Ferritin heavy chain supports stability and function of the regulatory T cell lineage

Article Ferritin heavy chain supports stability and function of the regulatory T cell lineage Qian Wu 1,2,15, Ana Rita Carlos 1,3,15,FaouziBraza 1,15, Marie-Louise Bergman 1,JamilZKitoko 1, Patricia Bastos-Amador 1,EloyCuadrado 4, Rui Martins1, Bruna Sabino Oliveira 1,VeraCMartins 1, Brendon P Scicluna 5, Jonathan JM Landry 6,FerrisEJung 6, Temitope W Ademolue1, Mirko Peitzsch 7, Jose Almeida-Santos 1, Jessica Thompson1, Silvia Cardoso1, Pedro Ventura1, Manon Slot4, Stamatia Rontogianni4,VanessaRibeiro 3, Vital Da Silva Domingues 1, Inês A Cabral1, Sebastian Weis 8,9,10,MarcoGroth 11, Cristina Ameneiro 12, Miguel Fidalgo 12, Fudi Wang 13, Jocelyne Demengeot 1, Derk Amsen4,14 &MiguelPSoares 1✉ Abstract Regulatory T (TREG) cells develop via a program orchestrated by the transcription factor forkhead box protein P3 (FOXP3). Maintenance of the TREG cell lineage relies on sustained FOXP3 transcription via a mechanism involving demethylation of cytosine-phosphate-guanine (CpG)-rich elements at conserved non-coding sequences (CNS) in the FOXP3 locus. This cytosine demethylation is catalyzed by the ten–eleven translocation (TET) family of dioxygenases, and it involves a redox reaction that uses iron (Fe) as an essential cofactor. Here, we establish that human and mouse TREG cells express Fe-regulatory genes, including that encoding ferritin heavy chain (FTH), at relatively high levels compared to conventional T helper cells. We show that FTH expression in TREG cells is essential for immune homeostasis. Mechanistically, FTH supports TET-catalyzed demethylation of CpG- rich sequences CNS1 and 2 in the FOXP3 locus, thereby promoting FOXP3 transcription and TREG cell stability. This process, which is essential for TREG lineage stability and function, limits the severity of autoimmune neuroinflammation and infectious diseases, and favors tumor progression. These findings suggest that the regulation of intracellular iron by FTH is a stable property of TREG cells that supports immune homeostasis and limits the pathological outcomes of immune-mediated inflammation. Keywords Regulatory T Cells; FOXP3; Iron Metabolism; Ferritin Heavy Chain; Ten–eleven Translocation Enzymes Subject Categories Cancer; Chromatin, Transcription & Genomics; Immunology https://doi.org/10.1038/s44318-024-00064-x Received 26 April 2023; Revised 15 February 2024; Accepted 20 February 2024 Published online: 18 March 2024 Introduction Identified and characterized (Powrie and Mason, 1990;Sakaguchi et al, 1982) originally on the basis of their critical involvement in maintaining peripheral immune tolerance (Coutinho et al, 1993), regulatory T (T REG ) cells partake in different aspects of immune homeostasis (Campbell and Rudensky, 2020; Dikiy and Rudensky, 2023;Josefowiczetal,2012; Panduro et al, 2016). One of the main functions of T REG cells, however, is most likely to restrain the breath of innate and adaptive immune responses against commensal microbes to prevent immunopathology (Belkaid, 2007;Demengeot et al, 2006). This evolutionarily conserved trait was probably coopted through evolution to prevent peripheral self-reactive T and B cells from eliciting autoimmune diseases (Lafaille et al, 1994; Sakaguchi et al, 1995). As an evolutionary trade-off (Stearns and Medzhitov, 2015), T REG cells are pathogenic, for example, when limiting immune-mediated inflammatory responses to pathogens to promote chronic infections (Belkaid, 2007; Demengeot et al, 2006) or when restraining anti-tumor immunity, to promote cancer progression (Curiel et al, 2004; Liu et al, 2016). T REG cell development and function are controlled by the X- chromosome-encoded transcription factor FOXP3 (Fontenot et al, 2003;Horietal,2003), together with auxiliary transcriptional 1Instituto Gulbenkian de Ciência, Oeiras, Portugal. 2International Institutes of Medicine, the Fourth Affiliated Hospital of Zhejiang University, School of Medicine, Yiwu, Zhejiang, China. 3Departamento de Biologia Animal, Centro de Ecologia, Evolução e Alterações Ambientais, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. 4Department of Hematopoiesis and Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, Amsterdam, The Netherlands. 5Department of Applied Biomedical Science, Faculty of Health Sciences, Mater Dei Hospital, and Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta. 6Genomic Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany. 7Institute for Clinical Chemistry and Laboratory Medicine, University Clinic Carl Gustav Carus, TU Dresden, Dresden, Germany. 8Department for Anesthesiology and Intensive Care Medicine, Jena University Hospital, Friedrich-Schiller University, Jena, Germany. 9Institute for Infectious Disease and Infection Control, Jena University Hospital, Friedrich-Schiller University, Jena, Germany. 10Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll Institute-HKI, Jena, Germany. 11Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany. 12Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela-Health Research Institute (IDIS), Santiago de Compostela, Spain. 13The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China. 14Department of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. 15These authors contributed equally: Qian Wu, Ana Rita Carlos, Faouzi Braza. ✉E-mail: [email protected] 1234567890();,: © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1445 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. regulators (Kanamori et al, 2016). The transcriptional program enforced by FOXP3 specifies T REG cell lineage commitment in the thymus and in the periphery (Fontenot et al, 2003;Horietal,2003; Lee et al, 2012), generating thymic T REG (tT REG ) cells and peripherally derived T REG (pT REG ) cells, respectively (Chen et al, 2003). Sustained FOXP3 transcription maintains T REG cell lineage stability (Williams and Rudensky, 2007), avoiding transdifferentiation towards pro-inflammatory T helper (T H ) cells (Gavin et al, 2007;Morikawaetal,2014). FOXP3 transcription is regulated by different signal transduction pathways, emanating from the T-cell receptor (TCR), interleukin (IL-2) receptor, and TGF-βreceptor (Bennett et al, 2001; Brunkow et al, 2001; Hori and Sakaguchi, 2004), among others. Sustained FOXP3 transcription is enforced epigenetically (Gavin et al, 2007; Morikawa et al, 2014), in response to environmental cues (Chapman et al, 2020; Shi and Chi, 2019)that regulate different aspects of T REG cell metabolism (Etchegaray and Mostoslavsky, 2016). These epigenetic modifications include the relative methylation status of cytosine-phosphate-guanine (CpG)- rich sequences in the FOXP3 conserved non-coding sequences (CNS) 1, 2, and 3 (Ohkura et al, 2012; Zheng et al, 2010), whereby cytosine methylation represses while demethylation sustains FOXP3 transcription (Ohkura et al, 2012; Zheng et al, 2010). Cytosine methylation is catalyzed by DNA methyltransferase (DNMT) (Ohkura et al, 2012), while demethylation is catalyzed by the ten–eleven translocation (TET) family of dioxygenases (Wu and Zhang, 2017;Yueetal,2016). Cytosine demethylation consists on redox-based reactions that oxidize 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC) and 5-carboxylcytosine (5caC) (Kohli and Zhang, 2013). TET dioxygenases catalyze cytosine demethylation at FOXP3 CNS1 and 2 (Ohkura et al, 2012), supporting T REG cell lineage stability (Nakatsukasa et al, 2019;WuandZhang,2017;Yueetal,2019; Yue et al, 2016), and preventing T REG cell transdifferentiating into inflammatory effector T H cells, also referred as ex-T REG cells (Duarte et al, 2009; Komatsu et al, 2009;Zhouetal,2009). TET dioxygenases use Fe as an essential cofactor and the intermediate metabolite α-ketoglutarate as an obligatory substrate (Huang and Rao, 2014;Pastoretal,2013). This TET reliance on Fe availability entertained the hypothesis that regulation of cellular Fe metabolism acts upstream of TET dioxygenases to modulate T REG cell lineage stability. Several studies have shown that Fe metabolism impacts on immunity. For example, intracellular Fe availability and redox activity is essential to support B and T-cell development (Vanoaica et al, 2014), via a cytoprotective mechanism exerted by the ferritin H chain (FTH) (Berberat et al, 2003;Phametal,2004), likely involving the mitochondria (Blankenhaus et al, 2019; Vanoaica et al, 2014). Regulation of cellular Fe content and redox activity also modulate cytokine production by effector T H cells, via a mechanism involving the PolyC-RNA-Binding Protein 1 (PCBP1) (Wang et al, 2018). Cellular Fe import, via the transferrin receptor 1 (TFR1/ CD71), supports T H type 1 (T H 1) cell immunity and its regulation by induced T REG (iT REG ) cells (Voss et al, 2023) as well as antibody responses to vaccination (Frost et al, 2021; Jiang et al, 2019)and immunity against infection by pathogens such as Plasmodium,the causative agent of malaria (Wideman et al, 2023). Here, we demonstrate that regulation of Fe metabolism by FTH operates upstream of TET dioxygenases to enforce cytosine demethylation at CpG-rich sequences in the CNS1 and 2 of the FOXP3 locus, sustaining FOXP3 transcription, expression and T REG cell lineage identity. This cell-intrinsic property of T REG cells is essential to maintain immune homeostasis while exerting a major impact on the outcome of immune-driven inflammation. Results T REG cells express relatively high levels of FTH In a previously unbiased proteomics analysis, we found that freshly isolated human naive CD45RA+CD25hi T REG (nT REG ) and memory CD45RA-CD25hi T REG (mT REG ) cells expressed relatively higher levels of Fe-regulatory proteins, including FTH and ferritin L chain (FTL), when compared to CD45RA+CD25-naive conventional (nTconv) cells or to CD45RA-CD25-activated/memory (mTconv) cells (Cuadrado et al, 2018). The relatively higher expression of the FTH and FTL components of the ferritin complex was maintained upon expansion of human CD4+CD127–CD25+T REG cells in vitro, in comparison to T CONV cells CD4+CD127+CD25–cells (Fig. 1A,B). Similarly, mouse CD4+Foxp3+T REG cells also expressed relatively higher levels of FTH protein, compared to CD4+Foxp3− CD44lowCD62Lhigh naive T H cells (T N )orCD4 +Foxp3− CD44highCD62Llow memory T H cells (T M ), as determined by western blot (Fig. 1C,D). This suggests that sustained and elevated levels of ferritin expression are a stable property of human and mouse T REG cells. Of note, mouse T REG cells express similar levels of Fth mRNA, compared to T N cells, while (CD4±Foxp3− CD44highCD62L− )T M cells express relatively higher levels of Fth mRNA, compared to T REG cells (Appendix Fig. S1). This suggests that the relatively higher level of FTH protein expression in T REG cells is enforced post-transcriptionally, similar to other cell types (Hentze et al, 1987; Meyron-Holtz et al, 2004; Muckenthaler et al, 2017; Rouault et al, 1988). We monitored FTH expression in induced T REG (iT REG ) cells, generated from mouse T N cells activated in vitro with anti-CD3/ CD28 mAb plus IL-2 and TGFβ(Chen et al, 2003) (Fig. 1E). To this aim we used Foxp3GFP T REG cell reporter mice, in which a green fluorescent protein (GFP) humanized Cre-recombinase (GFP- hCre) coding sequence is inserted downstream of the Foxp3 ATG translational start codon in a bacterial artificial chromosome (BAC) transgene carrying the intact Foxp3 promoter (Foxp3-GFP-hCre; referred herein as Foxp3GFP)(Chenetal,2003;Zhouetal,2008). FTH protein expression was higher under culture conditions containing TGFβand enriched in CD4+GFP+iT REG cells, compared to culture conditions lacking TGFβand enriched in CD4+GFP- T CONV cells (Fig. 1F). A similar trend was observed for Fth mRNA, which was upregulated in iT REG cells (Fig. 1G). The relative level of Fth mRNA expression was similar in thymic, peripheral and iT REG cells (Appendix Fig. S2). FTH expression in T REG cellsisessentialtomaintain immune homeostasis To determine the effect of regulation of intracellular Fe metabolism by FTH on T REG cells, we introduced an additional loxP-flanked Fth allele (Fthfl/fl) (Darshan et al, 2009)intoFoxp3GFP mice (Zhou et al, 2008), deleting Fth specifically in T REG cells from Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. EV1A). Fth deletion was associated with The EMBO Journal Qian Wu et al 1446 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. B TCONV TREG FTH E-Actin 0 0.02 0.04 0.06 0.08 0.1 FTH/ E-Actin A** 21 TCONV TREG IJ 42 H KDa KDa CD4 CD4 Foxp3GFP-Fth∆/∆ 11010 2103104105 101 102 103 104 105 106 12,4% 11010 2103104105 101 102 103 104 105 106 11010 2103104105 5,71% 11010 2103104105 2,65%13,3% 0 5 10 15 0 1 2 3 4 *** *** *** 11010 2103104105 101 102 103 104 105 106 11010 210310410 C Fth/H3 Fth H3 TNTREG TM D 21 17 KDa KDa TNTREGTM Spleen MLN TREG cell (CD4+GFP+ ) Thymus TREG cell (CD4+GFP+ ) TREG cell (CD4+GFP+ ) EF CD4 DCD3/28 IL-2 DCD3/28 IL-2 + TGFE TN T CONV G iT REG Fth H3 21 15 1,15% 105 104 103 0 -103 105 104 103 0 -103 105 104 103 0 79,4% DCD3/28 +IL-2 DCD3/28 +IL-2/TGFE0 0.2 0.4 0.6 0.8 Fth/H3 Protein ** TCONV iT REG 0 0.2 0.3 0.1 Fth/Arbp0 mRNA ** TCONV iT REG KDa TCONV iT REG Foxp3GFP Foxp3GFP-Fth∆/∆ Foxp3GFP Foxp3GFP-Fth∆/∆ Foxp3GFP 0 1 2 3 0 1 2 3 K L MLN Foxp3-GFP CD4 Foxp3GFP-Fth∆/∆ Foxp3GFP Foxp3-GFP Nrp1 Nrp1 + Nrp1 - Nbr. Foxp3-GFP+ Cells (x105) Nrp1 + Nrp1 - Foxp3-GFP+ (%) * * 0 1 2 3 2,15% 1,14% *** NS Foxp3-GFP+ (%) Foxp3-GFP+ (%) Nbr. Foxp3-GFP+ Cells (x105) Nbr. Foxp3-GFP+ Cells (x106) Foxp3-GFP+ (%) Nbr. Foxp3-GFP+ Cells (x105) Nrp1+ ( tTREG ) Nrp1- ( pTREG ) CD4+GFP+ cells 010 3104105010 3104105 0 20 40 60 80 100 0 1 2 3 4 ** *** *** 82% 72,6% 0 103 104 Thymus Spleen & MLN Events (%) Foxp3GFP-Fth∆/∆ Foxp3GFP *** *** *** 103 010 4 Foxp3-GFP Thymus Spleen MLN 0 1 2 3 4 Foxp3-GFP MFI (103) Thymus Spleen MLN TREG cell CD4+GFP+ Foxp3-GFP Foxp3-GFP Foxp3-GFP *** 0.5 2 1.5 1 00 5 10 15 20 Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1447 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. the accumulation of intracellular labile Fe2+in T REG cells, isolated from the mesenteric lymph nodes (MLN) of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. EV1B). The frequency of thymic CD4+GFP+T REG cells was lower in Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. 1H). This was not associated, however, with a concomitant reduction in the number of CD4+GFP+T REG cells (Fig. 1H). In contrast, there was a marked reduction of both the frequency and numbers of T REG cells in the spleen (Fig. 1I) and in the MLN (Fig. 1J) of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice. The frequency and number of CD4+GFP+CXCR5+PD1+ follicular T REG (FT REG ) cells was also decreased in the spleen of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. EV1C). These observations suggest that regulation of intracellular Fe by FTH is required to sustain the number of circulating T REG and FT REG cells in the periphery, without interfering with thymic T REG cell output. We noticed that the relative level of GFP expression, reporting on Foxp3 transcription under the control of an intact Foxp3 locus (Chen et al, 2003;Zhouetal,2008), was reduced in thymic, splenic and MLN T REG cells from Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. 1K). These observations suggest that FTH regulates Foxp3 transcription in the thymus as well as in circulating T REG cells, which is not sufficient however, to interfere with thymic T REG cell output. Thymic and peripheral T REG cell development give rise to tT REG and iT REG cells, expressing neuropilin1 (Nrp1) or not, respectively (Weiss et al, 2012; Yadav et al, 2012). The frequency and number of Nrp1+tT REG cells and Nrp1-iT REG cells was reduced, to the same extent, as assessed in the MLN (Fig. 1L) of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice. This suggests that regulation of Fe metabolism by FTH is required for the maintenance of thymic-derived and peripherally induced T REG cells. FTH restrains T REG cell transdifferentiation into inflammatory ex-T REG cells The reduction in T REG cells imposed by Fth deletion was associated with an accumulation of activated CD4+CD44highCD62Llow T CONV cells and CD8+CD44highCD62Llow cytotoxic T (T C )cells,inthe spleen (Fig. EV1D) and in MLN of Foxp3GFP-FthΔ/Δvs. control Fthfl/fl mice (Fig. EV1E). Concomitantly, there was an increase in the frequency of interferon-γ(IFN γ)-expressing activated CD4+T H cells and CD8+T C cells in the spleen (Fig. EV1F) and MLN (Fig. EV1G). These observations suggest that regulation of Fe metabolism in T REG cells is essential to maintain immune homeostasis, preventing the activation and accumulation of inflammatory CD4+T H cells and CD8+T cells. Secreted ferritin complexes can restrain human T-cell proliferation in vitro (Gray et al, 2001), entertaining the hypothesis that ferritin secretion supports the antiproliferative function of T REG cells. However, T REG cells from Foxp3GFP-FthΔ/Δmice inhibited T-cell proliferation in vitro, to a similar extent as T REG cells from Foxp3GFP (Fig. EV2A). This suggests that FTH is not essential to support the antiproliferative function of T REG cells, consistent with Foxp3GFP-FthΔ/Δ mice not developing overt autoimmune pathologic lesions, compared to control Fthfl/flmice (Fig. EV2B). To gain further insight into the mechanism via which FTH modulates T REG cell function in vivo, we performed RNA sequencing (RNAseq), to compare the gene expression profiles of CD4+CD25+GFP+T REG cells sorted from the lymph nodes of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. 2A). T REG cells from Foxp3GFP-FthΔ/Δmice upregulated 1832 genes and downregulated 1340 genes, compared to T REG cells from control Foxp3GFP mice (Fig. 2B). Fth deletion was associated with dysregulation of Foxp3- dependent and -independent “T REG transcriptional signature”(Hill et al, 2007), affecting at least 194 genes involved in T REG cell function and lineage maintenance (Fig. 2C). Pathway enrichment analysis (Fig. 2D,E) showed that T REG cells from Foxp3GFP-FthΔ/Δmice presented T H 1andtype2(T H 2) transcriptional signatures (Fig. 2D), as illustrated by the induction of the transcriptional master regulators of T H 1andT H 2 effector functions, Tbx21 (T-box transcription factor; T-bet) and Gata3 (GATA Binding Protein 3) as well as Runx3 (RUNX Family Transcription Factor 3), respectively (Fig. EV2C). Consistently, the percentage of activated CD4+GFP+ CD44highCD62Llow T REG cellswashigherinthespleenandMLN Figure 1. T REG cell ferritin expression is a stable property of human and mouse T REG cells. (A) FTH and β-Actin protein expression, detected by western blot in whole-cell extracts from human T conventional (CD4+CD45RA+CD127+CD25–;T CONV ) and T REG (CD4+CD127–CD45RA+CD25hi) cells after two weeks of expansion with anti-CD3, anti-CD28 mAb and IL-2. (B) Relative quantification of FTH protein expression, normalized to β-Actin, detected by western blot as in (A). n=3 independent experiments. (C) FTH and histone H3 protein expression detected by western blot in wholecell extracts from sorted mouse naive T cells (CD4+Foxp3-CD44lowCD62Lhigh;T N ), (CD4+Foxp3+GFP+)T REG cells and memory T cells (CD4+Foxp3−CD44highCD62Llow;T M ), by western blot. (D) Relative quantification of FTH, normalized to histone H3, protein expression, detected by western blot as in (C). Data were normalized to FTH expression in T N cells, pooled from four independent experiments. (E) Schematic representation of the protocol used for the generation of iT REG and representative flow cytometry dot plots of mouse iT REG generated from sorted naive T cells (T N ), stimulated with anti-CD3 and anti-CD28 mAb plus IL-2 and TGFβfor 5 days. Control T CONV cells were subjected to the same experimental conditions, without TGFβ.(F) FTH and histone H3 protein expression, detected by western blot in whole-cell extracts from iT REG and T CONV generated as depicted in (E). Data pooled from three independent experiments with similar trend. (G) The relative level of Fth mRNA expression, quantified by qRT-PCR, using Arbp0 as housekeeping gene. Data pooled from three independent experiments, with similar trend. (H–J) Schematic representation of the protocol used (top panels), representative flow cytometry dot plots (middle panels) and corresponding quantification of percentage (%) in CD4+cells and cell number (Nbr.) (bottom panels) of live (TCRβ+CD4+Foxp3+) GFP+T REG cells in (H) thymus, (I) spleen and (J) mesenteric LN (MLN). (H) Data from N=8 mice per genotype, per organ, from two independent experiments, with similar trend. (I,J) Data from N=12 mice per genotype, per organ, from three independent experiments, with similar trend. (K) Schematic representation of the experimental approach (top panel) used to monitor the expression of the GFP-hCre transgene in the thymus, spleen, and MLN of (CD4+GFP+)T REG cells. Representative flow cytometry histograms of GFP-hCre (bottom left panel). Relative quantification of GFP-hCre expression (bottom right panel), represented as mean fluorescence intensity (MFI). Data from N=8 mice per genotype, pooled from two independent experiments with similar trend. (L) Schematic representation of the experimental approach (left panel) used, representative flow cytometry dot plots (top panel) and corresponding quantification of percentage (%) (bottom left panel) and cell number (Nbr.) (bottom right panel) of live (TCRβ+CD4+GFP+) Nrp1+and Nrp1-T REG cells in MLN. Data from N=8 mice per genotype, pooled from two independent experiments with similar trend. Data information: Data in (B,F,G) are presented as mean ± SD. Data in (D) are presented as mean ± SEM. Circles in (H–L) correspond to individual mice. Pvalues in (B,F–J) determined using unpaired ttest with Welch’s correction, in (D,H–J) using ordinary one-way ANOVA, and in (K,L) using Two-way ANOVA with Sidak’s multiple comparisons test. NS not significant (P> 0.05); *P< 0.05; **P< 0.01; ***P< 0.001. Source data are available online for this figure. The EMBO Journal Qian Wu et al 1448 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. eIF4/p70S6 K signaling mTOR signaling EIF2 signaling AB 0 2 4 6 8 10 C F T GER erutangislanoitpircsnartllec Fold Expression (log 2 ) -2 -1 0 1 2 0246 AHR signaling Interferon signaling Acute phase response ER stress pathway BRCA1 in DNA damage response DNA damage (14-3-3-σ signaling) Inflammasome pathway CHK in cell cycle checkpoint control G1/S checkpoint regulation T H cell differentiation LXR/RXR activation Cyclins and cell cycle regulation GADD45 signaling T H 2 pathway NRF2-oxidative stress response Cell cycle control of chrom. replic. T H 1 pathway Estrogen-mediated S-phase entry G2/M DNA damage checkpoint T H 1 and T H 2 activation 35 1 Akt1 Nqo1 Dnajb11 Hmox1 Sod2 Mgst1 Sqstm1 Nfe2l2 Mafk Map2k3 Txnrd1 Dnajc15 Sod1 Abcc1 Fkbp5 Bach1 Ube2e3 Cat Pmf1 Gclm Txn1 Vcp Prdx1 Pik3c2a Gsr Gclc Abcc4 Gstt2 Hacd3 Enc1 Maff Ftl1 Mafg Maf Rras2 Actg2 Aox1 Map3k5 Oxidative stress Foxp3 GFP Foxp3 GFP-Fth'' −1 0 1 2 PF G -3pxo F CD25 de tro S 105 104 103 102 0 -102 105 104 103 0-103 laitinI Fth1 Mirt1 Top2a Tigit Hmox1 Anxa2 Myo1f Myo6 Hist1h1b Prune2 Mt1 Slc25a23 Stra6 Cdc42bpa Hdac9 Nqo1 Ryr2 Cyp26b1 Tmem136 Cacna1i Mt2 Gm13394 Mest Clec2g Pianp Pls1 Pnlip 0 50 100 150 200 250 −8 −4 0048 Adjusted p-value (-log10) Base mean expression 250000 500000 750000 Log2 fold change Foxp3GFP-FthΔ/Δ vs. Foxp3GFP Foxp3 GFP Foxp3 GFP-Fth'' 105 104 103 102 0 -102 Foxp3GFP Foxp3GFP-Fth'' Sorting Lymph nodes CD25+ Foxp3+(GFP+) RNAseq D P-value (-log 10) P-value (-log 10) E 8,12% 89,9% Fold Expression (log2) Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1449 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. from Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. EV2D). This was associated with an increase in the frequency of CD4+GFP+T REG cells expressing IFNγin the spleen (Fig. EV2E). This suggests that FTH is essential to prevent the transdifferentiation of T REG cells into inflammatory T H cells. RNAseq analysis also revealed that Fth deletion in T REG cells led to the induction of the canonical oxidative stress response controlled by the transcription factor nuclear factor erythroidderived 2-like 2 (NRF2) (Fig. 2D). This was associated with the activation of other canonical stress responses, including the cell cycle and DNA damage, unfolded protein, and hypoxic response (Fig. 2D) as well as an overall shutdown of eIF2, mTOR and eIF4/ p70s6K signaling transduction pathways (Fig. 2E). Activation of the oxidative stress response regulated by NRF2 was characterized by the induction of Nqo1 and Hmox1,amongseveralotherNRF2- regulated genes (Fig. 2F). These observations suggest that FTH is essential to support a transcriptional profile that maintains T REG cell redox homeostasis, similar to described in other cell types (Blankenhaus et al, 2019;Vanoaicaetal,2014). FTH supports T REG cell lineage maintenance To establish whether FTH enforces T REG cell lineage maintenance and prevents the transdifferentiation of T REG cells into inflammatory T H cells, an additional Rosa26-tandem dimer (td) Tomato-Flox-stop-Flox allele was introduced into Foxp3GFP-FthΔ/Δmice, driving the expression of tdTomato (tdT) by Cre-driven excision of a Flox-stop-Flox cassette, under the control of Foxp3 regulatory regions (Fig. 3A). The resulting Foxp3GFP-FthΔ/Δ-tdT mice allow distinguishing CD4+GFP+tdT+T REG from CD4+GFP-tdT+ex-T REG cells that at some point in their developmental history downregulated Foxp3 (i.e., GFP-), while retaining TdT expression (Fig. 3A). Fth deletion in T REG cells was associated with a progressive reduction in the frequency of circulating T REG cells from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice, as assessed from 2 to 24 weeks after birth (Figs. 3B and EV3A). Concomitantly, there was an increase in the frequency of circulating ex-T REG cells (Figs. 3C and EV3A), with 60% of circulating T REG cells becoming ex-T REG cells in Foxp3GFP-FthΔ/Δ-tdT, 24 weeks after birth (Figs. 3D and EV3A). This relative enrichment in the proportion of ex-T REG cells suggests that Fth deletion promotes the conversion of T REG cells into ex-T REG cells. We reasoned that if FTH prevents T REG cells from transdifferentiating into ex-T REG cells, then Fth deletion in T REG cells should be associated with an accumulation of ex-T REG cells in the spleen and/or lymph nodes. As expected, the percentage and number of T REG cells were reduced in the spleen from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice, as assessed at 19–30 weeks after birth (Fig. 3E). The percentage and number of splenic ex-T REG cells remained relatively stable (Fig. 3F), but the ratio of ex-T REG over tdT+cells was higher in the spleen from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice (Fig. 3G), with over 80% of T REG cells becoming ex-T REG cells (Fig. 3G). Fth was deleted in ex-T REG cells from Foxp3GFP-FthΔ/Δ-tdT mice, as determined by qRT-PCR (Fig. EV3B), confirming that the ex-T REG cells in Foxp3GFP-FthΔ/Δ-tdT mice do originate from T REG cells, in which the Fth allele was deleted under the control of the Foxp3 promoter. We then asked whether the transdifferentiation of T REG cells into ex-T REG cells was associated with the expression of proinflammatory cytokine, which is a feature of T H cells activation. In strong support of this hypothesis, a large percentage of T REG and ex-T REG cells in the LN and to a lesser extent in the spleen from Foxp3GFP-FthΔ/Δ-tdT mice expressed IFNγ,ascomparedtothelackof IFNγexpression in T REG and ex-T REG cells from control Foxp3GFP-tdT mice (Figs. 3H and EV3C). Moreover, a significant proportion of T REG and ex-T REG cells in the LN (Fig. 3I) and spleen (Fig. EV3D) from Foxp3GFP-FthΔ/Δ-tdT mice co-expressed the proliferation markers Ki67 and CD71 (i.e., transferrin receptor), as compared to T REG and ex-T REG cells from control Foxp3GFP-tdT mice (Figs. 3IandEV3D). This was not associated, however, with changes in the relative levels of CD71 expression (Fig. EV3E). Taken together these observations suggest that FTH is essential to restrain T REG cells from transdifferentiating into inflammatory ex-T REG cells. FTH supports T REG lineage maintenance in a cell-autonomous manner To disentangle cell-autonomous from systemic effects associated with Fth deletion in T REG cells we used mixed bone marrow (BM) chimeric mice. Briefly, sub lethally irradiated lymphogenic Rag2- deficient (Rag2-/-) mice were reconstituted with BM cells from CD45.2+Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice (50%), plus congenic BM cells (50%) from CD45.1+C57BL/6 mice (Fig. 4A). The proportion of CD45.2+vs. CD45.1+T REG cells in LN (Fig. 4A–C) was markedly reduced in BM chimeric mice, when reconstituted with BM cells from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice. In contrast, there were no differences in the relative proportion of CD45.2+vs. CD45.1+T REG cells in the thymus of these BM chimeric mice (Fig. EV3F,G). This suggests that FTH sustains the number of circulating T REG cells via a cell-autonomous mechanism that does notaffectT REG cell development in the thymus. The proportion of CD45.2+vs. CD45.1+Foxp3-CD3+CD4+T H cells and CD3+CD8+T C cells was indistinguishable in the LN (Fig. 4A–C) of mixed BM chimeras reconstituted with CD45.2+ BM cells from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice. Moreover, the percentage and number of activated CD45.2+CD4+ CD44highCD62Llow T CONV cells and CD45.2+CD8+CD44highCD62Llow T C cells in the LN (Fig. EV4A–C) were also similar in these BM chimeric mice. This confirms that FTH sustains the number of Figure 2. FTH expression in T REG cells prevents transdifferentiation into inflammatory ex-T REG cells. (A) Schematic representation of experimental approach (left panel) and representative flow cytometry dot plots of (CD4+CD25+GFP+)T REG cells from the lymph nodes before and after sorting. (B) Volcano plot representation of RNA sequencing data of genes overexpressed (red) or under-expressed (blue) in (CD4+CD25+GFP+)T REG cells sorted from Foxp3GFP-FthΔ/Δand control Foxp3GFP (N=5 per genotype) mice. Pvalues determined by Benjamini and Hochberg adjusted probabilities. (C) Heatmap representation of T REG transcriptional signature genes differentially expressed in T REG cells sorted from Foxp3GFP-FthΔ/Δvs. Foxp3GFP mice, as illustrated in (A,B). (D,E) Pathway enrichment analysis of genetic programs overexpressed (D) or under-expressed (E)inT REG cells sorted from Foxp3GFP-FthΔ/Δvs. Foxp3GFP mice, as illustrated in (A,B). Data were analyzed using g:SCS multiple testing correction method with a significance threshold of 0.05. (F) Heatmap representation of individual genes associated with oxidative stress-responsive programs, differentially expressed in T REG cells sorted from Foxp3GFP-FthΔ/Δvs. Foxp3GFP mice, as illustrated in (A,B). Source data are available online for this figure. The EMBO Journal Qian Wu et al 1450 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. B 0 20 40 60 80 2 4 7 9 11151924 Time [Weeks] 0 2 4 6 8 10 2 4 7 9 11151924 Time [Weeks] 0 1 2 3 4 5 247911151924 Time [Weeks] C **** **** H D GFP+TdT+ TREG Cells (%) GFP-TdT+ ex-TREG Cells (%) GFP-TdT+/TdT+ Cells (%) * E % (GFP +TdT+) TREG cells % GFP -TdT + ex-T REG cells Nbr. GFP+TdT+ TREG cells (x105) F Nbr. GFP-TdT+ ex-T REG cells (x10 6 ) G % GFP -TdT +/ TdT+ cells 0 2 4 6 0 2 4 6 0 5 10 15 20 25 0 0.5 1 15 2 2.5 ** ** NS NS 0 20 40 60 80 100 IFNJ Events (%) 100 0 20 40 60 80 LN 100 0 20 40 60 80 Spleen 103104105 102103104105 102 LN Spleen 01041050104105 10³ 10⁴ 10⁵ 106 0 107 10³ 10⁴ 10⁵ 106 0 107 CD71 Ki67 7 28% 14,3% 23,5% 16,1% TREG ex-TREG (%) Ki67+CD71+ ex-TREG 0 10 20 30 40 50 LN *** *** I TREG ex-TREG FMO A TREG cells (GFP+tdT+) ex-TREGcells (GFP-tdT+) Gfp hCre Stop LoxP LoxP tdT Rosa26 locus Foxp3 Promoter Fth locus LoxPLoxP Fth Promoter E1 BAC LN, Spleen IFN J+ TREG (GFP+tdT+) Ex-TREG (GFP-tdT+) LN Ki67 CD71 TREG (GFP+tdT+) Ex-TREG (GFP-tdT+) Foxp3GFP-Fth''-tdT Foxp3GFP-tdT TREG cells (GFP+tdT+) TREG cells (GFP-tdT+) Blood Blood Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT TREG cells (GFP+tdT+) TREG cells (GFP-tdT+) Spleen Spleen Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT Foxp3GFP-Fth''-tdT Foxp3GFP-tdT **** 0 20 40 60 **** **** ** * IFNJ+(%) TREG ex-TREG TREG ex-TREG TREG Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1451 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. circulating T REG cells, via a cell-autonomous mechanism that acts irrespectively of the systemic inflammatory response associated with Fth deletion in T REG cells from Foxp3GFP-FthΔ/Δmice (Fig. EV1D–G). We then asked whether FTH restrains the transdifferentiation of T REG cells towards ex-T REG cells via a cell-autonomous mechanism. In support of this notion, there was a marked reduction in the percentage and number of CD45.2+CD3+CD4+GFP+tdT+T REG cells (Fig. 4D,E) and CD45.2+CD3+CD4+GFP-tdT+ex-T REG cells (Fig. 4D,F) in the LN of mixed BM chimeric mice reconstituted with BM cells from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice. The ratio of CD45.2+ex-T REG cells over tdT+cells was increased in the LN from chimeric mice reconstituted with BM cells from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice (Fig. 4G). This suggests that FTH maintains peripheral T REG cell lineage stability, via a cell-autonomous mechanism, irrespectively of the systemic inflammatory response associated with Fth deletion in T REG cells from Foxp3GFP-FthΔ/Δmice (Fig. EV1D–G). FTH maintains T REG cell redox homeostasis via a cell-autonomous mechanism We then asked whether FTH regulates gene expression in T REG cells, irrespective of the systemic inflammatory response associated with Fth deletion in T REG cells from Foxp3GFP-FthΔ/Δmice (Fig. EV1D–G). To test this hypothesis, the gene expression profile of T REG cells sorted from the LN of mixed BM chimeras (Fig. 4H) was compared to that of T REG cells sorted from the LN of non-chimeric mice (Fig. 2A). Analysis of RNAseq data from BM chimeric mice showed that Fth-deleted T REG cells (CD45.2+GFP+tdT+) developing from the BM of Foxp3GFP-FthΔ/Δ-tdT mice, upregulated 149 genes and downregulated 96 genes, compared to Fth-competent T REG cells from control Foxp3GFP-tdT mice (Fig. 4I). In the same BM chimeric mice, Fth-deleted ex-T REG cells (CD45.2+GFP-tdT+) developing from the BM of Foxp3GFP-FthΔ/Δ-tdT mice upregulated 90 genes and downregulated 36 genes, compared to Fth-competent ex-T REG cells from control Foxp3GFP-tdT mice (Fig. 4J). The genes regulated in T REG and ex-T REG cells originating from the BM of Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice in BM chimeric mice, were associated with the oxidative stress response regulated by NRF2 (Fig. 4I,J). This suggests that FTH exerts cell-autonomous control of T REG cell redox homeostasis, irrespective of the systemic inflammatory response associated with Fth deletion in T REG cells from Foxp3GFP-FthΔ/Δmice (Fig. EV1D–G). The inflammatory transcriptional signature of T REG cells from Foxp3GFP-FthΔ/Δvs. Foxp3GFP mice (Fig. 2B–D) was not observed in T REG cells from BM chimeric mice, originating from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT (Fig. 4I,J). Among the 245 differentially expressed genes in Fth-deficient (CD4+GFP+)T REG cells from BM chimeric mice (Fig. 4I,J), 134 matched those differentially expressed in Fth-deficient T REG cells from non-chimeric mice (Fig. 4K,L). Gene ontology analyzes of the overlapping genes, showed an enrichment for pathways related to oxidative stress response, comprising several NRF2-regulated genes (Fig. 4K–M). In contrast, Fth-deficient (CD4+GFP+)T REG cells from BM chimeric mice did not show an enrichment for pathways associated with T H cell activation (Fig. 4K–M). This suggests that FTH acts in a cell-autonomous manner to support T REG cell redox homeostasis and restrain T REG cell transdifferentiation into ex-T REG cells (Fig. 4). In contrast, the inflammatory profile associated with the transition of Fth-deleted T REG cells towards inflammatory ex-T REG cells, observed in Foxp3GFP-FthΔ/Δmice (Fig. 2)andFoxp3GFP-FthΔ/Δ-tdT mice (Fig. 2) requires, in addition, the development of systemic inflammation. FTH acts in a cell-autonomous manner to support T REG cell homeostatic expansion We took advantage of the homeostatic expansion of T REG cells, upon adoptive transfer into lymphopenic Rag2−/−mice (Duarte et al, 2009), to compare the proliferative capacity of CD4+GFP+tdT+T REG cells from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice. The number of CD4+GFP+tdT+T REG cells recovered from the LN was markedly reduced when Rag2−/−mice received T REG cells from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice (Fig. 5A–C). While there were no differences in the number of CD4+GFP-tdT+ex-T REG , the ratio of T REG over tdT+cells (GFP-tdT+/TdT +)werehigherinRag2−/− mice receiving T REG cells from Foxp3GFP-FthΔ/Δ-tdT vs. Foxp3GFP-tdT mice, albeit without statistical significance (Fig. 5A–C). Fth expression in CD4+GFP+tdT+T REG cells used in the adoptive transfer was confirmed by qRT-PCR (Fig. EV4D,E). We considered the possibility of an increase in the ratio of ex-T REG to T REG cells associated with Fth deletion in T REG cells reflecting, to some extent, a T REG cell survival defect. Therefore, we asked whether FTH acts in a cell-autonomous manner to support T REG viability and proliferation in vitro. The frequency and number of induced T REG (iT REG )cellsgeneratedfromT N cells activated in vitro with anti-CD3/ CD28 mAb plus IL-2 and TGFβ, were indistinguishable regardless of Figure 3. FTH enforces T REG cell lineage stability. (A) Schematic representation of Foxp3GFP-FthΔ/Δ-tdT mice used to monitor the transition of (CD4+GFP+tdT+)T REG cells into (CD4+GFP-tdT+)ex-T REG cells that repressed GFP expression while retaining the expression of a tdT transgene. (B–D) Percentage of circulating: (B)T REG and (C)ex-T REG cells in Foxp3GFP-FthΔ/Δ-tdT and control Foxp3GFP-tdT mice, and (D) Percentage of ex-T REG cells among CD4+tdT+cells, calculated as the ratio of CD4+GFP-tdT+/CD4+tdT+cells in the same mice as (B,C). Data from N=4–6 mice per genotype was pooled from three independent experiments with a similar trend. (E–G) Percentage and number of splenic (E)T REG cells, (F)ex-T REG cells and (G) relative percentage of ex-T REG cells over total CD4+tdT+cells. Data from N=4 mice per genotype, pooled from two independent experiments with similar trends. (H) Representative flow cytometry histograms of IFNγexpression by live activated (CD4+GFP+tdT+)T REG and (CD4+GFP-tdT+)ex-T REG cells in lymph nodes and spleen from Foxp3GFP-FthΔ/Δ-tdT and control Foxp3GFP-tdT mice (left panels) and corresponding quantification of the percentage of IFNγexpressing (CD4+GFP+tdT+)T REG and (CD4+GFP-tdT+)ex-T REG cells (right panels). Expression of IFNγwas induced upon Phorbol-12-myristate-13-acetate (PMA) and Ionomycin re-activation in vitro. Data from N=3 wells per genotype, in one experiment, representative of two independent experiments with similar trend. (I) Representative flow cytometry dot plots (left panels) and corresponding quantification (right panel) of the percentage of (CD4+GFP+tdT+)T REG and (CD4+GFP-tdT+)ex-T REG cells expressing Ki67 and CD71 in the lymph nodes Foxp3GFP-FthΔ/Δ-tdT and control Foxp3GFP-tdT mice. Data from N=6 mice per genotype, pooled from two independent experiments, with similar trend. Data information: Data in (B–D) represented as mean ± SD. Circles correspond to mean values. Data in (E–I) circles correspond to individual mice and red bars to mean values. (H,I) represented as mean ± SD. Pvalues in (B–D,H,I) calculated using Two-way ANOVA analysis with Sidak’s multiple comparisons test and in (E–G) with unpaired ttest with Welch’s correction. NS not significant (P> 0.05); *P< 0.05; **P< 0.01; ***P< 0.001; ****P< 0.0001. Source data are available online for this figure. The EMBO Journal Qian Wu et al 1452 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. 0 40 80 120 CD8 + CD4 + Foxp3 + NS CD45.2 CD45.1 0 10³ -10³ 0 0 10³ -10³ 0 0 35.1% 64,8% 45% 54,2% 44% 54,4% 32,2% 67,7% 35,3% 63,8% 86,9% 12,7% Chimerism (%) Foxp3GFP-Fth''-tdT Foxp3GFP-tdT A 0 1 2 3 4 5 ** *** ** ** % GFP+TdT+ Nbr.GFP-TdT+(x104) Nbr. GFP +TdT +(x104) ex-TREG cells % GFP-TdT+/TdT+ 10 10³ 10 0 10³ 10 10 -10³ -10³ 0 10 10³ 10-10³ 0 TdT GFP 2,77% 13,2% 79,7% 4,34% 2,82% 3,43% 91,1% 2,68% B Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT 0 5 10 15 20 0 5 10 15 20 0 1 2 3 4 TREG cells Ratio DE 0 10 20 30 40 50 * GF Lymph Nodes Lymph Nodes % GFP-TdT+ CD45.1 CD45.2 Foxp3 GFP-Fth '' -td T Foxp3 GFP-tdT CD8 + CD4 + Foxp3 + C *** Rag2-/- Lymph Nodes CD45.1+ vs. CD45.2+ C57BL/6 50% Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT 50% BM Fth1 Hmox1 Nqo1 Vmn1r80 Prune2 Fes Mt1 Gm5441 Ly6c2 Mcoln2Cxcr2 Atp8b4 Fth1−ps Epdr1 Mt2 Padi4 Cnga1 Mageh1 Npdc1 Amz1 Susd4 Mrgprb1 Pde4c Large2 Vmn1r68 0 20 40 60 −10 −5 0 5 10 15 20 Adjusted p-value (-log10) Log2 fold change HI TREG cells (GFP+tdT+)J TREG cells (GFP+tdT+) ex-TREG cells (GFP-tdT+ ) Foxp3 GFP-tdT Foxp3 GFP-Fth'' -tdT vs. Base mean expression 50x105 10x105 15x105 KL M Rag2-/- Lymph nodes C57BL/6 50% BM Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT 50% CD45.2+ RNAseq Fth1 Hmox1 Nqo1 Nrn1 Ptpn5 Gm21909 Sema7a Ly6d Ccr6 Mt1 Gm16086 Hif3a Gm29243 Gja10 Gm19951 Ift172 Fth1−ps Sgcb Cfap54 Mfap3l Nr3c2 Cd83 Smim10l2a Arhgap24 Plpp1 0 20 40 60 −10 −5 0 5 10 15 20 Log2 fold change Adjusted p-value (-log10) ex-TREG cells (GFP-tdT+) Foxp3 GFP-tdT Foxp3 GFP-Fth'' -tdT vs. Base mean expression 50x105 10x105 15x105 Mixed BM chimeric/non-chimeric mice TREG cells (GFP+)TREG cells (GFP+)TREG cells (GFP+) C himeri c 111 134 Non−chimeric 3711 Upregulated Downregulated 300 100 20 10 0 10 20 100 150 -log10 (p-value) Mixed BM chimeras Non-chimeric Bio. proc. KEGG Reac. TF WP Cell redox homeostasis Cellular response to chemical stimulus Cell. response to IFN-β Detoxification Detoxification of ROS BACH2 IRF1 IRF2 IRF Ferroptosis Oxid. stress and redox pathway Oxid. stress response Response to oxid. stress Response to ROS Transcrip. activ. by NRF2 in response to phytochem. 2 1 4 6 7 5 3 −log10 (p value) Hmox1 Nqo1 Sqstm1 Sod1 Slc48a1 Txnrd1 Txn1 Prdx1 Mt1 Mafg Fth1 Ftl1 Gclm Gsr CD45.2+ CD45.1+ Sorting NS NS NS *** Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1453 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. expression in T REG cells favors tumor progression. In support of this hypothesis, the relative growth of syngeneic B16 melanoma cells was reduced in Foxp3GFP-FthΔ/Δvs. control Fthfl/flmice (Fig. 8H). This was associated with lower frequency of tumor-infiltrating T REG cells (Fig. 8I) and higher frequency of activated Foxp3-CD4+CD25+effector T H cells (Fig. 8J). The frequency and number of activated CD4+IFNγ+T H cells isolated from B16 melanomas was similar in Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (Fig. EV5F). In contrast, the number of activated CD8+IFNγ+T C cells was higher in B16 melanomas from Foxp3GFP-FthΔ/Δ vs. control Foxp3GFP mice (Fig. EV5F). This tendency was also observed for CD8+granzymeB+T C cells, albeit not statistically significant (Fig. EV5G). This suggests that regulation of Fe metabolism by FTH in T REG cells supports tumor progression, via a mechanism that hinders anti-tumor immunity. Discussion T REG cells respond to variations in the relative levels of nutrients, vitamins and metabolites in their environment (Chapman et al, 2020; Shi and Chi, 2019), via dedicated transporter-receptor coupled sensors that modulate T REG cell function and lineage stability (Kempkes et al, 2019; Shi and Chi, 2019). In keeping with Fe-regulatory genes being a core property of T REG cells (Cuadrado et al, 2018), we found that the Fe-regulatory protein FTH is essential to support T REG cell lineage maintenance in vivo (Figs. 1–4) and support immune homeostasis (Figs. 2,EV1D–G, and EV2C). FTH regulates T REG cell lineage stability (Figs. 3and 4A–G) without interfering with the antiproliferative function of T REG cells (Fig. EV2A). Instead, FTH targets the intracellular pool of redoxactive Fe2+(Fig. EV1B) to regulate T REG cell redox homeostasis (Figs. 2Fand4K–M) in a manner that controls T REG cell: (i) energy metabolism (Figs. 5F–IandEV5A–E), (ii) TET activity (Fig. 6H), (iii) cytosine demethylation at CpG-rich sequences at CNS1 and CNS2 in the FOXP3 locus (Fig. 6G), and (iv) FOXP3 transcription/ expression (Figs. 1K,L and 7F–H). As the latter is essential to maintain the transcriptional program supporting T REG cell lineage stability (Williams and Rudensky, 2007)(Nakatsukasaetal,2019; Ohkura et al, 2012;Yueetal,2019)Fth deletion is associated with a decrease of T REG cells, including tT REG cells and pT REG cells, without interfering with thymic T REG cell output (Fig. 1H,L). These observations are consistent with FTH acting upstream of TET methylcytosine dioxygenases, to control redox-based cytosine demethylation in T REG cells (Fig. 6)(HuangandRao,2014;Pastor et al, 2013). That FTH targets the intracellular pool of redox-active Fe2+to control TET enzymatic activity is suggested by the observation that overexpression of a ferroxidase deficient FTHmcompromised TET methylcytosine dioxygenase activity (Fig. 6H, I). Several nonmutually exclusive mechanisms might explain this observation. First, FTH could act “directly”via sequestration of catalytic Fe2+ (Fig. EV1B), controlling the availability of this essential cofactor of TET enzymatic activity (Kohli and Zhang, 2013;Pastoretal,2013). Second, FTH could act “indirectly”via the regulation of cellular redox homeostasis (Figs. 2D,F and 4K–M), preventing catalytic Fe2+from catalyzing oxidative stress, which compromises TET activity (Niu et al, 2015). Moreover, this should restrain NRF2 activation (Figs. 2D,F and 4K–M) from repressing Foxp3 expression and impair T REG cell function (Klemm et al, 2020). Consistent with our findings, intracellular Fe mobilization via lysosome-mediated ferritinophagy, was recently shown to regulate TET-driven (de)methylation of the peroxisome proliferatoractivated receptor γ(PPARγ) locus, the master regulators of adipocyte development (Suzuki et al, 2023). This suggest that FTH controls Fe-responsive epigenetic programs defining different cellular developmental programs. FTH regulates T REG cell mitochondrial TCA cycle and OXPHOS (Figs. 5F–I and EV5A–E), consistent with similar findings in other cell types (Blankenhaus et al, 2019; Oexle et al, 1999). While FTH does not modulate the intracellular concentration of intermediate TCA cycle metabolites (Fig. EV5E), including α-ketoglutarate (Fig. 5I), it does appear to modulate the rate of isocitrate conversion into α-ketoglutarate, likely acting irrespectively of α-ketoglutarate generation via glutaminolysis. It is possible therefore that FTH regulates the production of this obligatory substrate of TET cytosine dioxygenases (Kohli and Zhang, 2013;Pastoretal,2013) via regulation Figure 7. FTH is required to sustain Foxp3 transcription and expression. (A) FTH protein detected by western blot in whole-cell extracts from HEK293T cells infected with recombinant lentiviruses coding shRNAs targeting FTH (FTH429 and FTH432) or control (Ctrl.) recombinant lentiviruses non-targeting shRNA. (B) Mean fluorescence intensity (MFI) of FOXP3 expression, detected by flow cytometry in human (CD4+CD45RA+CD25+)T REG cells infected with the same recombinant lentiviruses as in (A). Data from N=6 samples per experimental group. (C) Schematic representation of the experimental approach (left panel) used to monitor GFP transgene expression in the mesenteric LN (MLN) of (CD4+GFP+Nrp1+)tT REG cells and (CD4+GFP+Nrp1−)pT REG cells. Representative flow cytometry histogram (middle panel) and quantification of relative GFP expression (right panel), shown as mean fluorescence intensity (MFI). Data from N=8 mice per genotype, pooled from two independent experiments with similar trend. (D) Schematic representation of the experimental approach (left panel) used to monitor Foxp3 expression by flow cytometry in mouse spleen and MLN (CD4+Foxp3+)T REG cells. Representative flow cytometry staining of Foxp3 (middle panel). Relative quantification of Foxp3 expression (right panel), represented as mean of fluorescence intensity (MFI). Data from N=9 mice per genotype, pooled from two to three independent experiments with similar trend. (E) Schematic representation of the experimental approach (left panel) used to monitor Foxp3 expression in (CD45.2+CD4+GFP+tdT+)T REG cells isolated from the spleen and LN of BM chimeras. Representative flow cytometry of Foxp3 staining (middle panel). Relative quantification of Foxp3 expression (right panel), shown as mean fluorescence intensity (MFI). Data in (E) from N=5–6 mice per genotype, representative of two independent experiments with similar trend. (F) Schematic representation of the experimental approach (left panel) used to monitor GFP expression in the spleen and LN of CD45.2+CD4+tdT+cells (T REG +ex-T REG ) from BM chimeras. Representative flow cytometry of GFP (F) staining (middle panel). Relative quantification of GFP expression (right panel), represented as mean fluorescence intensity (MFI). Data from N=10 mice per genotype, pooled from two independent experiments with similar trend. (G) Schematic representation of the experimental approach (left panel), where (CD4+tdT+) cells were sorted from the spleen and LN for qRT-PCR (G, left panel). Relative expression of Foxp3 (right panel). (H)Gfp, Fth, and tdT mRNA expression normalized to Arbp0 of cells sorted as in (G). Data in (G,H) from N=3–4 mice per genotype from one experiment. Data information: Data in (B) are presented as mean ± SD, circles correspond to individual wells and red bars to mean values. Data in (C–H) are presented as mean ± SD, circles correspond to individual mice and red bars to mean values. Pvalues in (B) were calculated using the Fiedman test with Dunn’s multiple comparison test, in (C–H) using two-way ANOVA with Sidak’s multiple comparison test. NS not significant (P> 0.05), *P< 0.05; **P< 0.01; ***P< 0.001; ****P< 0.0001. Source data are available online for this figure. The EMBO Journal Qian Wu et al 1460 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. H E D BC G F Fth fl/fl Foxp3 GFP-Fth'' Fth fl/fl Foxp3 GFP-Fth'' J I 0 40 80 67,7% 32,6% *** Foxp3+ (%) Foxp3 CD4 105 104 103 0 010 3104105010 3104105 10-2 10-3 10-2 1,32% 4,5% 0 2 4 6 8 Foxp3-CD25+ (%) CD25 Foxp3 *** 105 104 103 0 010 3104105010 3104105 Fth fl/fl Foxp3 GFP-Fth'' Fth fl/fl Foxp3 GFP-Fth'' 0 5 10 15 0 25 50 75 100 Percent survival *** Days 0 1 2 3 4 5 6 7 iRBCs/PL (x106) * Foxp3 GFP Foxp3 GFP-Fth'' Days 9/9 1/9 ** CD4 010 3104105010 3104105 105 104 103 0 0 5 10 15 Foxp3-GFP (x10 ) 0 2 4 1 3 NS 10% 4,86% Foxp3 GFP Foxp3 GFP-Fth'' Tumor Size B16 Melanoma Spleen CD4+Foxp3+ Pcc Pcc CD4 IFNJ 0 10³ 10 10 10 0 0 10 20 30 0 10 5 IFN J +CD4+ ( x10 6 ) IFN J +CD4+ ( % ) NS * 12,6% 21,4% CD4+ IFNJ Pcc 105 104107 106 0 105 104107 106 Spleen Foxp3 GFP Foxp3 GFP-Fth'' Foxp3 GFP Foxp3 GFP-Fth'' Foxp3 GFP Foxp3 GFP-Fth'' Survival Pathogen 03 9 15612 Disease Severity * 0 5 10 15 20 2530 0 0.5 1 1.5 2 2.5 3 Days 3.5 35 A Disease Incidence (%) EAE MOG35-55 +CFA 0 5 101520253035 0 50 100 Days 25 25 * Foxp3-GFP Foxp3-GFP (%) *** ** * 13 15 17 19 0 200 400 600 800 1000 Days Tumor size (mm3) IFNJ(%) IL-17A+(%) * Fth fl/fl Foxp3 GFP-Fth'' (IFNJ/IL-17) CD4+ Spinal Cord MOG35-55 0 5 10 15 20 0 10 20 30 40 0 2 4 6 8 (%) IFNJIL-17A+ * 16% 22% IFNJ IL-17A 18,7% 33,9% 105 104 103 0 010 3104105010 3104105 Fth fl/fl Foxp3 GFP-Fth'' +CFA NS 1,4% 4% Fth fl/fl Foxp3 GFP-Fth'' ** ** * ** * NS NS Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1461 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. of the TCA cycle. This interpretation is consistent with other signal transduction pathways regulating the TCA cycle, such as the one triggered by the programmed cell death 1 ligand 2 (PD-L2), regulating cytosine methylation at CpG sequences in the T REG -specific demethylation region, compromising Foxp3 stability in T REG cells (Hurrell et al, 2022). The metabolic program orchestrated by FOXP3, supports T REG cell antiproliferative function and lineage stability, via a mechanism that relies on the suppression of c-Myc, a transcription factor that represses mitochondrial OXPHOS and promotes glycolysis (Angelin et al, 2017). Importantly, c-Myc can repress FTH transcription and translation in other cell types, the later occurring via a mechanism involving the Fe-regulatory protein 2 (IRP2) (Wu et al, 1999). This suggests that the FOXP3-driven genetic program encompasses the induction of FTH via a mechanism that could involve the repression of c-Myc. Moreover, the FTH promoter contains at least one Foxp3 DNA binding site (Appendix Figs. S3 and 4) and as such it is possible that Foxp3 would regulate FTH expression directly. This would argue for a positive feedback loop in which FTH enforces Foxp3 expression, via the regulation of TET dioxygenases, and the later enforces the FTH expression transcriptionally. This hypothesis remains however to be tested experimentally. While FTH prevents the transdifferentiation of T REG cells toward inflammatory ex-T REG cells (Figs. 3and EV2C–E), via a non-cell- autonomous mechanism that relies on systemic inflammation (Fig. EV1D–G), this is not associated with the accumulation of inflammatory ex-T REG cells in Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice (Fig. 3E–G). The same is true in mixed BM chimeric mice reconstituted with BM cells from Foxp3GFP-FthΔ/Δ-tdT vs. control Foxp3GFP-tdT mice (Fig. 4A–G). One possible interpretation is that FTH is essential not only to restrain T REG cells from transdifferentiating into inflammatory ex- T REG cells but also to support the viability of highly proliferating T REG and ex-T REG cells. This is consistent with the lack of autoimmune lesions in Foxp3GFP-FthΔ/Δmice(Fig.EV2B).Wenote,however,thatFth deletion in T REG cells does not compromise the T REG cell thymic output (Fig. 1H) nor the generation and proliferation of iT REG cells in vitro (Fig. EV4F–J). One cannot exclude that the highly methylated status of the Foxp3 locus from ex-T REG cells (Komatsu et al, 2014; Miyao et al, 2012;Zhouetal,2009) together with the increase frequency of ex- T REG cells among CD45.2+CD4+tdT+cells (Fig. 4A–G), contributes to the observed increase in the methylation of the Foxp3 locus in Fth-deleted T REG cells (Fig. 6A–G). This does not invalidate however, that Fth deletion acts in a cell-autonomous manner to decrease Foxp3 transcription/expression (Figs. 1K,L and 7F–H), therefore increasing the frequency at which T REG cells transdifferentiate into ex-T REG cells (Fig. 4A–G). In contrast with genetic deletion of Tet2 and Tet3 in T REG cells, which promotes the transdifferentiation of T REG cells into inflammatory ex-T REG cells and the development of autoimmunity (Nakatsukasa et al, 2019;Ohkuraetal,2012;Yueetal,2019), Fth deletion in T REG cells is not associated with overt autoimmunity (Fig. EV2BG). This is consistent with FTH exerting additional effects, beyond the regulation of TET dioxygenases, preventing cellular stress from compromising the viability of the inflammatory ex-T REG cells that would otherwise elicit autoimmunity. Fth deletion in T REG cells led to an increase in EAE susceptibility and severity (Fig. 8A–C), induced by an immunization protocol leading to low-grade disease severity in control mice (Fig. 8C). This was associated with a higher accumulation of activated T H 1and T H 17 cells as well as pathogenic IFNγ+IL-17A+T H cells (Duhen et al, 2013) in the central nervous system (Fig. 8D), likely originating from auto-reactive T N cells and/from ex-T REG cells that lost Foxp3 expression to become inflammatory and presumably pathogenic (Bailey-Bucktrout et al, 2013). This is consistent with regulation of Fe metabolism modulating the incidence and severity of autoimmune conditions, as demonstrated for systemic lupus erythematosus (Gao et al, 2022a). Consistent with our findings, this was linked to modulation of T REG (Gao et al, 2022a), T H 17 (Teh et al, 2021), and FT H (Gao et al, 2022b) cells and was associated with regulation of DNA demethylation (Gao et al, 2022b; Teh et al, 2021). However, whether regulation of Fe metabolism in T REG cells affects systemic lupus erythematosus was, to the best of our knowledge, not established (Gao et al, 2022a). Fth deletion in T REG cells increased susceptibility to malaria (Fig. 8E), consistent with dysregulation of Fe metabolism promoting malaria lethality (Ferreira et al, 2008; Ramos et al, 2022; Ramos et al, 2019;Wuetal,2023). This was associated with an increase in hostparasite burden (Fig. 8E), in keeping with T REG cells being essential to counter the pathogenesis of severe presentations of malaria while limiting immune-driven resistance mechanisms driving parasite clearance (Hisaeda et al, 2004; Kurup et al, 2017; Walther et al, 2005). We infer that the protective effect of T REG cells against malaria acts via a mechanism that is not associated with a reduction of the Figure 8. FTH expression in T REG cells controls the pathologic outcome of experimental immune-driven inflammatory conditions. (A) Schematic representation of the induction experimental autoimmune encephalomyelitis (EAE) in response to MOG 35-55 immunization. (B) EAE incidence (percentage) and (C) EAE severity in MOG 35-55 immunized mice. Data from N=18–20 mice per genotype, pooled from three independent experiments, with similar trend. (D) Experimental approach (right panel), representative flow cytometry dot plots (middle panel) and corresponding quantification (left panel) of the relative percentage of activated T H 1 (CD3+CD4+IFN-γ+), T H 17 (CD3+CD4+IL-17A+); and double positive IFN-γ+IL-17A+T H cells in the spinal cord, 22 days after MOG 35-55 immunization. Data from N=3–4 mice per genotype. (E) Survival (left panel) and number of circulating Plasmodium chabaudi chabaudi (Pcc)-infected red blood cells (iRBC) per µL of whole blood (i.e., parasite burden) (right panel). N=9 mice per genotype, pooled from two independent experiments, with similar trend. (F,G) Representative flow cytometry dot plot (left panels) and corresponding percentage and cell numbers (right panels) of splenic (CD4+Foxp3-GFP+)T REG cells (F) and IFNγ+CD4+activated T H cells (G), 7 days after Pcc infection. Data from N=8–9 mice per genotype, pooled from two independent experiments, with similar trend. (H) Relative tumor (B16-F10-luc2) size, 13–19 days after inoculation (2 × 105cells). Data from N=7–11 mice per genotype, pooled from 3 independent experiments, with similar trend. (I,J) Representative flow cytometry dot plots (left panels) and corresponding percentage (right panels) of live tumor-infiltrating (CD4+Foxp3+)T REG cells (I) and (CD4+Foxp3-CD25+) effector T H cells (J), 3 weeks after tumor inoculation. N=7 mice per genotype, pooled from three independent experiments, with similar trend). Data information: Circles in (D,F,G,I,J) correspond to individual mice and red bars to mean values. Data in (C,H) are presented as mean ± SEM. Data in (E, right panel) are presented as mean ± SD. P values in (C), (E, right panel), and (H) were determined using Holm–Sidak method (multiple ttests), with alpha =0.05 under the assumption that both genotypes have similar SEM, in (B,E) by log-rank (Mantel–Cox) test, and in (D,F,G,I,J) by unpaired ttest with Welch’s correction. NS not significant (P> 0.05); *P< 0.05; **P< 0.01; ***P< 0.001. Source data are available online for this figure. The EMBO Journal Qian Wu et al 1462 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445–1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. host-pathogen burden, a defense strategy known as disease tolerance (Medzhitov et al, 2012; Soares et al, 2017). Presumably, the mechanism(s) via which FTH acts in T REG cells to establish disease tolerance to malaria is multifactorial, restraining unfettered immune activation to prevent the pathogenesis of severe forms of malaria. Dysregulation of Fe metabolism in Fth-deleted T REG cells was associated with better control of tumor progression (Fig. 8H), consistent with a relative reduction of tumor-infiltrating T REG cells (Fig. 8I) and a more pronounced activation and/or infiltration of activated T effector cells (Fig. 8J), including CD8+IFNγ+T C cells (Fig. EV5F). While the mechanism via which FTH expression in T REG cellspromotestumorprogressionisnotclear,these observations are consistent with the regulation of Fe metabolism in the tumor microenvironment impacting on tumor progression (Alaluf et al, 2020; Consonni et al, 2021). In conclusion, regulation of intracellular Fe metabolism by FTH is essential to maintain T REG cell lineage and function in vivo, reflecting how intracellular catalytic Fe controls the activity of TET dioxygenases that sustain FOXP3 transcription and support T REG cell lineage identity. Moreover, FTH might regulate other irondependent mechanisms supporting T REG function, for example, by enforcing the expression of c-Maf in T REG cells (Zhu et al, 2023)that control immunological tolerance to the microbiota (Xu et al, 2018). We propose that targeting Fe metabolism pharmacologically maybe considered when manipulating T REG cells for therapeutic purposes, either to enhance T REG cell function in the context of immunemediated inflammatory diseases or to dampen T REG cell function as in the context of cancer therapies. Methods Reagents and tools table Reagent/resource Reference or source Identifier or catalog number Experimental models Human: HEK293T ATCC ATCC®CRL-3216™ Mouse: Tumor cells B16-F10-luc2 (B16) CaliperLS B16-F10-luc2 Mouse: B6.C57BL/6 Fthfl/flLukas Kuhn, ETH, Switzerland (Darshan et al, 2009) Mouse: B6129S-Tg(Foxp3 EGFP/ icre)1aJbs/J backcrossed into B6.C57BL/6 background Jackson Laboratory JAX stock: 023161 Mouse: B6.Cg-Gt(ROSA) 26Sortm9(CAG-tdTomato)Hze/J Jackson Laboratory JAX stock: 007909 Mouse: RAG2 -/- (B6.129S6- Rag2<tm1Fwa>N12) Taconic Taconic # RAGN12 Plasmodium chabaudi chabaudi strains: PcAS clone AJ4916 Reece & Thompson, 2008 N/A Blood samples from anonymized healthy male donors were obtained in accordance with guidelines established by the Sanquin Medical Ethical Committee. This paper NA Reagent/resource Reference or source Identifier or catalog number Recombinant DNA psPAX Addgene Cat#12260 pMD2.G Addgene Cat#12259 pCMV-FTH-3tag3a This paper pCMV-FTHmut-3tag3a This paper pCMV-3×FLAG-TET3(human)- Neo miaolingBio P45302 Antibodies PE anti-human CD25 (Clone 2A3) BD Biosciences Cat#341011 (RRID: AB_2783790) PE-Cy7 anti-human CD45RA (Clone HI100) BD Biosciences Cat#560675 (RRID: AB_1727498) Brilliant Violet 421 Anti-human CD127 (Clone A019D5) Biolegend Cat#351309 (RRID: AB_10898326) PE-Cy7 anti-Human FOXP3 (Clone 236A/E7) eBioscience Cat#25-4777-42 (RRID: AB_2573450) Anti-Ferritin Heavy Chain Abcam ab65080 (RRID:AB_10564857) CD45 APC-eFluor780 eBioscience 30-F11, 47-0451-82 (RRID:AB_1548781) TCR-βBV421 BioLegend H57-597, 109229 (RRID:AB_10933263) TCR-βBV711 BioLegend H57-597, 109243 (RRID:AB_2629564) CD4 PE-Cy7 eBioscience RM4-5, 25-0042-82 (RRID:AB_469578) CD4 APC-eFluor780 eBioscience GK1.5, 47-0041-82 (RRID:AB_11218896) CD4 BV421 BioLegend GK1.5, 100438 (RRID:AB_10900241) CD8 PercpCy5.5 eBioscience 53-6.7, 45-0081-82 (RRID:AB_1107004) CD8 APC/Fire 750 BioLegend 53-6.7, 100766 (RRID:AB_2572113) CD44 eFluor450 eBioscience IM7, 48-0441-82 (RRID:AB_1272246) CD62L Pe-Cy7 eBioscience MEL-14, 25-0621-82 (RRID:AB_469633) CD304 (NRP1) PE BioLegend 3E12, 145204 (RRID:AB_2561928) PD1-PE eBioscience RMP1-30, 12-9981-82 (RRID:AB_466290) CXCR5-biotin BD Biosciences 2G8, 551960 RRID: AB_394301) Alexa Fluor®647 streptavidin BioLegend 405237 CD25 PE-Cy7 BioLegend PC61, 102016 (RRID:AB_312865) Foxp3 PE eBioscience FJK-16s, 12-5773-82 (RRID:AB_465936) Foxp3 FITC eBioscience FJK-16s, 11-5773-82 (RRID:AB_465243) Foxp3 eF450 eBioscience FJK-16s, 48-5773-82 (RRID:AB_1518812) Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1463 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Reagent/resource Reference or source Identifier or catalog number CD71 BioLegend RI7217, 113811 (RRID:AB_2203383) Ki67 eBioscience SolA15, 50-5698-82 (RRID:AB_2896285) IL-17A eBioscience eBio17B7, 50-7177-82 (RRID:AB_11220280) IFN-gamma eBioscience XMG1.2,12-7311-81 (RRID:AB_466192) CD45RA BD Biosciences HI100 (RRID: AB_1727498) CD45.1 Pacific blue Produced at IGC A20 CD45.2 AF647 Produced at IGC 104.2 CD4 MicroBeads, human Miltenyi Biotec 130-045-101 (RRID:AB_2889919) Thy1.1 Produced at IGC 19E12 Thy1.2 Produced at IGC 30H12 anti-CD3 mAb (Clone 1XE) Pelicluster M1654 (RRID:AB_10553652) anti-CD28 mAb (Clone CD28.2) eBioscience 16-0289-85 (RRID:AB_468927) HO-1 Enzo Life Sciences ADI-SPA-896 (RRID:AB_10614948) CD16/CD32 BioLegend 93, 101331 Oligonucleotides and other sequence-based reagents Human FTH1 shRNA: FTH429; TRCN0000029429; target sequence: GCCTCGGGCTAATTTCCCATA GPP Web Portal, Broad Institute RHS3979-9596837 Human FTH1 shRNA: FTH432; TRCN0000029432; target sequence: CCTGTCCATGTCTTACTACTT GPP Web Portal, Broad Institute RHS3979-9596840 Primers for RT-qPCR, see Table 1See Table 1N/A Chemicals, enzymes, and other reagents TMRE-Mitochondrial Membrane Potential Assay Kit Abcam ab113852 FerroFarRed™Goryo Chemical GC903-01 LIVE/DEAD™Fixable Aqua Stain ThermoFisher Scientific L34957 eBioscience™Cell Stimulation Cocktail ThermoFisher Scientific 00-4970-93 Protein Transport Inhibitor Cocktail ThermoFisher Scientific 00-4980 CFSE ThermoFisher Scientific C34554 Solid Phase Reversible Immobilization (SPRI) beads Beckman Coulter Software Include version where applicable ImageJ Schneider et al, 2012 https:// imagej.nih.gov/ij/ Flowjo BD Sciences Version 10.8.1 R R Core Team 2014, Vienna, Austria Version 3.5.1 Reagent/resource Reference or source Identifier or catalog number FastQC method Babraham bioinform. Version 0.11.5 Python (Linux/UNIX) Python Version 2.7.12 Trimmomatic Bolger et al, 2014 Version 0.36 HISAT2 Kim et al, 2015 Version 2.1.0 HTseq Anders et al, 2015 Version 0.6.1p1 DESeq2 Love et al, 2014 Version 1.26.0 Other QIAamp DNA Micro Kit QIAGEN 56304 NucleoSpin RNA XS Macherey-Nagel 740902 ChIP DNA Clean & Concentrator columns Zymo Research D5205 MagniSort™Mouse CD4 T-cell Enrichment Kit ThermoFisher Scientific 8804-6821-74 NEBNext®Enzymatic Methyl-seq Kit New England Biolabs E7120 Qubit HS dsDNA kit ThermoFisher Scientific Q32851 High Sensitivity DNA Bioanalyzer kit Agilent 5067-4626 Seahorse XF Cell Mito Stress Test Kit Agilent Technologies 103015-100 Animals Mice were bred and maintained under specific pathogen-free (SPF) conditions at the Instituto Gulbenkian de Ciência (IGC). All experimental protocols were approved by the Ethics Committee of the IGC, the “Órgão Responsável pelo Bem-estar dos Animais” (ORBEA) (license numbers A001-2017, A003-2017) and the Portuguese National Entity (Direcção Geral de Alimentação e Veterinária) (notification numbers 003722, 008830). Experimental procedures were performed according to the Portuguese (Portaria no. 1005/92, Decreto-Lei no. 113/2013 and Decreto-lei no.1/2019) and European (Directive 2010/63/EU) legislations, concerning housing, husbandry, and animal welfare. Foxp3GFP-FthΔ/Δmice were generated by crossing Foxp3GFP (i.e., B6129S-Tg(Foxp3 EGFP/icre) 1aJbs/J) mice (Chen et al, 2003;Zhouetal,2008)withFthfl/flmice (Darshan et al, 2009). Mouse progeny was genotyped for the presence of the Cre allele. The Foxp3GFP mice express a humanized Cre-recombinase (GFP-hCre) from a Foxp3 ATG translational start codon, inserted in a bacterial artificial chromosome (BAC) transgene (Chen et al, 2003;Zhouetal,2008). As Cre expression is not sex-dependent, this allows for Fth deletion in T REG cells from male and female Foxp3GFP-FthΔ/Δmice, using Foxp3GFP and Fthfl/flmice as controls. Foxp3GFP-FthΔ/Δ-tdT mice were generated by crossing Foxp3GFP-FthΔ/Δmice with Ai9 (RCL-tdT) mice. Control Foxp3GFP-tdT mice were generated by crossing Foxp3GFP mice with C57BL/6 Ai9 (RCL-tdT) mice, similar to described above. Progeny was genotyped for the presence of the humanized Cre-recombinase (Gfp-hCre) allele. The genetic background of the mouse strains used was C57BL/6J, including Foxp3GFP mice, backcrossed into C57BL/6/J The EMBO Journal Qian Wu et al 1464 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. background for over 10 generations. Rag2−/−mice used as recipients of BM precursor cells were in C57BL/6NTac background. Experimental autoimmune encephalomyelitis (EAE) C57BL/6 mice were immunized with the MOG 35–55 peptide (s.c.; 200 µg), emulsified in Complete Freund’s Adjuvant (CFA) containing Mycobacterium tuberculosis (4 mg/mL; BD Diagnostics). Mice received 200 ng of Pertussis toxin (i.v.; Sigma-Aldrich) at the time of immunization and 2 days thereafter. Clinical EAE severity scores were evaluated daily as follows: 0, normal; 1, limp tail; 2, partial paralysis of the hind limbs; 3, complete paralysis of the hind limbs; 4, hind-limb paralysis and forelimb weakness; 5, moribund or deceased, essentially as described (Chora et al, 2007). Plasmodium infection (malaria) Mice were infected by the inoculation of blood isolated from mice infected with a Plasmodium chabaudi chabaudi (Pcc) AS strain [i.p.; 2×10 6infected red blood cells (iRBC) per mouse]. Mice were monitored daily for parasitemia, weight, temperature, RBC number, and survival, essentially as described (Seixas et al, 2009). Tumor model Tumor cells B16-F10-luc2 (B16) (CaliperLS) were cultured at 37 °C in RPMI 1640 (Life Technologies) supplemented with 10% Fetal Bovine Serum (Biowest), 1% penicillin–streptomycin (Life Technologies), 50 µg/mL Gentamicin (Life Technologies), and 50 µM 2-Mercaptoethanol (Life Technologies). After trypsin (Life Technologies) treatment, single-cell suspensions were resuspended in ice cold calcium-free and magnesium-free HBSS (Life Technologies). Mice were injected subcutaneously in the right flank with 2×10 5B16 cells in 100 µL volume. Tumor size was measured with a caliper every 2 or 3 days, from day 8 post injection, and the tumor diameter (TD) was calculated as TD = (L +W)/2. Mice were sacrificed when TD ≥20 mm. By the end of each experiment, tumor clearance (TD ≤5 mm) was confirmed upon dissection. Human peripheral blood mononuclear cells (PBMC) Blood samples from anonymized healthy male donors were obtained in accordance with guidelines established by the Sanquin Medical Ethical Committee. Briefly, PBMC was isolated from fresh buffy coats using Ficoll-Paque Plus (GEHealthcare) gradient centrifugation. Next, CD4+T cells were isolated using magnetic sorting with CD4 microbeads (Miltenyi Biotec) and viable cells were separated using flow cytometric sorting based on the expression of CD25, CD45RA, and CD127 on a FACS Aria III (BD Biosciences). Mouse leukocyte isolation For isolation of leukocytes, spleen and lymph were harvested, disrupted, passed through a cell strainer (70 μm) in PBS (3% FBS, 1mMEDTA),pelleted(300×g,4°C,10min),andRBCwerelyzed (5 mL RBC lysis buffer; 5 min, RT). Lysis was stopped by adding 5 mL of medium, and cells were passed through a 40-μmcell strainer, centrifuged (300 × g, 4 °C, 10 min) and resuspended in PBS containing 3% FBS and 1 mM EDTA. Cell sorting Mice were sacrificed, and LN (i.e., inguinal, brachial, axillary, mandibular, superficial cervical, mesenteric, pancreatic, renal, and lumbar) or spleen were collected, and leukocytes isolated as described above. The negative fraction from CD4+T cells enrichment (MagniSort™Mouse CD4 T-cell Enrichment Kit) was recovered, centrifuged, and stained with the following antibody panel: anti- CD11b A647, anti-B220 A647, anti-CD8 A647, anti-CD4 PerCPCy5.5, anti-CD62L Pe-Cy7, and anti-CD44 eF450. Foxp3+cells were sorted based on endogenous Foxp3EGFP/icre expression (FACS Aria; BD Biosciences). When indicated, naive T cells (CD11b/B220/ CD8-CD4+Foxp3-CD44lowCD62Lhigh), memory/activated T cells (CD11b/B220/CD8-CD4+Foxp3-CD44 highCD62Llow)andT REG cells (CD11b/B220/CD8-CD4+Foxp3+) were sorted and recovered. A similar procedure was followed for sorting ex-T REG cells, based on endogenous expression of Tomato within GFP+(CD4+GFP+TdT+) and GFP-(CD4+GFP− TdT+)cellpopulations. Immunophenotyping Cells isolated as described in “Leukocyte isolation”were stained for flow cytometry analysis. For surface staining, cells were incubated with Fc block together with LIVE/DEAD™Fixable Aqua Stain in PBS, followed by incubation with antibodies against the following surface markers: CD8, CD4, CD62L, CD44, CD25, TCRβ,CD11b, and CD304 (Nrp1). Intracellular Foxp3 staining was performed using Foxp3/Transcription Factor Staining Buffer Set. Briefly, upon surface staining, cells were fixed, washed with permeabilization buffer, and incubated with anti-Foxp3 antibody in permeabilization buffer. For T REG cells (CD4+GFP+tdT+)andex-T REG cells (CD4+ GFP-tdT+) staining, cells were fixedandincubatedwithFcblock, followed by incubation with antibodies directed against the following surface markers: CD4, CD62L, CD44, CD3, TCRβ.T REG cells and ex-T REG cells were distinguished based on endogenous Foxp3EGFP/icre and Tomato expression. Follicular T cells were fixed and incubated with Fc block, followed by incubation with antibodies against surface markers: CD4, CD3, TCRβ,CXCR5, and PD1. Foxp3+cells were selected based on endogenous Foxp3EGFP/icre expression. For T REG cells lineage maintenance analysis comparing T REG cells (CD4+GFP+tdT+)andex-T REG cells (CD4+ GFP-tdT+), cells were incubated with Fc block together with LIVE/ DEAD™Fixable Aqua Stain in PBS, followed by incubation with antibodies against surface markers: CD4, CD3, CD71, and fixation with intracellular staining for the proliferation marker Ki67. Cell acquisition was performed using a CYTEK Aurora (Cytek Biosciences) flow cytometer, and data was analyzed using FlowJo software Version 10.8.1. Cytokine staining Cells were isolated as described in “Leukocyte isolation“and were stimulated using Cell Stimulation Cocktail together with Protein Transport Inhibitor Cocktail (4 h; 37 °C) in complete RPMI (10% FBS, 100 U/mL Penicillin and 100 µg/mL Streptomycin). For surface staining, cells were incubated with Fc block together with LIVE/DEAD™Fixable Aqua Stain, followed by incubation with antibodies directed against the following surface markers: CD8, CD4, and TCRβ.IntracellularFoxp3,IFNγ, Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1465 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. and IL-17 staining were performed using Foxp3/Transcription Factor Staining Buffer Set. Briefly, were fixed upon surface staining cells, washed with permeabilization buffer, and incubated with anti-Foxp3, anti-IFNγ, and anti-IL-17 antibodies in permeabilization buffer. Cell acquisition was performed using BD LSRFortessa X-20 (BD Biosciences) flow cytometer. Alternatively, cells were incubated with Fc block together with LIVE/DEAD™Fixable Yellow Stain, followed by incubation with antibodies against the following surface markers: CD8, CD4 and TCRβ. Intracellular Foxp3, IFNγ, IL-17 and IL10 staining were performed using Foxp3/Transcription Factor Staining Buffer Set. Briefly, upon surface staining cells were fixed, washed with permeabilization buffer, and incubated with anti-Foxp3, anti-IFNγ, anti-IL-17 and anti-IL-10 antibodies in permeabilization buffer. Cell acquisition was performed using a CYTEK Aurora (Cytek Biosciences) flow cytometer. Data were analyzed with FlowJo software Version 10.8.1. Leukocyte staining with fluorescent probes Cells were isolated as described in “Leukocytes isolation”. To evaluate mitochondrial membrane potential, cells were incubated with (tetramethylrhodamine, ethyl ester) TMRE-Mitochondrial Membrane Potential probe (20 nM) in RPMI without serum (20 min 37 °C). Control samples were pre-incubated with the ionophore uncoupler of oxidative phosphorylation FCCP (carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone; 20 μM, 10 min, 37 °C), to eliminate mitochondrial membrane potential and positive TMRE staining. To detect intracellular labile Fe2+, cells were incubated with FerroFarRed (5 μM; 1 h 37 °C) in RPMI without serum. After incubation with the fluorescent probes, cells were stained with Fc block together with LIVE/DEAD™Fixable Aqua Stain, followed by incubation antibodies against the following surface markers: CD4, CD44, CD62L, and TCRβ. Foxp3+cells were selected based on endogenous Foxp3GFP expression. Cell acquisition was performed using BD LSRFortessa X-20 (BD Biosciences) flow cytometer and data were analyzed using FlowJo software Version 10.8.1. Bone marrow transplants Bone marrow (BM) cells were harvested by flushing the femurs and tibias of donor mice, and T cells were depleted by antibodymediated complement killing. The rabbit complement was prepared fresh, via incubation on ice (30 min), centrifugation (300 × g,10min,4°C)andfiltering (0.22 µm). BM cell suspensions (1 × 107/mL in PBS) were incubated with an anti-Thy1.2 mAb (0.5 µg/mL) and mixed gently (every 15 min) with rabbit complement (LowTox-M, CL3051, CEDARLANE) at a ratio of 100 µL per mL of BM cell suspension (37 °C, 1 h). Complement activity was neutralized (FBS, 200 µL/mL), cell suspensions were filtered (70-μm mesh, cell strainer), washed (2x in PBS, 2% FBS and 1× in PBS without serum), and cell numbers adjusted (108/mL) in PBS. BM cells from C57BL/6 CD45.1+mice were mixed with BM cells from congenic C57BL/6 CD45.2+Foxp3GFP-FthΔ/Δ-tdT or control Foxp3GFP-tdT mice at a 1:1 ratio and injected (i.v.; tail vein, 200 µL) into congenic C57BL/6-recipient Rag2-deficient (Rag2−/−) female mice, 2 h after irradiation (600 Gys). Hematopoietic chimerism was monitored by immunophenotyping, 6 weeks after bone marrow reconstitution and thereafter. T REG cell in vivo homeostatic expansion LN (i.e., superficial, cervical, axillary, brachial, mesenteric, inguinal, lumbar, renal, caudal, and popliteal) and spleen were collected from Foxp3GFP-tdT and Foxp3GFP-FthΔ/Δ-tdT mice and gently disrupted in 70- μm mesh tissue to isolate leukocytes. Cell suspensions were washed in cold PBS, red blood cells were lyzed (i.e., ammonium chloride), passed through 40-μm cell strainer and CD4+GFP+TdT+T REG cells were sorted upon surface marker staining, as described in “Cell sorting”.TotestT REG cell stability in vivo, CD4+GFP+TdT+T REG cells from Foxp3GFP-tdT and Foxp3GFP-FthΔ/Δ-tdT mice were adoptively transferred (i.v., 1 × 105cells) to Rag2-/- mice. LN and spleen were collected six weeks later, disrupted, and RBC was lysed. The number of CD4+GFP+TdT+T REG cells and CD4+GFP-TdT+ex-T REG cells was quantified by flow cytometry, as described in “Immunophenotyping”. An additional aliquot of spleen cells was used to sort CD4+GFP+TdT+T REG cells, as described in “Cell sorting”.These were used to extract mRNA and monitor Fth mRNA expression in the CD4+GFP+TdT+T REG cells used for the adoptive transfer. T REG cell proliferation suppression assay Naive T cells were sorted, as described in “Cell sorting”, washed with PBS, and incubated with Cell Tracer Violet (CTV) (Thermofisher; 1/1000 in PBS without serum) at RT in the dark for 15 min. Staining was stopped by adding five volumes of complete media (containing 10% FBS). Sort-purified T REG cells were plated and serially twofold diluted, starting at 2.5 × 104cells/well in roundbottom 96-well plates with 1 µg/mL soluble anti-CD3 mAb and 5×10 4irradiated splenocytes. CTV-labeled T N cells were plated (2.5 × 104cells/well), resulting in T REG :T NAIVE ratio ranging from 1:1 to 1:64. Cultures were set in triplicates in a final volume of 200 µL. On day 3 of culture, CTV intensity was measured in responder Tcellsdefined as Thy1.1+Thy1.2-TCRb+CD4+, live lymphocytes. In vitro induction of T REG cells and flow cytometry analysis Naive T cells sorted, as described in “Cell sorting”, were cultured for 5 days on a maxisorb 96-well plate pre-coated with anti-CD3 mAb (1 µg/mL; 100 µL/well in PBS) in RPMI complete media (10% FBS, 100 U/mL Penicillin and 100 µg/mL Streptomycin), supplemented with anti-CD28 mAb (1 µg/mL), mouse recombinant IL-2 (20 ng/mL) and TGFβ(5 ng/mL) (iT REG cell differentiation medium). Alternatively, naive T cells were cultured in the same media, without TGFβ (conventional T-cell differentiation medium). For cell surface staining, cells were incubated with Fc block together with LIVE/DEAD™Fixable Aqua Stain, followed by incubation with anti-CD4 antibody. For intracellular Foxp3 staining cells were fixed after surface staining, washed with permeabilization buffer, and incubated with anti-Foxp3 antibody in permeabilization buffer, according to the Foxp3/ Transcription Factor Staining Buffer Set. Cells were acquired in a BD LSRFortessa X-20 (BD Biosciences) flow cytometer and analyzed using FlowJo software Version 10.8.1. For analysis at day 12 after T REG induction, induced iT REG cells were re-plated at day 5 in RPMI media supplemented with IL-2 (100 ng/mL) with or without Fe sulfate (20 µM) and cultured for 7 days. To determine cell proliferation, naive T cells (5 × 106/mL) were stained with CFSE (5 µM, 20 min RT), washed with complete medium to stop the reaction and re-cultured in T REG or conventional T cells medium. The EMBO Journal Qian Wu et al 1466 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Lentiviral transduction Lentivirus was produced by transfecting confluent human HEK293T cells with packaging (psPAX2) and envelope plasmids (pMD2.G) with pLKO.1. HEK293T cells were cultured in DMEM with HEPES (Life Technologies) supplemented with 10% fetal calf serum and 1% penicillin/streptomycin. Polyethylenimine (Polysciences, Hirschberg an der Bergstrasse, Germany) was used as a transfection reagent. After 24 h, the cultures were refreshed with medium with 2% FCS and 24 h later, lentiviral particles were concentrated and purified by ultracentrifugation at 50,000 × g, 2.5h,8°C.NaiveT CONV (CD4+, CD127+,CD25 −, CD45RA+)and naive T REG (CD4+, CD127-,CD25 +,CD45RA +) cells were isolated by FACS sorting (FACS Aria III, BD Biosciences) as described above. The cells were then cultured in presence of 0.1 µg/mL of anti-CD3 mAb (M1654, clone 1XE, PeliCluster) and anti-CD28 mAb (16-0289-85, clone CD28.2, eBioscience) for 5 days in IMDM containing 10% FCS and 300 U/mL IL-2 and restimulated one day prior to transduction. Cells were then infected in RetroNectin® (Clontech) coated plates for 24 h. After that, the cells were transferred into tissue culture-treated plates with medium containing 100 U/mL IL-2 and puromycin. After 5 days, the cells were directly used for FACS analysis or lysed for western blot assays. Western blot Human T conventional (CD4+CD45RA+CD127+CD25–), T REG (CD4+CD127–CD45RA+CD25hi) cells or HEK293T were washed (2× in PBS) and directly lysed in RIPA buffer. Cell lysates containing equal amounts of protein were boiled in a sample buffer prior to gel electrophoresis. SDS-PAGE gel electrophoresis was performed using the NuPAGE electrophoresis system (Novex, Life Technologies). Proteins were transferred using the iBlot system (Thermo Scientific) and analyzed using the corresponding antibodies. ECL signals on Western blots were developed using the Pierce ECL substrate kit (Pierce) followed by autoradiographic detection on film (Fuji Medical). Sorted cells mouse T REG ,T M ,and T N cells were directly lysed in 2× SDS-page sample buffer (20% glycerol, 4% SDS, 100 mM Tris pH 6.8, 0.002% bromophenol blue, 100 mM dithiothreitol). Samples were then sonicated or treated with Benzonase to degrade DNA, heated (10 min; 70 °C), and centrifuged. The supernatant was collected, and the protein was quantified using NanoDrop™1000. Anti-FTH1 (1:1000), anti- Histone H3 (1:1000) were detected using peroxidase-conjugated secondary antibodies (1 h; RT) and developed with SuperSignal™ West Pico PLUS Chemiluminescent Substrate (ThermoFisher Scientific). ECL signal was developed using Pierce ECL substrate kit followed by autoradiographic detection on film (Fuji Medical). Alternatively, western blots were developed using Amersham Imager 680 (GEHealthcare), equipped with a Peltier-cooled Fujifilm Super CCD. Densitometry analysis was performed with ImageJ using images without saturated pixels. qRT-PCR RNA was isolated from cells using NucleoSpin RNA XS kit (Macherey-Nagel) according to the manufacturer’s instructions. cDNA was transcribed from total RNA with transcriptor first strand cDNA synthesis kit (Roche) or Xpert cDNA Synthesis Kit (GRiSP). Quantitative real-time PCR (qRT-PCR) was performed using 1 μg cDNA and SYBR Green Master Mix (Applied Biosystems, Foster City, CA, USA) in duplicate on a 7500 Fast Real-Time PCR System (Applied Biosystems) under the following conditions: 95 °C/10 min, 40 cycles/95 °C/15 s, annealing at 60 °C/ 30 s, and elongation 72 °C/30 s. Primers listed in Table 1were designed using Primer Blast (Ye et al, 2012). Serology Mice were euthanized using CO 2 inhalation. Whole blood was collected by cardiac puncture and transferred into an EDTA for hemogram analyses or heparin tubes for serology (Iron, Transferrin, and Transferrin saturation). Analysis was performed by DNAtech (Lisbon). Histology Organs were harvested, fixed (10% formalin), embedded in paraffin, sectioned (3-μm-thick sections), and stained with Hematoxylin and Eosin (H&E). Whole sections were analyzed, and images acquired with a Leica DMLB2 microscope (Leica) and NanoZoomer-SQ Digital slide scanner (Hamamatsu). mtDNA/nDNA qPCR TotalisolatedDNAwasusedtoperformthequantification of mitochondrial DNA (mtDNA) in comparison to nuclear DNA (nDNA) using a qRT-PCR-based method (Quiros et al, 2017). Briefly, qRT-PCR was performed form using 20 ng of DNA and SYBR Green Master Mix (Bio-Rad), in duplicate on a 7500 Fast Real-Time PCR System (Applied Biosystems), under the following conditions: 50 °C/2 min and 95 °C/5 min (Hold stage), 45 cycles/ 95 °C/10 s, annealing at 60 °C/30 s, and elongation 72 °C/20 s, followed by melting curve: 95 °C/15 s, 60 °C/1 min, and gradual increase in temperature up to 95 °C. Primers for NADH- ubiquinone oxidoreductase chain 1 encoded by the mitochondrial gene MT-Nd1 (Nd1) and for the nuclear-encoded hexokinase 2 gene (Hk2) (Quiros et al, 2017) are listed in Table 1.Mitochondria number per cell was calculated according to the ratio of mRNA expression of the single copy mitochondrial gene Nd1 and the single copy nuclear gene Hk2. Seahorse assays Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured usingaSeahorseXFe96analyzer (Agilent Tech.) and the Seahorse XF Cell Mito Stress Test Kit according to instructions from the manufacturer. Specifically sorted TREG and TCONV cells were plated on poly-L lysine-coated Seahorse XF96 plates (15 × 103cells/well) XF medium (10 mM glucose, 1 mM sodium pyruvate, 2 mM L-glutamine, pH 7.4), and centrifuged (400 × g, for 5 min) to promote cell adhesion. Cells were incubated in a non-CO 2 incubator (37 °C; 1 h) prior to the assay. The analyzer was programmed to calibrate and equalize samples, followed by three baseline measurements (3 min each) and mixing (2 min) between measurements prior to inhibitor injection. The inhibitors were injected in the following order: Oligomycin (1 μM); FCCP (2 μM); Antimycin A/Rotenone (1 μM); and three Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1467 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. measurements (3 min each) were made following each injection with 2 min of mixing between measurements. RNA sequencing RNA was extracted from (CD4 +GFP +)T REG cells sorted from the LN of Foxp3GFP-FthΔ/Δvs. control Foxp3GFP mice (see Fig. 2) or from (CD45.2 +CD3 +CD4 +GFP+tdT +)T REG and (CD45.2 +CD3 + CD4 +GFP-tdT +)ex-T REG cells sorted from LN of BM chimeric mice (see Fig. 4). RNA sequencing data from non-chimeric mice was analyzed as follows: RNA sequencing was performed using Illumina’s next-generation sequencing (Bentley et al, 2008). Quality check and quantification of total RNA was done using the Agilent Bioanalyzer 2100 in combination with the RNA 6000 pico kit (Agilent Technologies). Library preparation was done using SMARTer Stranded Total RNA-Seq Kit v2-Pico Input Mammalian (Takara) following the manufacturer’s description. Library quantification and quality check was done using the Agilent Bioanalyzer 2100 in combination with the DNA 7500 kit. Libraries were sequenced on a HiSeq2500 running in 51cycle/single-end/rapid mode. All libraries were pooled and sequenced in two lanes. Sequence information was extracted in FastQ format using Illumina’s bcl2fastq v.2.19.1.403. Sequencing resulted in around 29mio reads per sample. RNA sequencing data from cells extracted from mixed BM chimeric mice was analyzed as follows: RNA was extracted and quality assessed using Agilent Bioanalyzer 2100 using the RNA 6000 pico kit (Agilent Technologies). Full-length cDNAs and sequencing libraries were generated following the SMART-Seq2 protocol62. Library preparation, including cDNA “tagmentation”, PCR-mediated adaptor addition and library amplification was performed using the Nextera library preparation protocol (Nextera XT DNA Library Preparation kit, Illumina). Libraries were sequenced (NextSeq 500, Illumina) using High Output kit v2.5 (75 cycles). Sequencing data was extracted in FastQ format, using Illumina’s bcl2fastq v.2.19.1.403, producing 30.14 × 106reads per sample on average. Library preparation and next-generation sequencing were performed at the IGC Genomics Unit. Fastq reads were aligned against the mouse reference genome GRCm39 using the GENCODE vM27 annotation to extract splice junction information (STAR; v.2.5.2a)64. Read summarization was performed by assigning uniquely mapped reads to genomic features using FeatureCounts (subread package v.1.5.0-p1). Gene expression tables were imported into the R environment (v.4.1.0) to perform differential gene expression, functional enrichment analyses, and data visualization (R Core-Team, 2021). Differential gene expression analysis was performed using the DESeq2 R package (v.1.32). Gene expression was modeled by genotype. Genes not expressed or with fewer than 10 counts across the samples were removed. We subsequently ran the function DESeq to estimate the size factors (by estimateSizeFactors), dispersion (by estimateDispersions) and fita binomial GLM fitting for βicoefficient and Wald statistics (by nbinomWaldTest). Pairwise comparisons were performed with the function results (alpha = 0.05), and the log 2 fold change for each pairwise comparison was shrunken with the function lfcShrink using the algorithm ashr (v.2.2-47)65. Differentially expressed genes were considered as genes with an adjusted Pvalue < 0.05 and an absolute log 2 fold change >0. Normalized gene expression counts were obtained with the function counts using the option normalized = TRUE. Regularized log-transformed gene expression counts were obtained with rlog, using the option blind = TRUE. Ensembl gene ids were converted into gene symbols from Ensembl (v.104 - May 2021— https://may2021.archive.ensembl.org) by using the mouse reference (GRCm39) database with biomaRt R package (v.2.48.2). All plots were created using the ggplot2 R package (v.3.3.5). Heatmaps were created with pHeatmap (v.1.0.12), using Euclidean distance and Ward.D2 methods for clustering estimation. For hierarchical clustering, gene expression counts were scaled (Z-score) with the function scale. Functional enrichment analysis was performed using the gprofiler2 R package (v.0.2.1). Enrichment was performed with the function gost based on the list of up- or downregulated genes, between each pairwise comparison, against annotated genes (domain_scope = “annotated”) Table 1. RT-qPCR primers. Oligonucleotides Sequences Reference Arbp0 Fwd 5′-CTTTGGGCATCACCACGAA-3′Blankenhaus et al, 2019 Arbp0 Rev 5′-GCTGGCTCCCACCTTGTCT-3′Blankenhaus et al, 2019 Fth Fwd 5′-CCATCAACCGCCAGATCAAC-3′Blankenhaus et al, 2019 Fth Rev 5′-GCCACATCATCTCGGTCAAA-3′Blankenhaus et al, 2019 Ftl Fwd 5’-AAGATGGGCAACCATCTGAC-3’This work Ftl Rev 5’-GCCTCCTAGTCGTGCTTGAG-3’This work Hk2 Fwd 5′-GCCAGCCTCTCCTGATTTTAGTGT-3′Quiros et al, 2017 Hk2 Rev 5′-GGGAACACAAAAGACCTCTTCTGG-3′Quiros et al, 2017 Nd1 Fwd 5′-CTAGCAGAAACAAACCGGGC-3′Quiros et al, 2017 Nd1 Rev 5′-CCGGCTGCGTATTCTACGTT-3′Quiros et al, 2017 GFP Fwd 5′-CGACGTAAACGGCCACAAGTTCAG-3′Liu et al, 2012 GFP Rev 5′-CCGTAGGTCAGGGTGGTCACGAG-3′Liu et al, 2012 Tdt Fwd 5′-GCCGACATCCCCGATTACAAGA-3′Wienert et al, 2015 Tdt Rev 5′-CGATGGTGTAGTCCTCGTTGTGG-3′Wienert et al, 2015 Foxp3 Fwd 5′-GGCCCTTCTCCAGGACAGA-3′Fontenot et al, 2003 Foxp3 Rev 5′-GCTGATCATGGCTGGGTTGT-3′Fontenot et al, 2003 The EMBO Journal Qian Wu et al 1468 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. of the organism Mus musculus (organism = “mmusculus”). Gene lists were sorted based on adjusted p value (ordered_query = TRUE) to generate GSEA (Gene Set Enrichment Analysis) style pvalues. Only statistically significant (user_threshold=0.05) enriched functions are returned (significant=TRUE) after multiple testing corrections with the default method g:SCS (correction_method = “analytical”). Gprofiler2 queries were run against the default functional databases for mouse which include Gene Ontology (GO:MF, GO:BP, GO:CC), KEGG (KEGG), Reactome (REAC), TRANSFAC (TF), miRTarBase (MIRNA), Human phenotype ontology (HP), WikiPathways (WP), and CORUM (CORUM). Gprofiler2 was performed using database versions Ensembl 104, and Ensembl gene 51 (database updated on 07/ 05/2021). RNA-sequencing data analysis Sequence read quality was assessed by means of the FastQC method (v0.11.5; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Trimmomatic version 0.36 was used to trim Illumina adapters and poor-quality bases (trimmomatic parameters: leading=3, trailing=3, sliding window=4:15, minimum length=40). The remaining highquality reads were used to align against the Genome Reference Consortium mouse genome build 38 (GRCm38). Mapping was performed by HISAT2 version 2.1.0 with parameters as default. Count data were generated by means of the HTSeq method and analyzed using the DESeq2 method in the R statistical computing environment (R Core Team 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). Statistically significant differences were defined by Benjamini & Hochberg adjusted probabilities <0.05. Canonical signaling pathways and biofunctions were generated by Ingenuity Pathway Analysis (IPA; QIAGEN) specifying mouse species and ingenuity database as reference. Benjamini & Hochberg adjusted probabilities <0.05 demarcated significance. Functional enrichment analysis was performed with gProfiler (Kolberg et al, 2023). Data were analyzed using g:SCS multiple testing correction method with a significance threshold of 0.05. Targeted metabolomics Targeted metabolomics analyses of cell extracts for intermediates of the tricarboxylic acid cycle (TCA cycle; citrate, isocitrate, α-ketoglutarate, succinate, fumarate, malate, cis-aconitate) and additional metabolites closely linked to the TCA cycle (pyruvate, lactate, DL-2-hydroxyglu- tarate, itaconate, and the amino acids aspartate, glutamate, glutamine) were performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), as described elsewhere (Richter et al, 2019). For metabolite quantification, ratios of analyte peak areas to peak areas of respective stable isotope labeled internal standards determined in cell extracts were compared to those in calibrators. Genome-wide methyl-sequencing (EM-seq) The initial genomic DNA (gDNA) was quantified using the Invitrogen Qubit 4 as per the manufacturer’s protocol (1 µL in 199 µL of Qubit working solution). For library preparation, NEB enzymatic Methy-Seq kit was used, following the manufacturer’s protocol for large insert libraries. Based on the quality assessment, samples were standardized to 10 ng of DNA in a volume of 50 µL, including a spike-in of pUC and Lambda DNA provided with the kit, as a control for methylation efficiency as per the manufacturer’s protocol. Samples were fragmented using the Covaris S2 system, with settings to achieve an average fragment size of 350-400 bp and individual barcoded during the PCR, using eight PCR cycles as per the manufacturer’s protocol. Libraries were quantified using the Qubit HS DNA assay as per the manufacturer’s protocol (1 µL of sample in 199 µL of Qubit working solution). The quality and molarity of the libraries were assessed using Agilent Bioanalyzer with the DNA HS Assay kit as per the manufacturer’s protocol. Molarity was used to equimolarly combine individual libraries into one pool, loaded and sequenced on an Illumina NextSeq 2000 platform (Illumina, San Diego, CA, USA) using a P3 300 cycle kit and a read-length of 155 bp paired-end reads. Raw sequencing reads were deposited to ENA under the accession number: Sequencing reads were processed by the methylseq (v1.6.1) (https://zenodo.org/record/2555454/export/xd) nf-core (Ewels et al, 2020). Default parameters were used with BWA-meth as the aligner, GRCm38 as the reference genome. The --em-seq and --methyl_kit options were also used for downstream analysis. R methylkit package (v1.22.0) (Akalin et al, 2012) was used to load the methylation calls, calculate the methylation rate, and generate plots presented in this paper. Sliding Linear Model (SLIM) multiple testing correction (Wang et al, 2011) was used to extract the significant methylated sites. TET activity assay TET enzymatic activity was monitored in human HEK293T cells, transiently transfected with human TET3 (pCMV-3×FLAG-TET3(human)-Neo; MiaolingBio P45302) plus human FTH (pCMV-FTH- 3Tag3a) or FTH mutant (FTHmut;pCMV-FTH mut-3Tag3a; Glu 62 and His 65 and Lys86 were mutated as Lys, Gly, Gln, respectively, to achieve fully ablation of ferroxidase activity) lacking ferroxidase mutant FTH (FTHmut), similar to previously described (Broxmeyer et al, 1991), followed by nuclear protein extraction and ELISA-based TET activity measurement. Briefly, HEK293T cells, at 60% confluency in six-well plates, were transiently transfected with the human TET3 (800 ng/well) together with FTH (200 ng/well), FTHmut (200 ng/well) cDNA expressing vectors or empty vector (pCMV-3Tag3a; 200 ng/well) using transfection reagent (YEASEN, 40802ES03). Nuclear proteins were extracted 48 h after transfection, using a commercial kit (EpiQuik, OP- 0002-1) and quantified (BCA assay; Meilunbio MA0082-2). TET activity was measured by ELISA-based the manufacturer’s instructions (EpiQuik, P-3087). Expression of flag-tagged TET3, FTH and FTHmut was monitored by western blot. Briefly, cytosol and nuclear extracts were loaded on a 4–20% gradient SDS-PAGE precast-Gel (ACE Biotechnology, ET15420LGel) and proteins were transferred on PVDF membranes. These were blocked (5% skimmed milk in TBST; 1 h) and incubated (4 °C; overnight) with an HRP-labeled anti-FLAG-HRP antibody (Sigma, A8592). Membranes were washed (3 × 10 min; TBST) and HRP activity was detected by ECL (Thermo Scientific, 32106). Images were capture by Molecular Imager®ChemiDocTM XRS+ with Image LabTM Software (BIO-RAD). Membranes were further incubated (1.5 h, RT) with anti-Lamin A/C (Proteintech, 10298-1-AP) and anti-GAPDH (Abclonal, AC033) antibodies, used as reference cytosolic and nuclear proteins, respectively. Membranes were washed (3 × 10 min; TBST), incubated with goat anti-rabbit (Abclonal, AS014) and goat anti-mouse (Abclonal, AS003), secondary antibodies, respectively, washed (3 × 10min in TBST), and HRP signal was detected by ECL to blot references proteins for cytosol and nuclear fractions, respectively, as above. Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 1469 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. A B TH cell activation Fold Expression (log2) −1 0 1 2 Icosl Il12rb1 Il4 Tbx21 Cd247 Gata3 Il17rb Il10 Il12rb2 Irf1 Psen1 H avcr2 Cd4 J ak2 Icos Il1 rl1 Il18r1 Stat1 N fatc2 Pik3c2a Cd40lg Il10 ra N fatc1 C rlf2 Ccr3 Icam1 Ccr8 Ccr4 Cxcr6 Cxcr3 Ifng Maf Nfil3 Il2rb Runx3 Cd80 Ccr5 Foxp3GFP-Fth'' 0 20 40 60 80 CD4 + Foxp3 + CD44 high CD62L low (%) * * Foxp3 10 5 10 4 10 3 0 -10 3 10 5 10 4 10 3 010 5 10 4 10 3 0 1,52% 5,05% 0 2 4 6 8 IFN- J + Foxp3 + ( % ) *** CD4+ Foxp3+ (GFP+) RNAseq C Spleen CD4+ Foxp3 + IFN J + IFNJ Foxp3GFP-Fth'' E Foxp3 GFP Foxp3 GFP-Fth'' SpleenMLN MLN & Spleen CD4+Foxp3 CD44 + CD62L + Foxp3GFP-Fth'' Fth fl/fl Fth fl/fl Fth fl/fl Fth fl/fl Foxp3 GFP-Fth'' Liver Lung Kidney Colon Pancreas 1.25x 1.25x 1.25x 1.25x 1.25x 10x 10x 10x 10x D 103104105 Cell Tracker Violet 8421 0 50 100 150 Inhibition of TN Proliferation (%) TN per TREG NS NS 8/1 4/1 2/1 1/1 8/1 4/1 2/1 1/1 TREG/TN NS NS TCONV cell division TREG/TN Foxp3GFP-Fth'' Foxp3GFP MLN T REG (GFP+) Sorting DCD3/28 +IL-2 Foxp3GFP Foxp3GFP-Fth'' T CONV cell proliferation Sorting The EMBO Journal Qian Wu et al EV3 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Figure EV2. FTH expression in T REG cells prevents systemic cellular inflammation. (A) Schematic representation of experimental approach (left panel) for (CD4+CD25+GFP+)T REG cell sorting from the mesenteric lymph nodes (MLN) and coculture with conventional activated T cells (αCD3/28 +IL-2) to evaluate suppressive function of T REG cells. Representative flow cytometry proliferation histograms (Cell Tracer Violet) of in vitro suppression assay of mouse T N by different ratios of T REG cells (middle panel). Inhibition of T N cell proliferation quantified as percentage of undivided cells (right panel). Data from 1 out of 3 representative experiments, with similar trend. (B) Representative images of H&E-stained liver, lung, kidney, colon, and pancreas from N=3–4 mice per genotype at 27–31 weeks after birth. (C) Schematic representation of the experimental approach (left panel) used to generate the Heatmap (right panel) of individual genes associated with T H effector function programs, differentially expressed in (CD4+GFP+)T REG cells sorted from Foxp3GFP-FthΔ/Δvs. Foxp3GFP mice (same experiment as Fig. 2A,B). (D) Schematic representation of the experimental approach used (left panel) to evaluate the percentage (right panel) of (CD4+Foxp3+CD44highCD62Llow) activated T REG cells in the MLN and spleen. Data from N=7–8 mice per genotype, pooled from three independent experiments, with similar trend. (E) Schematic representation of the experimental approach used (top panel), representative flow cytometry dot plots (bottom left panel) and percentage (bottom right panel) of splenic (CD4+Foxp3+) IFNγ-secreting T REG . Data from N=5 mice per genotype, pooled from two independent experiments, with similar trend. Data information: Data in (A,D,E) represented as mean ± SD. Circles in (A) represent individual wells, and red bars are mean values. Circles in (D,E) represent individual mice, and red bars are mean values. Pvalues in (A,D) calculated using Two-way ANOVA with Sidak’s multiple comparison test and in (E) using unpaired ttest with Welch’s correction. NS not significant (P> 0.05), *P< 0.05; ***P< 0.001. Source data are available online for this figure. Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 EV4 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. B 01010 01010 10³ 10 10 10 0 10 10³ 10 10 10 0 10 CD71 Ki67 TREG ex-TREG 0 20 40 60 80 ** Lorem 21,1% 47,4% 59,6% 32,1% Foxp3 GFP-Fth''-td T Foxp3 GFP-tdT * Fth/Arbp 0 mRNA C D ** 0 0.1 0.2 0.3 0.4 0.5 (%) Ki67 +CD71 + 4 8 12 IFNJMFI (x103) 0 LN Spleen *** *** * * TREG A tdT GFP Week 7Week 9Week 11 Week 15 Week 24 6,89% 1,83% 5,83% 2,19% 7,15% 1,77% 6,08% 1,79% 5,26% 1,42% 2,70% 2,77% 5,83% 2,17% 3,48% 3,48% 4,30% 3,56% 2,77% 2,64% 3,04% 3,25% Week 2Week 4 5,39% 1,2% 4,27% 1,65% 5,01% 1,37% 4,95% 1,61% Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Week 19 105 104 103 0 010 3104105010 3104105010 3104105010 3104105010 3104 105 010 3104105 010 3104105 010 3104105 102 105 104 103 0 102 Spleen 1 2 3 CD71 M FI (x104) 0 LN Spleen EF Thymus CD4+CD8-CD8+CD4- Foxp3+ CD45.2 CD45.1 0 10³ 10 10 -10³ 10 -10 -10 010 10 -10 010 10 -10 010 10 -10 010 10 -10 010 10 83,7% 16,2% 52,9% 46,6% 42,4% 55,6% 52,9% 47,1% 77,8% 21,9% 34,2% 64,5% 38,3% 61,2% 34,3% 64,7% 35,1% 63,5% 48,8% 51,2% 0 10³ 10 10 -10³ 10 -10 CD4+CD8+ CD4-CD8- 0 40 80 120 CD45.1 CD45.2 Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Chimerism (%) G Thymus CD4+CD8-CD8+CD4- Foxp3 + CD4+CD8+ CD4-CD8- NS NS NS NS ex-TREG TREG ex-TREG TREG ex-TREG TREG ex-TREG TREG ex-TREG TREG ex-TREG Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT 3,12% 3,5% NS NS NS NS NS NS NS NS NS NS Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT LN The EMBO Journal Qian Wu et al EV5 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Figure EV3. FTH expression in T REG cells prevents T REG transdifferentiation into inflammatory T REG cells. (A) Representative flow cytometry dot plots of GFP and tdT expression in circulating CD4+cells (same experiment as Fig. 3A–D). Numbers in quadrants correspond to percentages of positive cells at the indicated weeks after birth. (B) Relative quantification of Fth mRNA, by qRT-PCR, normalized to Arbp0 mRNA, in (CD4+GFP+tdT+)T REG and (CD4+GFP-tdT+)ex-T REG cells sorted from the lymph nodes (LN). Data from N=5–7 mice per genotype, pooled from two experiments with similar trend. (C) Mean fluorescence intensity (MFI) of IFNγin activated (CD4+GFP+tdT+)T REG cells and (CD4+GFP-tdT+)ex-T REG from the lymph nodes (LN) and spleen in the same experiment as (Fig. 3H). Data from N=3 wells per genotype in one experiment, representative of 2 independent experiments with similar trend. (D) Representative flow cytometry dot plots (left panel) and corresponding percentage of Ki67+CD71+(right panel) among splenic (CD4+GFP+TdT+)T REG cells and (CD4+GFP-TdT+)ex-T REG cells. Data from N=6 mice per genotype, pooled from two independent experiments, with similar trend. (E) Mean fluorescence intensity (MFI) of CD71 expression in Ki67+T REG and ex-T REG cells from the lymph nodes (LN) and spleen, from the same experiments as in (D). (F) Representative flow cytometry dot plots and (G) corresponding percentages of CD45.1+and CD45.2+double negative (DN), double positive (DP) thymocytes, T H cells, cytotoxic T cells and (CD4+Foxp3+)T REG cells in the thymus from the same BM chimeric mice illustrated in (Fig. 4A–G). Data from N=11–12 mice per genotype, pooled from two independent experiments with similar trend. Data information: Data in (B–E,G) are presented as mean ± SD, circles in (B,D,E) correspond to individual mice or individual wells (C) and red bars are mean values. Pvalues in Panel (B–E,G) were calculated using two-way ANOVA with Sidak’s multiple comparison test. NS not significant (P> 0.05), *P< 0.05; **P< 0.01; ***P< 0.001. Source data are available online for this figure. Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 EV6 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. 17,3% 5,71% 8,02% 1,71% A Rag2-/- CD45.2+ C57BL/6 50% Foxp3GFP-Fth''-tdT Foxp3GFP-tdT 50% Lymph Nodes Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT 4DC(L26DC + ) 8DC(L26DC + ) BC DE BM CD44 103104105 102106103104105 102106 -103 105 104 103 0 -103 105 104 103 0 0 0.05 0.1 0.15 Fth/Arbp0 mRNA *** tdT GFP 0-10³ 10³ 10 10 0-10³ 10³ 10 10 -10² 0 10² 10³ Post- Sorting Pre- Sorting TREG cells (GFP+tdT+) Sorting TREG cells (GFP+tdT+) 98,8%16,3% Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Foxp3 GFP-Fth'' Foxp3 GFP F 0 25 50 75 100 T N DCD3/28 +IL-2+TGFE CD3/28 +IL-2 Foxp3+Cells (%) 8,5% 96,8% 8,2% CD4 Foxp3 DCD3/28 IL-2 DCD3/28 IL-2 + TGF E 010 3104105010 3104105 -103 105 104 103 0 -103 105 104 103 0 96,9% TCONV iT REG 5d NS NS Foxp3 GFP Foxp3 GFP-Fth'' Foxp3 GFP Foxp3 GFP-Fth'' GH I 103 104 105 106 107 Foxp3+Cells (Nbr.) Foxp3 GFP Foxp3 GFPFth'' 12345678 0 10 20 30 Nbr. of cell divisions TREG (%) 0 20 40 60 80 100 0103 0 20 40 60 80 100 104105 1 2 3 4 5 6 7 8 1 2 3 4 6 7 8 5 Foxp3 GFP Foxp3 GFPFth'' TCONV iT REG J NS NS Events (%) CFSE Cells 0 10 20 30 40 50 0 0.5 1 1.5 2 CD44 high CD62L low (%) CD8 + CD4 + CD44 high CD62L low (x10 6 NS NS NS NS ) CD45.2+ CD8 + CD4 + Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Foxp3 GFP-Fth''-tdT Foxp3 GFP-tdT Foxp3 GFP Foxp3 GFPFth'' iTREG cells iTCONV cells The EMBO Journal Qian Wu et al EV7 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Figure EV4. FTH acts in a non-cell-autonomous manner to prevent T REG cells from transdifferentiating into inflammatory T REG cells. (A) Schematic representation of the experimental approach used for flow cytometry analysis from the lymph nodes of BM chimeric mice (same experiment as Fig. 4A). (B,C) Representative flow cytometry dot plots (B), quantification of percentage (left panel) and number (right panel) (C) of live activated (CD45.2+CD4+CD44highCD62Llow) and (CD45.2+CD8+CD44highCD62Llow) cells in the lymph nodes of BM chimeric mice from (A). Data in (C) from n=11–12 mice per genotype, pooled from 2 independent experiments, with similar trend. (D) Schematic representation of cell sorting for adoptive transfers in the experiment illustrated in Fig. 5A. (E) Representative flow cytometry dot plots of (CD4+GFP+tdT+)T REG cells and relative level of Fth mRNA expression in (CD4+GFP+tdT+)T REG cells (right panel) used for adoptive transfers in the experiment illustrated in Fig. 5A. (F) Schematic representation of experimental approach used for in vitro generation of induced T REG (iT REG ) cells and conventional T H (T CONV ) cells from sorted naive T H (T N ) cells, activated with anti-CD3 and anti-CD28 mAb plus IL-2 and TGFβ.(G) Representative flow cytometry dot plots of iT REG and T CONV cells, generated in (F). (H) Percentage (%) and (I) Number (Nbr.) of Foxp3+T CONV and iT REG cells, generated as described in (F). N=2–5 independent experiments with similar trend. Each experiment corresponds to the average of different wells. (J) Representative flow cytometry carboxyfluorescein succinimidyl ester (CFSE) staining (left panel) and quantification of percentage (%) (right panel) of proliferating (CD4+Foxp3+)iT REG cells, generated as described in (F). Data from 3 to 6 technical replicates in 1 out of 3 independent experiments, with similar trend. Data information: Data in (C,E) are presented as mean ± SD, circles correspond to individual mice and red bars are mean values. Circles in (H–J) correspond to individual wells and red bars are mean values. Pvalues in panels (C,H,I) were calculated using two-way ANOVA with Sidak’s multiple comparison test. Pvalues in (E) were calculated using Mann–Whitney test. NS not significant, ***P< 0.001. Source data are available online for this figure. Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 EV8 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. ABC Foxp3GFP FCCPOligo. A/A+Rot. OCR (pmol/min/106cells) 0 20406080100 0 200 400 600 800 -100 NS FCCPOligo. A/A+Rot. OCR (pmol/min/106cells) 0 20406080100 0 200 400 600 800 -100 Foxp3GFP-Fth'' REG CONV T T REG CONV T T Time (minutes) Time (minutes) OCR (pmol/min/106cells) **** TCONV TREG TCONV TREG Foxp3GFP REG CONV T T OCR (pmol/min/106cells) Foxp3GFP-Fth'' REG CONV T T D Metabolite Concentration [pmol/103 cells] Foxp3GFP-Fth''' Foxp3GFP Citrate Fumarate Isocitrate D-ketoglutarate D,L-2 hydroxyglutarate Succinate Cis-aconitate Pyruvate Malate Aspartate Glutamate Glutamine Itaconic acid Lactate 0 100 200 300 400 0.001 0.01 0.1 1 10 100 ** Trageted Metabolomics Spleen TREG cells (GFP+) Sorting E IFN 105 104 103 0 105 104 103 0 NS CD4 + CD8 + 0 20 40 60 80 NS 23,3% 105 104 103 0 105 104 103 0 28,8% 105 104 103 0 Granzyme B CD8 53,9% 54,9% 105 104 103 0105 104 103 0 CD4 CD8 0 2 4 6 per tumor (x10 5 ) CD4 + CD8 + NS * 15,0% 26,6% 0 20 40 60 NS 0 2.5 5 7.5 10 per tumor (x10 5 ) NS Tumor-infiltrating CD8+GrzmB+ cells B16 Melanoma G B16 Melanoma F Foxp3GFP-Fth'' Foxp3GFP Foxp3GFP-Fth'' Foxp3GFP CD8+GrzmB+ cells (%) Nbr. CD8+GrzmB+ cells 0 200 400 600 -100 0 100 200 300 400 The EMBO Journal Qian Wu et al EV9 The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 © The Author(s) Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182. Figure EV5. FTH regulates mitochondrial energy metabolism and CPG methylation in T REG cells. (A) Oxygen consumption rate (OCR) in live splenic (CD4+GFP+)T REG cells and (CD4+GFP-)T CONV cells from Foxp3GFP mice. (B) Quantification of spare respiratory capacity, from data represented in (A). (C) Oxygen consumption rate (OCR) in live splenic (CD4+GFP+)T REG cells and (CD4+GFP-)T CONV cells from Foxp3GFP-FthΔ/Δmice. (D) Quantification of spare respiratory capacity, from data represented in (C). Data in (A–D) pooled from N=3 mice per genotype, represented as mean ± SD. N=3–5 technical replicates in 1 out of 3 independent experiments, with similar trend. Oligomycin (Oligo.), carbonilcyanide p-triflouromethoxyphenylhydrazone (FCCP), Antimycin A/Rotenone (A/A+Rot.). (E) Schematic representation of sorting of splenic (CD4+GFP+)T REG cells used for targeted metabolomics (left panel). Quantification of intermediate metabolites from targeted metabolomics analyzes of splenic T REG cells (right panel). Data from N=3–4 mice per genotype in one experiment representative of 3 independent experiments with similar trend. (F) Schematic representation of the experimental approach used for flow cytometry analysis of tumor-infiltrating cells (left panel), representative flow cytometry dot plots (middle panel) and corresponding percentage and number (right panel) of live tumor-infiltrating (CD4+IFNγ+)T H cells (CD8+IFNγ+)T C cells, 3 weeks after tumor inoculation (2 × 105B16 cells). Data from N=6 mice per genotype, pooled from two independent experiments, with similar trend. (G) Schematic representation of the experimental approach used for flow cytometry analysis of tumor-infiltrating cells (left panel), representative flow cytometry dot plots (middle panel) and corresponding percentage and number (right panels) of live tumor-infiltrating (CD8+GrzmB+) T cells, 3 weeks after tumor inoculation (2 × 105 B16 cells). Data from N=6 mice per genotype, pooled from two independent experiments, with similar trend. Data information: Circles and triangles in (A,C) correspond to mean values, circles, and triangles in (B,D) correspond to individual wells, and circles in (E–G) correspond to individual mice, and red bars are mean values. Pvalues in (A,C) calculated using two-way ANOVA with Bonferroni’s(A,C) or Sidak´s (E,F) multiple comparisons test, in (B,D,G) using unpaired ttest with Welch’s correction. NS, not significant (P> 0.05); *P< 0.05; **P< 0.01; ****P< 0.0001. Source data are available online for this figure. Qian Wu et al The EMBO Journal © The Author(s) The EMBO Journal Volume 43 | Issue 8 | April 2024 | 1445 –1483 EV10 Downloaded from https://www.embopress.org on January 15, 2025 from IP 193.144.79.182.