Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Adipose tissue macrophages exert systemic metabolic control by manipulating local iron concentrations

Abstract

Iron is essential to many fundamental biological processes, but its cellular compartmentalization and concentration must be tightly controlled. Although iron overload can contribute to obesity-associated metabolic deterioration, the subcellular localization and accumulation of iron in adipose tissue macrophages is largely unknown. Here, we show that macrophage mitochondrial iron levels control systemic metabolism in male mice by altering adipocyte iron concentrations. Using various transgenic mouse models to manipulate the macrophage mitochondrial matrix iron content in an inducible fashion, we demonstrate that lowering macrophage mitochondrial matrix iron increases numbers of M2-like macrophages in adipose tissue, lowers iron levels in adipocytes, attenuates inflammation and protects from high-fat-diet-induced metabolic deterioration. Conversely, elevating macrophage mitochondrial matrix iron increases M1-like macrophages and iron levels in adipocytes, exacerbates inflammation and worsens high-fat-diet-induced metabolic dysfunction. These phenotypes are robustly reproduced by transplantation of a small amount of fat from transgenic to wild-type mice. Taken together, we identify macrophage mitochondrial iron levels as a crucial determinant of systemic metabolic homeostasis in mice.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Obesity leads to mitochondrial iron overload in adipose tissue macrophages.
Fig. 2: Mac-MitoNEETTG mice are protected from HFD-induced obesity and metabolic disorders.
Fig. 3: Mac-FtmtTG mice are more susceptible to high-fat-diet-induced obesity and metabolic disorders.
Fig. 4: Reducing mitochondrial iron content in macrophages after a long-term high-fat-diet feeding reverses obesity and metabolic dysfunction.
Fig. 5: Beneficial systemic effects in Mac-MitoNEETTG mice are due to adipose tissue macrophages.
Fig. 6: Deleterious systemic effects in Mac-FtmtTG mice are equally due to adipose tissue macrophages.
Fig. 7: Beneficial cross-talk between Mac-MitoNEETTG ATMs and adipocytes.
Fig. 8: Deleterious cross-talk between Mac-FtmtTG ATMs and adipocytes.

Similar content being viewed by others

Data availability

All data generated or analysed during this study are available with this paper. Source data are provided with this paper.

References

  1. Gustafson, B., Hedjazifar, S., Gogg, S., Hammarstedt, A. & Smith, U. Insulin resistance and impaired adipogenesis. Trends Endocrinol. Metab. 26, 193–200 (2015).

    CAS  PubMed  Google Scholar 

  2. Kloting, N. & Bluher, M. Adipocyte dysfunction, inflammation and metabolic syndrome. Rev. Endocr. Metab. Disord. 15, 277–287 (2014).

    PubMed  Google Scholar 

  3. Sun, K., Kusminski, C. M. & Scherer, P. E. Adipose tissue remodeling and obesity. J. Clin. Invest. 121, 2094–2101 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Sun, K., Tordjman, J., Clement, K. & Scherer, P. E. Fibrosis and adipose tissue dysfunction. Cell Metab. 18, 470–477 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chaurasia, B. & Summers, S. A. Ceramides—lipotoxic inducers of metabolic disorders: (Trends in Endocrinology and Metabolism 26, 538–550; 2015). Trends Endocrinol. Metab. 29, 66–67 (2018).

    CAS  PubMed  Google Scholar 

  6. Perry, R. J., Samuel, V. T., Petersen, K. F. & Shulman, G. I. The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature 510, 84–91 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Kusminski, C. M., Shetty, S., Orci, L., Unger, R. H. & Scherer, P. E. Diabetes and apoptosis: lipotoxicity. Apoptosis 14, 1484–1495 (2009).

    CAS  PubMed  Google Scholar 

  8. Winer, S. & Winer, D. A. The adaptive immune system as a fundamental regulator of adipose tissue inflammation and insulin resistance. Immunol. Cell Biol. 90, 755–762 (2012).

    CAS  PubMed  Google Scholar 

  9. Weisberg, S. P. et al. Obesity is associated with macrophage accumulation in adipose tissue. J. Clin. Invest. 112, 1796–1808 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Lumeng, C. N., Bodzin, J. L. & Saltiel, A. R. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J. Clin. Invest. 117, 175–184 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Orr, J. S. et al. Obesity alters adipose tissue macrophage iron content and tissue iron distribution. Diabetes 63, 421–432 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Hubler, M. J., Erikson, K. M., Kennedy, A. J. & Hasty, A. H. MFehi adipose tissue macrophages compensate for tissue iron perturbations in mice. Am. J. Physiol. Cell Physiol. 315, C319–C329 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Joffin, N. et al. Mitochondrial metabolism is a key regulator of the fibro-inflammatory and adipogenic stromal subpopulations in white adipose tissue. Cell Stem Cell https://doi.org/10.1016/j.stem.2021.01.002 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang, Z. et al. Adipocyte iron levels impinge on a fat-gut cross-talk to regulate intestinal lipid absorption and mediate protection from obesity. Cell Metab. 33, 1624–1639 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Ameka, M. K. & Hasty, A. H. Fat and iron don’t mix. Immunometabolism https://doi.org/10.20900/immunometab20200034 (2020).

  16. Gabrielsen, J. S. et al. Adipocyte iron regulates adiponectin and insulin sensitivity. J. Clin. Invest. 122, 3529–3540 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Zheng, J., Chen, M., Liu, G., Xu, E. & Chen, H. Ablation of hephaestin and ceruloplasmin results in iron accumulation in adipocytes and type 2 diabetes. FEBS Lett. 592, 394–401 (2018).

    CAS  PubMed  Google Scholar 

  18. Mills, E. L. & O’Neill, L. A. Reprogramming mitochondrial metabolism in macrophages as an anti-inflammatory signal. Eur. J. Immunol. 46, 13–21 (2016).

    CAS  PubMed  Google Scholar 

  19. Van den Bossche, J. et al. Mitochondrial dysfunction prevents repolarization of inflammatory macrophages. Cell Rep. 17, 684–696 (2016).

    PubMed  Google Scholar 

  20. Aerbajinai, W. et al. Glia maturation factor-gamma regulates murine macrophage iron metabolism and M2 polarization through mitochondrial ROS. Blood Adv. 3, 1211–1225 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Drapier, J. C. & Hibbs, J. B. Jr. Differentiation of murine macrophages to express nonspecific cytotoxicity for tumor cells results in l-arginine-dependent inhibition of mitochondrial iron-sulfur enzymes in the macrophage effector cells. J. Immunol. 140, 2829–2838 (1988).

    CAS  PubMed  Google Scholar 

  22. Soares, M. P. & Hamza, I. Macrophages and iron metabolism. Immunity 44, 492–504 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Tong, W. H. et al. TLR-activated repression of Fe–S cluster biogenesis drives a metabolic shift and alters histone and tubulin acetylation. Blood Adv. 2, 1146–1156 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Barros, M. H., Hauck, F., Dreyer, J. H., Kempkes, B. & Niedobitek, G. Macrophage polarisation: an immunohistochemical approach for identifying M1 and M2 macrophages. PLoS ONE 8, e80908 (2013).

    PubMed  PubMed Central  Google Scholar 

  25. Moreno-Navarrete, J. M. et al. HMOX1 as a marker of iron excess-induced adipose tissue dysfunction, affecting glucose uptake and respiratory capacity in human adipocytes. Diabetologia 60, 915–926 (2017).

    CAS  PubMed  Google Scholar 

  26. Lee, W., Yun, S., Choi, G. H. & Jung, T. W. Fibronectin type III domain containing 4 attenuates hyperlipidemia-induced insulin resistance via suppression of inflammation and ER stress through HO-1 expression in adipocytes. Biochem. Biophys. Res. Commun. 502, 129–136 (2018).

    CAS  PubMed  Google Scholar 

  27. Kusminski, C. M. et al. MitoNEET-driven alterations in adipocyte mitochondrial activity reveal a crucial adaptive process that preserves insulin sensitivity in obesity. Nat. Med. 18, 1539–1549 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kusminski, C. M., Park, J. & Scherer, P. E. MitoNEET-mediated effects on browning of white adipose tissue. Nat. Commun. 5, 3962 (2014).

    CAS  PubMed  Google Scholar 

  29. Corsi, B. et al. Human mitochondrial ferritin expressed in HeLa cells incorporates iron and affects cellular iron metabolism. J. Biol. Chem. 277, 22430–22437 (2002).

    CAS  PubMed  Google Scholar 

  30. Kusminski, C. M. et al. A novel model of diabetic complications: adipocyte mitochondrial dysfunction triggers massive beta-cell hyperplasia. Diabetes 69, 313–330 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhu, Q. et al. Suppressing adipocyte inflammation promotes insulin resistance in mice. Mol. Metab. 39, 101010 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Fernandez-Real, J. M., Lopez-Bermejo, A. & Ricart, W. Cross-talk between iron metabolism and diabetes. Diabetes 51, 2348–2354 (2002).

    CAS  PubMed  Google Scholar 

  33. Ford, E. S. & Cogswell, M. E. Diabetes and serum ferritin concentration among US adults. Diabetes Care 22, 1978–1983 (1999).

    CAS  PubMed  Google Scholar 

  34. Jiang, R. et al. Body iron stores in relation to risk of type 2 diabetes in apparently healthy women. JAMA 291, 711–717 (2004).

    CAS  PubMed  Google Scholar 

  35. Forouhi, N. G. et al. Elevated serum ferritin levels predict new-onset type 2 diabetes: results from the EPIC-Norfolk prospective study. Diabetologia 50, 949–956 (2007).

    CAS  PubMed  Google Scholar 

  36. Corna, G. et al. Polarization dictates iron handling by inflammatory and alternatively activated macrophages. Haematologica 95, 1814–1822 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Recalcati, S. et al. Differential regulation of iron homeostasis during human macrophage polarized activation. Eur. J. Immunol. 40, 824–835 (2010).

    CAS  PubMed  Google Scholar 

  38. Hubler, M. J., Peterson, K. R. & Hasty, A. H. Iron homeostasis: a new job for macrophages in adipose tissue? Trends Endocrinol. Metab. 26, 101–109 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. de Mello, A. H., Costa, A. B., Engel, J. D. G. & Rezin, G. T. Mitochondrial dysfunction in obesity. Life Sci. 192, 26–32 (2018).

    PubMed  Google Scholar 

  40. Caslin, H. L., Bhanot, M., Bolus, W. R. & Hasty, A. H. Adipose tissue macrophages: unique polarization and bioenergetics in obesity. Immunol. Rev. 295, 101–113 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Rodriguez-Prados, J. C. et al. Substrate fate in activated macrophages: a comparison between innate, classic, and alternative activation. J. Immunol. 185, 605–614 (2010).

    CAS  PubMed  Google Scholar 

  42. Pereira, M. et al. Acute iron deprivation reprograms human macrophage metabolism and reduces inflammation in vivo. Cell Rep. 28, 498–511 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    CAS  PubMed  Google Scholar 

  45. El Kasmi, K. C. & Stenmark, K. R. Contribution of metabolic reprogramming to macrophage plasticity and function. Semin. Immunol. 27, 267–275 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Galvan-Pena, S. & O’Neill, L. A. Metabolic reprograming in macrophage polarization. Front Immunol. 5, 420 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. Crooks, D. R. et al. Acute loss of iron-sulfur clusters results in metabolic reprogramming and generation of lipid droplets in mammalian cells. J. Biol. Chem. 293, 8297–8311 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Behmoaras, J. The versatile biochemistry of iron in macrophage effector functions. FEBS J. 288, 6972–6989 (2021).

    CAS  PubMed  Google Scholar 

  49. Kelly, B. & O’Neill, L. A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 25, 771–784 (2015).

    PubMed  PubMed Central  Google Scholar 

  50. Palsson-McDermott, E. M. et al. Pyruvate kinase M2 regulates Hif-1α activity and IL-1β induction and is a critical determinant of the warburg effect in LPS-activated macrophages. Cell Metab. 21, 65–80 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Mounier, R. et al. AMPKα1 regulates macrophage skewing at the time of resolution of inflammation during skeletal muscle regeneration. Cell Metab. 18, 251–264 (2013).

    CAS  PubMed  Google Scholar 

  52. Shapiro, H., Lutaty, A. & Ariel, A. Macrophages, meta-inflammation, and immuno-metabolism. ScientificWorldJournal 11, 2509–2529 (2011).

    PubMed  PubMed Central  Google Scholar 

  53. Angajala, A. et al. Diverse roles of mitochondria in immune responses: novel insights into immuno-metabolism. Front Immunol. 9, 1605 (2018).

    PubMed  PubMed Central  Google Scholar 

  54. Wang, H., Liu, C., Zhao, Y. & Gao, G. Mitochondria regulation in ferroptosis. Eur. J. Cell Biol. 99, 151058 (2020).

    CAS  PubMed  Google Scholar 

  55. Sumneang, N., Siri-Angkul, N., Kumfu, S., Chattipakorn, S. C. & Chattipakorn, N. The effects of iron overload on mitochondrial function, mitochondrial dynamics, and ferroptosis in cardiomyocytes. Arch. Biochem. Biophys. 680, 108241 (2020).

    CAS  PubMed  Google Scholar 

  56. Tajima, S. et al. Iron reduction by deferoxamine leads to amelioration of adiposity via the regulation of oxidative stress and inflammation in obese and type 2 diabetes KKAy mice. Am. J. Physiol. Endocrinol. Metab. 302, E77–E86 (2012).

    CAS  PubMed  Google Scholar 

  57. Green, A., Basile, R. & Rumberger, J. M. Transferrin and iron induce insulin resistance of glucose transport in adipocytes. Metabolism 55, 1042–1045 (2006).

    CAS  PubMed  Google Scholar 

  58. Cooksey, R. C. et al. Dietary iron restriction or iron chelation protects from diabetes and loss of beta-cell function in the obese (ob/ob lep−/−) mouse. Am. J. Physiol. Endocrinol. Metab. 298, E1236–E1243 (2010).

  59. Minamiyama, Y. et al. Iron restriction improves type 2 diabetes mellitus in Otsuka Long-Evans Tokushima fatty rats. Am. J. Physiol. Endocrinol. Metab. 298, E1140–E1149 (2010).

    CAS  PubMed  Google Scholar 

  60. Valenti, L. et al. Iron depletion by phlebotomy improves insulin resistance in patients with nonalcoholic fatty liver disease and hyperferritinemia: evidence from a case–control study. Am. J. Gastroenterol. 102, 1251–1258 (2007).

    CAS  PubMed  Google Scholar 

  61. Valenti, L. et al. Venesection for non-alcoholic fatty liver disease unresponsive to lifestyle counselling—a propensity score-adjusted observational study. QJM 104, 141–149 (2011).

    CAS  PubMed  Google Scholar 

  62. Gao, Y. et al. Adipocyte iron regulates leptin and food intake. J. Clin. Invest. 125, 3681–3691 (2015).

    PubMed  PubMed Central  Google Scholar 

  63. Tomay, F. et al. Purple corn extract induces long-lasting reprogramming and M2 phenotypic switch of adipose tissue macrophages in obese mice. J. Transl. Med 17, 237 (2019).

    PubMed  PubMed Central  Google Scholar 

  64. Weiss, G., Bogdan, C. & Hentze, M. W. Pathways for the regulation of macrophage iron metabolism by the anti-inflammatory cytokines IL-4 and IL-13. J. Immunol. 158, 420–425 (1997).

    CAS  PubMed  Google Scholar 

  65. Brock, J. H., Djeha, A., Ismail, M., Oria, R. & Sinclair, R. H. Cellular responses to iron and iron compounds. Adv. Exp. Med Biol. 356, 91–100 (1994).

    CAS  PubMed  Google Scholar 

  66. Weiss, G. et al. Iron modulates interferon-gamma effects in the human myelomonocytic cell line THP-1. Exp. Hematol. 20, 605–610 (1992).

    CAS  PubMed  Google Scholar 

  67. Sugimoto, M. et al. MMMDB: Mouse Multiple Tissue Metabolome Database. Nucleic Acids Res. 40, D809–D814 (2012).

    CAS  PubMed  Google Scholar 

  68. Fraenkel, P. G. Anemia of inflammation: a review. Med. Clin. North Am. 101, 285–296 (2017).

    PubMed  Google Scholar 

  69. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

  70. Peics, J. et al. Isolation of adipogenic and fibro-inflammatory stromal cell subpopulations from murine intra-abdominal adipose depots. J. Vis. Exp. https://doi.org/10.3791/61610 (2020).

    Article  PubMed  Google Scholar 

  71. Pinero, D. J., Li, N., Hu, J., Beard, J. L. & Connor, J. R. The intracellular location of iron regulatory proteins is altered as a function of iron status in cell cultures and rat brain. J. Nutr. 131, 2831–2836 (2001).

    CAS  PubMed  Google Scholar 

  72. Gao, Y. et al. Iron downregulates leptin by suppressing protein O-GlcNAc modification in adipocytes, resulting in decreased levels of O-glycosylated CREB. J. Biol. Chem. 294, 5487–5495 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the UTSW Animal Resource Center, Histology Core, Metabolic Phenotyping Core, the Live Cell Imaging Core, Transgenic Core and Flow Cytometry Facility for their excellent assistance with experiments performed in this paper. We also thank Shimadzu Scientific Instruments for the collaborative efforts in mass spectrometry technology resources. This study was supported by US National Institute of Health grants RC2-DK118620, R01-DK55758, R01-DK099110, R01-DK127274 and R01-DK131537 to P.E.S.; R01 DK108773 to D.Y.O.; C.C. is supported by K99-DK122019 and R00-DK122019. C.M.G. is supported by F32-DK-122623; V.A.P. was supported by an American Diabetes Association Minority Postdoctoral Fellowship (1-18-PMF-030). J.-B.F. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; grant 414232833).

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, N.J., C.M.G, V.A.P., D.Y.O. and P.E.S.; methodology, N.J. and C.M.G; formal analysis, N.J., V.A.P. and C.C; investigation, N.J., C.M.G., J.-B.F., V.A.P., C.C., S.C., R.G. and C.M.K.; resources, W.J.G.; original draft writing, N.J. and P.E.S.; manuscript review and editing, N.J., C.M.G., J.-B.F. and P.E.S.; supervision, P.E.S.; funding acquisition, P.E.S.

Corresponding author

Correspondence to Philipp E. Scherer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Alyssa Hasty and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt, in collaboration with the Nature Metabolism team.

Additional information

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

Extended data

Extended Data Fig. 1 Validation of MitoNEET overexpression in macrophages.

(a-j) Six-week-old male control and Mac-MitoNEETTG mice were fed chow dox for 2 weeks. (a) Breeding strategy. (b) Cisd1 mRNA levels relative to control in monocytes (control n = 4, TG n = 3), ATMs (n = 3), and peritoneal macrophages (n = 6). (c) MitoNEET protein expression relative to β-actin in peritoneal macrophages (n = 6). (d) Representative Western blots chosen from 2 independent experiments. (e-f) Mitochondrial respiration in peritoneal macrophages (control n = 3, TG n = 5). (g) Mitochondrial electron transport chain protein expression relative to prohibitin in peritoneal macrophages (n = 6). (h) Representative Western blots. (i-j) Glycolysis stress test in peritoneal macrophages (control n = 6, TG n = 6). Significance in (b-c, f-g, j) between control and Mac-MitoNEETTG was calculated using a two-tailed Student’s t-test. Significance in (e, i) was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 2 Validation of Ftmt overexpression in macrophages.

(a-j) Six-week-old male control and Mac-FtmtTG mice were fed chow dox for 2 weeks. (a) Breeding strategy. (b) Ftmt mRNA levels relative to control in peritoneal macrophages (control n = 7, TG n = 9). (c) FTMT protein expression relative to β-actin in peritoneal macrophages (control n = 5, TG n = 3). (d) Representative Western blots chosen from 2 independent experiments. (e-f) Mitochondrial respiration in peritoneal macrophages (control n = 6, TG n = 7). (g) Mitochondrial electron transport chain protein expression relative to prohibitin in peritoneal macrophages (control n = 5, TG n = 3). (h) Representative Western blots (control n = 5, TG n = 3). (i-j) Glycolysis stress test in peritoneal macrophages (control n = 4, TG n = 3). Significance in (b-c, f-g, j) between control and Mac-FtmtTG was calculated using a two-tailed Student’s t-test. Significance in (e, i) was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 3 Macrophages respond to mitoNEET overexpression by up-regulating iron storage.

(a-q) Male control and Mac-MitoNEETTG mice were fed chow dox for 2 weeks. (a) Labile iron pool (LIP) in mitochondrial and cytosolic compartments of peritoneal macrophages (control n = 7, TG n = 6). (b) mRNA levels of iron metabolism genes relative to control in peritoneal macrophages (control n = 10, TG n = 7). (c) Ferritin (control n = 9, TG n = 12), (d) Ferroportin (control n = 3, TG n = 5), (e) CD163 (control n = 3, TG n = 5), (f) CD91 (control n = 3, TG n = 5) and (g) FTMT (control n = 3, TG n = 5) protein expression relative to β-actin in peritoneal macrophages. (h) Representative Western blots chosen from 2 independent experiments (i) mRNA levels of inflammation markers relative to control in peritoneal macrophages (control n = 7, TG n = 10). (j-l) Flow cytometric measurement of total (control n = 6, TG n = 5), M1-like control n = 6, TG n = 5) and M2-like macrophages in eWAT (n = 6). (m) Body weight (control n = 3, TG n = 5). (n-o) Oral glucose tolerance test (control n = 3, TG n = 5). (n) Blood glucose levels. (o) Serum insulin levels. (p) Blood glucose levels during insulin tolerance test (control n = 3, TG n = 5). (q) Serum triglyceride levels during triglyceride clearance (n = 6). Significance in (a-g, i-m) between control and Mac-MitoNEETTG was calculated using a two-tailed Student’s t-test. Significance in (n-q) was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 4 Macrophages respond to Ftmt overexpression by up-regulating iron uptake.

(a-q) Male control and Mac-FtmtTG mice were fed chow dox for 2 weeks. (a) Labile iron pool (LIP) in mitochondrial and cytosolic compartments of peritoneal macrophages (n = 4). (b) mRNA levels of iron metabolism genes relative to control in peritoneal macrophages (control n = 5, TG n = 7). (c) CD91 (n = 8), (d) CD163 (n = 8), (e) Ferritin (n = 8), (f) Ferroportin (n = 4) and (g) MitoNEET (n = 8) protein expression relative to β-actin in peritoneal macrophages (n = 8). (h) Representative Western blots chosen from 2 independent experiments. (i) mRNA levels of inflammation markers relative to control in peritoneal macrophages (control n = 5; TG n = 7). (j-l) Flow cytometric measurement of total, M1-like and M2-like macrophages in eWAT (n = 6). (m) Body weight (n = 6). (n-o) Oral glucose tolerance test (n = 6). (n) Blood glucose levels (n = 6). (o) Serum insulin levels (control n = 7, TG n = 9). (p) Blood glucose levels during insulin tolerance test (control n = 6, TG n = 5). (q) Serum triglyceride levels during triglyceride clearance (n = 6). Significance in (a-g, i-m) between control and Mac-FtmtTG was calculated using a two-tailed Student’s t-test. Significance in (n-q) was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 5 Mitochondrial iron depletion in macrophages during HFD increases iron recycling, protecting adipocytes from iron overload.

(a-x) Six-week-old male control and Mac-MitoNEETTG mice were fed HFD dox for 6 weeks. (a) Labile iron pool (LIP) in ATMs from eWAT (control n = 7, TG n = 7) (b) Total iron content measured by ICP-MS in mitochondrial and cytosolic compartments in peritoneal macrophages (control n = 7, TG n = 5). (c-d) LIP in (c) adipocytes and (d) SVF from eWAT (control n = 7, TG n = 5). (e-f) mRNA levels of iron metabolism genes relative to control (e) adipocytes (control n = 5, TG n = 6) and (f) ATMs (control n = 6, TG n = 5) from eWAT. (g) Ferroportin and (h) Ferritin protein expression relative to β-actin in adipocytes from eWAT (n = 6). (i) Representative Western blots chosen from 2 independent experiments. (j) CD163 (n = 9), (k) CD91, (l) Ferroportin, and (m) Ferritin protein expression relative to β-actin in ATMs from eWAT (control n = 9, TG n = 6). (n) Representative Western blots chosen from 2 independent experiments. (o) mRNA levels of mitochondrial iron metabolism genes relative to control in ATMs from eWAT (control n = 5, TG n = 6). (p) MitoNEET and (q) Mitoferritin protein expression relative to β-actin in ATMs from eWAT (n = 3). (r) MitoNEET protein expression relative to β-actin in adipocytes from eWAT (control n = 9, TG n = 6). (s) LIP in mitochondrial and cytosolic compartments of eWAT (control n = 5, TG n = 4). (t-u) mRNA levels of inflammatory marker genes relative to control in (t) ATMs and (u) adipocytes from eWAT (control n = 5, TG n = 6). (v) Representative fluorescence microscopic images of 4-HNE, Perilipin and DAPI staining of eWAT chosen from 3 independent experiments. (w) Quantification of 4-HNE fluorescence in eWAT relative to control (control n = 8, TG n = 7). (x) Protein carbonylation in adipocytes from eWAT (control n = 10, TG n = 11). Significance in (a-h, j-u, w-x) between control and Mac-MitoNEETTG was calculated using a two-tailed Student’s t-test. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 6 Mitochondrial iron overload in macrophages during HFD promotes adipocyte iron overload and inflammation.

(a-q) Six-week-old male control and Mac-FtmtTG mice were fed HFD dox for 6 weeks. (a) Total iron content measured by ICP-MS in mitochondrial (n = 8) and cytosolic (control n = 4, TG n = 5) compartments in peritoneal macrophages. (b-d) Labile iron pool (LIP) in (b) ATMs (control n = 4, TG n = 5), (c) adipocytes (control n = 9, TG n = 12) and (d) SVF (control n = 7, TG n = 5) from eWAT. (e-f) mRNA levels of iron metabolism genes relative to control in (e) adipocytes (n = 5) and (f) ATMs (n = 6) from eWAT. (g) Ferritin (control n = 7, TG n = 12) and (h) Ferroportin (n = 3) protein expression relative to β-actin in adipocytes from eWAT. (i) Representative Western blots chosen from 3 independent experiments. (j) Ferritin (control n = 7, TG n = 9) and (k) Ferroportin protein expression relative to β-actin in ATMs from eWAT (n = 3). (l) Representative Western blots chosen from 3 independent experiments. (m) mRNA levels of mitochondrial iron metabolism genes relative to control in ATMs from eWAT (n = 6). (i) MitoNEET protein expression relative to β-actin in adipocytes from eWAT (control n = 4, TG n = 10). (o) LIP in mitochondrial and cytosolic compartments of eWAT (n = 4). (p-q) mRNA levels of inflammatory marker genes relative to control in (p) ATMs and (q) adipocytes from eWAT (n = 5). Significance in (a-h, j-k, m-q) between control and Mac-FtmtTG was calculated using a two-tailed Student’s t-test. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 7 Eliminating MitoNEET in macrophages during HFD promotes insulin resistance and inflammation.

(a-k) Six-week-old male control and Mac-MitoNEETKO mice were fed HFD dox for 7 weeks. (a) Breeding strategy. (b) MitoNEET protein expression relative to β-actin in peritoneal macrophages (n = 3). (c) Representative Western blots (n = 3). (d) Body weight (control n = 9, TG n = 5). (e) Tissue weights (control n = 9, TG n = 5). (f-g) Oral glucose tolerance test. (f) Blood glucose levels (control n = 5, TG n = 3). (g) Serum insulin levels (control n = 9, TG n = 5). (h) Blood glucose levels during insulin tolerance test (control n = 9, TG n = 5). (i-k) Flow cytometric measurement of total, M1-like and M2-like macrophages in eWAT (control n = 5, TG n = 3). Significance in (b, e, i-k) between control and Mac-MitoNEETKO was calculated using a two-tailed Student’s t-test. Significance in (d, f-h) was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 8 Metabolic analyses of Mac-MitoNEETTG mice.

(a-e) Six-week-old male control and Mac-MitoNEETTG mice were fed HFD for 20 weeks and then switched to HFD dox for 1 week. Metabolic analyses were performed at 3 d before and 7 d after diet change (n = 6). (a) Experimental design. (b) Body weight (n = 5). (c) Hourly food intake (n = 6). (d) Respiratory exchange ratio (n = 6). (e) Energy balance (n = 6). Significance in (b-e) between control and Mac-MitoNEETTG was calculated using a 2-way ANOVA with Sidak’s post-test for multiple comparisons. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Extended Data Fig. 9 Validation of eWAT transplantation to wild-type mice.

(a-i) Six-week-old male control and Mac-MitoNEETTG mice were injected with PKH67 dye before transplantation of their eWAT to wild-type mice (n = 5). Recipient mice were allowed to recover 3 weeks and then fed HFD dox for 6 weeks. (a-b) Flow cytometric measurement of PKH67 fluorescence in (a) graft and (b) recipient eWAT (n = 5). (c-d) mRNA levels of Cisd1, Ftmt, and Tfam relative to control in (c) graft and (d) recipient eWAT (n = 5). (e-g) Flow cytometric measurement of total, M1-like, and M2-like macrophages in recipient eWAT (n = 5). (h-i) Wild-type mice were transplanted with either control or Mac-FtmtTG eWAT. Transplanted mice were allowed to recover 3 weeks and then fed HFD dox for 6 weeks. (h-i) mRNA levels of Cisd1, Ftmt and Tfam relative to control in (c) graft and (d) recipient eWAT (control n = 7, TG n = 8). Significance in (a-i) between control and transgenic fat-transplanted animals was calculated using a two-tailed Student’s t-test. Error bars represent mean ± S.E.M. * (P < 0.05), ** (p < 0.01), *** (p < 0.0001), **** (p < 0.00001).

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Supplementary Tables 1–3

Reporting Summary

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 1

Statistics and raw data.

Source Data Fig. 2

Statistics and raw data.

Source Data Fig. 3

Statistics and raw data.

Source Data Fig. 4

Statistics and raw data.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 5

Statistics and raw data.

Source Data Fig. 6

Unprocessed western blots.

Source Data Fig. 6

Statistics and raw data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 7

Statistics and raw data.

Source Data Fig. 8

Unprocessed western blots.

Source Data Fig. 8

Statistics and raw data.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 1

Statistics and raw data.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistics and raw data.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 3

Statistics and raw data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 4

Statistics and raw data.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 5

Statistics and raw data.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 6

Statistics and raw data.

Source Data Extended Data Fig. 7

Unprocessed western blots.

Source Data Extended Data Fig. 7

Statistics and raw data.

Source Data Extended Data Fig. 8

Statistics and raw data.

Source Data Extended Data Fig. 9

Statistics and raw data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Joffin, N., Gliniak, C.M., Funcke, JB. et al. Adipose tissue macrophages exert systemic metabolic control by manipulating local iron concentrations. Nat Metab 4, 1474–1494 (2022). https://doi.org/10.1038/s42255-022-00664-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-022-00664-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing