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Hepcidin sequesters iron to sustain nucleotide metabolism and mitochondrial function in colorectal cancer epithelial cells

Nature Metabolism volume 3, pages 969–982 (2021)Cite this article

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Abstract

Colorectal cancer (CRC) requires massive iron stores, but the complete mechanisms by which CRC modulates local iron handling are poorly understood. Here, we demonstrate that hepcidin is activated ectopically in CRC. Mice deficient in hepcidin specifically in the colon tumour epithelium, compared with wild-type littermates, exhibit significantly diminished tumour number, burden and size in a sporadic model of CRC, whereas accumulation of intracellular iron by deletion of the iron exporter ferroportin exacerbates these tumour parameters. Metabolomic analysis of three-dimensional patient-derived CRC tumour enteroids indicates a prioritization of iron in CRC for the production of nucleotides, which is recapitulated in our hepcidin/ferroportin mouse CRC models. Mechanistically, our data suggest that iron chelation decreases mitochondrial function, thereby altering nucleotide synthesis, whereas exogenous supplementation of nucleosides or aspartate partially rescues tumour growth in patient-derived enteroids and CRC cell lines in the presence of an iron chelator. Collectively, these data suggest that ectopic hepcidin in the tumour epithelium establishes an axis to sequester iron in order to maintain the nucleotide pool and sustain proliferation in colorectal tumours.

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Fig. 1: Colorectal cancer produces an ectopic source of epithelial hepcidin that portends decreased patient survival.
Fig. 2: Local hepcidin–ferroportin signalling is necessary and sufficient for CRC growth.
Fig. 3: Hypoxia via HIF-2α activates hepcidin expression in CRC.
Fig. 4: Metabolomic profiling in patient-derived tumour enteroids and mouse models reveal an essential role for iron in nucleotide production.
Fig. 5: Nucleoside supplementation restores growth of 2D and 3D colorectal cancer models in the presence of DFO.
Fig. 6: DFO-mediated iron depletion alters mitochondrial metabolism.

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All data generated or analysed during this study are included in this published article and its Supplementary Information files. Source data are provided with this paper.

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Acknowledgements

We thank the laboratory of Y.M.S. and Zhaoqi Li for helpful input and suggestions. This work was supported by National Institutes of Health (NIH) grants R01CA148828, R01DK095201 and R01CA245546 (to Y.M.S.). C.A.L. was supported by the National Cancer Institute (R37CA237421, R01CA248160, R01CA244931 and R01CA215607). C.A.L. and Y.M.S. were supported by University of Michigan Rogel Cancer Center Core Grant (P30CA046592) and University of Michigan Center for Gastrointestinal Research (P30DK034933). Metabolomics studies performed at the University of Michigan were supported by NIH grant DK097153, the Charles Woodson Research Fund and the UM Pediatric Brain Tumor Initiative. X.X. was supported by the NIH (P20 GM130422 and K01DK114390), a Research Scholar Grant from the American Cancer Society (RSG-18-050-01-NEC), a Research Scholar Award from the American Gastroenterological Association and a Research Program Support Pilot Project Award from UNM Comprehensive Cancer Center (P30CA118100). P.P.H. (2T32CA071345-21A1), B.T.D. (T32GM007753), A.J.S. (F31DK116555), B.C. (T32GM007315) and S.A.K. (F31CA24745701) were supported by the indicated grants. Enteroid work was conducted with The Michigan Medicine Translational Tissue Modeling Laboratory, a funded initiative with support from the Endowment for Basic Sciences (Center for Gastrointestinal Research, Office of the Dean, Comprehensive Cancer Center, Departments of Pathology, Pharmacology and Internal Medicine).

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Authors and Affiliations

  1. Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA

    Andrew J. Schwartz, Joshua W. Goyert, Sumeet Solanki, Samuel A. Kerk, Brandon Chen, Cristina Castillo, Rashi Singhal, Ho-Joon Lee, Costas A. Lyssiotis & Yatrik M. Shah

  2. University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    Peggy P. Hsu, Costas A. Lyssiotis & Yatrik M. Shah

  3. Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA

    Peggy P. Hsu, Michael K. Dame, Jason R. Spence, Costas A. Lyssiotis & Yatrik M. Shah

  4. Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA

    Brian T. Do

  5. Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Brian T. Do & Matthew G. Vander Heiden

  6. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK

    Samira Lakhal-Littleton

  7. Dana-Farber Cancer Institute, Boston, MA, USA

    Matthew G. Vander Heiden

  8. Department of Biochemistry and Molecular Biology, University of New Mexico, Albuquerque, NM, USA

    Xiang Xue

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Contributions

A.J.S. and Y.M.S. conceived and designed the study. A.J.S., J.W.G., S.S., S.A.K., B.C., C.C., P.P.H., B.T.D., R.S., M.K.D. and H.-J.L. acquired the data. A.J.S., J.W.G., S.S., S.A.K., B.C., C.C., P.P.H., B.T.D., R.S., M.K.D., H.-J.L., J.R.S., S.L.-L., M.G.V.H., C.A.L., X.X. and Y.M.S. developed the methodologies. A.J.S., J.W.G., S.S., S.A.K., B.C., C.C., P.P.H., B.T.D., R.S., M.K.D., H.-J.L., J.R.S., S.L.-L., M.G.V.H., C.A.L., X.X. and Y.M.S. analysed and interpreted the data. A.J.S. and Y.M.S. wrote the manuscript. Y.M.S. supervised the study.

Corresponding authors

Correspondence to Xiang Xue or Yatrik M. Shah.

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Competing interests

P.P.H. is a consultant for Auron Therapeutics. C.A.L. is an inventor on patents pertaining to Kras-regulated metabolic pathways, redox control pathways in pancreatic cancer and targeting GOT1 as a therapeutic approach (US patent nos: 2012112933-A1, 2015126580-A1 and 20190136238). All other authors declare no competing interests.

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Peer review information Nature Metabolism thanks Kevin Myant and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Hepcidin expression is increased across many cancer types.

a, Hepcidin transcript abundance in normal and tumor tissue through The Cancer Genome Atlas. Log2 median centered ratio is plotted (Bladder Normal N = 24 and Bladder Tumor N = 152; Brain Normal N = 10 and Brain Tumor N = 547; Colorectal Normal N = 22 and Colorectal Tumor N = 215; Breast Normal N = 61 and Brest Tumor N = 532; Cervical Normal N = 5 and Cervical Tumor N = 155; Head/Neck Normal N = 74 and Head/Neck Tumor N = 388; Kidney Normal N = 329 and Kidney Tumor N = 727; Leukemia Normal N = 195 and Leukemia Tumor N = 197; Liver Normal N = 30 Liver Tumor N = 138; Lung Normal N = 71 and Lung Tumor N = 984, Lymphoma Normal N = 18 and Lymphoma Tumor N = 18, Ovary Normal N = 88 and Ovary Tumor N = 607; Pancreas Normal N = 10 and Pancreas Tumor N = 64; and Prostate Normal N = 34 and Prostate Tumor N = 171 biologically independent samples, Brain p = 0.0047. Breast p = 0.0009. Colorectal p = 0.0006. Kidney p < 0.0001, Lung p = 0.0092. Ovary p = 0.0002. Prostate p = 0.0222). b, qPCR analysis of hepcidin (Hamp) expression levels in tissues/cells of wild-type and colon epithelial-specific APC deficient mice (WT N = 4 and APC-/- N = 4 biologically independent samples from independent animals in each tissue). c, Hepcidin concentration in serum of wild-type and colon epithelial-specific APC deficient mice (WT N = 4 and APC-/- N = 4 biologically independent samples from independent animals). Data represent the mean ± SEM. Significance was determined by 2-tailed, unpaired t test. a-c, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 compared to normal tissue or wild-type animals.

Extended Data Fig. 2 Detection of the hepcidin protein by immunohistochemistry is ineffective.

Primary hepatocytes were generated from Hampfl/fl mice and treated in vitro with adenoviruses expressing either GFP or Cre recombinase for 48 hours. These cells were analyzed for hepcidin (Hamp) expression by qPCR analysis (a) and were stained for hepcidin protein (b) (AdenoGFP N = 3 and AdenoCre N = 3 biologically independent cell replicates, p = 0.0025). c, Representative hepcidin staining in mice that were placed on either an iron replete (350 PPM) or low iron (< 5 PPM) for seven days (N = 4 biologically independent samples from independent animals). d, Ferroportin staining of adjacent normal colon and tumor tissue in a sporadic model of CRC in wild-type and hepcidin-deficient mice (N = 3 biologically independent samples from independent animals). Data represent the mean ± SEM. Significance was determined by 2-tailed, unpaired t test (a). **P < 0.01.

Extended Data Fig. 3 Tumoral hepcidin regulation in vitro and in vivo.

qPCR analysis for hepcidin (Hamp) after treatment with FG4592 (100 μM) or vehicle for 16 hours in (a) HCT116 cells (N = 3 biologically independent cell replicates) and (b) enteroids generated from mice with inducible, colon epithelial deletion of APC and p53 and activation of KRAS (N = 3 biologically independent samples). c, qPCR analysis of Hamp in the colon of mice with embryonic, intestinal epithelial-specific overexpression of HIF-2α (HIF-2αOE) compared to wild-type mice (HIF-2αWT) (HIF-2αWT N = 6 and HIF-2αOE N = 10 biologically independent samples from independent animals). d, qPCR analysis of Hamp in the colon of colon epithelial-specific HIF-2αWT and HIF-2αOE mice that are also deficient for APC for 30 days HIF-2αWT N = 3 and HIF-2αOE N = 3 biologically independent samples from independent animals). e, qPCR analysis of Hamp in HCT116 cells treated for 24 hours with conditioned media (CM) from RAW 264.7 macrophages that had been treated with 10 ng/mL LPS for 16 hours (N = 3 biologically independent cell replicates). f, Relative luciferase activity of the human hepcidin promoter in HCT116 cells treated with vehicle (Veh), live bacteria (Live), or heat-killed bacteria (HK), or (g) vehicle (Veh) or bacteria-derived metabolites (N = 3 independent cell replicates). h, Methylation status of the human hepcidin promoter in human colorectal cancer tissue (Normal N = 37 and Tumor N = 313). i, HCT116 cells treated with vehicle (Veh) or 5AZA (10 μM) for 72 hours and then treated with vehicle (Veh) and/or FG4592 (100 μM) for 16 hours and analyzed via Western blot analysis for DNA methyltransferase 1 (DNMT1) and (j) qPCR analysis for HAMP expression (N = 3 biologically independent cell replicates). k, HCT116 cells pretreated with or without FG4592 (100 μM) for 16 hours and then administered a panel of epigenetic modifiers for 24 hours. qPCR was used to measure the expression of HAMP and the HIF-2α target, ANKRD37 (N = 3 biologically independent cell replicates for each treatment). l, Patient-derived enteroids were either pretreated with or without FG4592 (100 μM) for 16 hours and were then administered a panel of epigenetic modifiers for 24 hours. qPCR was used to measure the expression of HAMP and the HIF-2α target, ANKRD37 (N = 3 biologically independent samples for each treatment). Data represent the mean ± SEM. Significance was determined by 2-tailed, unpaired t test (a-e, h) or 1-way ANOVA with Tukey’s post hoc (f, j, k-l). ****P < 0.0001.

Source data

Extended Data Fig. 4 Colon cancer-derived cell lines are exquisitely sensitive to ferroportin-mediated iron loss.

a, qPCR analysis for the ferroportin transcript in late passages of cells that were made stable for a doxycycline (dox) inducible ferroportinGFP overexpression construct, following 16 hour treatment with dox (250 ng/mL) (N = 3 biologically independent cell replicates for each cell line and treatment, HEK293 p = 0.0001, IEC6 p = 0.0021, HT29 p < 0.0001, SW480 p = 0.0103) (b) Western blot analysis for GFP and ferroportin (FPN) in stable, normal ferroportinGFP overexpressing cell lines following 16 hour treatment with dox (250 ng/mL). c and d, Cell growth MTT assay in HEK293 ferroportinGFP (c) (N = 3 biologically independent cell replicates for each cell line, treatment, and time point, p < 0.0001) and (d) IEC6 ferroportinGFP cells (N = 3 biologically independent cell replicates for each cell line, treatment, and time point, p = 0.0008) treated with vehicle, dox (250 ng/mL), or dox (250 ng/mL) and recombinant hepcidin (1 μg/mL). e–h, Representative crystal violet staining images (e) and quantification (f) of HEK293 ferroportinGFP (N = 3 biologically independent cell replicates for each cell line and treatment, p = 0.0005) and IEC6 ferroportinGFP cells, images (g) and quantitation (h) (N = 3 biologically independent cell replicates for each cell line and treatment, p = 0.0025) ten days following treatment with vehicle, dox (250 ng/mL), or dox (250 ng/mL) and recombinant hepcidin (1 μg/mL) Data represent the mean ± SEM. Significance was determined by 2- tailed, unpaired t test (a) or by 1-way ANOVA with Tukey’s post hoc (c,d,f,h). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 comparing within each cell line or across treatment groups.

Source data

Extended Data Fig. 5 Intracellular iron deficiency blunts nucleotide metabolism.

a, Representative patient-derived CRC tumor enteroids were treated with vehicle or DFO (10 μM) (N = 3 biologically independent replicates). b, Quantification of enteroid growth following 4-days of either vehicle or DFO (10 μM) treatment (N = 3 biologically independent replicates). c, Pathway analysis was conducted on metabolomics data in enteroids after DFO treatment before any decrease in growth was seen. Significance was determined by 2-tailed, unpaired t test.

Extended Data Fig. 6 Intracellular iron deficiency blunts nucleotide metabolism.

a,b, Heatmap of metabolites of differential abundance in (a) HEK293 and (b) IEC6 doxycycline- inducible ferroportin overexpressing cell lines treated with doxycycline (d) as compared to vehicle (V) for 16 hours (N = 3 biologically independent cell replicates, only changes at p < 0.05 compared to V are shown). c, Pie chart showing the proportion of metabolites of differential abundance in both HEK293 and IEC6 ferroportin overexpressing cells that are involved in nucleotide metabolism.

Extended Data Fig. 7 Moderate doses of DFO do not remove iron from ferritin and individual nucleosides do not rescue DFO-mediated growth inhibition.

a and b, Western blot analysis for ferritin after 24 hours pre-loading with ferric ammonium citrate (FAC) (200 μM) and/or overnight DFO, as indicated in (a) HCT116 and (b) SW480 cells. c and d, Relative growth of HCT116 cells as measured by MTT at 72 hours after treating with DFO (10 μM) and individual nucleosides (100 μM) (c) (Vehicle N = 6, DFO N = 6, DFO + Nucloeside N = 8 biologically independent cell replicates, p < 0.0001), or (d) purine or pyrimidine (100 μM) nucleosides (N = 8 biologically independent cell replicates for all treatments, p < 0.0001. e, Cell growth MTT assay in IEC6 ferroportinGFP cells treated with vehicle, dox (250 ng/mL), or dox (250 ng/mL) and nucleoside cocktail (100 μM) 72 hours after treatment (Vehicle N = 6, Dox N = 9, Nuc N = 9, and Dox + Nuc N = 9 biologically independent cell replicates, p = 0.0006). f and g, Nucleoside (100 μM) rescue of DFO (10 μM) growth inhibition in DLD1 (f) (N = 8 biologically independent cell replicates for all treatments, p = 0.0007) and RKO (g) (N = 8 biologically independent cell replicates for all treatments, p = 0.0018 cells) after 72 hours of cotreatment using MTT. h and i, Representative crystal violet stains of DLD1 or RKO cells that were either pretreated with vehicle or DFO (10 μM) and then given vehicle or a nucleoside cocktail (100 μM) for ten days (N = 3 biologically independent cell replicates). Data represent the mean ± SEM. Significance was determined by one-way ANOVA followed by Tukey’s post hoc. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 compared to vehicle.

Source data

Extended Data Fig. 8 Acute DFO insult disrupts mitochondrial metabolism without decreased cell growth.

a, Cell growth assay in HCT116 and SW480 cells 24 hours after DFO treatment (10μM) and assessed by MTT (N = 3 biologically independent cell replicates for each treatment and cell line). b and c, Seahorse analysis of mitochondrial metabolism in DLD1 (b) and RKO (c) cells 24 hours after DFO administration (10μM) (N = 4 biologically independent cell replicates for each treatment condition). d and e, Western blot analysis of doxycycline (Dox)-inducible Lactobacillus NADH-oxidase (LbNox) and mitochondrial NADH-oxidase (Mito-LbNox) in stably generated in HCT116 (d) and SW480 (e) cells. f and g, MTT cell growth assays in LbNox and Mito-LbNox expressing cells following DFO (10μM) in (f) HCT116 (p = 0.014) and (g) SW480 cells (p = 0.0076) (N = 6 biologically independent cell replicates for each cell line and treatment). h and i, MTT cell growth assay in cells stably expressing yeast NADH-ubiquinone reductase (NDI1) following DFO (10μM) or phenformin (62.5μM) treatment in (h) HCT116 (p = 0.0006) and (i) SW480 (p = 0.0003) cells (N = 3 biologically independent cell replicates for each cell line and treatment). j and k, Western blot analysis of mitochondrial enzymes following 24-hours of DFO (10 µM) treatment in DLD1 (j) and RKO (k) cells. Data represent the mean ± SEM. Significance was determined by 2-tailed, unpaired t test (a, h, i)) or one-way ANOVA (f–g) followed by Tukey’s post hoc. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 compared to vehicle.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8

Reporting Summary

Supplementary Table 1

DepMap data analysis.

Supplementary Table 2

Metabolomics dataset.

Supplementary Table 3

Primer sequences.

Source data

Source Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 7

Unprocessed western blots.

Source Data Extended Data Fig. 8

Unprocessed western blots.

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Schwartz, A.J., Goyert, J.W., Solanki, S. et al. Hepcidin sequesters iron to sustain nucleotide metabolism and mitochondrial function in colorectal cancer epithelial cells. Nat Metab 3, 969–982 (2021). https://doi.org/10.1038/s42255-021-00406-7

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