen induction via 100mg/kg body weight I.P. injection in WT ( Sdhc +/+), Het ( Sdhc +/-) and KO ( Sdhc -/-) mice. F) Representative H&E images of Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. G) Inflammatory histology scored for small intestine and colon of Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. H) Representative immunohistochemistry images showing accumulation of 4 hydroxynonenal (4HNE) in small intestine and colon of Sdhc F/F and Sdhc ΔIE mice fed with 350 ppm or 1000ppm iron containing chow for 7 days. I) Schematic showing treatment protocol for colitis associated cancer model using AOM/DSS in Sdhc F/F (WT) and Sdhc Het IE (Het) mice. J) Tumor number measured in mice from I). K) Tumor burden measured in mice from I) L) Lipid ROS measured by C11-BODIPY581/591 in EPCAM positive epithelial cells derived from I). M) Schematic showing role of heme-SDHC-CoQ axis in mitigating iron induced cell death in CRC cells. All data are mean ± SEM. One-way ANOVA with Tukey’s multiple comparisons test for (B) and (G) and t test for (C), (J), (K) and (L). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To this end, mice were treated with azoxymethane (AOM) and subjected to three cycles of DSS to induce colitis-associated cancer. After completion of the three DSS cycles, at a stage when tumors are reliably established, tamoxifen was administered to delete one copy of Sdhc ( Fig. 7I ). Strikingly, Sdhc -IntHet mice developed markedly fewer tumors than their littermate controls, accompanied by increased lipid ROS and overall ROS accumulation ( Fig. 7J-L ; Fig. S8H-J ). Collectively, these findings define a previously unrecognized heme–SDHC axis that regulates iron-induced toxicity in vivo models ( Fig. 7M ). Discussion CRC cells are highly dependent on iron to support proliferation by fueling nucleotide synthesis and mitochondrial respiration. Yet, excess iron generates ROS and lipid peroxidation, which can trigger ferroptotic cell death. How CRCs thrive under these iron-rich conditions, which are cytotoxic to most other cell types, has remained poorly understood. While prior studies have largely focused on iron import, storage, and export via transferrin receptor, ferroportin, and ferritin, little is known about the cellular mechanisms that actively buffer high intracellular iron 5 , 8 . Our study identifies a previously unrecognized heme–SDH–CoQ axis that mediates this protective function. Cellular iron availability sustains SDHC protein abundance which in turn supports CoQ biosynthesis, which together contribute to lipophilic antioxidant defense, neutralizing iron-induced ROS and enabling CRC cells to exploit iron-rich environments for growth. This mechanism reveals a fundamental strategy by which CRCs resolve the iron paradox and maintain proliferation under potentially cytotoxic conditions. Iron dependent lipid ROS–driven cell death, or ferroptosis, has been extensively characterized in vitro. Canonical regulators such as SLC7A11 and GPX4 were largely discovered through chemical genetic screens and through synthetic compounds 47 , 48 . However, whether these pathways explain iron toxicity in physiological contexts remains unclear. In CRC, although canonical ferroptosis regulators are transcriptionally upregulated, their expression does not correlate with patient survival, and deletion of these genes fails to sensitize cells to iron or alter tumor progression. This suggests that alternative, physiologically relevant protective mechanisms exist. Several intrinsic (oncogenic mutations in KRAS, Tp53) and extrinsic (nutrient and oxygen availability) factors affect ferroptosis sensitivity in cancers; mechanistically these factors all converges to altered cellular iron homeostasis 49 – 52 . In PDAC, KRAS activation increases the labile iron pool via ferritinophagy to fuel mitochondrial metabolism via iron-sulfur cluster biogenesis, but also sensitizes cells to iron toxicity and consequently ferroptosis 53 , 54 . Interestingly, loss of iron sulfur clusters sensitize lung cancer cells to ferroptosis via IRP stabilization which increases the labile iron pool 55 , 56 . Our work positions SDH and CoQ as critical iron-specific antioxidant defenses in CRC cells, functioning in parallel to canonical ferroptosis pathways. By demonstrating that mitochondrial CoQ metabolism directly mitigates iron-induced lipid ROS, we expand the conceptual framework of ferroptosis and iron toxicity, particularly in vivo and in intestinal tissues where canonical ferroptosis effectors are dispensable. SDH has classically been studied as a metabolic hub, integrating TCA cycle flux with ETC activity, and as a tumor suppressor when mutated in paragangliomas, GIST, and renal cancers 36 , 57 – 59 . It remains unclear whether these tumor types experience significant iron accumulation; if not, SDH mutations in this context may have limited impact on iron buffering. In such cases, the signaling and metabolic advantages conferred by SDH loss may outweigh any detrimental effects of decreased protection against iron-induced stress. In contrast, our work identifies a previously unrecognized role for SDH in safeguarding cells against iron toxicity. Specifically, heme-dependent maintenance of SDHC preserves CoQ pools that neutralize lipid ROS. Notably, SDH also serves as a key regulator of CoQ redox status through its bidirectional catalytic capacity to oxidize succinate or reduce fumarate 60 , 61 . This property underlies tissue-specific variation in SDH function. In some tissues such as liver, kidney, and brain constitutively engage more in fumarate reduction than succinate oxidation, suggesting that cancers arising in these contexts may be less protected against iron overload by complex II activity 62 . Indeed, both liver and kidney are highly susceptible to iron-induced oxidative damage and ferroptosis, consistent with the notion that limited SDH-dependent succinate oxidation may compromise CoQ redox buffering capacity in these tissues 63 – 65 . Inhibition of SDHC dependent succinate ubiquinone reductase (SQR) activity has also been shown to increase sensitivity to BCL-2 antagonist venetoclax in multiple myeloma while heme supplementation induces resistance 66 , 67 . Whereas targeting SDHA dependent succinate oxidation reduces de novo purine synthesis, sensitizing cancer cells to inhibition of purine salvage pathways 68 . These findings also highlight the multifaceted role of mitochondrial ETC complexes where specific catalytic activity of the same complex can have vastly different affects. At present, the mechanisms by which iron or heme regulate SDHC remain unresolved. One possibility is that heme or iron regulates SDHC stability through mitochondrial quality-control pathways, which are tightly coupled to respiratory complex integrity 69 . Another intriguing possibility is that heme binding to SDHC or associated complex II subunits stabilize the holoenzyme, thereby protecting it from proteolytic degradation. How SDHC regulation leads to a net increase in the CoQ pool is also unclear, but it is possible that enhanced SDHC activity shifts electron flux or alters CoQ redox cycling, leading to increased CoQ synthesis, redistribution, or stabilization. Future work will be needed to understand these mechanisms. Beyond its canonical role as an electron carrier in the ETC, CoQ functions as a glutathione-independent, membrane-specific antioxidant 70 – 72 . Its redistribution to non-mitochondrial membranes lead to protection from iron-induced lipid peroxidation 38 , 39 . CoQ has been described as an radical trapping antioxidant in the membrane where it is reduced by FSP-1 and DHODH 43 , 70 , 71 . In this sense, the heme–SDH–CoQ axis may operate as a mitochondrial counterpart to the FSP1–CoQ system, with mitochondria actively supplying CoQ to the plasma membrane and other cellular compartments. This model is supported by the increased abundance of other mitochondrial CoQ reducing enzymes under heme deficiency where SDH is no longer functional. However, it remains unclear how CoQ is redistributed to non-mitochondrial membranes in response to iron. Our proteomic data did not detect changes in STARD7, a recently described CoQ transporter, suggesting that other factors may mediate this process 73 . These findings highlight a broader principle: cancer cells co-opt redox-active cofactors, such as heme and CoQ, not only to sustain metabolism but also to adapt to oxidative stress. Collectively, these findings uncover a novel mitochondrial antioxidant defense mechanism, redefine the landscape of ferroptosis regulation in vivo, and illuminate how CRCs buffer iron toxicity. By delineating the heme–SDH–CoQ axis, our study reveals a previously unknown metabolic adaptation that underlies CRC survival in iron-rich environments and highlights potential therapeutic opportunities to exploit iron-dependent synthetic vulnerabilities. In particular, our data suggest that pharmacologic inhibition of CII could disrupt this protective axis. When combined with approaches that elevate labile iron or impair parallel antioxidant defenses, such strategies could offer a synergistic means of inducing synthetic lethality in cancer cells. Thus, identifying the heme–SDH–CoQ axis not only advances our understanding of iron biology in cancer but also provides a promising therapeutic in CRC. Experimental Models Mice All mice used in this study are predominantly C557BL/6 background unless mentioned otherwise. 6-8 weeks old male and female mice were used for all the experiments and littermates were randomized in all experimental conditions. All the mice were housed at Unit for Laboratory Animal Management at University of Michigan in standard temperature controlled specific pathogen free environment with 12h light/dark cycle and had excess to standard chow and water ad libitum. Mouse lines used are Villin-ER T2 Cre; Gpx4 F/F 20 . To generate GPX4 deficient mice with Apc , Tp53 and Kras mutation, Gpx4 F/F mice were crossed CDX2-ER T2 Cre; Apc F/F mice (single mutant), CDX2-ER T2 Cre; Apc F/F , Tp53 F/F mice (double mutant-p53), CDX2-ER T2 Cre; Apc F/F , Kras G12D mice (double mutant-KRAS) and CDX2-ER T2 Cre; Apc F/F , Tp53 F/F , Kras G12D mice (triple mutant) 21 . And finally to generate intestine specific SDHC deficient mice, Villin-ER T2 Cre mice(Jax strain 032770) were crossed with Sdhc F/F mice 46 . All animal studies were performed in accordance with the Association of Assessment and Accreditation of Laboratory Animal Care International guidelines, approved by the University Committee on the Use and Care of Animals, University of Michigan. Tamoxifen induction Mice were intraperitoneally administered with 100mg/kg body weight tamoxifen dissolved in corn oil every other day 3 times. Mice were used for experiments 5 days post last tamoxifen dose unless mentioned otherwise. AOM-DSS induced colitis associated cancer (CAC) model Mice were injected with 10mg/kg body weight azoxymethane (AOM) intraperitoneally on Day 0. Day 3 mice were cycled on and off with 2% dextran sodium sulfate (DSS) in drinking water for 7 days, while body weights were recorded every other day 20 . 5 days after the last DSS cycle, mice were induced with tamoxifen as described above and euthanized 4 weeks after last DSS cycle, tissues were either flash frozen, fixed and Swiss rolled or processed live for molecular measurements, histological analysis for ROS measurements respectively. Syngeneic and Xenograft studies Wild type BalbC mice were anesthetized with 2-2.5% isoflurane and 2 million CT26 cells stable (Scr, SDHC-sh2/3) were implanted into the lower flanks subcutaneously. Mice were then placed on standard or doxycycline containing chow 7 days after the implantation after the tumors were palpable. Mice were euthanized roughly 2 weeks after or the tumors reached 2cm 3 in size, the humane end point 26 . Tumor volume/weight was measured, and the tissues were either flash frozen, fixed and Swiss rolled or processed live for molecular measurements, histological analysis for ROS measurements respectively. High Iron Diet For high iron studies mice were given tamoxifen to induce Cre-mediated recombination as described above. After administering the last tamoxifen dose, mice were fed with standard diet chow or high iron containing chow i.e., AIN-93G Purified Rodent Diet chow with 350 ppm Fe (Dyets, 115180) or 1000ppm Fe (Dyets, 115842) added from Ferric Citrate respectively for 7 days. Body weight was recorded every day and % body weight change was calculated i.e. (body weight on day 7/ body weight on day 1) *100. Human Subjects Paraffin embedded blocks of de-identified human adult colon biopsies were collected with approval from the University of Michigan Institutional Review Board. 10uM sections were cut from the blocks and uses for Immunohistochemistry (IHC). Deidentified human CRC enteroids and matched control enteroids were established and cultured as previously described 20 . Cell lines Cancer cell lines HCT116, SW480, DLD1, KM12, RKO-1, HT29, MC38, CT26, CD-1, NIH3T3, HT1080, DANG, ASPC, UOS2 were obtained from ATCC and maintained at 37°C, 5% CO 2 , 21% O 2 humidified environment in DMEM supplemented with 10% heat inactivated fetal bovine serum (FBS) and 1% antibiotic/antimycotic agent). Method Details Clonogenic Assay 250-500 cells were seeded in a 12 well plate in biological triplicates with 1mL media. The cells were treated as described in the legends for Fig. 2C, D ; Fig S2F; Fig. 3C, D ; Fig. S3B, E, G ; Fig. S4 ; Fig. S5B-D, F-G ; Fig 5B ; 24h. 2 weeks after the treatment, cells were fixed in 10% buffered formalin and stained with 0.5% crystal violet, 20% methanol solution. Colonies were then blindly counted using Cytation 5 Imaging Multi-Mode reader. Cell Growth Assays Cell growth was measured using live cell imaging by the Cytation 5 Imaging Multi-Mode reader with attached BioSpa. 1000 cells/well were seeded in 96 well clear flat bottom plates and treated 24h later in biological triplicates or quadruplicate as described in the legends for Fig. 2A-B ; Fig. S2D-E ; Fig. 3G ; Fig. S4B-C ; Fig. S6C . Images were taken every 24h and normalized to the baseline values counted from images taken right before the treatments (0h). Relative growth was plotted using Graphpad Prism software. Cell Death Assay Cell death was measured using SYTOX Green Nucleic Acid stain (20nM, Invitrogen #S7020). 3500-5000 cells / well were seeded in 96 well clear flat bottom plates and treated 24h later in biological triplicates or quadruplicate as described in the legends for Fig. 2D ; Fig. S2B-C ; Fig. 4J-K ; Fig. 5I-J ; Fig. 6J . Imaged were taken every 8h using Cytation 5 Imaging Multi-Mode reader with attached BioSpa and % cell death was calculated by dividing number SYTOX + cells by total number of cells. Histology Colonic and tumor tissues were rolled and fixed in 10% buffered formalin overnight, dehydrated in 70% ethanol and embedded in paraffin. 5μM thick sections were then stained for H&E and mounted with Permount Mounting Medium (Thermo Fisher Scientific). The slides were imaged using Leica Aperio Slide Scanner and representative images are shown at 10x or 20x magnification. Histological scoring was performed evaluating inflammation severity (0–3), depth of injury (0–3), and crypt damage (0–4). Each parameter was multiplied by the percentage of tissue involvement (1, 0–25%; 2, 26–50%; 3, 51–75%; 4, 76–100%), and the values were summed to yield a total injury score (maximum 40). SDHC and 4HNE staining and quantification For SDHC staining, 5μM thick sections obtained from the paraffin blocks containing human CRC tissues were rehydrated and antigen retrieval was done using 10mM sodium citrate buffer. Followed by blocking with 5% goat serum in PBS and sections were then probed with primary antibody against SDHC (Proteintech, 14575-AP) or 4HNE (R&D Systems, MAB3249-SP) diluted at 1:500 in the blocking solution. After 3 washes with PBS, slides were incubated HRP-conjugated anti-rabbit IgG at 1:500 dilution in the blocking solution for 1h. Sections were washed with PBS and incubated with DAB substrate and monitored for the color reaction (∼5minutes). The sections were then counter staining with hematoxylin, dehydrated and mounted with Permount mounting medium. The slides were imaged using Leica Aperio Slide Scanner and representative images are shown at 10x or 20x magnification. Qupath software was used for quantification and cells were categorized as SDHC negative, low, medium, or high based on mean cell DAB OD and represented as % of the total positive cells in Fig. 6H . The annotated images show SDHC negative, low, medium, and high cells as blue, yellow, orange, and red respectively ( Fig 6. G ). Western Blotting Protein lysates were prepared by lysing the cells or tissues in RIPA buffer supplemented with protease and phosphatase inhibitors. After lysis solubilized proteins were resolved on 12% SDS-polyacrylamide gels and transferred to PVDF membrane, blocked with 5% milk in TBST and probed overnight with indicated primary antibodies diluted at 1:1000 in the blocking buffer for GPX4 (Proteintech, 67763-1-Ig), FTH1 (Cell Signaling,3998S), UROD (Santa Cruz, SC-365297), FECH (Proteintech, 14466-1-AP), SDHA (Cell Signaling, 11998), SDHB (Proteintech, 10620-1-AP), SDHC (Proteintech, 14575-AP), β-actin (Proteintech 66009-1-Ig). Protein bands were then visualized by electrochemical luminescence using HRP conjugated secondary antibody against mouse (Cell Signaling, 7076S) or rabbit (Cell Signaling 7074S). Real Time Quantitative PCR RNA was isolated from cells or tissues using TRIzol chloroform method. RNA was reverse transcribed using MMLV reverse transcriptase using primers listed in the table 1. Quantitative PCR was done with Radiant Green qPCR Master Mix (Alkali Scientific Inc.) and primers listed in table 1. Quantification cycle values were normalized to β-actin and expressed and log 2 fold change. CRISPR screen and Analysis The human CRISPR metabolic gene knockout library was a gift from David Sabatini containing 29,790 sgRNAs targeting 2981 human metabolic genes. A total of 75 × 10 6 cells were seeded at 0.5X10 6 cells/mL in 6 well plates and transfected with the CRISPR library virus through spinfection at 1200g for 1h in presence of 10μM polybrene. 24h after spinfection the virus was removed by changing the media and after additional 24h, 1μM/mL puromycin was added transfected cells were selected for 3 days. About 30 million cells were pelleted and saved as doubling 0 population for normalization purposes described later. 30 million cells/ condition were then treated with 2.5mM FAC, 100μM FG4592 or vehicle control and passaged every 3-4 days until 14 doublings. Genomic DNA was isolated from 15 million cells from doubling 0 control and doubling 14 FSC, FG4592 and vehicle treated cells using DNeasy kit (Qiagen). sgRNA inserts were PCR amplified using Takara Ex Taq polymerase, purified, and sequenced using NextSeq Illumina. The sequence reads were mapped to the sgRNA reference library and abundance of sgRNA was quantified (normalized to doubling 0 abundances). For gene level differential score the log2 fold change abundance between vehicle, FAC and FG4592 for all sgRNA targeting the same gene was averaged. Finally, the gene score for each condition was calculated as median log2 fold change in abundance between FAC or FG492 and vehicle treated population. Generation of shRNA/sgRNA stable cell lines shRNA targeting UROD, FECH and SDHC were cloned into pLKO.1-Tet on plasmid to generate doxycycline inducible knock down. While shRNA targeting SDHA and SDHB were cloned into pLKO.1 plasmid to generate constitutively active knockdown cells. Corresponding empty vectors were used as controls. shRNA sequences were sequence validated and packaged into lentivirus by University of Michigan vector core. Cells were transfected through spinfection at 900g for 1h and selected for 2 weeks in puromycin (2μM/mL). All clones were validated by measuring protein expression 24, 48 and 72h post doxycycline (250ng/mL) induction. Cell ROS and Lipid ROS measurement For cells, 1million cells/well were seeded in 6 well plates and 24h later were treated as indicated in the legends of Fig. 3F , and Fig. 5E-F for 72h and 24h respectively. Cells were harvested using PBS-EDTA (5mM) buffer, washed with HBSS and stained for lipid ROS or cell ROS with 5μM C11-BODIPY (Thermo Fisher, D3861) and 10μM cell permeable free radical sensor carboxy-H2DCFDA (Thermo Fisher, C10444) for 30minutes at 37°C respectively. Cells were pelleted, washed, and resuspended in FACS buffer (1x PBS, 2%BSA, 1mM EDTA) for analysis on Cytek aurora spectrum flow analyzer. Fluorescence intensity was measured in the FITC channel for a minimum of 20,000 cells, data analyzed using FlowJo software and values expressed as percentage of positive cells 26 . For tumor and colon tissue: Middle region of the colon or tumors were collected from mice as indicated in the legend of Fig. 7E, K . The tissues were digested with 1mg/mL collagenase type IV dissolved in RPMI for 30 minutes at 37°C. Cell suspension was then filtered, collected, washed, and resuspended in FACS buffer (1x PBS, 2%BSA, 1mM EDTA). A single cell suspension was stained with 7-AAD (1:200; BD Pharmingen, catalog number 559925) and APC anti-mouse CD326 (EpCAM) antibody (1:200; BioLegend, catalog number 118214) for 30 min on ice. Subsequently, the single suspension was stained with 5μM C11-BODIPY (Thermo Fisher, D3861) and 10μM cell permeable free radical sensor carboxy-H2DCFDA (Thermo Fisher, C10444) for 30minutes at 37°C respectively. On Cytek aurora spectrum flow analyzer, forward and side scatter was used to identify single cells while 7AAD negative, APC positive cells were identified as live epithelial or tumor cells. Fluorescence intensity for C11-BODIPY or carboxy-H2DCFDA was measured in the FITC channel for previously identified live cell population 20 . The data was collected for a minimum of 20,000 cells, analyzed using the FlowJo software and values expressed as percentage of positive cells. Metabolomics HCT116 cells were seeded at 1 million cells/well seeding density and treated with FAC (100μM), Succinyl Acetone (500μM) and heme (10μM) for 72h, 24h post seeding. Metabolites were extracted in 80% methanol, lyophilized, and reconstituted in 50:50 methanol water mixture, normalized based on protein concentrations obtained from a well processed in parallel. Samples were then analyzed on Agilent 1290 Infinity II Bio LC ultra-high performance liquid chromatography system paired with an Agilent 6470 QQQ 74 . Data was processed and analyzed via MassHunter Quantitative Analysis v10.Metabolite abundance was median centered across samples and statistical significance was determined by one-way ANOVA with a significance threshold of 0.05. 13 C 5 L-Glutamine Labeling HCT116 cells were seeded at 1million cells/ well in a 6 well plate and treated 24h later with 0.5mM FAC or 150μM DFO for 24h. For stable isotope tracing, glutamine free DMEM (Thermo Fisher) was supplemented with 2mM 13 C 5 -L glutamine (Sigma), 1mM pyruvate, 10% FBS, and 1% antibiotic/antimycotic solution. A parallel plate was treated similarly except it was supplemented with regular 12 C 5 -L glutamine for normalization purposes. The cells were labeled for 8h and metabolites were extracted as described above. Samples were run on a Thermo Scientific IQX OrbiTrap LCMS system using a Waters Acquity UPLC BEH amide 1.7 um 2.1 × 100 mm column with a Waters UPLC BEH Amide 1.7 um VanGuard Pre-Column column of amide for high throughput acquisitions. Solvent A consists of 10 mM ammonium acetate in water with 10 mM Ammonia pH 9.2. Solvent B consists of acetonitrile. Pump Seal wash and autosampler wash is 50% isopropanol with 0.1% formic acid. The LC profile is 0.2 ml/min of 85% B at 0-0.5 min: 0.5-15 min, 15%B; 15-17 min, 15% B: 17.1 min, 85%B; 22 min, 85% B. The column compartment temperature is maintained at 30 °C and the autosampler is at 5 °C. The injection volume is 3 µl. Data was analyzed using Compound Discoverer v3.4. Heme measurement Total cellular heme was measured using fluorometric assay that measures protoporphyrin IX fluorescence upon release of free iron from heme as previously described 75 . 1X10 6 cells were seeded in 6 well plate and 24h later treated with FAC, Heme and Succinyl acetone for 72h as described in the legend for Fig. 3B . Cells were washed with PBS, harvested, and resuspended in 250μL 20mM oxalic acid and incubated overnight at 4°C in dark. Next day, 250uL of supersaturated prewarmed 2M oxalic acid was added to the cells to achieve a final 1M oxalic acid concentration. The samples were then split in half and boiled at 100°C for 1h while the other half was kept at room temperature for background normalization. The samples were centrifugated at full speed for 5inutes at room temperature, 50uL of the supernatant was transferred to 96 well black wall, clear bottom plates and fluorescence was measured at 400nM excitation and 604nM emission. The fluorescence of unboiled sampled representing background was subtracted from the fluorescence of boiled sample and the heme concentration was calculated using a standard curve of hemin chloride as μM. Oxygen Consumption Rate Cells were seeded (5000 cells/ well) in a 96 clear flat bottom plate and treated 24h later as described in the legend of Fig. 3H and Fig. 4G-H . Plate was then interfaced with Resipher system by placing the Resipher sensors over the plate as per manufacturer’s instructions for live, continuous monitoring of oxygen consumption. OCR values computed by the Lucid Lab software were normalized to cell numbers. Proteomics Mitochondria were isolated using 3 million cells were seeded in a 15cm cell culture plate in 25mL media and treated with 0.5mM FAC, 0.5mM SA for 72h and 50μM TFFA for 24h in biological triplicates. Cells were washed, harvested in ice cold PBS and resuspended in homogenization buffer (containing 200mM sucrose, 10mM TrisCl pH7.5, 10mM EDTA and protease inhibitor cocktail). Cells were then homogenized with 30-40 strokes in the dounce homogenizer. Homogenate was the centrifuged twice at 1000g for 10minutes at 4°C to removed nuclear pellet and the mitochondrial pellet was obtained by centrifuging the supernatant at 9000g for 20 minutes at 4°C. The obtained mitochondrial pellet was washed with the homogenization buffer twice to remove any cellular debris and nuclear contaminants. 100ug of mitochondrial proteins were then digested into peptides with trypsin, labeled with tandem mass tag labeling kit (ThermoFisher) according to manufacturer’s instructions, analyzed on mass spectrometer by the University of Michigan proteomics core. The values are represented as log2 fold change in abundance compared vehicle treated group. Co-enzymeQ 10 measurement For whole cell CoQ measurements, 1.5million cells were seeded in 10cm cell culture plates in 20mL media in biological triplicates, 24h later they were treated as indicated in the legends of Fig. 5D-H and Fig. 6K . Cells were then washed, harvested, and pelleted in ice cold PBS in pre-weighed tubes and wet weight of cell pellets was recorded. Cell pellets were then rapidly resuspended in n-propanol for CoQ extraction, where volume of n-propanol was normalized based on cell pellet weight. Samples were allowed to stand at room temperature and centrifuged at 12,000g for 5 minutes at 10°C to avoid CoQ precipitation and stored at -80°C. HPLC analysis for CoQ measurement was performed on a Hypersil ODS column (150 × 4.6mm, 3μm bead size, thermo Fischer) as described previously described 76 . The samples were run twice, first to measured oxidized CoQ, followed by oxidation of reduced CoQ by p-benzoquinone to measure total CoQ. Reduced CoQ was calculated by total CoQ – Oxidized CoQ. For plasma membrane CoQ levels, 3 million cells were seeded in 15cm cell culture plate in 25mL media, treated 24h later and homogenized as described above for mitochondrial extraction. The remaining supernatant after pelleting mitochondrial content was centrifuges at 100,000g for 1h at 4°C to get plasma membrane pellet. Wet weight of the plasma membrane pellet was determined, samples were prepared and analyzed as described above. Funder Information Declared University of Michigan, https://ror.org/00jmfr291 , Barbour fellowship National Institute of General Medical Sciences National Cancer Institute, https://ror.org/040gcmg81 , F99CA284256-01 Crohn's and Colitis Foundation, https://ror.org/05f2xbq60 , 623914 National Cancer Institute National Institute of Diabetes and Digestive and Kidney Diseases, https://ror.org/00adh9b73 , 1F31DK143736-01 Paradifference Foundation National Cancer Institute National Institute of General Medical Sciences National Cancer Institute, https://ror.org/040gcmg81 , R01 CA148828 University of Michigan Comprehensive Cancer Center Core , P30CA046592 Footnotes Conflict of Interest Disclosure Statement: Over the past three years, CAL has served as a consultant for Astellas Pharmaceuticals, Odyssey Therapeutics, Third Rock Ventures, and T-Knife Therapeutics. He is also an inventor on patents related to KRAS-regulated metabolic pathways, redox control mechanisms in pancreatic cancer, and therapeutic targeting of the GOT1-ME1 pathway (U.S. Patent No. 2015126580-A1, 2015; U.S. Patent No. 20190136238, 2019; and International Patent No. WO2013177426-A2, 2015). RB is a consultant for Zyphore Therapeutics and Alnylum Pharmaceuticals. The remaining authors declare no competing interests. Funding Details: CJ – Rackham Babour Fellowship; NJR – Systems and Integrative Biology Training Grant (NIGMS T32 GM150581); BC was supported by NCI F99CA284256-01; SS was supported by Crohn’s and Colitis Foundation Research fellow award (623914); PJD – Oncology Research Training Grant (NCI T32 CA009357), University of Michigan Pioneers Fellowship; SM was supported by NIDDK 1F31DK143736-01; JM is supported by Paradifference Foundation; CAL – NCI R37 CA237421, R01 CA248160, R01 CA244931; RB – R35 GM130183; YMS – NCI R01 CA148828; University of Michigan Comprehensive Cancer Center Core – P30CA046592 References 1. ↵ Siegel , R.L. , Kratzer , T.B. , Giaquinto , A.N. , Sung , H. , and Jemal , A . ( 2025 ). Cancer statistics, 2025 . CA: A Cancer Journal for Clinicians 75 , 10 – 45 . doi: 10.3322/caac.21871 . OpenUrl CrossRef PubMed 2. ↵ Hanahan , D. , and Weinberg , R.A . ( 2011 ). Hallmarks of Cancer: The Next Generation . Cell 144 , 646 – 674 . doi: 10.1016/j.cell.2011.02.013 . OpenUrl CrossRef PubMed Web of Science 3. ↵ Drakesmith , H. , Nemeth , E. , and Ganz , T . ( 2015 ). Ironing out Ferroportin . Cell Metabolism 22 , 777 – 787 . doi: 10.1016/j.cmet.2015.09.006 . OpenUrl CrossRef PubMed 4. ↵ Brissot , P. , Pietrangelo , A. , Adams , P.C. , de Graaff , B. , McLaren , C.E. , and Loréal , O. ( 2018 ). Haemochromatosis . Nat Rev Dis Primers 4 , 18016 . doi: 10.1038/nrdp.2018.16 . OpenUrl CrossRef PubMed 5. ↵ Xue , X. , and Shah , Y.M . ( 2013 ). Intestinal Iron Homeostasis and Colon Tumorigenesis . Nutrients 5 , 2333 – 2351 . doi: 10.3390/nu5072333 . OpenUrl CrossRef PubMed Web of Science 6. ↵ Sasso , A. , and Latella , G . ( 2018 ). Role of Heme Iron in the Association Between Red Meat Consumption and Colorectal Cancer . Nutrition and Cancer 70 , 1173 – 1183 . doi: 10.1080/01635581.2018.1521441 . OpenUrl CrossRef PubMed 7. Bouvard , V. , Loomis , D. , Guyton , K.Z. , Grosse , Y. , Ghissassi , F.E. , Benbrahim-Tallaa , L. , Guha , N. , Mattock , H. , and Straif , K . ( 2015 ). Carcinogenicity of consumption of red and processed meat . The Lancet Oncology 16 , 1599 – 1600 . doi: 10.1016/S1470-2045(15)00444-1 . OpenUrl CrossRef PubMed 8. ↵ Gamage , S.M.K. , Lee , K.T.W. , Dissabandara , D.L.O. , Lam , A.K.-Y. , and Gopalan , V . ( 2021 ). Dual role of heme iron in cancer; promotor of carcinogenesis and an inducer of tumour suppression . Experimental and Molecular Pathology 120 , 104642 . doi: 10.1016/j.yexmp.2021.104642 . OpenUrl CrossRef PubMed 9. ↵ Xue , X. , Ramakrishnan , S.K. , Weisz , K. , Triner , D. , Xie , L. , Attili , D. , Pant , A. , Győrffy , B. , Zhan , M. , Carter-Su , C. , et al. ( 2016 ). Iron Uptake via DMT1 Integrates Cell Cycle with JAK-STAT3 Signaling to Promote Colorectal Tumorigenesis . Cell Metabolism 24 , 447 – 461 . doi: 10.1016/j.cmet.2016.07.015 . OpenUrl CrossRef PubMed 10. ↵ Xue , X. , Taylor , M. , Anderson , E. , Hao , C. , Qu , A. , Greenson , J.K. , Zimmermann , E.M. , Gonzalez , F.J. , and Shah , Y.M . ( 2012 ). Hypoxia-Inducible Factor-2α Activation Promotes Colorectal Cancer Progression by Dysregulating Iron Homeostasis . Cancer Res 72 , 2285 – 2293 . doi: 10.1158/0008-5472.CAN-11-3836 . OpenUrl Abstract / FREE Full Text 11. Schwartz , A.J. , Das , N.K. , Ramakrishnan , S.K. , Jain , C. , Jurkovic , M.T. , Wu , J. , Nemeth , E. , Lakhal-Littleton , S. , Colacino , J.A. , and Shah , Y.M . ( 2019 ). Hepatic hepcidin/intestinal HIF-2 α axis maintains iron absorption during iron deficiency and overload . J Clin Invest 129 , 336 – 348 . doi: 10.1172/JCI122359 . OpenUrl CrossRef PubMed 12. ↵ Brookes , M.J. , Hughes , S. , Turner , F.E. , Reynolds , G. , Sharma , N. , Ismail , T. , Berx , G. , McKie , A.T. , Hotchin , N. , Anderson , G.J. , et al. ( 2006 ). Modulation of iron transport proteins in human colorectal carcinogenesis . doi: 10.1136/gut.2006.094060 . OpenUrl Abstract / FREE Full Text 13. ↵ Schwartz , A.J. , Goyert , J.W. , Solanki , S. , Kerk , S.A. , Chen , B. , Castillo , C. , Hsu , P.P. , Do , B.T. , Singhal , R. , Dame , M.K. , et al. ( 2021 ). Hepcidin sequesters iron to sustain nucleotide metabolism and mitochondrial function in colorectal cancer epithelial cells . Nat Metab 3 , 969 – 982 . doi: 10.1038/s42255-021-00406-7 . OpenUrl CrossRef PubMed 14. ↵ Dixon , S.J. , Lemberg , K.M. , Lamprecht , M.R. , Skouta , R. , Zaitsev , E.M. , Gleason , C.E. , Patel , D.N. , Bauer , A.J. , Cantley , A.M. , Yang , W.S. , et al. ( 2012 ). Ferroptosis: An Iron-Dependent Form of Nonapoptotic Cell Death . Cell 149 , 1060 – 1072 . doi: 10.1016/j.cell.2012.03.042 . OpenUrl CrossRef PubMed Web of Science 15. ↵ Yang , W.S. , Kim , K.J. , Gaschler , M.M. , Patel , M. , Shchepinov , M.S. , and Stockwell , B.R . ( 2016 ). Peroxidation of polyunsaturated fatty acids by lipoxygenases drives ferroptosis . Proceedings of the National Academy of Sciences 113 , E4966 – E4975 . doi: 10.1073/pnas.1603244113 . OpenUrl Abstract / FREE Full Text 16. ↵ Diao , J. , Jia , Y. , Dai , E. , Liu , J. , Kang , R. , Tang , D. , Han , L. , Zhong , Y. , and Meng , L . ( 2024 ). Ferroptotic therapy in cancer: benefits, side effects, and risks . Molecular Cancer 23 , 89 . doi: 10.1186/s12943-024-01999-9 . OpenUrl CrossRef PubMed 17. ↵ Le , N.T.V. , and Richardson , D.R . ( 2002 ). The role of iron in cell cycle progression and the proliferation of neoplastic cells . Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1603 , 31 – 46 . doi: 10.1016/S0304-419X(02)00068-9 . OpenUrl CrossRef PubMed 18. ↵ Xia , C. , Xing , X. , Zhang , W. , Wang , Y. , Jin , X. , Wang , Y. , Tian , M. , Ba , X. , and Hao , F . ( 2024 ). Cysteine and homocysteine can be exploited by GPX4 in ferroptosis inhibition independent of GSH synthesis . Redox Biology 69 , 102999 . doi: 10.1016/j.redox.2023.102999 . OpenUrl CrossRef PubMed 19. ↵ Harris , I.S. , Treloar , A.E. , Inoue , S. , Sasaki , M. , Gorrini , C. , Lee , K.C. , Yung , K.Y. , Brenner , D. , Knobbe-Thomsen , C.B. , Cox , M.A. , et al. ( 2015 ). Glutathione and Thioredoxin Antioxidant Pathways Synergize to Drive Cancer Initiation and Progression . Cancer Cell 27 , 211 – 222 . doi: 10.1016/j.ccell.2014.11.019 . OpenUrl CrossRef PubMed 20. ↵ Huang , W. , Zhang , Y. , Das , N.K. , Solanki , S. , Jain , C. , El-Derany , M.O. , Koo , I. , Bell , H.N. , Aabed , N. , Singhal , R. , et al. ( 2025 ). Fibroblast lipid metabolism through ACSL4 regulates epithelial sensitivity to ferroptosis in IBD . Nat Metab 7 , 1358 – 1374 . doi: 10.1038/s42255-025-01313-x . OpenUrl CrossRef PubMed 21. ↵ Bell , H.N. , Rebernick , R.J. , Goyert , J. , Singhal , R. , Kuljanin , M. , Kerk , S.A. , Huang , W. , Das , N.K. , Andren , A. , Solanki , S. , et al. ( 2022 ). Reuterin in the healthy gut microbiome suppresses colorectal cancer growth through altering redox balance . Cancer Cell 40 , 185 – 200 .e6. doi: 10.1016/j.ccell.2021.12.001 . OpenUrl CrossRef PubMed 22. ↵ Mayr , L. , Grabherr , F. , Schwärzler , J. , Reitmeier , I. , Sommer , F. , Gehmacher , T. , Niederreiter , L. , He , G.-W. , Ruder , B. , Kunz , K.T.R. , et al. ( 2020 ). Dietary lipids fuel GPX4-restricted enteritis resembling Crohn’s disease . Nat Commun 11 , 1775 . doi: 10.1038/s41467-020-15646-6 . OpenUrl CrossRef PubMed 23. ↵ Modulation of the HIF2α-NCOA4 axis in enterocytes attenuates iron loading in a mouse model of hemochromatosis | Blood | American Society of Hematology https://ashpublications.org/blood/article/139/16/2547/483338/Modulation-of-the-HIF2-NCOA4-axis-in-enterocytes . 24. ↵ Mancias , J.D. , Wang , X. , Gygi , S.P. , Harper , J.W. , and Kimmelman , A.C . ( 2014 ). Quantitative proteomics identifies NCOA4 as the cargo receptor mediating ferritinophagy . Nature 509 , 105 – 109 . doi: 10.1038/nature13148 . OpenUrl CrossRef PubMed Web of Science 25. ↵ Birsoy , K. , Wang , T. , Chen , W.W. , Freinkman , E. , Abu-Remaileh , M. , and Sabatini , D.M . ( 2015 ). An Essential Role of the Mitochondrial Electron Transport Chain in Cell Proliferation Is to Enable Aspartate Synthesis . Cell 162 , 540 – 551 . doi: 10.1016/j.cell.2015.07.016 . OpenUrl CrossRef PubMed 26. ↵ Singhal , R. , Mitta , S.R. , Das , N.K. , Kerk , S.A. , Sajjakulnukit , P. , Solanki , S. , Andren , A. , Kumar , R. , Olive , K.P. , Banerjee , R. , et al. ( 2021 ). HIF-2 α activation potentiates oxidative cell death in colorectal cancers by increasing cellular iron . J Clin Invest 131 . doi: 10.1172/JCI143691 . OpenUrl CrossRef PubMed 27. ↵ Belot , A. , Puy , H. , Hamza , I. , and Bonkovsky , H.L . ( 2024 ). Update on heme biosynthesis, tissue-specific regulation, heme transport, relation to iron metabolism and cellular energy . Liver International 44 , 2235 – 2250 . doi: 10.1111/liv.15965 . OpenUrl CrossRef 28. ↵ Grandchamp , B. , Bissell , D.M. , Licko , V. , and Schmid , R . ( 1981 ). Formation and disposition of newly synthesized heme in adult rat hepatocytes in primary culture . Journal of Biological Chemistry 256 , 11677 – 11683 . doi: 10.1016/S0021-9258(19)68459-0 . OpenUrl Abstract / FREE Full Text 29. ↵ Jansen , T. , and Daiber , A . ( 2012 ). Direct Antioxidant Properties of Bilirubin and Biliverdin. Is there a Role for Biliverdin Reductase? Front. Pharmacol . 3 . doi: 10.3389/fphar.2012.00030 . OpenUrl CrossRef 30. ↵ Shan , Y. , Lambrecht , R.W. , Ghaziani , T. , Donohue , S.E. , and Bonkovsky , H.L . ( 2004 ). Role of Bach-1 in Regulation of Heme Oxygenase-1 in Human Liver Cells: INSIGHTS FROM STUDIES WITH SMALL INTERFERING RNAS* . Journal of Biological Chemistry 279 , 51769 – 51774 . doi: 10.1074/jbc.M409463200 . OpenUrl Abstract / FREE Full Text 31. ↵ Chen , J.-J . ( 2025 ). HRI protein kinase in cytoplasmic heme sensing and mitochondrial stress response: Relevance to hematological and mitochondrial diseases . Journal of Biological Chemistry 301 . doi: 10.1016/j.jbc.2025.108494 . OpenUrl CrossRef 32. ↵ Role of Bach1 and Nrf2 in up-regulation of the heme oxygenase-1 gene by cobalt protoporphyrin - Shan - 2006 - The FASEB Journal - Wiley Online Library https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.06-6346fje . 33. ↵ Andreini , C. , Putignano , V. , Rosato , A. , and Banci , L . ( 2018 ). The human iron-proteome† . Metallomics . 10 , 1223 – 1231 . doi: 10.1039/c8mt00146d . OpenUrl CrossRef PubMed 34. ↵ Yang , C. , Ko , B. , Hensley , C.T. , Jiang , L. , Wasti , A.T. , Kim , J. , Sudderth , J. , Calvaruso , M.A. , Lumata , L. , Mitsche , M. , et al. ( 2014 ). Glutamine Oxidation Maintains the TCA Cycle and Cell Survival during Impaired Mitochondrial Pyruvate Transport . Molecular Cell 56 , 414 – 424 . doi: 10.1016/j.molcel.2014.09.025 . OpenUrl CrossRef PubMed 35. ↵ Yang , L. , Venneti , S. , and Nagrath , D . ( 2017 ). Glutaminolysis: A Hallmark of Cancer Metabolism . Annual Review of Biomedical Engineering 19 , 163 – 194 . doi: 10.1146/annurev-bioeng-071516-044546 . OpenUrl CrossRef PubMed 36. ↵ Rutter , J. , Winge , D.R. , and Schiffman , J.D . ( 2010 ). Succinate dehydrogenase – Assembly, regulation and role in human disease . Mitochondrion 10 , 393 – 401 . doi: 10.1016/j.mito.2010.03.001 . OpenUrl CrossRef PubMed Web of Science 37. ↵ Kim , H.J. , Khalimonchuk , O. , Smith , P.M. , and Winge , D.R . ( 2012 ). Structure, function, and assembly of heme centers in mitochondrial respiratory complexes . Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1823 , 1604 – 1616 . doi: 10.1016/j.bbamcr.2012.04.008 . OpenUrl CrossRef PubMed Web of Science 38. ↵ Guerra , R.M. , and Pagliarini , D.J . ( 2023 ). Coenzyme Q biochemistry and biosynthesis . Trends in Biochemical Sciences 48 , 463 – 476 . doi: 10.1016/j.tibs.2022.12.006 . OpenUrl CrossRef 39. ↵ Navas , P. , Villalba , J.M. , and de Cabo , R. ( 2007 ). The importance of plasma membrane coenzyme Q in aging and stress responses . Mitochondrion 7 , S34 – S40 . doi: 10.1016/j.mito.2007.02.010 . OpenUrl CrossRef PubMed Web of Science 40. ↵ Bonkovsky , H.L. , Guo , J.-T. , Hou , W. , Li , T. , Narang , T. , and Thapar , M . ( 2013 ). Porphyrin and Heme Metabolism and the Porphyrias . Comprehensive Physiology 3 , 365 – 401 . doi: 10.1002/j.2040-4603.2013.tb00488.x . OpenUrl CrossRef PubMed 41. ↵ Xue , X. , Bredell , B.X. , Anderson , E.R. , Martin , A. , Mays , C. , Nagao-Kitamoto , H. , Huang , S. , Győrffy , B. , Greenson , J.K. , Hardiman , K. , et al. ( 2017 ). Quantitative proteomics identifies STEAP4 as a critical regulator of mitochondrial dysfunction linking inflammation and colon cancer . Proceedings of the National Academy of Sciences 114 , E9608 – E9617 . doi: 10.1073/pnas.1712946114 . OpenUrl Abstract / FREE Full Text 42. ↵ Rouault , T.A . ( 2006 ). The role of iron regulatory proteins in mammalian iron homeostasis and disease . Nat Chem Biol 2 , 406 – 414 . doi: 10.1038/nchembio807 . OpenUrl CrossRef PubMed Web of Science 43. ↵ Cao , J. , Chen , X. , Chen , L. , Lu , Y. , Wu , Y. , Deng , A. , Pan , F. , Huang , H. , Liu , Y. , Li , Y. , et al. ( 2025 ). DHODH-mediated mitochondrial redox homeostasis: a novel ferroptosis regulator and promising therapeutic target . Redox Biology 85 , 103788 . doi: 10.1016/j.redox.2025.103788 . OpenUrl CrossRef PubMed 44. ↵ Lee , N. , Park , S.J. , Lange , M. , Tseyang , T. , Doshi , M.B. , Kim , T.Y. , Song , Y. , Kim , D.I. , Greer , P.L. , Olzmann , J.A. , et al. ( 2024 ). Selenium reduction of ubiquinone via SQOR suppresses ferroptosis . Nat Metab 6 , 343 – 358 . doi: 10.1038/s42255-024-00974-4 . OpenUrl CrossRef PubMed 45. ↵ Chandrashekar , D.S. , Karthikeyan , S.K. , Korla , P.K. , Patel , H. , Shovon , A.R. , Athar , M. , Netto , G.J. , Qin , Z.S. , Kumar , S. , Manne , U. , et al. ( 2022 ). UALCAN: An update to the integrated cancer data analysis platform . Neoplasia 25 , 18 – 27 . doi: 10.1016/j.neo.2022.01.001 . OpenUrl CrossRef PubMed 46. ↵ Khazal , F.A. , Holte , M.N. , Bolon , B. , White , T.A. , LeBrasseur , N. , and Iii , L.J.M . ( 2019 ). A conditional mouse model of complex II deficiency manifesting as Leigh-like syndrome . The FASEB Journal 33 , 13189 – 13201 . doi: 10.1096/fj.201802655RR . OpenUrl CrossRef PubMed 47. ↵ Dolma , S. , Lessnick , S.L. , Hahn , W.C. , and Stockwell , B.R . ( 2003 ). Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells . Cancer Cell 3 , 285 – 296 . doi: 10.1016/S1535-6108(03)00050-3 . OpenUrl CrossRef PubMed Web of Science 48. ↵ Yang , W.S. , and Stockwell , B.R . ( 2008 ). Synthetic Lethal Screening Identifies Compounds Activating Iron-Dependent, Nonapoptotic Cell Death in Oncogenic-RAS-Harboring Cancer Cells . Chemistry & Biology 15 , 234 – 245 . doi: 10.1016/j.chembiol.2008.02.010 . OpenUrl CrossRef PubMed Web of Science 49. ↵ Bartolacci , C. , Andreani , C. , Vale , G. , Berto , S. , Melegari , M. , Crouch , A.C. , Baluya , D.L. , Kemble , G. , Hodges , K. , Starrett , J. , et al. ( 2022 ). Targeting de novo lipogenesis and the Lands cycle induces ferroptosis in KRAS-mutant lung cancer. Nat Commun 13 , 4327 . doi: 10.1038/s41467-022-31963-4 . OpenUrl CrossRef PubMed 50. Doll , S. , Proneth , B. , Tyurina , Y.Y. , Panzilius , E. , Kobayashi , S. , Ingold , I. , Irmler , M. , Beckers , J. , Aichler , M. , Walch , A. , et al. ( 2017 ). ACSL4 dictates ferroptosis sensitivity by shaping cellular lipid composition . Nat Chem Biol 13 , 91 – 98 . doi: 10.1038/nchembio.2239 . OpenUrl CrossRef PubMed 51. Jiang , L. , Kon , N. , Li , T. , Wang , S.-J. , Su , T. , Hibshoosh , H. , Baer , R. , and Gu , W . ( 2015 ). Ferroptosis as a p53-mediated activity during tumour suppression . Nature 520 , 57 – 62 . doi: 10.1038/nature14344 . OpenUrl CrossRef PubMed 52. ↵ Lu , Y. , Yang , Q. , Su , Y. , Ji , Y. , Li , G. , Yang , X. , Xu , L. , Lu , Z. , Dong , J. , Wu , Y. , et al. ( 2021 ). MYCN mediates TFRC-dependent ferroptosis and reveals vulnerabilities in neuroblastoma . Cell Death Dis 12 , 511 . doi: 10.1038/s41419-021-03790-w . OpenUrl CrossRef PubMed 53. ↵ Santana-Codina , N. , del Rey , M.Q. , Kapner , K.S. , Zhang , H. , Gikandi , A. , Malcolm , C. , Poupault , C. , Kuljanin , M. , John , K.M. , Biancur , D.E. , et al. ( 2022 ). NCOA4-Mediated Ferritinophagy Is a Pancreatic Cancer Dependency via Maintenance of Iron Bioavailability for Iron–Sulfur Cluster Proteins . Cancer Discov 12 , 2180 – 2197 . doi: 10.1158/2159-8290.CD-22-0043 . OpenUrl CrossRef PubMed 54. ↵ Ravichandran , M. , Hu , J. , Cai , C. , Ward , N.P. , Venida , A. , Foakes , C. , Kuljanin , M. , Yang , A. , Hennessey , C.J. , Yang , Y. , et al. ( 2022 ). Coordinated Transcriptional and Catabolic Programs Support Iron-Dependent Adaptation to RAS–MAPK Pathway Inhibition in Pancreatic Cancer . Cancer Discov 12 , 2198 – 2219 . doi: 10.1158/2159-8290.CD-22-0044 . OpenUrl CrossRef PubMed 55. ↵ Alvarez , S.W. , Sviderskiy , V.O. , Terzi , E.M. , Papagiannakopoulos , T. , Moreira , A.L. , Adams , S. , Sabatini , D.M. , Birsoy , K. , and Possemato , R . ( 2017 ). NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis . Nature 551 , 639 – 643 . doi: 10.1038/nature24637 . OpenUrl CrossRef PubMed 56. ↵ Terzi , E.M. , Sviderskiy , V.O. , Alvarez , S.W. , Whiten , G.C. , and Possemato , R . ( 2021 ). Iron-sulfur cluster deficiency can be sensed by IRP2 and regulates iron homeostasis and sensitivity to ferroptosis independent of IRP1 and FBXL5 . Science Advances 7 , eabg4302 . doi: 10.1126/sciadv.abg4302 . OpenUrl FREE Full Text 57. ↵ Miettinen , M. , and Lasota , J . ( 2014 ). Succinate dehydrogenase deficient gastrointestinal stromal tumors (GISTs) – A review . The International Journal of Biochemistry & Cell Biology 53 , 514 – 519 . doi: 10.1016/j.biocel.2014.05.033 . OpenUrl CrossRef PubMed 58. Burnichon , N. , Brière , J.-J. , Libé , R. , Vescovo , L. , Rivière , J. , Tissier , F. , Jouanno , E. , Jeunemaitre , X. , Bénit , P. , Tzagoloff , A. , et al. ( 2010 ). SDHA is a tumor suppressor gene causing paraganglioma . Hum Mol Genet 19 , 3011 – 3020 . doi: 10.1093/hmg/ddq206 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Szymanski , M. , Rusetska , N. , Jancewicz , I. , Armatowska , A. , Ligaj , M. , Chrzan , A. , Hincza , K. , Kowalik , A. , Mika , P. , Kisiel , M. , et al. ( 2021 ). Succinate Dehydrogenase-Deficient Renal Cancer Featuring Fructose-1,6-Biphosphatase Loss, Pyruvate Kinase M2 Overexpression, and SWI/SNF Chromatin Remodeling Complex Aberrations: A Rare Case Report . Oncologist 26 , e1652 – e1655 . doi: 10.1002/onco.13825 . OpenUrl CrossRef PubMed 60. ↵ Kumar , R. , Landry , A.P. , Guha , A. , Vitvitsky , V. , Lee , H.J. , Seike , K. , Reddy , P. , Lyssiotis , C.A. , and Banerjee , R . ( 2022 ). A redox cycle with complex II prioritizes sulfide quinone oxidoreductase-dependent H2S oxidation . J Biol Chem 298 , 101435 . doi: 10.1016/j.jbc.2021.101435 . OpenUrl CrossRef PubMed 61. ↵ Banerjee , R. , and Kumar , R . ( 2022 ). Gas regulation of complex II reversal via electron shunting to fumarate in the mammalian ETC . Trends in Biochemical Sciences 47 , 689 – 698 . doi: 10.1016/j.tibs.2022.03.011 . OpenUrl CrossRef PubMed 62. ↵ Spinelli , J.B. , Rosen , P.C. , Sprenger , H.-G. , Puszynska , A.M. , Mann , J.L. , Roessler , J.M. , Cangelosi , A.L. , Henne , A. , Condon , K.J. , Zhang , T. , et al. ( 2021 ). Fumarate is a terminal electron acceptor in the mammalian electron transport chain . Science 374 , 1227 – 1237 . doi: 10.1126/science.abi7495 . OpenUrl CrossRef PubMed 63. ↵ Valeros , J. , Jerome , M. , Tseyang , T. , Vo , P. , Do , T. , Palomino , D.F. , Grotehans , N. , Kunala , M. , Jerrett , A.E. , Hathiramani , N.R. , et al. ( 2025 ). Rhodoquinone carries electrons in the mammalian electron transport chain . Cell 188 , 1084 – 1099 .e27. doi: 10.1016/j.cell.2024.12.007 . OpenUrl CrossRef PubMed 64. Chen , J. , Li , X. , Ge , C. , Min , J. , and Wang , F . ( 2022 ). The multifaceted role of ferroptosis in liver disease . Cell Death Differ 29 , 467 – 480 . doi: 10.1038/s41418-022-00941-0 . OpenUrl CrossRef PubMed 65. ↵ Li , J. , Zheng , S. , Fan , Y. , and Tan , K . ( 2023 ). Emerging significance and therapeutic targets of ferroptosis: a potential avenue for human kidney diseases . Cell Death Dis 14 , 628 . doi: 10.1038/s41419-023-06144-w . OpenUrl CrossRef PubMed 66. ↵ Bajpai , R. , Sharma , A. , Achreja , A. , Edgar , C.L. , Wei , C. , Siddiqa , A.A. , Gupta , V.A. , Matulis , S.M. , McBrayer , S.K. , Mittal , A. , et al. ( 2020 ). Electron transport chain activity is a predictor and target for venetoclax sensitivity in multiple myeloma . Nat Commun 11 , 1228 . doi: 10.1038/s41467-020-15051-z . OpenUrl CrossRef 67. ↵ Nair , R. , Vu , A.H. , Freer , A.K. , Bhatia , K.S. , Wang , D. , Savani , M.R. , Matulis , S.M. , Lonial , S. , Jaye , D.L. , Boise , L.H. , et al. ( 2025 ). Heme promotes venetoclax resistance in multiple myeloma through MEK-ERK signaling and purine biosynthesis . Blood 145 , 732 – 747 . doi: 10.1182/blood.2024025690 . OpenUrl CrossRef PubMed 68. ↵ Nengroo , M.A. , Klein , A.T. , Carr , H.S. , Vidal-Cruchez , O. , Sahu , U. , McGrail , D.J. , Sahni , N. , Gao , P. , Asara , J.M. , Shah , H. , et al. ( 2025 ). Succinate dehydrogenase activity supports de novo purine synthesis . Preprint at bioRxiv , doi: 10.1101/2025.02.26.640389 https://doi.org/10.1101/2025.02.26.640389. OpenUrl Abstract / FREE Full Text 69. ↵ Huang , P. , Peslak , S.A. , Lan , X. , Khandros , E. , Yano , J.A. , Sharma , M. , Keller , C.A. , Giardine , B. , Qin , K. , Abdulmalik , O. , et al. ( 2020 ). The HRI-regulated transcription factor ATF4 activates BCL11A transcription to silence fetal hemoglobin expression . Blood 135 , 2121 – 2132 . doi: 10.1182/blood.2020005301 . OpenUrl CrossRef PubMed 70. ↵ Bersuker , K. , Hendricks , J.M. , Li , Z. , Magtanong , L. , Ford , B. , Tang , P.H. , Roberts , M.A. , Tong , B. , Maimone , T.J. , Zoncu , R. , et al. ( 2019 ). The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis . Nature 575 , 688 – 692 . doi: 10.1038/s41586-019-1705-2 . OpenUrl CrossRef PubMed 71. ↵ Doll , S. , Freitas , F.P. , Shah , R. , Aldrovandi , M. , da Silva , M.C. , Ingold , I. , Goya Grocin , A. , Xavier da Silva , T.N. , Panzilius , E. , Scheel , C.H. , et al. ( 2019 ). FSP1 is a glutathione-independent ferroptosis suppressor . Nature 575 , 693 – 698 . doi: 10.1038/s41586-019-1707-0 . OpenUrl CrossRef PubMed 72. ↵ Wu , K. , Vaughan , A.J. , Bossowski , J.P. , Hao , Y. , Ziogou , A. , Kim , S.M. , Kim , T.H. , Nakamura , M.N. , Pillai , R. , Mancini , M. , et al. ( 2025 ). Targeting FSP1 triggers ferroptosis in lung cancer . Preprint at bioRxiv , doi: 10.1101/2025.08.07.668766 https://doi.org/10.1101/2025.08.07.668766. OpenUrl Abstract / FREE Full Text 73. ↵ Deshwal , S. , Onishi , M. , Tatsuta , T. , Bartsch , T. , Cors , E. , Ried , K. , Lemke , K. , Nolte , H. , Giavalisco , P. , and Langer , T . ( 2023 ). Mitochondria regulate intracellular coenzyme Q transport and ferroptotic resistance via STARD7 . Nat Cell Biol 25 , 246 – 257 . doi: 10.1038/s41556-022-01071-y . OpenUrl CrossRef PubMed 74. ↵ Kerk , S.A. , Lin , L. , Myers , A.L. , Sutton , D.J. , Andren , A. , Sajjakulnukit , P. , Zhang , L. , Zhang , Y. , Jiménez , J.A. , Nelson , B.S. , et al. ( 2022 ). Metabolic requirement for GOT2 in pancreatic cancer depends on environmental context . eLife 11 , e73245 . doi: 10.7554/eLife.73245 . OpenUrl CrossRef PubMed 75. ↵ Hanna , D.A. , Moore , C.M. , Liu , L. , Yuan , X. , Dominic , I.M. , Fleischhacker , A.S. , Hamza , I. , Ragsdale , S.W. , and Reddi , A.R . ( 2022 ). Heme oxygenase-2 (HO-2) binds and buffers labile ferric heme in human embryonic kidney cells . Journal of Biological Chemistry 298 . doi: 10.1016/j.jbc.2021.101549 . OpenUrl CrossRef 76. ↵ Vitvitsky , V. , Kumar , R. , Diessl , J. , Hanna , D.A. , and Banerjee , R . ( 2024 ). Rapid HPLC method reveals dynamic shifts in coenzyme Q redox state . Journal of Biological Chemistry 300 . doi: 10.1016/j.jbc.2024.107301 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted November 06, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death Chesta Jain , Muqit Essani , Roshan Kumar , Nupur K. Das , Rashi Singhal , Nicholas J. Rossiter , Brandon Chen , Wesley Huang , Zheng Hong Lee , Sumeet Solanki , Yuezhong Zhang , Peter Sajjakulnukit , Li Zhang , Prarthana J. Dalal , David A Hanna , Shannon McCollum , Elena M. Stoffel , Joel K. Greenson , L James Maher III , Costas A. Lyssiotis , Ruma Banerjee , Yatrik M. Shah bioRxiv 2025.11.05.686813; doi: https://doi.org/10.1101/2025.11.05.686813 Share This Article: Copy Citation Tools Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death Chesta Jain , Muqit Essani , Roshan Kumar , Nupur K. Das , Rashi Singhal , Nicholas J. Rossiter , Brandon Chen , Wesley Huang , Zheng Hong Lee , Sumeet Solanki , Yuezhong Zhang , Peter Sajjakulnukit , Li Zhang , Prarthana J. Dalal , David A Hanna , Shannon McCollum , Elena M. Stoffel , Joel K. Greenson , L James Maher III , Costas A. Lyssiotis , Ruma Banerjee , Yatrik M. 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