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Meta-Analysis
. 2024 Dec 6;7(1):1631.
doi: 10.1038/s42003-024-07115-3.

Novel loci and biomedical consequences of iron homoeostasis variation

Elias Allara #  1   2   3 , Steven Bell #  4   5 , Rebecca Smith  6   7 , Spencer J Keene  6   7 , Dipender Gill  8 , Liam Gaziano  6   7   9   10 , Deisy Morselli Gysi  9   11   12 , Feiyi Wang  13 , Vinicius Tragante  14 , Amy Mason  6   7 , Savita Karthikeyan  6   7 , R Thomas Lumbers  15 , Emmanuela Bonglack  6   7   16 , Willem Ouwehand  17   18   19   20 , David J Roberts  21   22 , Joseph Dowsett  23 , Sisse Rye Ostrowski  23   24 , Margit Hørup Larsen  23 , Henrik Ullum  25 , Ole Birger Pedersen  24   26 , Søren Brunak  27 , Karina Banasik  27 , Christian Erikstrup  28 ; DBDS Genomic Consortium; Jonathan Mitchell  29 , Christian Fuchsberger  29 , Cristian Pattaro  29 , Peter P Pramstaller  29   30 , Domenico Girelli  31 , Mikko Arvas  32 , Jarkko Toivonen  32 , Sophie Molnos  33   34 , Annette Peters  34   35   36 , Ozren Polasek  37 , Igor Rudan  38 , Caroline Hayward  39 , Ciara McDonnell  38   40 , Nicola Pirastu  38   41 , James F Wilson  38   39 , Katja van den Hurk  42   43 , Franke Quee  42   43 , Luigi Ferrucci  44 , Stefania Bandinelli  45 , Toshiko Tanaka  44 , Giorgia Girotto  46   47 , Maria Pina Concas  46 , Alessandro Pecori  46 , Niek Verweij  48   49 , Pim van der Harst  48   50 , Yordi J van de Vegte  48 , Lambertus A Kiemeney  51   52 , Fred C Sweep  53 , Tessel E Galesloot  51 , Patrick Sulem  14 , Daniel Gudbjartsson  14   54 , Egil Ferkingstad  14 ; FinnGen Consortium; Luc Djousse  9   10   12   55 , Kelly Cho  9   10   12 , Michael Inouye  6   7   16   56   57   58 , Stephen Burgess  6   7   16   59 , Beben Benyamin  60   61 , Konrad Oexle  62   63 , Dorine Swinkels  53   64 , Kari Stefansson  14   65 , Magnus Magnusson  14   65 , Andrea Ganna  13 , Michael Gaziano  9   10   12 , Kerry Ivey  9   10   12 , John Danesh  6   66   7   16   57   67 , Alexandre Pereira  9   10   12 , Angela M Wood  6   66   7   16   57   68 , Adam S Butterworth  6   66   7   16   57 , Emanuele Di Angelantonio  69   70   71   72   73   74
Collaborators, Affiliations
Meta-Analysis

Novel loci and biomedical consequences of iron homoeostasis variation

Elias Allara et al. Commun Biol. .

Abstract

Iron homoeostasis is tightly regulated, with hepcidin and soluble transferrin receptor (sTfR) playing significant roles. However, the genetic determinants of these traits and the biomedical consequences of iron homoeostasis variation are unclear. In a meta-analysis of 12 cohorts involving 91,675 participants, we found 43 genomic loci associated with either hepcidin or sTfR concentration, of which 15 previously unreported. Mapping to putative genes indicated involvement in iron-trait expression, erythropoiesis, immune response and cellular trafficking. Mendelian randomisation of 292 disease outcomes in 1,492,717 participants revealed associations of iron-related loci and iron status with selected health outcomes across multiple domains. These associations were largely driven by HFE, which was associated with the largest iron variation. Our findings enhance understanding of iron homoeostasis and its biomedical consequences, suggesting that lifelong exposure to higher iron levels is likely associated with lower risk of anaemia-related disorders and higher risk of genitourinary, musculoskeletal, infectious and neoplastic diseases.

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Conflict of interest statement

Competing interests: R.S. is currently employed at Astra Zeneca. N.V. is an employee and stockholder of Regeneron Pharmaceuticals. J.Da. serves on scientific advisory boards for AstraZeneca, Novartis, and UK Biobank, and has received multiple grants from academic, charitable, and industry sources outside of the submitted work. All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Study overview.
GWAS, genome-wide association study. sTfR soluble transferrin receptor, TSAT transferrin saturation, TIBC total iron-binding capacity, eQTL expression quantitative trait loci, pQTL protein quantitative trait loci, MR Mendelian randomisation, MVP Million Veteran Programme. The genetic variants from Moksnes 2022 were obtained from the paper’s Supplementary Data 1.
Fig. 2
Fig. 2. Results of GWAS meta-analysis of hepcidin and sTfR and correlations with common iron traits.
A Miami plot for hepcidin (upper plot, N = 91,675 participants) and sTfR (lower plot, N = 45,330 participants). For each locus (N = 16 loci for hepcidin, N = 27 for sTfR), we show the candidate gene name for the sentinel variant with the lowest p value. B Genetic and phenotypic correlations between the iron traits analysed in this study (hepcidin, sTfR) and those investigated in previous studies (ferritin, iron, TIBC, TSAT). Phenotypic correlations were estimated in the INTERVAL study (up to 40,197 participants). Genetic correlations were estimated using associations from the present study (hepcidin, sTfR; up to 91,675 participants) and Moksnes et al. 2022 (ferritin, iron, TIBC, TSAT; up to 257,953 participants).
Fig. 3
Fig. 3. Established and potential candidate genes mapped to variants associated with hepcidin or sTfR: summary of their role and contextual information.
A This figure summarises the genes mentioned in Table 2 of this study, as well as other iron-homoeostasis genes provided for contextual information. Genes with an established role in iron homoeostasis are shown in red and italic; genes with a potential role are presented in dark grey and italic. Relevant references to other studies are included in Supplementary Data 8 ❶ Hepcidin is tightly regulated by several pathways. TMPRSS6, ERFE (via the BMP pathway), and ZFPM1 suppress hepcidin expression in hepatocytes. HFE, TFR2, the Wnt pathway, and the JAK/STAT pathway increase hepcidin expression. Activation of the Wnt pathways is observed in iron overload, with involvement of AXIN1. Activation of JAK/STAT signalling has been proposed as a possible link between inflammation and iron homoeostasis. ❷ In presence of iron abundance, hepcidin suppresses function of ferroportin (FPN), an iron transporter coded by SLC40A1 that mediates dietary intestinal iron uptake and iron recycling by macrophages from senescent erythrocytes. NDFIP1 prevents degradation of ferroportin in vitro. ❸ Hypoxia-inducible factor 2α (HIF-2 α), coded by EPAS1 and regulated by EGLN3, also controls duodenal iron absorption by promoting the expression of divalent metal transporter 1 (DMT1), coded by SLC11A2, on the luminal side of enterocytes. NDFIP1 regulates DMT1 expression in mice. EGLN3 hydroxylates key prolyl residues on HIF-2α, providing a recognition motif for its degradation. ❹ Several genes appear relevant to intestinal iron absorption: (i) DUOX2 regulates interactions between the intestinal microbiota and the mucosa to maintain immune homoeostasis in mice, which likely enables intestinal iron absorption; (ii) FUT2 codes for fucosyltransferase 2, an enzyme responsible for maintaining host-microbiota symbiosis via fucosylation of intestinal epithelial cells; (iii) VANGL1 encodes a protein involved in mediating intestinal trefoil factor-induced wound healing in the intestinal mucosa. ❺ Iron released through ferroportin is bound to iron carrier transferrin (referred to as apotransferrin when not bound to iron), forming iron-loaded transferrin (holotransferrin), which delivers iron to most cells, especially erythrocytes. ❻ In presence of hypoxia, raised levels of HIF-2 α result in increased erythropoietin (EPO) production. ❼ EPO stimulates erythropoiesis, which is also modulated by several genes involved in erythroblast proliferation and differentiation: (i) the HBS1L/MYB intergenic region regulates erythroid cell proliferation, maturation, and foetal haemoglobin expression; (ii) HK1 mutations lead to haemolytic anaemia via hexokinase deficiency, which in turn likely affects erythropoiesis; (iii) IRS2 expression plays a role in erythroid cell differentiation through binding to cellular receptors involved in normal haematopoiesis; (iv) ARHGAP9 regulates adhesion of haematopoietic cells to the extracellular matrix, which can influence their localisation and differentiation potential, and R3HDM2 has been mapped to haemoglobin and red blood cell traits in large-scale GWASs; (v) CPS1 is directly related to glycine, which is an essential requirement for haem synthesis; (vi) SLC22A5 is involved in the active cellular uptake of carnitine, which stimulates erythropoiesis; (vii) SOX7 blocks differentiation of hematopoietic progenitors to erythroid and myeloid lineages. In erythroblasts, TFR2 is a sensor of holotransferrin, and is thought to protect against excessive erythrocytosis in the presence of iron deficiency. ❽ Finally, the immune response to external pathogens, which compete for iron, may also influence overall iron availability. Among the genes identified, LVRN may play a role in the synthesis of defensins and defensin-like peptides such as hepcidin, potentially contributing to iron homoeostasis via immune response; (ii) MFSD6 recognises major histocompatibility complex type I (MHC-I) molecules and mediates MHC-I restricted killing by macrophages; (iii) MPO catalyses the production of hypohalous acids, primarily hypochlorous acid in physiologic situations, and other toxic intermediates that greatly enhance microbicidal activity. Images from Servier Medical Art (https://smart.servier.com), licensed under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) Licence. B This figure summarises the genes mentioned in Table 2 of this study, as well as other iron-homoeostasis genes provided for contextual information. Genes with an established role in transferrin receptor synthesis, recycling, or degradation are shown in red and italic; genes with a potential role are presented in dark grey and italic. Relevant references to other studies are included in Supplementary Data 8. ❶ TFRC codes for transferrin receptor 1, which is constitutively expressed in most cells, especially erythrocytes. TFR2 codes for transferrin receptor 2, linked to iron sensing and maintenance of body iron homoeostasis. PGS1 is involved in the synthesis of cardiolipin, a phospholipid of mitochondrial membranes implicated in the regulation of transferrin receptor expression. ❷ After O-linked glycosylation, possibly mediated by the protein product of GALNT6, transferrin receptor 1 is expressed on the external surface of the cytoplasmic membrane. ❸ HFE interactswith transferrin receptor 1, facilitating cellular iron-sensing function and playing an important part in the regulation of hepcidin expression in response to body iron status. ❹ Iron-loaded transferrin (holotranferrin) binds to the receptor and the complex is internalised through clathrin-mediated endocytosis. ❺ A proton pump acidifies the endosome, which causes release of iron from holotransferrin; iron-deprived transferrin (apotransferrin) remains bound to its receptor. ❻ The endosome is usually recycled to the plasma membrane, a process likely regulated by (i) LRBA, known to influence recycling of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) via the classical recycling pathway used by receptors such as transferrin and (ii) UBXN6, which negatively regulates the adenosine triphosphate (ATP) hydrolytic activity of valosin containing protein (VCP), an ATP-driven segregase; VCP depletion delays transferrin receptor recycling. ❼ At neutral pH, apotransferrin dissociates from transferrin receptor and is ready to bind to free iron. The transferrin receptor may also be ubiquitinated and directed to lysosomal degradation, which is mediated by MARCH8, a membrane-associated zinc-finger factor, and, possibly, also by RPS6KB1, a protein kinase involved in the mammalian target of rapamycin-protein S6 kinase (mTOR-S6K) pathway, which is implicated in the degradation of transferrin receptor 1. ❽ Finally, PCSK7 mediates the shedding of soluble transferrin receptor (sTfR) from the transferrin receptor. When iron availability is limited, sTfR levels increase at least in part by downregulating expression of PCSK7 or neighbouring genes. Images from Servier Medical Art (https://smart.servier.com), licensed under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) Licence.
Fig. 4
Fig. 4. Putative causal effects of genetically predicted iron-related loci: Bonferroni-significant and nominal associations (null findings not presented).
A Locus-based MR associations with disease outcomes in up to 1,469,361 deCODE, FinnGen, MVP, and UK Biobank participants. Only loci that are associated (P < 5.2 × 10−6) with at least one disease and have suggestive evidence of colocalization are shown. The terms in parenthesis indicate the trait that has been used for rescaling. B Locus-based MR associations with biomedical traits in up to 854,977 MVP and UK Biobank participants. Only loci that are associated with at least one disease outcome are shown. The terms in parenthesis indicate the trait that has been used for rescaling.
Fig. 5
Fig. 5. Putative causal effects of genetically predicted systemic iron status: Bonferroni-significant and nominal associations (null findings not presented).
A MR associations of systemic iron status with disease outcomes in up to 1,492,717 deCODE, FinnGen, MVP, and UK Biobank participants. The estimates are expressed in odds ratio per one standard deviation (SD) higher transferrin saturation (TSAT) with confidence intervals shown between brackets. The plot shows estimates with the pC282Y variant in HFE (left-hand Forest plot) and without that variant (right-hand Forest plot), presenting diseases that have MR point estimates with the same direction in all the biobanks included in the meta-analysis. The instrument was generated using six variants mapped to ERFE, HAMP, HFE, SLC25A37, TFR2 and TMPRSS6 not affected by horizontal pleiotropy, indirect vertical pleiotropy, or collider bias and that: (i) were associated (P < 5 × 10−8) with at least one trait; (ii) were nominally associated (P < 0.05) with all the other iron traits except for hepcidin (as its levels are influenced by systemic iron status); and (iii) displayed a direction of association consistent across all traits. B MR associations of systemic iron status, using the same instrument, with biomedical traits in up to 860,060 MVP and UK Biobank participants. The estimates are expressed in mean change (beta) per one SD higher TSAT. The plot shows estimates with the pC282Y variant in HFE (left-hand Forest plot) and without that variant (right-hand Forest plot), presenting traits that have MR point estimates with the same direction in all the biobanks included in the meta-analysis.
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