TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition

TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition Marion Turpin , Thierry Madigou , Maud Bizot , Rachael Acker , Stephane Avner , Gérard Benoît , Martin Braud , Cynthia Fourgeux , Gaëlle Palierne , Jeremie Poschmann , Katie Sawvell , Erwan Watrin , Christine Le Péron , Gilles Salbert doi: https://doi.org/10.1101/2024.04.01.587571 Marion Turpin 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thierry Madigou 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maud Bizot 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachael Acker 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France 3 University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephane Avner 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gérard Benoît 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Braud 2 CHU-Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie UMR1064, Nantes Université , Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cynthia Fourgeux 2 CHU-Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie UMR1064, Nantes Université , Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gaëlle Palierne 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeremie Poschmann 2 CHU-Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie UMR1064, Nantes Université , Nantes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katie Sawvell 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France 4 Cincinnati Children’s Hospital Medical Center , Cincinnati, OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erwan Watrin 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Le Péron 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gilles Salbert 1 Univ Rennes, CNRS, Institut de Génétique et Développement de Rennes, UMR 6290 , F-35000 Rennes, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: gilles.salbert{at}univ-rennes1.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Acquisition of cell identity is associated with a remodeling of the epigenome in part through active DNA demethylation. The T:G mismatch DNA glycosylase (TDG) participates to this process by removing 5-methylcytosines that have been oxidized by Ten-Eleven-Translocation (TET) enzymes. Despite this well-defined molecular function, a comprehensive view of the biological function of TDG is still lacking, especially during cell differentiation. Here, we combined transcriptomic and epigenomic approaches in a Tdg knock-out epiblast stem-like cell model to decipher TDG function in pluripotent cells and their retinoic acid-induced progeny. We determined that TDG occupies a majority of active promoters, a large fraction of which are also engaged by the transcription factor ATF4. Consistently, neural fate commitment upon retinoic acid treatment is associated with a TDG-dependent sustained expression of ATF4-dependent genes, in relation with a TDG-associated nucleosome positioning at promoters. We further evidenced that TDG maintains ATF4 pathway activity by positively regulating the mammalian target of rapamycin complex 1 (mTORC1), favoring neural cell fate commitment. These observations highlight the central role of TDG in cell differentiation and support a model linking metabolic reprogramming to cell fate acquisition. INTRODUCTION During differentiation, acquisition of a given cell identity relies on the establishment of specific gene expression profiles. These transcriptional changes involve both remodeling and covalent modifications of chromatin, among which stands DNA methylation. Genome-wide DNA methylation patterns constitute a crucial layer of epigenetic information involved in transcription regulation and result from the equilibrium between methylation reactions catalyzed by DNA methyltransferases (DNMTs) and active demethylation reactions, which rely on the Ten-Eleven-Translocation (TET) and thymidine:guanidine mismatch thymine DNA glycosylase (TDG) enzymes ( Wu and Zhang, 2017 ). The transfer by DNMTs of a methyl group from S-adenosyl-L-methionine to cytosine at position 5 (5mC) occurs mainly in the context of CpG dinucleotides and can exert either negative or positive effects on gene expression by regulating the binding of transcription factors to DNA, and by the interaction of 5mCpGs with methyl-DNA binding proteins which are believed to recruit repressive complexes ( Nan et al., 1998 ; Kaluscha et al., 2022 ). However, in the context of CpG island (CGIs) promoters, dense DNA methylation is associated with gene silencing ( Deaton and Bird, 2011 ). TDG was initially discovered as an enzyme catalysing the removal of U and T mismatched with G ( Neddermann and Jiricny, 1993 ; 1994 ) thereby participating in the maintenance of genome stability. Its role has expanded since the discovery of an active DNA demethylation pathway that involves TDG and TET enzymes ( Kriaucionis and Heintz, 2009 ; Tahiliani et al., 2009 ; Maïti and Drohat, 2011). During this demethylation process, TETs iteratively oxidize 5mC into 5-hydroxy-mC (5hmC), 5-formyl-C (5fC) and 5-carboxyl-C (5caC), collectively referred to as oxi-mCs. The two terminal oxidized forms, 5fC and 5caC, are excised by TDG that hydrolyses the sugar-base bond, creating an abasic site that is then converted into an intact, unmodified cytosine by the base excision repair machinery. The newly incorporated base can then be targeted for de novo methylation by DNA methyltransferases. One crucial aspect of DNA methylation resides in the dynamic state that exists between modified and unmodified forms, which varies among different types of genomic regions. The turnover of 5mC is notably high at enhancers, a particular class of gene regulatory elements that physically contact distant promoters for expression of cognate genes. As enhancer/promoter wiring plays an essential role in cell specific gene expression patterns, it is tempting to consider that such highly dynamic methylation of CpGs at enhancers is indicative of its involvement in programming and acquisition of cell identity ( Sérandour et al., 2012 ; Caron et al., 2015 ; Mahé et al., 2017 ; Ginno et al., 2020 ). As an actor of the demethylation pathway, TDG has been involved in this cyclic methylation/demethylation process in breast cancer cells, a role that was later extended to embryonic stem cells (ESCs - Métivier et al., 2008 ; Rulands et al. 2018 ; Parry et al., 2021 ). Furthermore, TDG interaction with DNMT3A and DNMT3B results in a decreased catalytic activity of both methyltransferases ( Li et al., 2007 ; Boland and Christman, 2008 ; Sandoval et al., 2019 ). Hence, the opposing activities of DNMTs, TETs and TDG finely tune methylation dynamics that allow proper cell differentiation and identity acquisition. Besides this function in the CpG methylation cycle, TDG has also been shown to interact physically with the histone acetyltransferase complex CBP and with a number of transcription factors such as p53 and nuclear receptors, including retinoic acid (RA) receptors (RARs), and thereby participates in rendering chromatin accessible to DNA binding factors ( Um et al., 1998 ; Tini et al., 2002 ; Onabote et al., 2022 ; Aranda et al., 2023 ). Hence, TDG is central to mechanisms regulating gene expression during cell-type specification processes, including those triggered by RA. The RA/RAR pathway exerts pleiotropic effects during development, in particular on cell fate commitment and identity acquisition processes, some of which can be recapitulated in vitro . Notably, RA treatment triggers neural differentiation of ESCs as well as of embryonal carcinoma cells (ECCs - Jones-Villeneuve et al., 1982 ; Okada et al., 2004 ). Investigating the role of TDG in RA-induced neural differentiation has been previously attempted through Tdg inactivation approaches both in mESCs and in mouse primary neural stem cells (NSCs - Cortazar et al., 2011; Wheldon et al., 2014 ; Steinacher et al. 2019 ). However, massive cell death occurred in both models early during neural differentiation, precluding an in-depth study of TDG function in neural differentiation processes. To circumvent these limitations, we took advantage of a mouse ECC line called P19 ( McBurney, 1993 ). P19 ECCs derive from epiblast stem cells (EpiSCs) and as such present genic and morphological characteristics of EpiSCs ( Han et al., 2013 ). Furthermore, P19 ECCs can differentiate in the presence of RA ( Jones-Villeneuve et al., 1982 ), and are known as an appropriate alternative model to ESCs and EpiSCs when high cell death is observed ( Magnúsdóttir et al., 2013 ). Using P19 ECCs, we succeeded in establishing a viable Tdg -null model cell line that withstands RA treatment, providing us with a unique opportunity to study the role of TDG during cell differentiation. Using this model, we established the impact of Tdg inactivation on the transcriptome and demonstrated that TDG is required for balancing cell differentiation towards a neural fate at the expense of a cardiac mesoderm one. During differentiation, Tdg inactivation led to changes in nucleosome positioning and to a down-regulation of ATF4-target genes, revealing that TDG is required to maintain ATF4-dependent gene transcription. ATF4, a bZIP transcription factor, confers to cells adaptability to various stress conditions like high levels of ROS, amino-acid (aa) deprivation and endoplasmic reticulum stress, through direct binding to promoters and co-activation by LSD1 and p300/CBP of a large set of genes grouped into the so-called integrated stress response (ISR) pathway ( Lassot et al., 2005 ; Pakos-Zebrucka et al., 2016 ; Faletti et al., 2021 ). We finally report that TDG maintains ATF4-target gene expression through regulation of the mTORC1 signaling complex and propose that TDG regulates cell fate acquisition through diverse molecular mechanisms converging on ATF4 activity and which might be relevant to human cancer. Materials and Methods Cell Culture and treatments P19 mouse embryonal carcinoma cells (ECCs) were grown and differentiated as described ( Sérandour et al. 2012 ). Briefly, cells were cultured on cell culture plastic dishes in DMEM containing 10% FCS, penicillin and streptomycin. To obtain cell aggregates, 10 6 cells were grown in bacterial grade plastic dishes with or without 1 μM RA (Sigma-Aldrich, R2625). For mTOR inhibition experiments in undifferentiated ECCs, subconfluent monolayers of cells grown in 6-well plates (10 5 cells per well) were treated with 20 nM rapamycin (ApexBio, A8167) for 16 hours. To evaluate the impact of mTOR inhibition during RA-induced differentiation, 4×10 4 cells were seeded in 6-well plates and first treated with 1 μM RA for 24 hours and then with 20 nM rapamycin for an additional 24 hours. Halofuginone (8 nM, Supelco, 32481) or L-proline (0.5 mM, Sigma-Aldrich, P0380) were applied to monolayers of ECCs for 48 hours. The ferroptosis inducer RSL3 (Sigma-Aldrich, SML2234) was applied to monolayers of ECCs for 24 hours. The ferroptosis inhibitor ferrostatin-1 (Fer1, Sigma-Aldrich, SML0583) was applied to cells at 20 μM for 24 hours, in combination with 100 nM RA. MTT assays were run as previously described ( Laurent et al., 2022 ). CRISPR-Cas9-mediated Tdg knock-out Knock-out was performed using the Tdg mouse Gene Knock-Out kit (Origene, KN317363). This kit contained two gRNAs that targeted the first exon of Tdg gene and a donor vector that contained a GFP-Puromycin cassette to facilitate the screening process. Briefly, wt ECCs were seeded in P100 Petri dishes transfected with 1 µg of either gRNA vectors and 1 µg of the donor vector using the JetPEI transfection reagent (Polyplus-transfection). Five passages after the transfection, Puromycin was added to the medium (0.5 µg/ml). After the ninth passage, fluorescence activated cell sorting was carried out to isolate GFP-positive cells (LSR Fortessa X-20, Becton Dickinson). A limiting dilution strategy was then applied to isolate and expand clones from single cells. These clones were analyzed by RT-PCR and PCR on genomic DNA (primers are listed in Supplementary Table 1) to assess for the absence of Tdg mRNA and the presence of the GFP-Puromycin cassette respectively. CRISPR-Cas9-mediated Cyp26a1 knock-out Two guide RNAs (gRNAs) flanking the region of interest were designed using CRISPOR ( http://crispor.tefor.net/ ) and introduced either into pX458 or pX459 (one gRNA, Addgene) at the BbsI (NEB, R0539) site. Wt ECCs were seeded into P100 Petri dishes and transfected for 24 hours with vectors containing the gRNAs, i.e., pX458, carrying a GFP coding sequence, and pX459 with a ratio 1:1.2. GFP-positive cells were isolated using flow cytometry and seeded into a 96-well plate (LSR Fortessa X-20, Becton Dickinson). Clones were analyzed by PCR and knock-out was confirmed by Sanger sequencing. MNase assay For MNase-seq assays, 10-20×10 6 cells were incubated for 10 min in 1 % formaldehyde in PBS, washed twice and scraped off of plates in ice cold PBS and spun down at 100g for 10min at 4°C. Cells were re-suspended in digestion buffer (50 mM Tris-HCl pH7.6, 2 mM CaCl2, 0.2 % NP40) containing protease inhibitors (complete EDTA-free, Roche) and prewarmed to 37°C for 2 min. Then, 4U of MNase were added to wt ECCs and the reaction mixture was incubated at 37°C for 5 min. For Tdg -null ECCs, 2U of MNase were added, due to the limited amount of recovered mono-nucleosomal DNA when using 4U of MNase. The reaction was stopped by adding EDTA to a final concentration of 10 mM. Cells were then incubated for 10 min on ice in SDS lysis buffer (1 % SDS, 50 mM Tris pH8, 10 mM EDTA pH8). An equal volume of TE/RNase buffer (10 mM Tris pH8, 0.1 mM EDTA pH8, 0.4 µg/ml RNase A) was added before incubating at 37°C for 2 hours. A Proteinase K digestion (1 mg/ml) was then performed at 55°C overnight. The mixture was then extracted twice with Phenol/chloroform and the DNA was precipitated 1 hour at −20°C in the presence of glycogen (20 µg). After centrifugation, DNA pellets were washed by ethanol, dissolved in TE buffer (10 mM Tris pH8, 1 mM EDTA pH8) and purified using NucleoSpin Gel and PCR clean-up (Macherey-Nagel). DNA was loaded on a 2 % agarose gel. The mono-nucleosome band was excised and the corresponding DNA was purified using MinElute Gel Extraction kit (Qiagen). Mono-nucleosomal DNA was further analyzed by deep sequencing. MNase-qPCR assays followed the same procedure except that increasing concentrations of MNase were used. After PCR amplification of selected nucleosomes, values were normalized to the amplification values of undigested purified genomic DNA. Primers are listed in Supplementary Table 1. Chromatin Immunoprecipitation (ChIP) ChIP assays of H3K27ac were run as previously described ( Sérandour et al., 2012 ). For TDG and ATF4 ChIP assays, chromatin from wt ECCs was cross-linked using 1% formaldehyde for 10min at room temperature. Cells were rinsed with cold PBS, harvested and lyzed in Farnham lysis buffer (5 mM PIPES pH8, 85 mM KCl, 0.5 % NP40, Protease inhibitor cocktail). Nuclei were pelleted and resuspended in RIPA buffer (1xPBS, 1 % NP40, 0.5 % Sodium deoxycholate, 0.1 % SDS, protease inhibitor cocktail). Chromatin fragmentation was performed by sonicating samples 14 min (30 sec on/off cycles) using a Bioruptor (Diagenode). The fragmented chromatin was incubated at 4°C overnight either with a rabbit polyclonal antibody against mTDG ( Gallais et al., 2007 ), or a rabbit monoclonal antibody against hATF4 (Cell Signaling, D4B8) in RIPA buffer. Complexes were recovered after incubation with 50 μl protein A–conjugated Sepharose bead slurry at 4°C. Beads were then washed four times by LiCl washing buffer (100 mM Tris pH7.5, 1 % Sodium deoxycholate, 1 % NP40, 500 mM LiCl, protease inhibitor cocktail) and twice by TE buffer (10 mM Tris HCl pH8, 1 mM EDTA). Fragments were eluted with extraction buffer (1 % SDS, 100 mM NaHCO3). Cross-linking was reversed by overnight incubation at 65°C and DNA fragments were purified using NucleoSpin Gel and PCR Clean-up columns (Macherey-Nagel). RNA preparation, reverse transcription and real-time PCR (qPCR) RNA was isolated using Trizol reagent (Ambion, Life Technology) according to the manufacturer’s protocol. Reverse transcription was performed using 1 µg of total RNA as template, 200 U of the M-MLV Reverse Transcriptase (Invitrogen) and 250 ng of Pd(N6) random hexamers (Jena Bioscience). Real-time PCR was performed using SYBR Green Master Mix (Bio-Rad) on a Bio-Rad CFX96 machine. All primers were designed with Primer3 ( Untergasser et al., 2012 ) and purchased from Sigma (Supplementary Table 1). RNA Sequencing Methodology Total RNA was extracted using Trizol and RNA Integrity Number (RIN) score were determined with a 2100 Bioanalyzer (Agilent). The protocol for 3’ digital gene expression profiling was carried out as previously described ( Chaumette et al., 2022 ). Library preparation was initiated from 10 ng of total RNA per sample. mRNA poly(A) tails were tagged with universal adapters, well-specific barcodes and unique molecular identifiers (UMIs) during the template-switching reverse transcription process. Barcoded complementary DNAs (cDNAs) from multiple samples were pooled, amplified, and subjected to tagmentation, focusing on enriching the 3’ ends of cDNAs. The resulting library, with fragment sizes ranging from 350 to 800 bp, underwent sequencing in a 100-cycle S2 run on the NovaSeq 6000 system at the Genomics Atlantic platform (Nantes, France). Raw sequencing data were deposited in GEO under accession number GSE262587. On average, each sample yielded approximately 5 million 75 bp single-end reads. Post-sequencing, data demultiplexing was performed using Illumina bcl2fastq. The resulting FASTQ files were analyzed using the 3’ Sequencing RNA Profiling (SRP) pipeline ( Charpentier et al., 2021 ). This methodology aligns with previous DGEseq analyses in various studies ( Chaumette et al., 2022 ; Letellier et al., 2022 ; Ménoret et al., 2023 ). The SRP pipeline incorporates cutadapt for read trimming and bwa aln (version 0.7.17) for alignment to the mm10 Mus musculus reference genome and transcriptome. A custom Python script was employed to parse and count UMIs, leading to the generation of a raw matrix containing unique transcript counts. Gene annotation was based on the RefSeq database. The dataset encompassed a total of 26,214 genes, where 3,453 genes had zero reads across all samples, while 22,761 genes had at least one read in at least one sample. The tests for differential expression between two conditions were performed using the R package DESeq2 ( Love et al., 2014 ) version 1.42.0. A gene was declared modulated if it displayed a significant difference between the two indicated conditions with a cut-off fixed at 5.10 −2 for adjusted P-value (Benjamini-Hochberg). MA-plots show the shrunken log2 fold changes between the indicated conditions, using the adaptive shrinkage estimator from the ashr package ( Stephens, 2016 ). Heatmap in figures 2B and 3F were computed using the ggplot2 R package ( Wickham, 2016 ). Library preparation, sequencing, and bioinformatics All MNase-seq libraries were prepared using the TrueSeq ChIP Sample Prep kit (Illumina) and single-end sequenced using a HiSeq 1500 sequencing system at the “Human and Environmental Genomics” platform (now EcoGeno) (Rennes, France). Raw sequencing data were deposited in GEO under accession number GSE261602. Sequencing reads were mapped to mm8 using Bowtie ( Langmead et al., 2009 ) and processed by SAMtools ( Li et al., 2009 ) to generate bam files followed by wig file generation with MACS ( Zhang et al., 2008 ). Peak calling followed a previously described procedure ( Sérandour et al., 2012 ). Sequencing reads from publicly available datasets used in this study were downloaded from GEO at NCBI and were mapped and treated following the same procedure. For comparison, sequencing data were normalized with respect to the number of reads. To generate heatmaps, except for Fig. 4C which was obtained online with Cistrome ( Liu et al., 2011 ; http://cistrome.org/ap/root ), signal matrix was computed using DeepTools (v 3.5.1) computeMatrix tools ( Ramírez et al., 2016 ) and processed using Profileplyr R package (v 1.20.0) ( Carroll and Barrows, 2024 ). To compare the genome-wide distribution of TDG with candidate cis-regulatory elements (cCREs - ENCODE Project Consortium et al., 2020 ), cCRE coordinates (mm10) from the 2nd version of the registry were downloaded ( https://screen.encodeproject.org/index/cversions ). CpG island coordinates (mm10) were obtained from UCSC genome browser ( https://genome.ucsc.edu/ ). Transcription start sites (TSSs) from the mouse genome (mm10) were extracted from the Ensembl v102 gtf ( https://www.ensembl.org/ ). Motif enrichment in TDG ChIP-seq peaks was run using the SeqPos tool from Cistrome. The online Dependency Map (DepMap) portal was used in custom analysis mode ( https://depmap.org/portal/interactive/custom_analysis ) to investigate the correlation between TDG gene expression and protein levels in human cancer cell lines, as well as between the occurrence of damaging mutations in the TDG gene and growth of human cancer cell lines following CRISPR mediated gene inactivation (Gene effect). Bar graphs (mean +/- SD, n=3 to 6) were generated with GraphPad Prism 5.0 and analyzed by unpaired t-test (*: P<0.05, **: P<0.01, ***: P<0.001, ****: P<0.0001). Total cell extract and immunoblot Total cell extracts were prepared and immunoblotting experiments performed as described previously ( Watrin et al, 2014 ). Rabbit antibodies against U2AF65 (sc-53942) were purchased from Santa-Cruz Biotechnologies, p70S6K (# 9202) and T389p70S6K-Ph (# 9234) from Cell Signalling Technology, and MEIS1 from Abcam (#19867). Rabbit anti-TDG antibodies were already described ( Gallais et al., 2007 ). All antibodies used in this study were affinity-purified. Results TDG maintains retinoic acid-induced differentiation of ECCs towards neural fate In order to characterize the dynamics of gene expression during RA-induced ECC differentiation, we first ran a time-course analysis of the transcriptome of P19 ECCs treated with 1 μM RA, by 3’RNA-seq (Supplementary figure 1A). Results indicated that P19 ECCs expectedly lost expression of pluripotency genes upon RA treatment and acquired a posterior fate, as revealed by the rapid extinction of Otx2 expression and induction of Cdx1 and Hoxa1 (Supplementary Fig. 1A). Appearance of posteriorization markers was rapidly followed by the expression of neuromesodermal progenitor (NMP) markers, all of which except Sox2 dropped at 24 hours of RA exposure while neural progenitor cell (NPC) markers rose markedly (Supplementary Fig. 1A). In parallel, mesoderm progenitor cell (MPC) and paraxial mesoderm markers showed no to low induction (except for Meox1 ) while cardiomyocyte markers were induced (Supplementary Fig. 1A). These data indicate that ECCs mainly engage into a neuroectodermal fate upon RA treatment but a fraction of them adopt a cardiac mesoderm fate. Consistent with this observation, signaling pathways favoring NMP establishment and maintenance as well as mesoderm induction ( i.e. , Fgf, Wnt and Nodal; Henrique et al., 2015 ; Edri et al., 2019 ) were turned down at the time when NPC markers started to be expressed (Supplementary Fig. 1A). Hence, upon RA addition, the P19 ECC system recapitulates key steps of EpiSC differentiation into NPCs and cardiomyocyte precursors and therefore stands as a suitable model for investigating mechanisms involved in cell fate acquisition. To interrogate the role of TDG in RA-induced differentiation of ECCs, the Tdg gene was inactivated by CRISPR/Cas9 in wt P19 ECCs (Supplementary Fig 1B). Transcriptomes were determined by 3’-RNA-seq for both wt and Tdg -null ECCs, before and after treatment with RA. Differential gene expression analysis between untreated wt and Tdg -null ECCs highlighted 977 differentially expressed genes (DEGs, adjusted P value < 0.05, Supplementary Table 2). Gene ontology (GO) enrichment analysis revealed no specific annotation for up-regulated genes and “RNA processing” and “amino acid metabolism” annotations for down-regulated genes (Supplementary Fig. 1C), suggesting that TDG is not involved in the repression of a particular set of genes in the absence of RA. Upon treatment with RA for 48 hours, 700 DEGs were significantly up and 609 down (adjusted P value < 0.05) in Tdg -null cells compared with wt cells ( Fig. 1A , Supplementary Table 3). In particular, Tdg -null cells showed a decreased expression of NPC-specific markers like Pax6 and Dbx1 and an increased expression of mesoderm markers such as Acta2 , Myl9 and Meox1 , indicating differentiation skewing towards cardiac mesoderm ( Fig. 1A ). In addition, the RA-inducible Meis1 gene encoding a transcription factor shared between neural and mesodermal lineages showed similar expression levels in both cell lines 48 hours post RA treatment ( Fig. 1A ), indicating that Tdg -null cells retained the ability to respond to RA. Of note, RA-induced expression of the Cyp26a1 gene was blunted in the absence of TDG ( Fig. 1A and supplementary Fig. 1D). CYP26A1 catabolizes the first step of RA elimination by oxidizing RA into 4-oxoRA and, by doing so, reduces the intracellular levels of RA ( Langton and Gudas, 2008 ). Consistent with a reduced catabolism of RA, RA-induction of Meis1 expression was triggered by lower concentrations of RA in Tdg -null cells than in wt cells (Supplementary Fig. 1E). This higher sensitivity to RA of Tdg -null cells was also evidenced phenotypically since RA-induced cell growth as 3D-clusters was triggered by lower RA concentrations in Tdg -null cells than in wt ECCs (Supplementary Fig. 1F). In order to investigate whether alterations in RA catabolism could account for the differentiation skewing that we observed in Tdg -null cells, we generated Cyp26a1 -null ECCs (Supplementary Fig. 1G,H). In line with results obtained by treating Cyp26a1 -null ESCs with high concentration of RA ( Russo et al., 2022 ), RT-qPCR analysis of differentiation markers in RA-treated Cyp26a1 -null ECCs did not show a reduction in expression of neural markers compared to wt ECCs ( Fig. 1B ). Thus, reduced CYP26A1 level does not account for the differentiation skewing observed in Tdg -null cells. Alternatively, it remains possible that the observed reduced expression of NPC markers in Tdg -null cells arises from a mere delay in cell’s response to RA. To test this possibility, we characterized expression levels of these markers 48, 72 and 96 hours after RA treatment by RT-qPCR. Results revealed reduced mRNA levels of the neural markers Pax6 and Irx3 and increased mRNA levels of the cardiogenic gene Wnt11 and the cardiac mesoderm marker Acta2 in Tdg -null cells compared with wt cells at all time points ( Fig. 1C ). Therefore, the NPC marker reduction and cardiac fate marker increase observed upon Tdg inactivation were not due to a delayed gene activation in response to RA but, instead, to a switch from a neural to a cardiac mesoderm fate. In line with this observation and consistent with the role of early NODAL signaling in favoring cardiomyocyte differentiation at the expense of neural differentiation in mESCs ( Parisi et al., 2003 ), expression of Nodal as well as of Lefty1 and Lefty2 , two genes upregulated by NODAL signaling, showed higher levels in Tdg -null than in wt cells shortly after RA addition ( Fig. 1D ). Finally, GO enrichment analysis of up- and down-DEGs (absolute value of Log2 fold change (FC) > 1 and adjusted P value < 0.05) further established the skewing from neural to mesodermal differentiation in RA-treated Tdg -null cells ( Fig. 1E ). Indeed, down-DEGs were associated with neural developmental processes, whereas up-DEGs were associated with cardiac morphogenetic processes ( Fig. 1E ). In addition, GO terms that relate to aa transport and metabolism were significantly enriched for down-regulated genes in Tdg -null versus wt ECCs treated with RA ( Fig. 1E ). Collectively, these results indicate that TDG is required for efficient RA-induced neural differentiation and suggest that cell lineage bifurcation and aa-related metabolic processes could be functionally linked in differentiating ECCs. Download figure Open in new tab Figure 1: Transcriptomic analysis of wt and TDG-null ECCs undergoing retinoic acid-induced differentiation. ( A ) Log ratio (M) and mean average (A) representation (MA plot) showing differentially expressed genes in 1 μM RA-treated Tdg -null ECCs compared to RA-treated wt ECCs grown as monolayers. Up-regulated genes are highlighted in red and down-regulated genes in blue. Specific marker genes are shown in green and their names indicated. ( B ) Quantification by RT-qPCR of mRNA levels of the pluripotency gene Oct4 and of neural ( Pax6 , Dbx1 ) and cardiac mesoderm ( Acta2 ) differentiation marker genes in wt ECCs, Tdg -null and Cyp26a1 -null cells before and after 1 μM RA for 48 hours. For each gene analyzed, expression values are expressed relative to the average expression values observed in untreated wt cells and are shown as “fold change”. ( C ) Quantification of the mRNA levels (RT-qPCR) of the neural markers Pax6 and Irx3 , the mesoderm inducer Wnt11 , and the cardiac mesoderm marker Acta2 at the indicated time points upon RA treatment of wt and Tdg -null ECCS. ( D ) Nodal , Lefty1 and Lefty2 expression profiles (RT-qPCR) during RA treatment of wt and Tdg -null ECCs. ( E ) Gene ontology (GO) enrichment analysis of down- and up-regulated genes described in (A). TDG protects ECCs against ferroptosis and regulates ATF4-target genes Tdg inactivation has been associated with high cell death in mouse ESCs and NSCs upon RA treatment ( Wheldon et al., 2014 ; Steinacher et al. 2019 ). Consistently, we observed that Tdg inactivation in ECCs led to increased cell death upon RA treatment, suggesting that TDG also protects ECCs from cell death during differentiation. Indeed, when investigating mitochondrial reductase activity, used as an indirect readout for cell viability through MTT assays, results showed lower viability in monolayers of Tdg -null compared to wt ECCs (Supplementary Fig. 2A), suggesting increased cell death in Tdg -null cells upon RA treatment. In line with this possibility, a high number of floating dead cells were observed in Tdg -null cells treated with 1 μM RA (Supplementary Fig. 2B). Careful examination of transcriptomic data from untreated ECCs did not reveal changes in major components of the apoptotic machinery (data not shown) but instead showed alterations in expression of genes involved in ferroptosis, a non-apoptotic type of cell death that is triggered by an iron-dependent increase in reactive oxygen species (ROS) and in lipid peroxidation that leads to a loss of membrane integrity and eventually to cell death ( Jiang et al., 2021 ). Remarkably, 32% (15 genes, Supplementary Fig. 2C) of the top 47 differentially expressed genes between Tdg -null and wt ECCs were previously shown to regulate ferroptosis. These include several genes that protect from ROS accumulation and lipid peroxidation like Gpx7 ( Zhou et al., 2022 ) and Mgst1 ( Kuang et al., 2021 ) as well as genes that stimulate ferroptosis like Upp1 ( Lai et al., 2023 ) and Phb2 ( Yang et al., 2022 ; Fig. 2A,B ; Supplementary Fig. 2C). In addition, the solute carrier genes Slc3a2 and Slc7a11 , the products of which form the xc-complex that allows cystine entry into the cell and glutathione synthesis hence protecting cells from ferroptosis ( Jiang et al., 2021 ; He et al., 2023 ; Fig. 2A ), were markedly downregulated in Tdg -null versus wt ECCs upon RA treatment ( Fig. 2B ). Additional time-course study of major antioxidant effectors revealed a RA-dependent increase in Gpx4 , Gpx7 and Mgst1 mRNA levels in wt ECCs that was significantly reduced in Tdg -null cells ( Fig. 2C ). Since expression of a number genes involved in protection against ferroptosis was affected in Tdg -null cells, we next evaluated directly the sensitivity of wt and Tdg -null ECCs to ferroptosis. In that aim, cells were treated for 24 hours with increasing concentrations of RSL3, a compound inducing ferroptosis by inhibiting the major glutathione peroxidase GPX4 ( Yang et al., 2014 ). Consistent with a dysregulation of genes preventing ferroptosis, Tdg -null cells were about 3 times more sensitive than wt cells to RSL3-mediated ferroptosis induction, as determined through mitochondrial activity assays ( Fig. 2D ). In addition, the ferroptosis inhibitor Fer1 ( Dixon et al., 2012 ) partially restored viability of Tdg -null cells treated with 100 nM RA (Supplementary Fig. 2D). These data demonstrate that TDG protects from ferroptosis. Download figure Open in new tab Figure 2: TDG protects cells from ferroptosis. ( A ) Outline of ferroptosis regulation. Ferroptosis is triggered by excessive lipid peroxidation driven by iron-dependent reactions. This can be counteracted by GPX4 and MGST1 in combination with reduced glutathione (GSH). In addition, GPX4 and GPX7, together with GSH, allow to detoxify cells from H2O2, thus lowering ROS production and lipid oxidation. The activity of GPX4 relies on the presence of selenocysteine supplied through degradation of selenoprotein P (SELENOP) upon binding to its receptor LRP8 and endocytosis. Resistance to ferroptosis is also provided by high levels of SLC7A11 and SLC3A2, which allow cystine entry into cells, thus increasing GSH synthesis. ( B ) Log2 fold change (Log2FC) in expression levels of the indicated genes in wt and Tdg -null ECCs treated or not (NT) with 1 μM RA for 48 hours. All values are expressed relative to the untreated wt ECCs. ( C ) Quantification of Gpx4 , Gpx7 and Mgst1 mRNA levels (RT-qPCR) during 1 μM RA-response of wt ECCs and Tdg -null cells. ( D ) RSL3 dose-response curves in MTT assays. RSL3 EC50 is indicated for both wt and Tdg -null cells. ( E ) Log2 fold change (Log2FC) and adjusted P value (Adj P val) of the 17 most significantly down-regulated genes in 1 μM RA-treated monolayers of Tdg -null ECCs compared to RA-treated wt ECCs. ( F ) Network showing functional interactions between the 17 genes and their products shown in (E). The network was drawn using the online tool String ( https://string-db.org/ ). The thickness of the full string network edges indicates confidence in the corresponding supporting data. It has been established that the ATF4 transcription factor mediates protection against ferroptosis by upregulating the xc-/GPX4 axis ( Ahola et al., 2022 ; He et al., 2023 ; Swanda et al., 2023 ). As we observed that the two xc-transporter genes Slc3a2 and Slc7a11 were down-regulated in Tdg -null cells, we hypothesized that TDG could broadly participate in the transcriptional control of ATF4-target genes. In agreement with this hypothesis, further expression analysis revealed that, strikingly, the 17 most significantly down-regulated genes between RA-treated wt and Tdg -null cells all belonged to the so-called integrated stress response pathway (ISR - Pakos-Zebrucka et al., 2023), a cell signaling pathway that depends on ATF4 activity and exerts broad control on protein synthesis in response to metabolic stress ( Fig. 2E,F ). Remarkably, these 17 downregulated genes are all known direct targets of the transcription factor ATF4 and, consistent with their role in the ISR, are involved in one-carbon metabolism as well as in aa metabolism, including biosynthesis, transport and tRNA charging. Interestingly, expression of the top 3 genes ( i.e. , Asns , Mthfd2 and Psat1 , in red Fig. 2E ) has been shown to depend on the catalytic activity of TET1 and TET2 in ESCs ( Mulholland et al., 2020 ). As both TDG and TET enzymes act in active DNA demethylation, this observation supports a crucial role of the DNA methylation/demethylation machinery in the control of ATF4 target gene expression in response to RA. Collectively, these data indicate that, in a model recapitulating cell differentiation into NMPs followed by an engagement towards neural or mesoderm fates, TDG prevents ferroptosis and favors the acquisition of a neural fate, possibly by allowing proper expression of ATF4-dependent ISR genes. TDG maintains expression of stress-response genes in differentiating ECCs Since we observed a significant down-regulation of ATF4-target genes in RA-treated Tdg -null cells ( Fig. 2E ), we next addressed the expression dynamics of ATF4-target genes and the role of TDG in their regulation. The time-course response to RA treatment of wt ECC monolayers was analyzed by 3’RNA-seq and revealed that ATF4-target genes exhibited a decrease in mRNA levels during the first 24 hours followed by a recovery between 24 and 48 hours ( Fig. 3A ). This shared expression pattern was confirmed by RT-qPCR assays for a number of genes (Supplementary Fig. 3A). As an illustration, the solute carrier ( Slc ) family gene members that displayed such expression pattern were ATF4 targets, while ATF4-independent Slc genes did not (Supplementary Fig. 3B,C). These changes in gene expression were mirrored by similar variations in acetylation levels of histone H3 lysine 27 (H3K27ac, GEO dataset GSE82314), an active chromatin mark, at ATF4-target gene promoters but not at the ATF4-independent gene Nampt ( Fig. 3B ). Thus, the observed variations in ATF4-target mRNA levels are likely to be regulated transcriptionally, which prompted us to search for genes that are common targets between TDG and ATF4. In order to identify such genes, we reanalyzed publicly available mouse TDG and ATF4 ChIP-seq datasets (GSM1341311 from ESCs and GSM5440974 from cardiomyocytes, respectively). First, we could evidence that promoters from the top down-regulated gene in Tdg -null cells ( Fig. 2E ) are bound by both TDG and ATF4, whereas the Nampt promoter was bound by TDG only ( Fig. 3C ). These data suggest that TDG and ATF4 act directly at promoters in the transcriptional control of ATF4-target genes. Consistently, mRNA levels of ATF4-target genes were decreased in 48h RA-treated Tdg -null cells compared to wt ECCs, whereas Nampt mRNA levels remained unchanged ( Fig. 3D ). In addition, acetylation of H3K27 immediately downstream of the transcription start site (TSS) of ATF4-target genes was significantly reduced in RA-treated Tdg -null cells compared to wt ECCs, whereas no change was observed for Atf4 and Nampt ( Fig. 3E ). These data further consolidate the notion that TDG positively regulates ATF4-target genes at the transcriptional level by acting directly at promoters. Download figure Open in new tab Figure 3: TDG and mTORC1 regulate ATF4-mediated gene transcription during cell differentiation. ( A ) Variations in mRNA levels for the indicated ATF4-target genes and for the unrelated gene Nampt (used as a control) extracted from RNA-seq data of a 1 μM RA treatment time-course experiment in wt ECCs. Data are expressed as Log2FC compared to untreated cells. ( B ) Variations in H3K27 acetylation profiles at gene loci shown in (A) during a 1 μM RA treatment time-course experiment in wt ECCs. ( C ) TDG and ATF4 ChIP-seq signals at selected loci. ( D ) Expression levels of the indicated genes in wt and Tdg -null ECCs grown as monolayers and treated with 1 μM RA for 48 hours (extracted from RNA-seq data and expressed in counts per million reads - CPM). ( E ) ChIP-qPCR analysis of H3K27ac enrichment immediately downstream of the TSS of ATF4-target genes. For the negative control gene Nampt , two different sequences were interrogated ( Nampt -nuc1 and Nampt -nuc2). Values were normalized to the H3K27ac signal in untreated wt ECCs and are expressed as fold change. ( F ) Table displaying Log2 fold change of expression levels for the indicated genes in wt and Tdg -null ECCs grown either as monolayers (Mon) or as aggregates (Agg) and treated or not (NT) with 1 μM RA for 48 hours. All conditions are expressed relative to the untreated wt ECCs grown as monolayers. ( G ) Table depicting the overlap between the indicated lists of genes. Agg. up and Agg. down are genes up- or down-regulated by cell aggregation of wt ECCs. Nascent mesoderm (NM) and epiblast gene modules have been previously established ( Cheng et al., 2022 ). ( H ) RT-qPCR analysis of selected ATF4-target gene mRNAs in wt and Tdg -null ECCs grown as monolayers and treated or not with 20 nM rapamycin for 16 hours (+ rap.). ( I ) Western blot analysis of the mTORC1 target p70S6K in wt and Tdg -null ECC monolayers treated or not with 1 μM RA for 48 hours. Detection of p70S6K phosphorylated on T389 is shown on top, p70S6K in the middle, and the loading control SMC1A on the bottom. ( J ) RT-qPCR analysis of mRNAs encoding the indicated differentiation markers in monolayers of wt ECCs treated or not with 1 μM RA for 48 hours. Twenty-four hours after RA addition, rapamycin was added to the cell culture medium (20 nM; RA+rap.) and cells were further grown for 24 hours. The observations that the RA-induced formation of 3D clusters of ECCs (Supplementary Fig. 1F) was accompanied by the down-regulation of ATF4-target genes, and that both processes are exacerbated in Tdg -null cells, which display increased cell aggregation in response to low concentrations of RA, prompted us to hypothesize that cell aggregation by itself triggers repression of ISR gene expression through the downregulation of ATF4 activity. To test this possibility, wt and Tdg -null ECCs were grown as monolayers or as aggregates, treated or not with RA, and the different corresponding transcriptomes were determined by 3’RNA-seq and analyzed. Differential gene expression analysis first revealed that, in accordance with our hypothesis, expression was significantly reduced for 10 out of 16 tested ATF4-target genes when wt ECCs were grown in the absence of RA as aggregates when compared to monolayers ( Fig. 3F ). When looking for specific cell signatures associated with up- or down-regulated genes in wt ECCs grown as aggregates versus monolayers, we evidenced that aggregated cells were primed for pluripotency exit and nascent mesoderm formation. Indeed, 40% (6 out of 15) of the epiblast module genes ( Cheng et al., 2022 ) overlapped with aggregation down-regulated genes, and 80% (12 out of 15) of the nascent mesoderm module genes ( Cheng et al., 2022 ) overlapped with aggregation up-regulated genes ( Fig. 3G ), indicating that an engagement towards a mesodermal fate associates with a reduction in expression of ATF4-target genes. In the absence of TDG, aggregation had a stronger impact on ATF4-dependent genes since 15 out of these 16 genes showed a significant lower expression together with a higher fold-change amplitude ( Fig. 3F ). In addition, whereas treatment with RA amplified the effect of cell aggregation on ATF4-target gene expression in wt ECCs, RA did not further reduce expression of most of the tested genes in Tdg -null cells ( Fig. 3F ). Importantly, aggregation had no impact on the expression of the two non-ATF4-target genes Slc2a3 and Nampt ( Fig. 3F ). Collectively, these data indicate that TDG positively regulates ATF4-target genes in aggregates and that the transient RA-mediated down-regulation of these genes observed in RA-treated monolayers is probably initiated through the 3D reorganization of cells in culture (Supplementary Fig. 3D). In line with these results, we propose that 3D growth of ECCs per se induces a transient metabolic stress state that triggers a TDG-dependent ATF4-mediated adaptative response. ATF4 activity can be tuned by distinct molecular pathways that lead to expression of overlapping sets of ATF4-dependent ISR genes. Cell adaptation to metabolic stress through the ISR relies in part on the stimulation of the Atf4 mRNA translation by the GCN2 pathway ( Harding et al., 2000 ). In addition, upon growth factor-induced proliferation, Atf4 mRNA stability and translation are sustained by the mechanistic target of rapamycin complex 1 (mTORC1) in mouse embryonic fibroblasts and various cancer cell lines ( Park et al., 2017 ; Torrence et al., 2021 ). Notably, ATF4 activation by mTORC1 upregulates a subset of ISR genes that are involved in aa uptake and synthesis as well as in tRNA charging ( Torrence et al., 2021 ). To determine whether one or both of these pathways control ATF4-target gene expression in ECCs, we selectively modulated their activity by a pharmacological approach. First, the GCN2 pathway was investigated either using Halofuginone, a compound that blocks the prolyl-tRNA synthetase, which mimics aa starvation and hence activates GCN2, or using an excess of L-Proline, which depletes cells of uncharged tRNAs thus lowering GCN2 activity ( D’Aniello et al., 2015 ). As shown in Supplementary Fig. 3E, and contrary to what has been observed in ESCs ( D’Aniello et al., 2015 ), Halofuginone did not increase Asns or Psat1 mRNA levels whether in wt or in Tdg -null ECCs. In the case of an active GCN2 pathway, L-Proline would induce a decrease in Asns and Psat1 mRNA levels in cells. However, the observed decrease was not significant (Supplementary Fig. 3E). These results indicate that the GCN2 pathway plays no role in the expression of these two ATF4-target genes in ECCs, under these conditions. Next, we addressed the involvement of the mTOR pathway by treating wt and Tdg -null ECCs with the mTORC1 inhibitor rapamycin and measuring expression of ATF4-target genes by RT-qPCR assays. As shown in Fig. 3H , all but one ATF4-target genes exhibited reduced expression upon rapamycin treatment in wt cells, indicating that mTOR activity is indeed required for proper expression of ATF4-target genes. Further, these genes showed no to small reduction in expression upon rapamycin treatment in Tdg -null cells ( Fig. 3H ), suggesting that TDG is necessary to maintain sufficient levels of mTORC1 activity. The requirement of TDG for mTORC1 activity was confirmed by western blot analysis that revealed reduced levels of the phosphorylated form of p70S6K, a direct target of mTORC1 and a marker of its activity, in Tdg -null cells versus wt ECCs ( Fig. 3I ). In order to investigate how TDG regulates mTORC1 activity, transcriptomic data were interrogated and revealed that known activators of mTORC1 ( i.e ., Mgst1 , Gpx7 , Rpl22l1 and Slc7a5 ) showed reduced expression levels in Tdg -null compared to wt ECCs (Supplementary Fig. 3F). In addition, the mTORC1 inhibitor encoding gene Sesn1 was down-regulated in RA-treated wt ECCs but not in Tdg -null ECCs (Supplementary Fig. 3F). Knowing that sestrins (SESN1 and SESN2) are leucine sensors that lose their ability to inhibit mTORC1 when bound to leucine ( Wolfson et al., 2016 ; Cangelosi et al., 2022 ), the expected decrease in leucine uptake in Tdg -null cells through reduced SLC7A5 levels could explain the lower mTORC1 activity detected in Tdg -null cells. In addition, reduced mTORC1 activity upon decreased cystine influx through SLC7A11 has been shown to sensitize cells to ferroptosis ( Zhang et al., 2021 ). Hence, TDG appears to be a key regulator of mTORC1 activity in differentiating cells. The activation of the mTOR pathway observed upon RA treatment ( Fig. 3I ) and the impact of its inhibition on ATF4-target gene expression ( Fig. 3H ) strongly suggest that mTOR pathway activity participates in cell differentiation induced by RA. To directly evaluate this possibility, wt ECCs were treated with rapamycin 24 hours after differentiation had been initiated by RA, and expression levels of differentiation markers were determined by RT-qPCR. Whereas expression of the mixed-lineage marker Meis1 and the early neural marker Pax6 were not affected by rapamycin treatment, late neural markers like Irx3 and Dbx1 were down-regulated, and the cardiac mesoderm marker Acta2 increased ( Fig. 3J ). Collectively, these data reveal that TDG ensures a sufficient level of mTORC1 activity to sustain the transcriptional activity of ATF4-regulated genes and that the mTOR pathway is essential for proper cell fate choice in RA-treated ECCs. TDG is engaged at ATF4-target gene promoters and regulates nucleosome positioning To gain further insight into the role of TDG in the control of gene expression at genome scale, we next compared the genome-wide distribution of TDG in ESCs with candidate cis-regulatory elements (cCREs) of the mouse genome defined by DNase1 accessibility and enrichment in H3K27ac and H3K4me3 (ENCODE Project Consortium et al., 2020 ). Among the 49,699 identified TDG peaks, 23.5 % (n=11,666) were located at promoters, 20.7 % (n=10,275) mapped within proximal enhancers and 23.5 % (n=11,663) to distal enhancers ( Fig. 4A ). In agreement with a functional relationship between TDG and ATF4 at promoters, most TDG-bound promoters were also ATF4-bound ( Fig. 4A ). In addition, 3.4 % (n=1,675) overlapped with strong CTCF binding sites in both ESCs (GSE98671) and ECCs (GSE103198- Fig. 4A ). Consistent with a high prevalence of CpG islands (CGIs) at promoters, 69 % (8,049 out of 11,666) of TDG sites overlapping with promoters were included in CGIs ( Fig. 4B ). Further examination of TDG engagement at CGIs showed that TDG binds to almost all CGIs ( Fig. 4C ), suggesting that TDG engagement in chromatin depends on CpG density. Search for transcription factor DNA binding motifs within the TDG peaks identified the Sp1 motif (5’-CCCCGCCCC-3’), consistent with an engagement of TDG at CpG-rich sequences, and the CTCF motif as highly enriched ( Fig. 4D ). In order to unveil any correlation between transcription factor binding at promoters and gene expression levels, genes were next ranked according to their expression in untreated wt ECCs, and the binding of TDG, ATF4 and CTCF around TSSs was analyzed. As shown in Fig. 4E , TSSs of highly expressed genes were found to be associated with both TDG and ATF4 binding and the presence of CGIs, whereas TSSs of low expressed genes were not ( Fig. 4E ). On the contrary, CTCF showed low signal at TSSs ( Fig. 4E ). Further, CTCF was not found at TSSs of ATF4-target genes down-regulated by TDG depletion, except for Mthfd2 in ESCs (Supplementary Fig. 4A). This strongly suggests that CTCF is not directly involved in TDG-mediated regulation at TSSs of ATF4-target genes, both in ECCs and ESCs since CTCF showed a similar distribution in these two cell types (Supplementary Fig. 4B). Consistent with data shown in Fig. 3 , down-regulated genes in RA-treated Tdg -null cells could be distinguished from up-regulated genes by their association with TDG and ATF4 binding within +/- 100 bp of their TSS (p=0.0014, exact Fisher test) whereas such a difference was not observed for RA-treated wt ECCs (p=0.3645, exact Fisher test). These data suggest that TDG positively regulates ATF4-target genes through direct binding at or near their TSSs. Download figure Open in new tab Figure 4: TDG binds active gene promoters and regulates nucleosome positioning. ( A ) Heatmaps of TDG, ATF4, and CTCF ChIP-seq signals in clusters of TDG binding sites overlapping or not with cCREs. The signals are centered on the middle of the TDG peaks (P enh: proximal enhancers, D enh: distal enhancers, Prom: promoters, CTCF: CTCF binding sites, H3K4me3: cCREs marked by H3K4me3 only, No overlap: TDG binding sites not overlapping with cCREs). ( B ) Fraction of TDG binding sites overlapping with CGIs and categorized by their overlap with different types of cCREs. Same color code as in (A). ( C ). Heatmap of TDG ChIP-seq signal in mESCs at annotated CGIs. ( D ) Enrichment in transcription factor motifs at TDG binding sites. ( E ) Heatmap of TDG, ATF4 and CTCF ChIP-seq signals centered on the TSSs of genes ranked by their expression level in wt ECCs. TSSs embedded in CGIs are indicated by a dark gray bar and others by a pink bar on the right side of the heatmaps. ( F ) Clustering heatmap of ATF4 binding sites according to their enrichment in TDG in mESCs. ( G ) Genomic distribution of ATF4 binding sites from clusters C3, C4 and C5 as defined in (F). ( H ) Average profiles of CpG density, 5fC/5caC-seq (MAB-seq) signal, and TDG and ATF4 ChIP-seq signals centered on the oriented TSS (arrow) of genes associated with ATF4 binding (ATF4 ChIP-seq peak within -/+ 500 bp of a single TSS) and included in clusters C3, C4 and C5. ( I ) ChIP-qPCR assay of TDG at ATF4-target gene promoters in wt ECCs. ( J ) ATF4 ChIP-qPCR assay at its target gene promoters in wt ECCs. ( K ) Average profiles of MNase-seq signal at the TSS of genes ranked by quartiles of expression in untreated wt and Tdg -null ECCs. ( L ) Average profiles of MNase-seq signal around C3 TSSs in wt and Tdg -null ECCs treated with 1 μM RA. The ATF4 ChIP-seq profile is also shown (dashed blue line). Average positions of nucleosome dyads relative to the TSS are indicated. ( M ) Heatmap representation of TDG, CHD4, and CHD2 ChIP-seq signals at TDG binding sites in mouse ESCs. ( N ). Average CHD4 (top panel) and CHD2 (bottom panel) ChIP-seq profiles around C3, C4 and C5 TSSs. The nucleosome density profile around C3 TSSs is also shown. ( O ) Average profiles of MNase-seq signal at non-promoter CTCF binding sites common between ESCs and ECCs in RA-treated wt and Tdg -null ECCs and grouped as CHD4-low (top panel) or CHD4-high (bottom panel). The CHD4 ChIP-seq profile has been added in each panel (dashed blue line). The values of CHD4 ChIP-seq signal at CHD4-high CTCF sites have been reduced to fit within the range of nucleosome density values. Average positions of nucleosome dyads relative to the CTCF binding sites are indicated. To further characterize the functional relationship between ATF4 and TDG engagement genome-wide, we clustered all ATF4 binding sites according to the TDG ChIP-seq signal intensity from ESCs ( Fig. 4F ). Due to the off-centered distribution of TDG in clusters 1 and 2 ( Fig. 4F ), these two clusters were not further analyzed. Whereas ATF4 binding sites that were not bound by TDG were mostly intronic and intergenic (cluster C5), sites that were highly enriched in TDG (cluster C3) were mostly associated with promoters and 5’UTRs, reminiscent of CGI distribution along the genome ( Fig. 4G ). Accordingly, C3 TSSs (n=3,010) showed high TDG and ATF4 binding and high CpG density, whereas C5 TSSs (n=347) showed high ATF4 binding but low CpG density and low TDG engagement ( Fig. 4H ). Cluster C4 TSSs (n=940) showed high ATF4 binding but intermediate engagement of TDG and lower CpG density than C3 TSSs ( Fig. 4H ). Similar to TDG distribution patterns, 5fC/5caC accumulation which reflects active turnover of 5mC was higher in C3 than in C4 and almost absent from C5 regions ( Fig. 4H ), as determined by methylation assisted bisulfite sequencing (MAB-seq) in Tdg knock-down ESCs (GEO dataset GSE62631; Neri et al., 2015 ). Importantly, TDG engagement at Atf4 and selected ATF4-target promoters was independently confirmed by ChIP-qPCR experiments performed in wt P19 ECCs ( Fig. 4I ), indicating that TDG regulates ATF4-target gene expression likely through direct binding to their promoter in wt ECCs. Next, we interrogated ATF4 binding to the same selected ATF4-target gene promoters by ChIP-qPCR. Except for Atf4 itself, data evidenced a marked engagement of ATF4 at all tested promoters as well as a significant decrease in ATF4 binding in RA-treated Tdg -null ECCs, consistent with a lower activity of the corresponding genes ( Fig. 4J ). Together, these results indicate that TDG engagement and activity at ATF4-bound TSSs facilitate gene transcription. In order to interrogate a putative role of TDG on chromatin organization at ATF4-bound promoters, genome-wide nucleosome maps were generated through MNase-seq experiments that were performed on wt and T dg -null ECCs treated or not with RA for 48 hours. First, TSSs were ranked in quartiles of expression in RA-untreated wt and Tdg -null ECCs. As previously observed in mESCs ( Voong et al., 2016 ), average profiles of nucleosome occupancy showed a positive correlation with gene expression in both cell lines ( Fig. 4K ). Nucleosome occupancy around TSSs of genes from cluster C3, cluster C4 and cluster C5 followed this correlation with high nucleosome occupancy around C3 and C4 TSSs being associated with high gene expression and low nucleosome occupancy around C5 TSSs being associated with low gene expression (Supplementary Fig. 4C,D). In addition, a slight decrease in nucleosome occupancy at C3 and C4 TSSs upon RA treatment was observed in both cell lines, an effect that was more pronounced for C3 TSSs (Supplementary Fig. 4C). This observed reduction in nucleosome occupancy was further confirmed by MNase-qPCR targeting the +1 nucleosome of selected ATF4-target genes (Supplementary Fig. 4E). Interestingly, and although nucleosome density did not seem to vary between wt and Tdg -null ECCs, nucleosome positioning was markedly altered around the TSSs of C3 genes where the dyad of −1 and +1 nucleosomes appeared shifted towards the TSS by 20 bp in RA-treated Tdg -null cells compared to wt ECCs ( Fig. 4L ). Such a shift was also detected for C4 TSS +1 nucleosomes, whereas C5 genes did not show well-defined nucleosome positions around TSSs (Supplementary Fig. 4F). Examination of ATF4 distribution around C3 and C4 TSSs further indicated that ATF4 binds to the nucleosome depleted region (NDR) of its target genes ( Fig. 4L , Supplementary Fig. 4F). Overall, these data strongly suggest that TDG regulates the position of nucleosomes flanking the NDR at promoters of active ATF4-target genes, a process that might favor ATF4 engagement. Given the impact of the +1 nucleosome position and of the organization of a promoter NDR on the transcriptional activity of genes ( Bai and Morozov, 2010 ; Abril-Garrido et al., 2023 ), these observations support a direct role of TDG in the regulation of ATF4-target gene activity. The observed shift in nucleosome positioning in the absence of TDG is likely to reflect a change in the activity of a remodeler such as CHD4, which is known to associate with nucleosome positioning at active promoters and CTCF binding sites in mouse ESCs (de Dieuleveult et al., 2016; Clarkson et al., 2019 ). To investigate a putative functional relationship between TDG and CHD4, we next compared the distribution of TDG, CHD4 (GSM1581292, mouse ESCs) and the related remodeler CHD2 (GSM1581290, mouse ESCs), which has been shown to be enriched in gene bodies in correlation with the transcription elongation mark H3K36me3 (de Dieuleveult et al., 2016), around TDG-bound sites in mouse ESCs. Results showed that CHD4 was bound to a large fraction of TDG sites whereas CHD2 showed no binding at these sites ( Fig. 4M ). Enrichment profiles of CHD4 around C3, C4 and C5 TSSs were consistent with a role of CHD4 in nucleosomal organization around C3 and C4 TSSs since the level of CHD4 peaks at nucleosome positions, as shown for C3 TSSs ( Fig. 4N ). On the contrary, CHD2 was not enriched at these nucleosome position ( Fig. 4N ). Since CTCF-bound sites are known to have well positioned nucleosomes ( Clarkson et al., 2019 ), we next interrogated the role of TDG in nucleosome positioning at non-promoter CTCF binding sites that are common between ESCs and ECCs (n=29,207). Similar to the situation at TSSs, the positioning of nucleosomes directly flanking CTCF binding sites was altered in Tdg -null cells compared to wild-type cells in both RA-treated and control conditions, whereas the position of more distant nucleosomes remained unaltered (Supplementary Fig. 4G). Whereas the dyad of these CTCF-flanking nucleosomes was positioned 175 to 185 bp away from the CTCF binding sites in wt ECCs, as shown for CTCF sites in ESCs ( Clarkson et al., 2019 ), its distance to CTCF sites was lowered by 20 to 30 bp in Tdg -null ECCs compared to wt ECCs, at either low or high CHD4 sites ( Fig. 4O ). Consistent with a role of CHD4 in mediating TDG role in positioning these CTCF-flanking nucleosomes, CHD4 was found to preferentially bind next to these two nucleosomes ( Fig. 4O ). Collectively, these data indicate that TDG is involved in the precise positioning of nucleosomes flanking the TSS of ATF4-target genes and CTCF-bound sites by regulating the recruitment and/or activity of chromatin remodelers. TDG expression correlates with expression of ATF4-target genes in human Our data show that TDG plays a role in the regulation of the ATF4/ISR pathway in mouse ECCs. To investigate whether such regulatory function is conserved in humans and relevant to pathologies, we interrogated a putative correlation between the expression levels of TDG and ASNS (as a paradigmatic stress response gene) expression, using gene expression data from 37 cohorts of patients that cover a wide range of cancer types and are publicly available through the UCSC Xena browser ( https://xenabrowser.net/ ). This analysis revealed that a handful of cancer types exhibit a strong positive association (Pearson’s rho ≥ 0.4, p ≤ 0.05) between TDG and ASNS expression levels ( Fig 5A ). The highest correlation was found for patients affected by pediatric brain tumors (Children’s Brain Tumor Tissue Consortium-CBTTC, 854 tumors from 33 different types) where TDG expression was of adverse prognosis (p = 1.12E-6), as was that of ASNS (p = 2.48E-13). Further examination of ASNS and TDG expression in the most represented tumor types within pediatric brain tumors showed variable expression and correlation levels of these two genes, with the highest correlation being observed for Low grade glioma ( Fig. 5B,C ). Interestingly, activation of the ASNS gene by ATF4 in asparagine-depleted acute lymphoblastic leukemia cells was shown to be counteracted by DNA methylation ( Jiang et al., 2019 ), suggesting that expression of this ISR gene is repressed by DNA methylation in human and is activated by oxidation and/or erasure of the methylation mark. Thus, we next tested the existence of such an inverse correlation between DNA methylation and ASNS expression in the Cancer Genome Atlas (TCGA) lung cancer cohort, in which correlation between TDG and ASNS expression was also high (rho = 0.543, p = 1.285E-87, Fig. 5A ). While most CpGs located immediately upstream of the ASNS gene were unmethylated in patient cells (not shown), cg25906151 methylation did exhibit an inverse correlation with both ASNS and TDG expression ( Fig. 5D,E ), suggesting that increased DNA methylation at this CpG negatively impacts on ASNS expression in lung cancer patients. Consistent with the positive role of TDG in the regulation of ATF4-target genes in mouse ECCs, the expression of other ATF4 target genes such as MTHFD2 and the aa transporters SLC7A1 and SLC7A5 also showed a positive correlation with TDG expression in lung cancer patients ( Fig. 5F ). Conversely, expression of the glucose transporter gene SLC2A3 , which is not an ATF4 target in mouse, did not show any correlation with that of TDG ( Fig. 5F ). Finally, TDG expression was negatively correlated with that of the mTOR pathway inhibitor SESN1 , whereas a positive correlation was observed with RPL22L1 , a positive regulator of mTOR signaling ( Fig. 5F , Supplementary Fig. 3F). In the TCGA lung cancer cohort, expression correlation with the selected genes was higher for TDG than for ATF4 expression ( Fig. 5G ). These analyses confirmed that TDG levels impact on ATF4 and mTOR pathways at gene expression level and revealed that the TDG-ISR axis is likely involved in the pathogenesis of some human cancer types In line with a putative implication of TDG in the control of ferroptosis sensitivity in human cancer cells, analysis of the correlation between TDG expression and protein levels through interrogating proteomics data obtained in cancer cell lines ( https://depmap.org/portal/interactive/custom_analysis ) revealed that highly significant correlations were observed for ferroptosis-related proteins ( Fig. 5H ), such as the pro-ferroptotic proteins MVP, FOSL1 and CTSB ( Kuang et al., 2020 ; Shao et al., 2023 ; Xia et al., 2024 ), or the anti-ferroptotic protein CD44 ( Bian et al., 2023 ). The role of TMPO in ferroptosis has not been reported yet but the oncogenic TMPO antisense RNA 1 (TMPO-AS1) is part of a long non-coding RNA signature of ferroptosis and positively regulates TMPO production ( Li et al., 2020 ; Yao et al., 2021 ). Such positive (TMPO) or negative (MVP) correlation with TDG was also observed at the mRNA level in tumors from the TCGA lung cancer cohort (Supplementary Fig. 5A). When interrogating gene dependencies (DepMap CRISPR screen project Chronos) in cancer cell lines harboring TDG damaging mutations compared to other cell lines, we found that only two genes showed a significant correlation with TDG mutations: the ferroptosis resistance gene FBXL5 ( Liu et al., 2024 ) and the mTOR inhibitor gene GNAO1 ( Kim et al., 2024 - Fig. 5I , Supplementary Fig. 5B). Consistent with our results in mouse ECCs showing that Tdg -null cells are primed for ferroptosis, the growth of cells with damaging mutations of TDG was more sensitive than other cells to FBXL5 disruption ( Fig. 5I , Supplementary Fig. 5B). Also, as Tdg -null ECCs showed lower mTORC1 activity, the growth of TDG mutant cancer cells was positively impacted by GNAO1 disruption ( Fig. 5I , Supplementary Fig. 5B). In lung cancer patients, the higher expression of FBXL5 in tumor cells expressing low levels of TDG and low levels of SLC7A11 ( Fig. 5J ) is anticipated to protect cells from ferroptosis, hence allowing for tumor growth. Extending our findings to humans, these data indicate that TDG is likely to be involved in ATF4-mediated gene regulation, as well as in the control of ferroptosis and of the mTOR pathway in cancer patients. Beside its essential role in cell homeostasis, the ATF4-controlled ISR also stands crucial for the growth of certain cancer types ( Tian et al., 2021 ; Lines et al., 2023 ). Hence, our data support a conserved role of TDG in the control of ISR genes and suggests and involvement of dynamic DNA methylation processes in cancer cell growth through the regulation of ATF4-dependent genes. Download figure Open in new tab Figure 5: TDG and ATF4-target gene expression correlate in human cancer. ( A ) Pearson’s correlation between TDG and ASNS expression levels in patient tumors from 37 TCGA cohorts. ( B ) Violin plots of ASNS and TDG expression levels in pediatric brain tumors (ATRT: Atypical Teratoid Rhabdoid Tumor). ( C ) Pearson’s correlation between TDG and ASNS expression levels in pediatric brain tumors. ( D ) Heatmap representation of cg25906151 methylation levels and ASNS and TDG expression levels in lung cancer samples (n=812; high methylation/expression: red, low methylation/expression: blue). ( E ) Expression levels of TDG and ASNS in lung cancer samples classified by quartiles of cg25906151 methylation (Q1: lowest DNA methylation, Q4: highest DNA methylation). ( F ) Heatmaps showing the expression levels of the indicated ISR genes and mTOR pathway genes in lung tumor samples (TCGA lung cancer cohort, n=1,129) ranked according to TDG expression levels. ( G ) Pearson’s correlation between expression levels of either TDG or ATF4 and selected genes in lung cancer patients. Rho and P values are indicated for each combination. ( H ) Correlation between TDG expression (mRNA levels) and protein levels in human cancer cell lines (DepMap custom analysis, proteomics). Proteins with a correlation qVal ≤ 0.05 have been highlighted in green. ( I ) Correlation between the occurrence of TDG damaging mutations and the gene effect observed in CRISPR experiments (DepMap Public 24Q2+Score, Chronos). Gene effects with a correlation qVal ≤ 0.05 have been highlighted in green. ( J ) Heatmaps showing the expression levels of the indicated genes in lung tumor samples (TCGA lung cancer cohort, n=1,129) ranked according to TDG expression levels. ( K ) Working model depicting ISR gene regulation by TDG. ATF4 and TDG co-binding at ATF4 target gene promoters is associated with H3K27 acetylation by CBP and +1 nucleosome positioning by chromatin remodeler(s), leading to expression of ISR genes, which include aa transporter genes such as Slc7a5 , and favoring resistance to ferroptosis (PM: plasma membrane). The resulting leucine (Leu) influx stimulates mTORC1 activity by inhibiting sestrins (SESN). This, in turn, favors Atf4 mRNA stability and translation, sustaining ISR gene activity. Discussion Deciphering the role of TDG in cell lineage commitment has been hampered so far by the cell lethality that is caused by RA treatment in Tdg knock-out mESCs as well as in Tdg knocked-down primary neural stem cells ( Wheldon et al., 2014 ; Steinacher et al. 2019 ). Here, we describe the establishment of a Tdg knock-out murine cell model derived from wt ECCs. These cells proliferate in vitro and maintain viability upon RA-induced differentiation, therefore representing a relevant substitute model for EpiSCs that allowed us to investigate the molecular function of TDG in gene control and cell fate commitment. In the course of this study, we could evidence that TDG is required for efficient neural differentiation, as Tdg -null cells underwent differentiation skewing towards a cardiac mesoderm fate when treated with RA. Similarly, a transient Tdg knock-down followed by a recovery period before embryoid body differentiation of mESCs led to an increase in cardiac mesoderm formation, possibly through a down-regulation of repressors of mesoderm differentiation ( Aranda et al., 2023 ). Differentiation skewing towards cardiac mesoderm has also been observed in vivo in TET mutants ( Li et al., 2016 ), suggesting a common requirement of various components of the active DNA demethylation machinery for neural differentiation. Consistent with the idea that DNA demethylation associates with neural differentiation, the knock-out of the DNA methyltransferase gene Dnmt3b in mESCs inversely skews differentiation towards neurectoderm ( Lauria et al., 2023 ). Altogether, these observations converge to a model where mechanisms controlling DNA methylation dynamics are important components of cell fate commitment. Although a number of differentially methylated regions (DMRs) observed in EpiSC-like cells derived from Dnmt3b -KO ESCs ( Lauria et al., 2023 ) were found to overlap with the TSSs of genes repressed in Tdg -null ECCs, they also overlapped in a similar proportion with TSSs of activated genes (data not shown). This argues against a simple model in which the co-engagement of TDG at DNMT3b-bound TSSs would prevent DNA methylation by inhibiting DNMT3B activity, thus favoring gene activity. Instead, we propose that an active methylation/demethylation reaction cycle is required to enable positive gene regulation. Consistent with this model, previous mapping of 5fC and 5caC through MAB-seq indicated that a fraction of these oxidized bases is found at promoters, including in CpG islands, although the role of TDG at promoters remained elusive in this case ( Neri et al., 2015 ). Nonetheless, highly expressed gene promoters appear to undergo DNA methylation cycling that would be disrupted in Tdg -null cells. The nucleosome maps we have generated suggest that TDG fine-tunes nucleosome positioning at promoters as well as at other genomic sites such as CTCF binding sites. Indeed, the - 1/+1 nucleosomes around TSSs and those immediately flanking CTCF binding sites were differently positioned in the absence of TDG, consistent with a direct impact of 5mC turnover on the accurate positioning of specific nucleosomes. DNA methylation has already been proposed to favor nucleosome positioning and stability ( Chodavarapu et al., 2010 ) but other studies reported opposite relationship ( Li et al., 2022 ). In addition, 5fC has been shown to associate with well-positioned nucleosomes, likely through its capacity to directly interact with histones ( Teif et al., 2014 , Raiber et al., 2018 ), and sites that gain 5mC in Tet1/Tet2 KO ESCs also have a higher nucleosome occupancy ( Wiehle et al., 2019 ). Hence, part of the transcriptional effect of TDG depletion may very well arise from alterations in nucleosome positioning caused by defects in 5mC turnover. Accordingly, we observed that nucleosomes flanking CTCF sites are shifted 30 bp towards CTCF sites in Tdg -null ECCs compared to wt ECCs, whereas +2, +3 and +4 nucleosome positions remain unchanged. The size of this nucleosome-shift effect is very reminiscent of that observed upon alterations in the activity of chromatin remodelers such as BRG1 ( Hu et al., 2011 ; Ren et al., 2024 ). Hence, TDG could participate in coordinating nucleosome positioning and gene expression through functional interaction with chromatin remodelers such as CHD4, which associates with TDG binding sites (this study), and also with CTCF binding sites ( Clarkson et al., 2019 ) and active promoters, where it participates to the definition of the NDR in mouse ESCs (de Dieuleveult et al., 2016). Although moderate, the estimated 40 bp narrowing of the NDR in Tdg -null cells is highly comparable to the 33 bp shrinkage of the NDR observed in yeast cells depleted of the chromatin remodeler RSC ( Ganguli et al., 2014 ). Therefore, we propose that upon gene activation, TDG molecules that are pre-bound or newly recruited at promoters participate in CpG methylation/demethylation cycling and interact functionally with chromatin remodelers, which together allow for a proper positioning of the nucleosomes flanking the NDR, thereby facilitating access of NDR-binding transcription factors such as ATF4 to promoters. In this study we also evidenced a functional link between TDG and ATF4-dependent transcription of ISR genes. Indeed, TDG binds to the promoters of Atf4 and ATF4-target genes in ECCs, and a large subset of these ATF4-target genes are down-regulated in Tdg -null cells. This global effect likely results from the integration of several interdependent molecular mechanisms that include: regulation of ATF4 and its partner C/EBPb binding to DNA by 5mC turnover, nucleosome (re)positioning and acetylation of H3K27, and control of ATF4 protein levels via regulating mRNA stability and translation of its mRNA ( Fig. 5K - Wortel et al., 2017 ). DNA binding of ATF4 and C/EBPb occurs at 5’-TGACGTCA-3’ sequences and ATF4 binding is inhibited by methylation of the central CpG ( Kribelbauer et al., 2017 ; Yin et al, 2017 ). ATF4-C/EBPb heterodimers also bind to the non-canonical sequence 5’-CGATGCAA-3’ with higher affinity when the CpG is methylated ( Mann et al., 2013 ). In addition, C/EBPb binds with a slightly higher affinity to the 5’-TTGCGTCA-3’ motif when the CpG is methylated, formylated or carboxylated ( Sayeed et al., 2015 ; Yin et al., 2017 ). Given the documented impact of CpG methylation status on ATF4 and C/EBPb binding to DNA, a complete ablation of TDG activity is predicted to result in altered chromatin binding of these major stress integrators in various ways depending on the sequence of the binding site and the type of dimer engaged. Upon stress conditions like aa starvation, ATF4 protein levels rise, favoring transcription of ATF4-target genes, the products of which act in a concerted manner during ISR. Members of the ISR pathway include aa synthetases, tRNA charging and solute carrier genes ( Wortel et al., 2017 ; Lines et al., 2023 ). Here we show that both RA-induced cell aggregation and the mere formation of embryoid bodies are associated with a decrease in several Slc mRNAs encoding amino-acid transporters, including the leucine transporter SLC7A5. This suggests that aggregate formation induces leucine shortage, hence lowering mTORC1 activity through sestrin activation, and thereby reducing ATF4-dependent gene expression. In wt ECCs, treatment with RA leads to a TDG-dependent down-regulation of the leucine sensor and mTORC1 inhibitor Sesn1 , thus likely increasing ATF4 production to sustain ATF4-target gene expression. Importantly, the implication of TDG in down-regulating Sesn1 in ECCs is in line with the inverse correlation between TDG and SESN1 expression in cancer patients. The cellular stress caused by tridimensional growth, which is reflected by the down-regulation of Slc genes, was found to be amplified in Tdg -null cells, implying a defective stress response upon Tdg inactivation. Amino-acid levels are sensed by two major pathways involving GCN2- and mTOR-regulated translation of ATF4 mRNA ( Harding et al., 2000 ; Park et al., 2017 ; Torrence et al., 2021 ). In addition, mTORC1 induces a stabilization of ATF4 mRNA ( Park et al., 2017 ). In the GCN2-ATF4 pathway, amino-acid starvation results in an increase in uncharged tRNAs, which bind to and activate the kinase GCN2, leading to phosphorylation of eIF2A and selective translation of the ATF4 mRNA. In growth factor-stimulated cells, uptake of essential amino-acids through SLC7A5 activates mTORC1, which allows for sustained growth and inhibition of TFE3-regulated autophagy ( Nicklin et al., 2009 ). Our data unequivocally identified the mTORC1 pathway as being required for ATF4 activity in P19 ECCs and being affected in Tdg -null cells. In particular, depletion of TDG down-regulates Slc7a5 mRNA levels in RA-treated cells. Also, the down-regulation of Slc7a11 and Slc3a2 in Tdg -null cells might prime cells for ferroptosis and participate in the reduction of mTORC1 activity ( Zhang et al., 2021 ). In line with our observations, the mTORC1 pathway is involved in pluripotency exit of ESCs through TFE3 inhibition ( Betschinger et al., 2013 ; Villegas et al., 2019 ). Furthermore, and consistent with the differentiation skewing observed in the absence of TDG, mTORC1 inhibition favors differentiation of ESCs into cardiomyocytes ( Zheng et al., 2017 ). Thus, by controlling the activity of mTORC1 and ATF4, TDG takes part in metabolic reprogramming during cell differentiation, a role that may also well be of relevance to tumor cell growth and survival in human cancers. Funding This work was funded by the Centre National de la Recherche Scientifique and the University of Rennes. MT was a recipient of a Doctoral Fellowship from the University of Rennes. RA was a recipient of a Fullbright grant. KS was a recipient of a scholarship from the Northern Kentucky University STEM International Research and Scholarly Exchange Program. Author contributions MT, TM, EW, RA and MB: methodology and investigation. GB: methodology and formal analysis. SA: software and data curation. KS, GP, CF, MB, JP and CLP: investigation. GS: conceptualization, project administration, funding acquisition, investigation, visualization and original draft preparation. All authors reviewed and edited the manuscript. Acknowledgements We thank L. Deleurme and A. Aimé for conducting cell sorting experiments at the Biosit cell sorting platform ( https://biosit.univ-rennes.fr/cytometrie-en-flux-et-tri-cellulaire ). We also thank V. Dupé for providing the Pax6 antibody, M. Pucéat for providing the Acta2 antibody, E. Chevet and E. Lafont for the gift of p70S6K and Phospho-p70S6K antibodies, PA Bidaud-Meynard for the gift of rapamycin, and A. Sérandour for providing sequencing reagents. We are grateful to R. Gibeaux for access to the fluorescence microscope and to A. Laurent for MNase-seq library preparation. Appreciation is extended to the Genomics Core Facility GenoA and the Bioinformatics Core Facility BiRD, both members of Biogenouest and France Genomique, as well as the Institut Français de Bioinformatique for their resources and technical support. Footnotes New experiments related to to Figure 1 (kinetic study of Nodal, Lefty 1 and Lefty 2 expression), Figure 2 (ferroptosis regulation), Figure 3 (epiblast and nascent mesoderm module analysis) Figure 4 (new heatmaps and average ChIP-seq profiles including CHD2 and CHD4 remodelers) and Figure 5 (new analysis of TDG expression correlation with gene effect and protein levels in human cancer cell lines) have been added. A supplementary Figure 5 has also been created. References ↵ Abril-Garrido J , Dienemann C , Grabbe F , Velychko T , Lidschreiber M , Wang H , Cramer P . ( 2023 ). Structural basis of transcription reduction by a promoter-proximal +1 nucleosome . Mol Cell , 83 ( 11 ): 1798 – 1809.e7 . 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Share TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition Marion Turpin , Thierry Madigou , Maud Bizot , Rachael Acker , Stephane Avner , Gérard Benoît , Martin Braud , Cynthia Fourgeux , Gaëlle Palierne , Jeremie Poschmann , Katie Sawvell , Erwan Watrin , Christine Le Péron , Gilles Salbert bioRxiv 2024.04.01.587571; doi: https://doi.org/10.1101/2024.04.01.587571 Share This Article: Copy Citation Tools TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition Marion Turpin , Thierry Madigou , Maud Bizot , Rachael Acker , Stephane Avner , Gérard Benoît , Martin Braud , Cynthia Fourgeux , Gaëlle Palierne , Jeremie Poschmann , Katie Sawvell , Erwan Watrin , Christine Le Péron , Gilles Salbert bioRxiv 2024.04.01.587571; doi: https://doi.org/10.1101/2024.04.01.587571 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7651) Biochemistry (17746) Bioengineering (13928) Bioinformatics (42066) Biophysics (21499) Cancer Biology (18650) Cell Biology (25579) Clinical Trials (138) Developmental Biology (13409) Ecology (19947) Epidemiology (2067) Evolutionary Biology (24374) Genetics (15633) Genomics (22557) Immunology (17775) Microbiology (40505) Molecular Biology (17217) Neuroscience (88796) Paleontology (667) Pathology (2845) Pharmacology and Toxicology (4836) Physiology (7664) Plant Biology (15179) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9839) Zoology (2272)