Cell MetabolismCancer BiologyPeer-reviewed journal article
Iron addicted colorectal cancers exploit Heme-Complex II axis to resist oxidative cell death
Chesta JainiD, Muqit Essani, Roshan KumariD, Nupur Kanti DasiD, Rashi SinghaliD, Nicholas J. RossiteriD, Brandon CheniD, Wesley HuangiD, Zheng Hong LeeiD, Sumeet A. SolankiiD, Yuezhong ZhangiD, Peter SajjakulnukitiD, Li ZhangiD, Prarthana DalaliD, David A. HannaiD, Cristina CastilloiD, Harrison S. GreenbaumiD, Shannon E. McCollumiD, Elena M. StoffeliD, Joel K. GreensoniD, Louis James Maher IIIiD, Costas Andreas LyssiotisiD, Ruma V. BanerjeeiD, Yatrik Madhukar ShahiD
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.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 4
- FWCI
- 18.81
- Field percentile
- top 1%
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