Deciphering the role of histone modifications in memory and exhausted CD8 T cells

Deciphering the role of histone modifications in memory and exhausted CD8 T cells | 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 Deciphering the role of histone modifications in memory and exhausted CD8 T cells Hua Huang , Amy E. Baxter , Zhen Zhang , Charly R. Good , Katherine A. Alexander , Zeyu Chen , Paula A. Agudelo Garcia , Parisa Samareh , Sierra M. Collins , Karl M. Glastad , Lu Wang , Gregory Donahue , Sasikanth Manne , Josephine R. Giles , Junwei Shi , Shelley L. Berger , E. John Wherry doi: https://doi.org/10.1101/2025.04.16.649198 Hua Huang 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy E. Baxter 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 5 Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhen Zhang 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 6 Institute of Health and Medicine, Hefei Comprehensive National Science center , Hefei, Anhui 230601, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charly R. Good 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine A. Alexander 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 7 Cold Spring Harbor Laboratories, Cold Spring Harbor , NY 11724, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zeyu Chen 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 8 Gene Regulation Observatory, Broad Institute of MIT and Harvard , Cambridge, MA 02142, USA ; Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA 02215, USA ; Department of Cell Biology and Pathology, Harvard Medical School , Boston, MA 02115, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paula A. Agudelo Garcia 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 9 Department of Biomedical Engineering, The Ohio State University , OH 43210, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Parisa Samareh 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sierra M. Collins 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl M. Glastad 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 10 Department of Biology, University of Rochester , NY 14620, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lu Wang 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 11 Sam and Ann Barshop Institute for Longevity and Aging Studies, UT University of Texas Health Science Center at San Antonio , San Antonio, TX 78229, USA ; Department of Biochemistry and Structural Biology, UT Health San Antonio , San Antonio, TX 78229, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregory Donahue 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sasikanth Manne 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josephine R. Giles 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 16 Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Junwei Shi 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 12 Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, USA 13 Abramson Family Cancer Research Institute, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shelley L. Berger 3 Epigenetics Institute, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 4 Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 14 Department of Genetics, University of Pennsylvania , Philadelphia PA 19104, USA 15 Department of Biology, University of Pennsylvania , Philadelphia PA 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: bergers{at}pennmedicine.upenn.edu wherry{at}pennmedicine.upenn.edu E. John Wherry 1 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 2 Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA 16 Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: bergers{at}pennmedicine.upenn.edu wherry{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Exhausted CD8 T cells (T EX ) arising during chronic infections and cancer have reduced functional capacity and limited fate flexibility that prevents optimal disease control and response to immunotherapies. Compared to memory (T MEM ) cells, T EX have a unique open chromatin landscape underlying a distinct gene expression program. How T EX transcriptional and epigenetic landscapes are regulated through histone post-translational modifications (hPTMs) remains unclear. Here, we profiled key activating (H3K27ac and H3K4me3) and repressive (H3K27me3 and H3K9me3) histone modifications in naive CD8 T cells (T N ), T MEM and T EX . We identified H3K27ac-associated super-enhancers that distinguish T N , T MEM and T EX , along with key transcription factor networks predicted to regulate these different transcriptional landscapes. Promoters of some key genes were poised in T N , but activated in T MEM or T EX whereas other genes poised in T N were repressed in T MEM or T EX , indicating that both repression and activation of poised genes may enforce these distinct cell states. Moreover, narrow peaks of repressive H3K9me3 were associated with increased gene expression in T EX , suggesting an atypical role for this modification. These data indicate that beyond chromatin accessibility, hPTMs differentially regulate specific gene expression programs of T EX compared to T MEM through both activating and repressive pathways. INTRODUCTION CD8 T cells are a proliferative and functional differentiation hierarchy. Following activation, quiescent naive CD8 T cells (T N ) undergo a complex and extensive rewiring of epigenetic, transcriptional regulation and gene expression programs. In the days after activation, T N differentiate into two major divergent populations: short-lived cytotoxic effector CD8 T cells (T EFF ) and the precursors for long-lived, quiescent memory CD8 T cells (T MEM ) 1 , 2 . Whereas T EFF mediate antiviral and anticancer effector functions, migrate throughout the body and help control initial disease, T MEM precursors are more restrained. Following antigen clearance, most T EFF die, but T MEM precursors survive and differentiate into mature T MEM that are quiescent and slowly self-renew. Furthermore, T MEM can rapidly reactivate effector functions and proliferate following encounter with the same antigen, mounting robust recall responses. However, if antigen persists, such as during chronic viral infections and cancer, these early precursors instead differentiate into exhausted CD8 T cells (T EX ). In contrast to T MEM , T EX are maintained by persistent stimulation, resulting in chronic activation and altered effector capacity. Thus, T EX mount weak recall responses to antigen restimulation and are associated with poor disease control compared to T MEM 3 . Despite both cell populations originating from a common precursor cell (T N ), T MEM and T EX represent divergent differentiation paths, resulting in highly distinct cell types. This ability of a common progenitor population to give rise to differentiation hierarchies consisting of diverse cell types with distinct functions has parallels throughout developmental biology, for example in the gastrointestinal tract where an intestinal stem cell niche gives rise to all mucosal cell types 4 . Although much work has focused on the functional differences between T MEM and T EX , the precise epigenetic and transcriptional mechanisms associated with differentiation of these divergent cell states remains to be fully defined. Epigenetic profiling of chromatin accessibility by Assay for Transposase-Accessible Chromatin with sequencing (ATAC-seq) has revealed that T MEM and T EX are as epigenetically distinct from each other as each cell type is from T N 5 – 10 . Furthermore, T EX have a unique chromatin accessibility profile with distinct open chromatin sites compared to other CD8 T cell subsets 5 , 6 . These observations support the idea that T MEM and T EX are separate lineages of mature CD8 T cells. The unique chromatin accessibility landscape of T EX is established in part by the thymocyte selection associated high mobility group transcription factor (TF) TOX 11 – 16 . TOX is essential for the formation of T EX but is dispensable for the generation of T MEM despite transient expression of TOX following acute stimulation 13 , 17 . In addition, TOX may regulate further differentiation once the T EX population is established 18 , coordinating transitions between progenitor, intermediate and terminal T EX subsets 19 – 25 , 18 . Thus, TOX plays a key role in establishing the unique T EX open chromatin landscape profiled by ATAC-seq. However, the associations between this T EX open chromatin landscape and regulation of gene expression through histone post-translational modifications (hPTMs), including how these hPTMs change as T N differentiate into T MEM or T EX , remains incompletely understood. Indeed, CD8 T cell differentiation is regulated by the addition or removal of hPTMs by a variety of epigenetic enzymes. In all multicellular organisms, histone 3 lysine 27 acetylation (H3K27ac) is associated with active enhancers, whereas methylation at the same site (H3K27me3) is associated with decreased gene expression and the formation of facultative heterochromatin. In CD8 T cells, EZH2, a histone methyltransferase that establishes H3K27me3, and KDM6B, a lysine demethylase that removes methyl groups from H3K27, have both been reported to regulate cell fate specification between T EFF and T MEM populations, as well as formation of T MEM capable of mounting robust recall responses 26 – 28 . Furthermore, the histone deacetylase HDAC3, which leads to chromatin compaction in part by removing acetyl groups from H3K27ac, restrains T EFF development and dampens effector function 29 . In addition to H3K27me3, H3K9me3 is also associated with heterochromatin and regulates constitutive repression of repeated DNA elements and long-term repression of inactive regions. The methyltransferase Suv39h1 that establishes H3K9 tri-methylation silences T MEM -associated genes to enable T EFF differentiation, and may regulate T EX effector function 30 , 31 . Furthermore, epigenetic enzymes such as protein arginine methyltransferase PRMT4 (CARM1) 32 , chromatin remodelers SWI/SNF family members BAF and PBAF 33 – 37 , and ASXL1 38 , as well as enzymes catalyzing DNA methylation and demethylation (TET2 39 , 40 and DNMT3A 41 – 43 ) have all been implicated in regulating fate transitions between and/or within CD8 T cell subsets. The diverse functions of the epigenetic enzymes that potentially regulate CD8 T cell differentiation suggests that complex patterns of hPTMs may be a feature of the distinct T N , T MEM and T EX epigenetic landscapes. Interrogating the differences in these hPTM patterns may provide insights into the diverse transcriptional regulation and gene expression programs of these CD8 T cell states. Once established, T EX are fate inflexible and do not differentiate into T EFF or T MEM cells. Furthermore, T EX retain the epigenetic “scars” of exhaustion even after removal of antigen and “cure” of chronic infection 44 . In contrast, T MEM are fate-flexible and are poised to rapidly respond when re-encountering antigen by differentiating into highly functional T EFF . This T EX fate inflexibility limits disease control. Targeted immunotherapies such as PD-1 pathway blockade “reinvigorate” T EX and have revolutionized cancer therapy 45 – 47 . However, not all patients experience clinical benefit, in part because the burst of effector activity in reinvigorated T EX is transient. This inability to provoke durable changes in T EX function and differentiation is due, at least in part, to the failure of T EX -targeted immunotherapies to rewire the T EX chromatin landscape and epigenetically reprogram these cells into T EFF or T MEM cells 5 , 6 , 18 . Indeed, because PD-1 pathway blockade does not change the T EX open chromatin landscape 5 , 10 , reinvigorated T EX revert to their original exhausted state over time 5 . Thus, although the open chromatin landscape of T EX has been defined by ATAC-seq, a more comprehensive understanding of how changes in chromatin accessibility and hPTMs are associated with gene expression is required to develop immunotherapy strategies that provoke durable responses in T EX . Furthermore, how epigenetic regulation through combinations of hPTMs might regulate the maintenance of T EX epigenetic scars, but enable fate flexibility in T MEM , remains unclear. To address these questions, we interrogated hPTMs patterns in T N , T MEM and T EX to investigate how active and repressive hPTMs were associated with the diverse gene expression and chromatin accessibility landscapes of these distinct CD8 T cell differentiation states. The epigenetic transition from a quiescent T N state into antigen-experienced populations (i.e. from T N to T MEM or T EX ) was associated with broad genome-wide alterations, highlighting the extensive epigenetic remodeling associated with CD8 T cell activation. Moreover, these genome-wide changes were associated with distinct chromatin features for T MEM and T EX cells, highlighting a key role for hPTM patterns in the development of fate-flexible T MEM versus fate-inflexible T EX . Defining these hPTM patterns may provide insight into the control of gene expression in T cell populations with diverse functions. These data provide a foundation for future epigenetic-based therapeutic approaches. RESULTS Histone modifications are associated with distinct gene expression landscapes of T MEM and T EX To study how hPTMs could regulate T EX development, we profiled activating and repressive histone modifications in T EX compared to T MEM and T N . We used different strains of lymphocytic choriomeningitis virus (LCMV) to induce either an acutely resolving infection [Armstrong (Arm)] with development of T EFF followed by T MEM , or to establish a chronic infection [clone13 (Cl13)] that results in T EX formation 3 . We adoptively transferred a physiological number of naive T cell receptor (TCR) transgenic LCMV D b GP33-41-specific CD8 T cells (P14 cells) into congenically distinct recipient mice and infected these recipient mice with LCMV Arm or Cl13 ( Fig. 1a ; 48 , 49 ). At ∼day 30 post-infection, we isolated P14 cells from mice infected with either Arm (T MEM , Fig. S1a-S1c) or Cl13 (T EX , Fig. S1d-S1f). P14 cells from an uninfected mouse were isolated as naive controls (T N ). We then performed Cleavage Under Targets and Release Using Nuclease (CUT&RUN) for histone modifications H3K27ac, H3K4me3, H3K27me3, and H3K9me3 on T N , T MEM , and T EX cells, alongside RNA-sequencing ( Fig. 1a ). Download figure Open in new tab Figure 1. Histone modifications act in concert to regulate gene expression in T EX and T MEM ( a ) Experimental design. ( b ) PCA of H3K27ac, H3K4me3, H3K27me3 and H3K9me3 data for T N , T MEM and T EX . For T MEM and T EX , n = 4 biological replicates; for T N , n = 8 biological replicates. ( c ) Correlation of change in RNA expression between T MEM and T EX and change in H3K27ac (top), or H3K27me3 (bottom). R and associated P-value represent Pearson correlation. Each dot represents one gene with one associated peak. ( d-e ) Genome tracks showing RNA-seq, ATAC-seq and hPTM data. Differentially modified regions between T MEM and T EX for each hPTM are highlighted in black boxes. ( f ) Heatmap of DEGs showing K-mean clusters for all pairwise comparisons between T N , T MEM , and T EX . ( g-h ) Meta plot (top) and heatmap plot (bottom) of H3K27ac at TSS for DEGs (g) cluster 1 (C1) and ( h ) cluster 5 (C5). ( i-j ) Meta plot (top) and heatmap plot (bottom) of H3K27me3 at TSS for DEGs (i) cluster 1 (C1) and (j) cluster 1 (C5). ( k-l ) Comparison of hPTM patterns between T MEM and T EX for (k) genes with increased expression in T EX compared to T MEM or (l) genes with increased expression in T MEM compared to T EX . Top six most frequent groups for each set of genes plotted. We first investigated whether hPTM patterns were distinct between CD8 T cells from acute and chronic infection compared to naive control cells. Principal component analysis (PCA) revealed that T N , T MEM and T EX occupied separate regions of PCA space for each hPTM studied ( Fig. 1b ). Furthermore, for all hPTMs, T N clustered separately from T EX and T MEM in PC1 whereas T MEM and T EX separated in PC2 ( Fig. 1b ). Thus, the highest numbers of differential hPTMs were identified between T N and either T MEM or T EX (Fig. S1g), suggesting that the greatest magnitude of hPTM changes occurred as T N were activated and differentiated into T EX or T MEM cell fates. To examine how hPTMs were associated with changes in gene expression between the three CD8 T cell subtypes, we first mapped each hPTM region to the nearest gene. We selected the peak for each gene that was the most variable across conditions, mapping one peak per gene. We then correlated the fold change in RNA expression of these genes to the fold change in hPTMs at the gene-associated peak. Genes with higher H3K27ac were associated with increased gene expression (R = 0.67, Fig. 1c , top and Fig. S1h-S1i). In contrast, higher H3K27me3 was only weakly correlated with lower gene expression (R = -0.16, Fig. 1c , bottom and Fig. S1h-S1i). However, key CD8 T cell genes had concurrent changes in H3K27ac and H3K27me3. For example, the TF Tcf7 was highly expressed in T N , moderately expressed in T MEM but lower in T EX (Fig. S1j, upper tracks). These differences in RNA expression were associated with concordant changes in activating hPTMs: H3K27ac was highest in T N , moderate in T MEM and minimal in T EX . In contrast, the repressive modification H3K27me3 was low in both T N and T MEM , but higher in T EX (Fig. S1j, lower tracks). Thus, reduced Tcf7 expression in T MEM compared to T N was associated with a decrease in activating hPTMs, whereas in T EX this gene had a combination of both lower H3K27ac and higher H3K27me3. This combination of changes was associated with the lowest Tcf7 RNA expression between CD8 T cell subsets. The exhaustion-associated TF Tox is highly expressed in T EX compared to T MEM and the Tox locus has extensive open intronic chromatin in T EX 13 ( Fig. 1d ). This region of the Tox gene was extensively marked with both activating H3K27ac and H3K4me3 in T EX , concurrent with low H3K27me3 ( Fig. 1d , Fig. S1k). In contrast, reduced expression of IL-2 receptor alpha ( Il2ra ) in T EX compared to T MEM was associated with higher H3K27me3 and lower H3K27ac and H3K4me3 ( Fig. 1e , Fig. S1k). These analyses suggest that combinatorial changes of activating and repressive hPTMs accompany changes in gene expression between CD8 T cell fates. To probe how the interplay between different hPTMs might regulate gene expression between CD8 T cell states, we next focused on hPTMs at promoters. H3K27ac at promoters is associated with active transcription, whereas H3K27me3 is present at inactive or poised promoters 50 . K-mean clustering identified 7 groups of genes differentially expressed between T N , T EX and T MEM ( Fig. 1f ). One set of differentially expressed genes (DEGs) was highly expressed in T EX compared to both T N and T MEM ( Fig. 1f : C1; 1126 genes), whereas a second set of DEGs was highly expressed in T MEM compared to both T N and T EX ( Fig. 1f : C5; 528 genes). For both clusters of DEGs, H3K27ac levels were highest and broadest around the transcription start site (TSS) in the corresponding CD8 T cell type ( Fig. 1g - 1h ). For example, for genes upregulated in T EX (C1), H3K27ac peaks at the TSS were highest and broadest in T EX ( Fig. 1g ), whereas C5 DEGs (upregulated in T MEM ) had higher H3K27ac in T MEM than T EX and T N ( Fig. 1h ). In contrast, the association between H3K27me3 and gene expression varied between cell state-associated genes. H3K27me3 was lower at the TSS in both T EX and T MEM compared to T N for C1 genes, despite elevated gene expression for this cluster only in T EX ( Fig. 1i ). However, H3K27me3 was lowest at the TSS of C5 genes (upregulated in T MEM ) in T MEM compared to T N and T EX ( Fig. 1j ), suggesting that loss of this repressive mark may have a distinct role in enforcing gene expression in T MEM . Furthermore, H3K27me3 was higher at the TSS of C5 genes in T EX compared to both T MEM and T N ( Fig. 1j ) provoking the hypothesis that a subset of genes expressed in T MEM are actively repressed in T EX . Finally, consistent with the role of silencing hPTMs in gene repression, H3K27me3 deposition was distributed over ∼10 kb around the TSS ( Fig. 1j ). These findings further support a role for changes in both activating and repressive hPTMs in regulating gene expression in T MEM and T EX , including at and outside the promoter, and also indicate that gain of activation marks, rather than loss of repressive modifications, may be a more common feature associated with increased gene expression. We next investigated the genome-wide association of combinatorial changes in hPTMs with gene expression. DEGs between T EX and T MEM were binned into patterns based on higher or lower abundance of the hPTMs analyzed. For genes upregulated in T EX compared to T MEM , 5 of the 6 most frequent patterns were characterized by increased H3K27ac deposition in T EX ( Fig. 1k ; G1, G2, G3, G5, and G6), which commonly co-occurred with higher H3K4me3 in T EX ( Fig. 1k ; G1, G3, G5, G6). In contrast, associations between T EX gene expression and repressive marks were less consistent. H3K27me3 and H3K9me3 levels were unchanged in T EX versus T MEM in 3 of the 6 patterns (G1, G2 and G4), with a shift to lower H3K27me3 in T EX only in patterns G3 and G6. Instead, H3K9me3 levels increased concurrently with H3K27ac and H3K4me3 in 2 patterns (G5 and G6), including for Tox (G6). Similar features were identified for genes upregulated in T MEM ( Fig. 1l ). Indeed, the top 3 most frequent hPTM patterns were similar between genes upregulated in T MEM or T EX ( Fig. 1k - 1l ; G1, G2, G3). Together, these analyses suggest that gain of H3K27ac is the most prevalent and homogenous feature of differential gene expression between T EX and T MEM . In contrast, loss of the H3K27me3 is linked to increased expression of only a subset of genes whereas H3K9me3 may be associated with increased expression of some genes in T EX . H3K27ac identifies putative enhancers and predicts families of transcription factors acting at these enhancers in T EX Analysis of TF binding sites in differentially accessible chromatin regions has identified TFs driving distinct T MEM and T EX cell fates 10 , 51 . However, chromatin accessibility defined by ATAC-seq alone may not be sufficient to identify functional enhancers. Therefore, analysis of TF activity at total accessible chromatin sites may not reflect the TF regulatory networks active at enhancers controlling gene regulation. As H3K27ac amplitude is strongly correlated with enhancer activity 52 , 53 , we hypothesized that identifying TF activity at H3K27ac sites could predict TFs active at putative enhancers. Thus, we performed Taiji PageRank analysis 54 on H3K27ac data to identify TF networks at putative enhancers that might drive T EX differentiation. Taiji PageRank analysis combines peak intensity, TF motif binding site accessibility, and TF expression to predict TF activity. TFs associated with quiescence such as LEF1 and TCF7 ranked highly in T N and higher in T MEM than in T EX ( Fig. 2a ) 55 , 56 , whereas TFs reported to coordinate memory versus effector responses, including RUNX1 and RUNX2, ranked highest in T MEM 57 ( Fig. 2a ). BATF, NFAT and IRF family members (IRF1, IRF3, IRF4 and IRF8) ranked highly in T EX ( Fig. 2a ), supporting identified roles for these TFs in exhaustion 58 – 62 . To identify TFs with predominant roles at putative enhancers (i.e. H3K27ac sites) rather than all open chromatin regions, we compared Taiji PageRank scores generated using H3K27ac ( Fig. 2a ) to published ATAC-seq data (Fig. S2a) 5 . The majority of TFs that ranked highly in T EX compared to T MEM using H3K27ac also ranked highly in ATAC-seq-based analysis, including NFAT, AP-1 and BATF ( Fig. 2b ). Correspondingly, TFs that ranked more highly in T MEM compared to T EX in the H3K27ac analysis also ranked highly using ATAC-seq (e.g. TCF7) ( Fig. 2b ). However, some TFs were differentially ranked between the two analyses. Whereas NUR77 (NR4A1) was highly ranked in T EX compared to T MEM based on chromatin accessibility 63 , 64 , this TF had comparable rankings in T EX and T MEM when assessed using enhancer-biased H3K27ac sites ( Fig. 2b ). In contrast, ZEB1 scored highly using H3K27ac in T EX compared to T MEM , but had similar PageRank scores in T MEM and T EX by ATAC-seq ( Fig. 2b ). The ZEB family of TFs, ZEB1 and ZEB2, may have reciprocal and potentially opposing roles in mature CD8 T cell differentiation. Of note, the ZEB2 binding motif remains undefined, preventing Taiji PageRank analysis. Whereas ZEB1 is required for T MEM survival and function 10 , 65 , ZEB2 promotes terminal differentiation and effector function 66 , 67 . In T EX , ZEB1 regulates T EX survival and persistence, whereas ZEB2 mediates T EX cytotoxic function, suggesting these TFs modulate distinct, potentially opposing, gene regulatory networks 10 , 68 . A deeper understanding of TFs acting at putative enhancers may uncover relevant T EX transcriptional networks, including transcriptional pathways regulated by the TF pair ZEB1/ZEB2. Download figure Open in new tab Figure 2. Identification of predicted TF binding motifs under H3K27ac identities role for ZEB1 in T EX ( a ) Heatmap of normalized Taiji PageRank scores determined using RNA-seq and H3K27ac data. ( b ) Correlation plot comparing Taiji PageRank scores from H3K27ac to ATAC-seq data. Axes represent log2 fold change (log2FC) in Taiji PageRank scores between T EX and T MEM ( c ) Venn diagram comparing number of ZEB1 motifs in regions with increased H3K27ac in T EX to regions with increased chromatin accessibility (ATAC) in T EX . ( d ) Heatmap showing DEGs between T MEM and T EX associated with regions with increased H3K27ac in T EX that contain ZEB1 motifs. ( e ) Bar graph showing cell type expression of DEGs associated with ZEB1 motifs in regions with H3K27ac enriched in T EX without concurrent increases in chromatin accessibility. ( f ) Genome tracks highlighting ZEB1 motifs in regions with increased H3K27ac levels in T EX without changing chromatin accessibility. Differentially modified regions for H3K27ac and open chromatin are highlighted in boxes under tracks. Predicted ZEB1 binding sites are shown in red. ( g ) Number of differentially expressed TFs between T MEM and T EX , with representative TFs indicated. ( h ) Comparison of hPTMs between T EX and T MEM for TFs with increased expression in T EX . Top six most frequent groups plotted. ( i ) Genome tracks showing RNA-seq, ATAC-seq and hPTM data for TFs with increased expression in T EX . Differentially modified regions for each modification are highlighted in black bars. To further investigate the roles of ZEB1 and NUR77 at H3K27ac sites compared to accessible chromatin regions, we used motif analysis to identify predicted ZEB1 and NUR77 binding sites within differentially H3K27ac-modified regions and differentially accessible chromatin regions. Although ∼20% (38/193) of ZEB1 motifs were found in regions where both H3K27ac and chromatin accessibility were increased in T EX compared to T MEM , ∼45% (86/193) of predicted ZEB1 binding sites had increased H3K27ac without a concurrent increase in chromatin accessibility ( Fig. 2c ). Of these 86 ZEB1 predicted binding sites with increased H3K27ac deposition in T EX , but without increased chromatin accessibility ( Fig. 2c ), the vast majority, ∼91%, were associated with genes increasing in expression in T EX ( Fig. 2d - 2e ). Genes potentially regulated by ZEB1 binding at sites with changing H3K27ac deposition included the TFs Eomes and Tox, and the mitotic regulator Plk1 ( Fig. 2d and 2f ). In contrast, NUR77 motifs were much more prevalent in regions of increased chromatin accessibility in T EX than regions with increased H3K27ac, representing 71% of predicted binding sites (Fig. S2b). We identified an enrichment in these predicted binding sites toward genes with increased expression in T EX (Fig. S2c-S2d), including the genes encoding TFs Setbp1 and Ikzf2 (HELIOS), the pro-survival factor Bcl2 and Tnfsf4 (OX40L) (Fig. S2c and S2e). However, ∼30% of these NUR77 motifs were located in genes highly expressed in T MEM . These analyses support the differential rank in Taiji PageRank analysis for ZEB1 and NUR77 and suggest a preferential role for ZEB1 in T EX through binding sites marked by H3K27ac. Assessment of TF binding motifs at H3K27ac sites revealed TFs potentially acting at putative T EX enhancers. However, both the accessibility of TF binding sites and expression of these TFs themselves must be tightly regulated to orchestrate broad changes in transcriptional networks during CD8 T cell differentiation. Therefore, we next asked how the TF genes were themselves regulated by hPTMs. First, we identified TFs from the AnimalTFDB database 69 with differential gene expression between T MEM and T EX cells. The majority of TFs identified in this pairwise comparison increased in expression in T EX , with 120 TF genes upregulated in T EX versus T MEM compared to only 47 TFs with increased expression in T MEM ( Fig. 2g ). We then assessed the genes encoding these TFs for associated changes in hPTMs ( Fig. 2h ). For TFs that increased in expression in T EX compared to T MEM , H3K27ac, H3K4me3 or both increased in all 6 of the hPTM patterns; however, only 2 patterns (G2 and G6) had decreased H3K27me3 and none had decreased H3K9me3 ( Fig. 2h ). For example, Irf4 , Batf and Ikzf2 all gained H3K27ac and H3K4me3 in T EX , whereas H3K27me3 was unchanged for Batf , but lost for Irf4 and Ikzf2 ( Fig. 2i ). Similar patterns were observed for TFs with higher expression in T MEM , where all of the top 6 patterns were associated with increased H3K27ac levels in T MEM (Fig. S2f). Together, these analyses indicate that upregulation of TF expression is more frequently associated with an increase in the activating hPTMs rather than removal of repressive hPTMs. This pattern of regulation (gain of activation-associated modifications in T EX ) was not unique to TFs, but was observed for all genes differentially expressed between T EX and T MEM ( Fig. 1k ). Thus, these analyses indicate that both expression of the TFs that coordinate CD8 T cell differentiation and the genes downstream of these TFs are regulated by similar patterns of hPTMs. Furthermore, these data suggest that regulating gene expression through the acquisition of activating hPTMs may be a common feature of CD8 T cell differentiation. Super enhancers associate with distinct transcriptional wiring of T EX Super enhancers (SEs) are large clusters of enhancers with the potential to bind numerous TFs and recruit co-factors 52 , 70 , playing key roles in defining cell fate, controlling cell identity and/or driving disease 52 . We identified enhancers in each cell type based on the presence of H3K27ac at non-promoter regions (Fig. S3a), and ranked “stitched” enhancers using the ROSE algorithm 70 , 71 . Enhancers with high signal intensity were defined as super enhancers, whereas those with low signal intensity were defined as typical enhancers (TEs) ( Fig. 3a and S3b). As expected, SEs were associated with higher expression of nearby genes compared to TEs (Fig. S3c). To investigate whether SEs were associated with the distinct T EX and T MEM cell fates, we ranked SEs within each CD8 T cell population ( Fig. 3a ). A number of top-ranked SEs were shared between all three CD8 T cell populations, such as Ikzf1 (IKAROS), Rapgef1 , Fyn and the transcriptional regulator Id2 , whereas the TFs Tbx21 (TBET) , Zeb2 and Runx2 were shared between T MEM and T EX ( Fig. 3a ). In contrast, the SEs near genes involved in quiescence, such as Bach2 and Foxp1 , ranked highly in T N ( Fig. 3a , left). Although several SEs were highly ranked in both T MEM and T EX , key differences were identified. SEs more highly ranked in T MEM than T EX were located near genes associated with effector biology, such as Rora , Klrb1b and Klrg1, persistence-associated TFs Bhlhe40 68 , 72 , 73 and Tcf7 (TCF1), and the cytokine receptor Il2ra were more highly ranked in T MEM than T EX ( Fig. 3a , middle). In contrast, SEs close to the T EX -associated TFs Tox, Eomes and Batf , as well as the inhibitory receptors Pdcd1 and Havcr2 ranked highly in T EX ( Fig. 3a , right). Furthermore, top-ranked SEs in T EX were associated with unannotated RNAs or lncRNAs, including 2310001H17Rik ( Fig. 3a , right). Thus, H3K27ac-associated SEs likely play a role in regulating expression of key lineage-defining TFs, effector molecules and other genes driving the distinct T N , T MEM and T EX cell differentiation paths. Download figure Open in new tab Figure 3. Super enhancers drive transcriptional phenotype of T EX ( a ) Distribution of H3K27ac signal across stitched enhancer regions in T N , T MEM and T EX .. Top 10 ranked putative SEs plus selected SEs are highlighted. N indicates the number of putative SEs identified for each cell type. Stitched enhancers above horizontal dashed lines are associated with putative SEs; enhancers below horizontal dashed lines are typical enhancers (TE). ( b ) Comparison of SE ranks identified using H3K27ac signal (x-axis) and ATAC signal (y-axis). Selected SEs are labeled. ( c ) Genome tracks showing RNA-seq, ATAC-seq and H3K27ac data. Putative SE regions identified using H3K27ac data but not ATAC-seq data are highlighted in black boxes below tracks. Individual enhancers within SE regions are highlighted in grey. Promoter regions (±2,500bp of TSS) are indicated in red. ( d ) Venn diagram showing cell-type specificity of SEs identified by H3K27ac. ( e ) Heatmap showing gene expression of genes within 50 kb of SE identified only in T MEM and T EX , respectively. Selected DEGs are highlighted. ( f ) Genome tracks showing RNA-seq and H3K27ac data. Putative SE regions identified using H3K27ac data are highlighted in black box. Individual enhancers within SE regions as highlighted in grey. Promoter regions (±2,500bp of TSS) are indicated in red. Chromatin accessibility can infer SEs 52 , 70 and in T EX , such analysis has been used to investigate the regulation of Tox expression 13 . Therefore, we investigated whether defining SEs by H3K27ac rather than chromatin accessibility could provide additional insights into SE regulation of the T EX cell fate. We directly compared SEs defined by chromatin accessibility 5 (Fig. S3d) to SEs identified via H3K27ac ( Fig. 3a - 3b ). Many SEs were highly ranked using both approaches, including SEs associated with Tox and Pdcd1 ( Fig. 3b ). However, the majority of SEs identified by chromatin accessibility were not identified using H3K27ac (Fig. S3e). For example, Tigit , encoding an inhibitory receptor 74 , Nr4a2 , encoding a TF reported to promote T cell exhaustion 13 , 15 , 64 , and TNF family member Tnfsf10 , all ranked highly for SE activity defined by chromatin accessibility but were not identified as H3K27ac-defined SEs ( Fig. 3b and S3f). In contrast, Havcr2, encoding the inhibitory receptor TIM3, and killer cell lectin-like receptor Klra8 ranked highly when SEs were identified by H3K27ac, but not by chromatin accessibility ( Fig. 3b - 3c ). Furthermore, Gene Ontology (GO) analysis revealed functional divisions within SEs. Whereas GO terms for gene-associated SEs identified by chromatin accessibility were more likely to have roles in cell survival and differentiation, genes associated with SEs identified based on H3K27ac were enriched for GO terms involved in cell adhesion, division and inflammatory responses (Fig. S3g). Therefore, H3K27ac identified additional potential SE-regulated genes both with known roles in T cell exhaustion and genes that have not previously been deeply interrogated in T EX . As antigen-experienced cells, T EX and T MEM share a core epigenetic and transcriptional network that distinguishes these cells from T N . However, T EX and T MEM also have distinct chromatin accessibility and transcriptional circuits that are cell-type specific and define these two differentiation trajectories 5 , 6 , 8 , 10 . More than half of all SEs we identified (52%) were shared between T N , T MEM and T EX , suggesting a common role in CD8 T cell biology ( Fig. 3d ). Furthermore, 161 SEs were shared between T MEM and T EX , reflecting common pathways in non-naive T cells. Moreover, only 6% of SEs were unique to T EX (58/985), and even fewer were unique to T MEM (3%; 33/985; Fig. 3d ). Therefore, we next examined whether T cell fate-specific SEs were associated with cell type specific expression of the SE-associated gene. Indeed, T MEM -specific SEs were associated with high gene expression only in T MEM and included Il2ra and Cd44 ( Fig. 3e ). In contrast, genes with SEs specific to T EX were highly expressed only in T EX , including the SE-associated genes Tox and the inhibitory receptors Entpd1 , Pdcd1 and Havcr2 ( Fig. 3e - 3f ). This analysis also revealed T EX -enriched SE-associated genes that have not been extensively studied in T EX , including Setbp1 13 , Trps1 and Ubash3b ( Fig. 3e - 3f ). Together, these data add further support to the hypothesis that SEs play key roles driving both the shared and distinct chromatin regulatory and transcriptional circuitry of T EX versus T MEM cells and identify previously understudied H3K27ac-enriched SEs associated with genes in T EX that warrant further investigation. Chromatin state analysis reveals state-specific transitions from T N to T MEM or T EX To study how genome-wide chromatin states change during CD8 T cell differentiation, we applied ChromHMM, an algorithm that uses multiple hPTMs to segment the genome into distinct states 75 . We used ATAC-seq, H3K27ac, H3K4me3, H3K27me3, and H3K9me3 data to identify the four major promoter states; active (I), poised (II), repressive (III) and repetitive/heterochromatin (IV) promoters. Active promoters (I) were defined by open chromatin and by the active marks H3K27ac and H3K4me3 (Fig. S4a-S4b). As expected, the vast majority of (∼95%) of genes with active promoters in one CD8 T cell population were highly expressed in that cell type (Fig. S4c). Pathway analysis of genes with active promoters in T N revealed that these promoters were associated with core cellular function pathways, including DNA repair, chromatin segregation and translation (Fig. S4d), suggesting that, in T N , genes involved in basic cellular functions are regulated by promoters with active marks. In contrast, repressed (III) and repetitive/heterochromatin (IV) promoters were defined by deposition of the repressive marks H3K27me3 and/or H3K9me3 (Fig. S4a-S4b). Accordingly, the vast majority of genes with repressed or repetitive/heterochromatin promoters were not expressed in the cell type with those repressed or repetitive/heterochromatin features (Fig. S4c). In T N , genes with repressed promoters were enriched for more specialized pathways with limited roles in CD8 T cells, such as muscle contraction, sensory perception of pain and response to pheromone (Fig. S4d). The poised promoter state was first described in embryonic stem cells (ESCs) as nucleosomes bearing both H3K4me3 and H3K27me3 76 – 78 . The genes associated with these dual modified promoters were not expressed in ESCs, but instead were turned on as cells acquired identity and lineage commitment in the developing embryo 79 , 80 . This poised state has also been described in “multipotent” T N 81 . To investigate how genes with poised promoters are associated with T MEM and T EX cell fates, we first identified genes with poised (state II) promoters in T N (Fig. S4a-S4b). As expected, in T N the majority (∼57.7%) of genes with promoters bearing both H3K4me3 and H3K27me3 were not highly expressed (Fig. S4c), supporting the assignments of these promoters as poised. Within the 4,945 poised T N promoters, we identified promoters that shifted to an activated state only in T EX or only in T MEM ( Fig. 4a , indicated by star and triangle respectively). We then focused on promoters that were associated with increased gene expression in each cell type ( Fig. 4b ). For example, the Src family kinase Yes1 and the IL-2 receptor alpha ( Il2ra ) showed this pattern of activation only in T MEM ( Fig. 4b - 4c ). Furthermore, promoters for genes posited to have roles in CD8 T cell persistence and tissue residency, including Prss12 82 , Nt5e (CD73 83 ), and Ier3 (IEX-1 84 ) transitioned from poised-to-active state only in T MEM ( Fig. 4b ). Gene ontology (GO) analysis revealed functions in metabolism and signaling, including cytokine signaling (Fig. S4e). Together these analyses suggest that genes poised in T N that become activated in T MEM are involved in key aspects of T MEM biology. Download figure Open in new tab Figure 4. Key T MEM and T EX genes are poised in T N and activated upon differentiation ( a ) Alluvial plot showing how hPTMs at promoters poised in T N change in T MEM and T EX . Active promoters were defined as either significantly gaining H3K4me3 or losing H3K27me3 or both, repressed promoters were defined as either significantly losing H3K4me3 or gaining H3K27me3 or both. Statistical cutoff of FC > 1.5. Number represents total number of T N poised protomers; inset table shows number of poised-to-active and poised-to-repressed promoters in T MEM and T EX . ( b ) RNA expression heatmap for DEGs with poised promoters which were activated in either T MEM (triangle) or T EX (star). Selected DEGs are highlighted in bold. ( c-d ) Genome tracks showing poised promoters in T N that switched to active (c) only in T MEM or (d) only in T EX . Promoters were excluded from analysis and are highlighted in grey bars. ( e ) Bubble plot showing changes in predicted TF binding site accessibility for poised-to-active T MEM or T EX genes, with select TFs highlighted. In contrast, genes with promoters that shifted from T N poised to active in T EX included the TFs Eomes , which drives terminal differentiation of T EX 19 , 61 and Tox2 , which has a reported role in T EX 15 , as well as other T EX -associated genes including ItgaV (CD51) 61 , Srgap3 85 , and Ndfip2 85 ( Fig. 4b and 4d ). Several cell cycle-associated genes also showed this pattern of regulation, including Mki67 (KI67), Incenp and Chn2 ( Fig. 4b and 4d ) consistent with the more extensive cell division history of T EX including ongoing cell cycle 10 , 19 . Of note, the promoter of Id2 was poised in T N , and shifted to an activated state only in T EX , despite RNA expression in both T MEM and T EX ( Fig 4b 86 – 88 ) . Additional analysis revealed that, although H3K27me3 was lost at the Id2 promoter in both T MEM and T EX , T EX retained activating H3K4me3 and this modification decreased in T MEM compared to T EX (Fig. S4f). These analyses highlight the complexity of gene expression regulation by a suite of hPTMs at and around the promoter. Finally, several genes that have not previously been well studied in T EX were also identified, including Setbp1 ( Fig. 4b ), which was also associated with a SE in T EX ( Fig. 3e - 3f ). GO analysis identified pathways linked to development, differentiation, and proliferation (Fig. S4e). Together, these data suggest that genes with poised promoters in T N are key genes involved in the divergent differentiation trajectories of T MEM and T EX , including lineage-driving TFs and genes associated with T EX function (e.g. continued proliferation and survival during high antigen stress). We next investigated TFs associated with expression of genes with poised-to-active promoters and might therefore drive the distinct T EX and T MEM cell fates. We performed TF motif analysis on these poised-to-active promoters ( Fig. 4a - 4b ). Motifs for ZNF41, a zinc finger family TF, were enriched in T MEM poised-to-active promoters, as were both STAT1 and STAT5 motifs ( Fig. 4e ). Indeed, STAT1 is required for CD8 T cell clonal expansion and memory formation 89 , and STAT5 has a key role in early effector and memory-precursor CD8 T cell differentiation 90 . In contrast, predicted MafK binding sites were enriched in poised-to-active T EX promoters ( Fig. 4e ). MafK forms heterodimers with BACH2 to help direct the repressive activity of BACH2 to specific genes 91 . BACH2 is a key transcriptional coordinator involved in T cell quiescence in T N , T MEM including stem cell memory cells, but also in stem cell-like progenitor T EX 92 , 93 suggesting a potential T EX -associated BACH2-MafK regulatory module. Furthermore, Fli1, an ETS family TF, dampens effector CD8 T cell transcriptional networks 94 . The EWS:ERG fusion motif, which is also predicted to be bound by Fli1, was enriched in poised-to-active promoters in T EX ( Fig. 4e ), supporting a potential role for Fli1 or other ETS family members in restraining effector biology in T EX . Finally, NFkB-p65 motifs were enriched in T EX poised-to-active promoters ( Fig. 4e ). NFkB signaling has broad roles in T cells, regulating initial TCR-mediated T cell activation, proliferation and effector function as well as T MEM survival 95 , 96 . Moreover, NFkB transcriptional circuitry is augmented after treatment with immunotherapies targeting inhibitory receptors, such as PD-1 blockade, or costimulation, such as CD137 (41BB) agonism 5 , 97 . The enrichment for NFkB-p65 motifs at T EX poised-to-active promoters suggests that reengaged NFkB circuitry in immunotherapy-reinvigorated T EX may be driving expression of previously poised genes, with potential implications for improving therapies. Together, these data suggest that a subset of the distinct T EX and T MEM transcriptional programs consist of genes with poised promoters in T N that then acquire either active or repressed chromatin states during differentiation. We next investigated how chromatin modifications could reinforce distinct T MEM and T EX cell states through repression of alternative fates. We identified promoters that were poised in T N and then became repressed in T MEM or T EX either through gain of H3K27me3, loss of H3K4me3 or both potentially to silence genes of alternative cell fates ( Fig. 4a ). In total, 901 promoters switched from a poised to repressed state in T EX compared to only 142 promoters for T MEM ( Fig. 4a , blue line for T EX compared to blue circle for T MEM ). Genes with promoters that switched from poised-to-repressed as T N differentiated into T MEM on average lost the activating mark H3K4me3 and also maintained the repressive mark H3K27me3 (Fig. S4g). For example, several genes associated with T cell differentiation including the TFs Tox2, Eomes and Ikzf2 (HELIOS), the exhaustion-associated ectonuclease Entpd1 (CD39), costimulatory molecule Tnfsf4 (OX40L) and glycoprotein Itm2a all had higher H3K27me3 and lower H3K4me3 at the promoters for these genes in T MEM compared to T N and T EX (Fig. S4h). This poised-to-repressed promoter state was associated with lower RNA expression of these genes in T MEM than in T EX . (Fig. S4i), indicating that genes highly expressed in T EX and poised in T N are actively repressed in T MEM . In contrast, genes that became repressed during T EX differentiation gained repressive H3K27me3 at the promoter, however there was variable loss of H3K4me3 at these promoters (Fig. S4j). For example, in T EX , whereas Ier3 (IEX1) , Tnfrsf25 (DR3) , Hdac10 and Mapk12 gained H3K27me3 at the promoter, only Mapk12 lost H3K4me3 (Fig. S4h, S4j-S4k). Furthermore, a subset of genes with promoters that switched from poised-to-repressed in T EX switched from poised-to-active in T MEM (e.g. Ier3 , Il2ra , Tnfrsf25 ) and vice versa (e.g. Tox2 ) ( Fig. 4b and S4h). Together, these data suggest that active repression of genes poised in T N may help enforce the distinct T MEM and T EX states and potentially limit conversion between these two populations, with gain of H3K27me3 predominantly driving this repression in T EX . Atypical H3K9me3 narrow peaks are enriched for CTCF motifs and occur at distinct repeat classes H3K9me3 is typically associated with constitutive heterochromatin and is classically involved in silencing gene expression, playing a critical role in cell differentiation 98 , 99 . This hPTM classically exhibits broad peaks across the genome 98 , however analysis of H3K9me3 peak width in CD8 T cells revealed a wide range in peak size, from 100 kb, with most peaks less than 10 kb (Fig. S5a) 100 , 101 . Therefore, we examined how H3K9me3 localization and deposition changed during CD8 T cell differentiation. Regions with higher H3K9me3 in T MEM compared to T EX (T MEM -enriched peaks; n=1565) were broad, covering ∼40.1kb bases on average ( Fig. 5a , left; Fig 5b ). In contrast, regions with higher H3K9me3 in T EX compared to T MEM (T EX -enriched peaks; n=1279) were narrower, averaging only ∼15.7kb bases ( Fig. 5a , right; Fig. 5c ). Directly comparing the distribution of peak sizes between T EX -enriched and T MEM -enriched H3K9me3 peaks showed that T EX -enriched peaks were substantially narrower than T MEM -enriched peaks ( Fig. 5d ). Moreover, only 10.2% (n=130) of T EX -enriched peaks were broad (>=15 kb), compared to 42.9% (n=672) of T MEM -enriched peaks ( Fig. 5e ). Analysis of the number of base pairs in the genome covered by H3K9me3 revealed that while the majority of the genome is covered by broad peaks, over 25% of the T EX -enriched base pairs are in narrow peaks (Fig. S5b). The majority of narrow T EX -enriched H3K9me3 peaks were found in intergenic and intronic regions, with only a small proportion (∼3%) located in promoter regions (Fig. S5c). These results suggest that H3K9me3 deposition patterns at T MEM or T EX -enriched peaks have distinct characteristics, with H3K9me3 enriched in narrow peaks in T EX and broad peaks in T MEM . Download figure Open in new tab Figure 5. T EX -enriched atypical H3K9me3 peaks cover CTCF sites and are associated with gene expression ( a ) Signal intensity heatmap of T MEM -enriched and T EX -enriched H3K9me3 regions. ( b ) Genome track showing broad H3K9me3 regions enriched in T MEM . ( c ) Genome track showing narrow H3K9me3 regions enriched in T EX . ( d ) Peak size distribution of T MEM - and T EX -enriched H3K9me3 regions. ( e ) Percentage of H3K9me3 peaks that are broad versus narrow. Peaks >=15 kb are defined as broad, peaks <15 kb are defined as narrow. N.s. = non-significantly different between T MEM and T EX . ( f ) Heatmap of Z-scored repeat element class coverage of T MEM -enriched, non-significant (n.s.) and T EX -enriched H3K9me3 narrow and broad peaks. ( g ) Predicted TF binding motifs in T MEM - and T EX -enriched narrow and broad peaks compared to n.s. H3K9me3 peaks. Top 5 motifs shown per comparison. ( h ) Venn diagram showing overlap between locations of CTCF binding sites and T EX - enriched narrow H3K9me3 peaks. P value represents a hypergeometric test for the comparison. ( i ) Pie chart showing change in CTCF binding at sites within T EX -enriched narrow H3K9me3 peaks. ( j ) Violin plot of log2 fold change in RNA expression between T MEM and T EX near differentially modified regions for each hPTM (H3K27ac, H3K4me3, H3K27me3 and H3K9me3). ( k ) Pie chart showing changes in RNA expression between T MEM and T EX of genes near T EX -enriched narrow H3K9me3 peaks. ( l ) Heatmap of RNA expression for genes from Fig. 5k . ( m ) Genome track showing DEGs near to narrow T EX -enriched H3K9me3 regions. A major function for H3K9me3 is to repress repetitive genomic elements, maintaining genome integrity 102 . Therefore, we examined whether the atypical narrow H3K9me3 peaks enriched in T EX also were associated with repetitive elements like broad H3K9me3 peaks. We first assessed repeat coverage of narrow, broad and H3K9me3 peaks that were not significantly different (n.s.) between T EX and T MEM . Analysis of T EX -enriched H3K9me3 peaks shows that 53% of narrow peaks covered repeats, compared to around 34% of broad peaks (Fig. S5d). This analysis suggests that broad and narrow H3K9me3 peaks may function to repress repetitive genomic elements, but that narrow H3K9me3 peaks could be additionally specialized to this role. We next investigated whether specific families of repetitive elements were enriched underneath the T EX -enriched narrow H3K9me3 peaks. T EX -enriched narrow H3K9me3 peaks had reduced coverage of LINE elements (27.7%) (Fig. S5e, top) compared to n.s. narrow peaks (60.8%) and T MEM -enriched narrow peaks (52%), as did T EX -enriched broad peaks (Fig. S5e, bottom). However, T EX -enriched narrow peaks showed an increase in LTR element coverage compared to other H3K9me3 peak sets (Fig. S5e). To further investigate this finding, we assessed repeat element subclasses. Narrow H3K9me3 peaks that were not enriched in T MEM or T EX were strongly associated with LINE:L1 and LTR:ERV subclasses compared to n.s. broad H3K9me3 peaks ( Fig. 5f ). T MEM -enriched broad and narrow peaks had similar patterns of repeat element coverage, wheras T EX -enriched broad and narrow peaks were associated with distinct repeat element subclasses ( Fig. 5f ). For example, T EX -enriched narrow H3K9me3 peaks showed a unique enrichment in the LTR subclass ERVL-MaLR and the retrotransposon SINE B2 ( Fig. 5f ), suggesting a distinct regulatory role for these elements in T EX cells. Repeat elements have been co-opted to serve critical roles in chromatin organization, enhancer function, and gene regulation 103 , 104 . SINE B2 is one example of repeat elements serving functional roles. These elements are rodent-specific retrotransposons that contain binding sites for CTCF 105 , a zinc finger protein that acts as a transcriptional activator, repressor, and genome organization 106 . H3K9me3 at SINE B2 repetitive elements can regulate CTCF binding at these sites 107 . Therefore, we performed an unbiased motif analysis of T MEM - and T EX -enriched H3K9me3 peaks. Indeed, the top most enriched motifs under T EX narrow peaks were CTCF and the CTCF related factor (CTCFL or BORIS) ( Fig. 5g ). To validate whether CTCF bound directly at T EX -enriched H3K9me3 narrow peaks, we performed CUT&RUN for CTCF in T N , T MEM , and T EX . Nearly a quarter (24%, n=271) of T EX -enriched H3K9me3 narrow peaks were bound by CTCF ( Fig. 5h, p = 0.037). Of these, 45% had differential CTCF binding: 26.7% showed increased CTCF binding in T EX compared to T MEM , while 18.5% had reduced CTCF binding in T EX ( Fig. 5i ). For example, CTCF binding increased in H3K9me3 peaks associated with Ctsc (CAPTHESIN C), a peptidase that coordinates activation of serine proteases including granzymes, whereas CTCF binding decreased at a H3K9me3 peak close to Klra3 , an NK cell receptor expressed in a cytotoxic subset of T EX (Fig. S5f). Together, these findings suggest that narrow H3K9me3 peaks may have distinct associations with repetitive elements in different CD8 T cell subtypes, and that altered H3K9me3 deposition in T EX may impact CTCF binding at specific sites in the genome, with potential implications for cell type-specific genome organization. To investigate whether these T EX -enriched narrow H3K9me3 peaks were associated with chromatin accessibility, we examined the overlap between these peaks and T EX -accessible chromatin regions. Approximately 25% (287/1149) of T EX -enriched narrow H3K9me3 peaks were located in regions of open chromatin in T EX (Fig. S5g), for example near Tox (Fig. S5i). Genes with narrow H3K9me3 peaks associated with open chromatin, were significantly enriched in cytokine-mediated signaling and leukocyte cell-cell adhesion pathways (Fig. S5h). These observations suggests that the association of a subset of narrow H3K9me3 peaks with open chromatin may contribute to the role for this hPTM to regulate expression of key cell-type associated genes. T EX -enriched H3K9me3 peaks correlate with gene activation H3K9me3 is typically associated with repression of gene expression 108 . However, a subset of genes with increased expression in T EX compared to T MEM had higher H3K9me3 deposition in T EX ( Fig. 1k , G5 and G6). Thus, we next investigated the association between H3K9me3 and gene expression in T EX . Regions with differentially enriched hPTMs between T MEM or T EX were identified, then filtered for nearby genes that were differentially expressed between T MEM and T EX ( Fig. 5j ). As expected, genes near regions with increased H3K27ac or H3K4me3 in one cell type had overall higher expression in that cell type ( Fig. 5j ), consistent with gene-activating functions of H3K27ac and H3K4me3. In contrast, genes near regions of increased H3K27me3 or H3K9me3 in T MEM had lower expression in T MEM than T EX ( Fig. 5j ), consistent with the repressive roles of H3K27me3 and H3K9me3. However, although increased H3K27me3 was negatively associated with gene expression in T EX , H3K9me3 was positively associated with gene expression, with nearby genes more highly expressed in T EX versus T MEM ( Fig. 5j , purple box). Given the observations above that increased H3K9me3 deposition in T EX was predominantly localized to narrow peaks ( Fig 5a and 5e ; 89.8% of peaks), we tested if the link between H3K9me3 and increased gene expression was associated with narrow H3K9me3 peaks, or a general feature of H3K9me3 in T EX . Both narrow and broad H3K9me3 peaks showed the same pattern of increased deposition of H3K9me3 near genes with higher expression in T EX (Fig. S5j). To assess the breadth of impact of these H3K9me3 peaks on regulating the transcriptional programs of T EX , we next asked what proportion of T EX -enriched peaks were associated with changes in gene expression ( Fig. 5k ). Given that the vast majority of H3K9me3 peaks in T EX were narrow, we focused the subsequent analyses on these atypically narrow peaks. The majority of genes close to T EX -enriched H3K9me3 narrow peaks did not change expression (n.s., Fig. 5k ), suggesting that not all H3K9me3 has gene regulatory functions. However, 17% of H3K9me3 T EX -enriched narrow peaks were associated with gene upregulation in T EX cells, whereas 7.7% were associated with decreased expression ( Fig. 5k ), such that narrow H3K9me3 T EX -enriched peaks were twice as likely to be associated with gene upregulation compared to downregulation. Of note, T MEM -enriched narrow H3K9me3 peaks did not show this positive association with gene expression (Fig. S5k). Furthermore, 12.3% of genes upregulated in T EX compared to T MEM had an increase in H3K9me3 deposition in a narrow peak within 50 kb (Fig. S5l). This fraction increased to 34% of genes when assessing H3K9me3 deposition within 250 kb of the gene (Fig. S5l), suggesting that a subset of T EX upregulated genes were potentially regulated at least in part by atypical “activating” H3K9me3. Finally, we performed a GO analysis to examine differences in the biological processes associated with H3K9me3 deposition in T EX . For example, genes nearby T EX -enriched narrow peaks with decreased expression in T EX , indicative of a repressive function for H3K9me3, were enriched for negative regulation of lymphocyte activation (Fig. S5m). In contrast, genes with increased expression close to regions with increased H3K9me3 deposition in T EX , i.e. “activating” H3K9me3, were associated with cell cycle, cell death, response to cytokine stimulus and leukocyte activation (Fig. S5m). Thus, genes potentially regulated by both canonical repressive and non-canonical “activating” H3K9me3 play broad roles in T EX biology. Indeed, upregulated genes in T EX located near T EX -enriched narrow H3K9me3 peaks included the key T cell exhaustion TFs Tox, Prdm1 (BLIMP-1) and Ikzf2 , inhibitory receptors Cd244a (2B4) and Tigit , and functional molecules Ifng , Il10 and Pdgfb ( Fig. 5c , 5l and 5m ). Together, these results suggest that the H3K9me3 modification may have distinct characteristics in T EX , with increased deposition localized to non-conventional narrow peaks, a subset of which are located near key genes that increase in expression in T EX , including Tox . DISCUSSION Here we profiled the hPTMs and chromatin epigenetic landscape of CD8 T cells as they differentiate from T N into two functionally different cell fates, T MEM and T EX . T N , T MEM and T EX cells had distinct epigenetic profiles across both activating (H3K27ac and H3K4me3) and repressive (H3K27me3 and H3K9me3) hPTMs, with the majority of hPTM changes occurring as T N were activated and differentiated into T MEM or T EX . The unique transcriptional networks of T MEM and T EX were co-regulated by combinations of hPTMs, with gain of activating hPTMs playing a dominant role compared to loss of repressive modifications. Differentiation from T N into T MEM or T EX resulted in both activation and repression of discrete subsets of genes that were poised in T N , suggesting that hPTMs play a role in both upregulating T MEM versus T EX transcriptional networks, and repressing transcription of genes associated with the opposing cell fate. Whereas increased deposition of H3K9me3 in T MEM was associated with decreased gene expression in T MEM , a subset of genes with increased expression in T EX , including the TF Tox , had increased H3K9me3 in nearby non-canonical narrow peaks. Thus, our analyses reveal the complexity of hPTMs in guiding alternative CD8 T cell fates, with potentially atypical roles in T EX . T EX have a unique open chromatin accessibility and transcriptional landscape compared to T MEM . However, precisely how this cell-fate specific chromatin accessibility may mediate cell-fate associated gene expression remains poorly understood. Analysis of individual hPTMs revealed that in both T MEM and T EX gain of activating modifications in one cell type was strongly associated with increased gene expression in this cell type. In contrast, loss of repressive modifications was only loosely correlated with increased gene expression in this cell type. Supporting this observation, we found that activating modifications were frequently gained in the most common combinatorial patterns of hPTMs associated with cell-type specific increases in gene expression, but that often these changes in H3K27ac and H3K4me3 did not co-occur with loss of H3K27me3 and/or H3K9me3. Thus, these analyses suggest that active modifications are key components for gene activation in CD8 T cells, whereas repressive hPTMs may serve a fine-tuning role, selectively regulating a subset of potentially cell-fate related genes. To further investigate how combinations of activating and repressive marks regulate T MEM and T EX gene expression, we focused on poised chromatin states (with both H3K4me3 and H3K27me3) and the dynamics of these states as CD8 T cells differentiate. Genes in a poised state displayed high cellular plasticity, enabling them to quickly respond to antigen stimulation and facilitate rapid cell differentiation 109 – 111 . Thus, poised chromatin states are linked to genes driving cellular identity 110 . Since T MEM and T EX share a common progenitor (T N ), we investigated how genes in a poised state in T N might shape the distinct transcriptional networks of these populations. Discrete subsets of genes poised in T N shifted to an active state in either T MEM or T EX . In T MEM , this subset included genes such as Ier3 and Il2ra, and in T EX included multiple TFs such as Eomes , Setbp1 , Tox2 and Id2 in addition to genes such as Ki67 . Furthermore, genes that shifted from poised-to-active promoter states only in T MEM or only in T EX were regulated by distinct families of TFs, with STAT family binding motifs enriched in T MEM poised-to-active genes compared to MafK and NFkB-p65 in T EX . Thus, the distinct functions of T MEM and T EX were regulated by discrete sets of TFs that coordinated upregulation of key genes primed for activation in quiescent T N . Whether this poised state is imprinted during thymic development when many T cell genes are “tested” or reflects even earlier developmental poising will be interesting to examine in the future. Furthermore, T MEM and T EX are distinct endpoints in complex differentiation trajectories originating from a common T N precursor. It will be interesting to investigate the dynamics of how promoters poised in T N are activated/repressed throughout the full trajectory of T MEM and T EX differentiation, for example as early T MEM precursors differentiate into T MEM . T EX are epigenetically inflexible and do not convert to T EFF or T MEM cell states. Analysis of genes that were poised in T N but shifted to a repressed state in T MEM or T EX highlighted the role of active repression in forming and maintaining these distinct CD8 T cell populations. For example, expression of the inhibitory receptor Entpd1 was repressed in T MEM , whereas Il2ra was repressed in T EX . Together, these data suggest that, although gain of activating hPTMs plays a dominant role in the upregulation of discrete TF regulatory networks in T MEM and T EX , repressive hPTMs also coordinate the repression of opposing CD8 T cell fates. These findings highlight the multidimensional roles of hPTMs in T EX , and emphasize the importance of both activating and repressive modifications. Understanding the combinations of these hPTMs provides a comprehensive view of the regulatory landscape, and reveals the complex patterns regulating gene expression in T EX . The top three “patterns” of hPTM changes associated with gene upregulation were the same between T MEM and T EX , indicating that the broad associations of these hPTMs and gene expression are comparable between CD8 T cell states. However, in T EX , a subset of upregulated genes was associated with gain of both H3K27ac and H3K4me3, but also a gain of H3K9me3, typically a repressive modification. These genes included key T EX TFs, such as Tox and Ikzf2 , effector genes including Ifng, and the gene including the inhibitory receptor Cd244a (2B4). This finding was in contrast to the widely understood role of H3K9me3 and its association with gene repression, especially in embryonic stem cells 108 , 112 , suggesting that this modification may have a distinct function in T EX . Indeed, we found that classically broad H3K9me3 was, as expected, associated with decreased gene expression in T MEM , indicating that H3K9me3 performs this typical role in T MEM . However, in T EX , H3K9me3 was predominantly gained in atypically narrow peaks and, furthermore, these narrow H3K9me3 peaks were associated with gene activation. Narrow H3K9me3 peaks were enriched for CTCF motifs and the repetitive elements SINE:B2 and LTR:ERVL-MaLR, suggesting that CTCF binding could be regulated by H3K9me3 deposition in T EX . Specifically, increased H3K9me3 at SINE B2 sites in T EX may influence CTCF binding patterns and therefore genome organization during CD8 T cell exhaustion, potentially contributing to increased gene expression at these locations. These data provoke the hypothesis that gene activation in T EX is, at least in part, mediated by atypical H3K9me3 deposition that influences CTCF binding to alter higher order chromatin structure, which may in turn regulate gene expression. This result highlights an unusual role of H3K9me3 in gene regulation in T EX . Understanding whether and how this role impacts chromatin organization, and what the relationship is between these hPTM patterns and CTCF binding, for example, in T EX biology will be of interest for future studies. Multiple recent studies have used ATAC-seq to examine the unique chromatin accessibility landscape of T EX and to investigate how this landscape impacts T EX biology 5 , 6 , 8 , 10 , 7 , 9 , 113 , 114 . However, chromatin accessibility is only one feature of a dynamic epigenetic landscape and analysis of hPTMs may provide additional insights into CD8 T cell subset differentiation and T EX biology. Using H3K27ac deposition to identify cell-state associated SEs uncovered additional SEs compared to examination of chromatin accessibility alone. For example, we discovered a potential role for SEs in regulating expression of Havcr2 (TIM3) and Klra8 using H3K27ac, whereas ATAC-seq did not identify these SEs. In addition, analysis of predicted TF activity at H3K27ac sites provided further insight into TF function at enhancers and SEs. For example, the TF NUR77 was highly ranked in T EX compared to T MEM only when activity was predicted using open chromatin data, but not when H3K27ac-decorated regions were analyzed. This observation suggests that NUR77 may be predominantly acting in T EX at genomic locations not associated with enhancer or SE activities. In contrast, the TF ZEB1 was predicted to have high importance in T EX compared to T MEM at H3K27ac-associated enhancers and SEs, but not across open chromatin regions in general. Thus, understanding the additional layer of regulatory networks modulated by hPTMs on top of chromatin accessibility provides further insight into how the distinct T MEM and T EX transcriptional programs are established. Further work is required to investigate how TFs such as ZEB1 function at SEs. In this study, we interrogated the epigenetic landscape of two distinct CD8 T cell fates, T MEM and T EX , and their common precursor T N . Despite these two populations representing endpoints of an antigen-driven differentiation hierarchy, T MEM and T EX themselves are heterogeneous and contain further proliferative and functional hierarchies, including subsets that function as stem cell-like reservoirs for more terminally differentiated, effector-like populations 19 – 25 , 18 , 10 . It is likely that observations made for bulk T EX and T MEM populations reflect an average hPTM landscape and average gene expression across distinct T MEM and T EX subsets. For example, it is likely that additional hPTM associations with transcriptional circuits will be identified in subsets of T EX such as the key progenitor T EX and the downstream T EX intermediate and terminal populations. This subset-specific variation in hPTMs could contribute to functional differences between T EX subsets, potentially shaping the diversity of T cell responses. Similar heterogeneity exists in T MEM as well. Thus, future research should explore how hPTMs mediate control of gene expression within these additional T MEM and T EX subsets, including at earlier timepoints in the differentiation trajectories of these subsets. Our analysis of hPTMs uncovered a potential interplay between hPTM deposition and higher-order chromatin structure in regulating gene expression in T EX . How the deposition of these hPTMs, including atypical H3K9me3 in T EX , is orchestrated remains unknown, including the role of T MEM and T EX TF networks in recruiting epigenetic enzymes to sites of both histone modification and chromatin remodeling. It will be interesting in the future to investigate how TF networks, hPTMs, and three-dimensional genome structure coordinate establishment and maintenance of the distinct transcriptional landscape of T MEM and T EX and their subsets. Thus, understanding how the T MEM and T EX differentiation hierarchies are epigenetically regulated will provide key insight into the epigenetic scar of exhaustion, fate-flexibility, and could be used to inform effective clinical therapies. MATERIALS AND METHODS Mice Animals were housed in a specific pathogen-free facility at the University of Pennsylvania at ∼20°C with 55% humidity and a dark-light cycle of 12hr-12hr. Animals were provided with ad libitum access to food and water throughout the duration of the experiment. All experiments and breeding were approved by the Institutional Animal Care and Use Committee guidelines for the University of Pennsylvania. All procedures were performed in accordance with Institutional Animal Care and Use Committee Protocol 803071. Transgenic mice expressing a TCR specific for the LCMV peptide D b GP 33-41 (P14 donor mice) were bred in-house at the University of Pennsylvania on a C57BL/6 background purchased from Charles River. Donor mice were used at ∼8 weeks of age. Recipient C57BL/6 mice were purchased from Charles River and used at 6-8 weeks of age. Recipient mice were sex matched with donor mice. Euthanasia was performed using CO 2 inhalation in a CO 2 unit as recommended by the Panel on Euthanasia of the American Veterinary Medical Association and the University of Pennsylvania. Infections LCMV Armstrong (Arm) and LCMV clone 13 (Cl13) were grown in house and titrated as previously described 5 . Recipient mice were either infected intraperitoneally (i.p) with 2 x 10^5 PFU LCMV Armstrong to model an acute infection or intravenously (i.v.) with 4 x 10^6 PFU LCMV Cl13 to establish a chronic infection. Adoptive cell transfer PBMCs were isolated from the peripheral blood of naive P14 donor mice using gradient centrifugation (Histopaque-1083). 1,000 naive P14 cells were adoptively transferred i.v. into sex-matched recipient mice. P14 cells were isolated from donor mice of a distinct congenic background than recipient mice to enable donor P14 cells to be distinguished from recipient CD8 T cells. Recipient mice were infected with LCMV Arm or LCMV Cl13 one day following adoptive cell transfer. Cell sorting Spleens were collected at d30 of LCMV Arm and d32 of LCMV Cl13 infection. Donor P14 mice or littermates were used for the naive condition. Single cell suspensions were prepared by mechanical disruption of spleens through a 70 µm cell strainer. Red blood cells were lysed in ACK buffer (3min, RT) and CD8 T cells isolated using EasySep CD8 T cell negative selection kit (Stem Cell, Cat# 19853) following manufacturer’s instructions. CD8 T cells were washed in FACS buffer (2% FCS in PBS) and surface stained with an antibody cocktail in FACS buffer for 30 min at 4°C. Donor CD8+ P14 cells were sorted on a BD FACS Aria II using congenic markers for identification. Samples were sorted to >95% purity. CUT&RUN CUT&RUN was performed as previously described with slight modifications 94 , 115 . 10,000 sorted cells were washed twice (600 g x 5 min) with 1 ml of cold wash buffer (20 mM HEPES-NaOH, pH 7.5, 150 mM NaCl, 0.5 mM Spermidine (Sigma 85558-1G) supplemented with protease inhibitor cocktail (Sigma 4693132001) in 1.5ml tubes. Next, cells were resuspended in 1 ml of cold wash buffer, 20 µl of BioMagPlus Concanavalin A beads (Bangs laboratories BP531) were added and samples were mixed by rotation (4°C, 20 min). Samples were briefly spun at 100 g, placed on DynaMag™-2 Magnet (Thermo 12321D), and liquid was removed. Primary antibodies were diluted 1:100 in 250 µl of cold antibody buffer (20 mM HEPES-NaOH pH 7.5, 150 mM NaCl, 0.5 mM Spermidine, 2 mM EDTA, 0.1% digitonin (Millipore 300410-1GM) supplemented with protease inhibitor cocktails) and incubated with samples (4°C, overnight, with rotation). The following day, samples were washed once with 1 ml cold wash buffer. Protein A-MNase (pA-MN) was diluted 1:200 in 250 µl of cold digitonin buffer (20 mM HEPES-NaOH pH 7.5, 150 mM NaCl, 0.5 mM Spermidine, 0.1% digitonin supplemented with protease inhibitor cocktails) and added to samples (4°C, 1 h, with end-to-end rotation). Samples were washed twice with 1 ml of cold digitonin buffer, resuspended in 150 µl of cold digitonin buffer and placed on a pre-cooled metal block on ice for 5 min. pA-MN digestion was initiated by adding 3 µl of 0.1 M CaCl2 to samples, mixed by gently flicking tubes 20 times and samples placed back on metal block for 30 min. Digestion was stopped by adding 150 µl of 2 x stop buffer (340 mM NaCl, 20 mM EDTA, 4 mM EGTA, 0.02% Digitonin, 50 ug/ml RNase A (Thermo EN0531), 50 ug/ml Glycogen (Thermo R0561), and 4 pg/ml yeast heterologous spike-in DNA). Samples were incubated at 37°C for 10 min and then spun (16,000 g, 5 min, 4°C). Supernatant containing cleaved chromatin was transferred to a new tube, 3 µl of 10% SDS and 2.5 µl of 20 mg/ml proteinase K (Denville Scientific CB3210-5) were added and samples were incubated at 70°C for 10 min, followed by phenol:chloroform:isoamyl alcohol (Thermo 15593049) and chloroform (Sigma 288306) extraction. Supernatant containing DNA (∼300 ul) was transferred to new tubes pre-loaded with 20 ug of glycogen and then mixed with 750 µl of cold 100% ethanol for precipitation at -20°C overnight. Tubes were centrifuged at 20,000 g for 30 min at 4°C. DNA pellets were washed once with 1ml of cold 100% ethanol, air-dried, and stored at -20°C. DNA libraries were prepared as previously described with slight modifications 94 , 116 , 117 . DNA pellets were dissolved in nuclease free H 2 O and library preparation performed using NEBNext Ultra II DNA Library Prep Kit (NEB E7645L). Adaptor was diluted to 1:25 for adaptor ligation. For samples labeled with CTCF, DNA was barcoded and amplified for 12 PCR cycles. For histone modification samples, adaptor-ligated DNA was first selected with 25 µl and second with 45 µl of AMPure XP beads, followed by PCR amplification (H3K4me3: 11 cycles, H3K9me3: 9 cycles, H3K27ac: 14 cycles, and H3K27me3: 10-12 cycles). All libraries were cleaned up using AMPure XP beads (Beckman Coulter A63881). CUT&RUN Antibodies View this table: View inline View popup Download powerpoint RNA isolation RNA-seq was performed as previously described with minor modifications 118 . 90,000 sorted cells were pelleted (600 g, 5 min, 4°C) and pellets washed once with cold PBS. Pellets were resuspended in 0.5 ml of TRIzol (Thermo 15596018) and stored at -80°C. Total RNA (∼30-120 ng) was extracted using RNA Clean & Concentrator-5 (ZYMO R1013) following manufacturer’s instruction and immediately followed by RNA library preparation. mRNA was isolated using NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB E7490L). Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit (NEB E7760L) and following manufacturer’s instructions. Sequencing Library quality was assessed using the Agilent 2100 Bioanalyzer (Agilent G2939BA) and libraries were quantified using a Qubit 2.0 fluorometer (Thermo Q32866) and by qPCR using NEBNext Library Quant Kit for Illumina (NEB E7630L) according to manufacturer’s instructions. Libraries were pooled at equal molarity and sequenced with NextSeq 500/550 High Output Kit (75 cycles) v2.5 kit (Illumina 20024906) on NextSeq 550 sequencing system (Illumina SY-415-1002). 20-30 million reads for each library were sequenced using paired-end sequencing (42:6:0:42). RNA-seq data processing and analysis Paired-end reads were aligned and processed using STAR 119 v2.7.1a with mm10 Gencode reference genome and default parameters. Paired-end read counts of genes were quantified by featureCounts using Gencode primary assembly annotation reference genome version vM24. Genes with raw reads greater than 5 were used for downstream analysis. Normalized read counts and differential analyses were generated using DESeq2 120 . Differentially expressed genes (DEGs) were identified with filters absolute fold change >1.5 and adjusted P-value <0.05. All pairwise DEGs across different cell types were combined and grouped into 7 clusters using the K-mean algorithm. Heatmap plots were generated using ComplexHeatmap 121 , 122 packages. Other summary bar plots, violin plots, volcano plots were generated using R. Pathway analysis of differentially expressed genes was performed using Metascape 123 . DESeq2 120 normalized factors were used to normalize bam files. Normalized bigwig files were generated using bamCoverage 124 with parameters -ignore chM -- minMappingQuality 5 -ignoreDuplicates -skipNAs and were visualized in UCSC genome browser and R package Gviz 125 . ATAC-seq data analysis and processing ATAC-seq data were aligned and processed using Bowtie2 126 v2.3.5 using mouse mm10 reference genome. Picard 127 tools v1.96 was used to remove presumed PCR duplicates using the MarkDuplicates command. Bam files containing uniquely mapped reads were created using Samtools 128 v1.1. Blacklist regions defined by ENCODE 129 , random chromosomes and mitochondria were removed, and filtered bam files were used for downstream analysis. Union peaks were downloaded from GEO. Read per million (RPM/CPM) normalized bigwig files were created using deepTools bamCoverage 124 , 130 . Replicates were pooled together using wiggleTools and UCSC toolkit bedGraphToBigwig 131 , and tracks were imported and viewed using UCSC genome browser 132 . CUT&RUN data processing FastQC 134 v0.11.2 and MultiQC 135 were used to check data quality. Reads were aligned to the mouse mm10 Gencode reference genome using Bowtie2 126 v2.3.5, following parameters suggested by Skene et al. 115 --local --very-sensitive-local --no-unal --no- mixed --no-discordant --phred33 -I 10 -X 700 -k1 -N1. Picard 127 tools v1.96 was used to remove presumed PCR duplicates using MarkDuplicates command. Bam files containing uniquely mapped reads were created using Samtools 128 , 136 v1.1. Fragments between 40-700 bp were kept. Blacklist regions defined by ENCODE, random chromosomes and mitochondria were removed, and filtered bam files were used for downstream analysis. CUT&RUN signals were called using MACS 133 v2.1 using the broadPeak setting with adjusted P-value cutoff 0.01. In broad histone modifications, customized parameters were adjusted for different hPTMs based on length of signals and sample background variation. Consensus peaks shown in at least two biological replicates were used, and merged conditional peaks with IgG peaks removal were finally used as a union peak list for downstream quantification. Venn diagrams were generated using the ChIPpeakAnno 137 package findOverlapsOfPeaks() and makeVennDiagram() function. Read counts were quantified across all samples based on union peak using featureCounts 138 , and validated using bedtools coverage. All sample read counts were normalized using DESeq2, and principal component analysis (PCA) plots of all replicates were generated using R function prcomp. Statistical significantly differential hPTMs analyses were performed using DESeq2. The histone modification regions with adjusted P-value 1.5 were defined as significantly differential modification regions, and were quantified in bar plots using ggplots. The volcano plots were generated using R ggplot2. Approximate posterior estimation for GLM shrinkage method “apeglm” 139 in DESeq2 was applied to H3K4me3 to alleviate its batch effects and final fold change calculation. Binding motif enrichment of selected differential histone modification regions were identified using findMotifsGenome.pl from HOMER 140 v4 using each corresponding union peaks as background and size as given with mask options. For consistent visualization, DESeq2 normalization factors were used to adjust bam files to create normalized bigwig files using bamCoverage. Bigwig files of replicates were pooled together using WiggleTools 141 mean setting. Tracks were loaded to UCSC genome browser and Gviz 125 R package for visualization. Heatmaps and metaplots were generated using deepTools plotHeatmap 124 , 130 . CUT&RUN data analysis Annotations -- Chromatin modification region annotation Genes proximal to peaks (hPTMs) were annotated against mm10 genome using annotatePeaks.pl from HOMER 140 v4, ChIPseeker 142 with 10,000 base pairs (bp) flank regions, as well as GREAT 143 . Gene position information was extracted from the Gencode mm10 database, excluding pseudo genes or ambiguous undefined genes. Regions within 2,500bp of the TSS were defined as promoters. Annotation pie chart and bar chart of gene locations were generated based on filtered gene annotation. For one-gene-one-peak mapping, the peak with maximum variations across different conditions representing the gene were selected. For one-gene-two-peak mapping, two peaks including the gene promoter and the non-promoter peak with maximum variation were used. For one-peak-multi-gene mapping, all genes annotated to target peaks were used. For hPTM patterns analysis and gene expression correlation, one-gene-one-peak mapping was used. Taiji transcription factors analysis Taiji 54 analyses were performed using H3K27ac CUT&RUN bam data and RNA-seq read count data to predict key TFs. For comparison, the same analysis was performed using RNA-seq data and ATAC-seq from published papers 5 , 6 . The identified Taiji page rank scores of TFs were Z-score normalized across three conditions T N , T MEM and T EX to identify key TFs corresponding to each condition genomewide. Heatmaps were generated based on the page rank Z-scores, and plotted using ComplexHeatmap 121 , 122 . Specific TF binding sites were identified using the MEME suite FIMO tool 144 . Venn diagrams of motif binding sites in H3K27ac and ATAC-seq were generated using ChIPpeakAnno 137 . Super enhancer analysis Promoter regions were defined as regions located within 2,500bp of TSS of each gene using the Gencode mm10 reference genome. Enhancers were defined as non-promoter regions with H3K27ac bound or ATAC-seq open accessibility. The enhancers between T N , T MEM and T EX were compared using ChIPpeakAnno 137 . Super enhancers were identified using the stitching and rank ordering algorithm, ROSE 52 , 70 , using enhancers defined by H3K27ac and ATAC-seq, respectively. In brief, nearby enhancers were stitched together, ranked and plotted by signal enrichment levels. The enhancers with signals above tangent point (slope=1) were defined as super enhancers and the rest as typical enhancers. The SEs with only one peak stitched were further filtered out. SE were annotated to potential regulated genes using Homer 140 and GREAT 143 , and further verified in the UCSC genome track. The SEs were ranked by signal intensity and plotted using R. The comparison of SEs across T N , T MEM and T EX were performed as follows. First, an initial comparison and venn diagram were generated using ChIPpeakAnno 137 . Next, the SE signal intensities for overlapping and conditional-specific SEs were quantified for each cell type by summing the normalized reads of individual enhancers under the examined region. SEs were then filtered and refined: SEs with a fold change greater than 1.5 between any two conditions were classified as conditional-specific SEs, whereas other SEs were designated as shared respectively. An updated venn diagram was generated to reflect the refined SEs. The enrichment signal intensity of SE and TE per condition was compared, and metaplots were generated using deepTools plotProfile 124 , 130 . Nearest genes were used to access gene expression differences between SE and TE, which were displayed in boxplot. Comparison of SEs identified using H3K27ac and ATAC-Seq were generated using ChIPpeakAnno 137 , with hypergeometric P-values calculated for each pairwise comparison. Bivalency analysis Chromatin states were identified using chromHMM 75 by separating promoters with non-promoters, respectively. Promoter regions were defined as regions located within 2,500bp of TSS of each gene and the rest of the regions were defined as non-promoter regions. ChromHMM was performed using consensus peaks of histone modifications H3K27ac, H3K27me3, H3K9me3 and ATAC with predefined 10 states, using concatenated mode with binarizing the peaks. The chromatin states coverage was quantified by base pairs. Initial states with non-low signals were further categorized into 4 major states, including I. active, II. poised, III. repressed and IV. repetitive states, based on the presence of histone modifications, and validated through heatmap plot of all marks in naive state. Alluvial plots were generated to examine poised promoter dynamics from T N to T MEM and T EX states. The significant changes were defined as histone changing with absolute fold change >1.5. The poised-to-activated promoters were defined as either gaining H3K4me3 or losing H3K27me3 or both, and poised-to-repressed promoters were defined as either losing H3K4me3 or gaining H3K27me3 or both. The identified dynamic regions were compared to significant DEGs. The enriched motifs were identified using Homer 140 v4 with parameters -size given and - mask, and all coding gene promoters were used as background for identifying all poised promoter motifs, while random genomes were used as background for identifying unique motifs enriched in the dynamic poised promoters. Pathway analyses were generated using Metascape on major chromatin states and dynamic promoters with consistent gene expression. Heatmaps were generated using R package ComplexHeatmap 122 . Examples of gene tracks were generated from UCSC genome tracks and Gviz 125 R package. Summary bar plots and dot plots were generated using R ggplot2. H3K9me3 analysis Genome-wide H3K9me3 bound regions were split into broad and narrow peaks based on 15 kb cutoff. Peaks were annotated to nearest genes and genes within 250 kb. Mouse (mm10) repeat database was downloaded from UCSC RepeatMasker tool 145 , 146 . Five runs of random background controls were generated using non-H3K9me3 bound genome regions with peak amount and width matching to T EX -enriched H3K9me3. The 5 random background regions were merged and used as one background control, average values were used for repeat coverage calculation. Three levels of repeats, including repeat family, class and name, were used to calculate repeat coverage over peaks. Repeat covered regions were identified using bedtools 147 intersect of query regions with the repeat database. Repeat coverage was calculated as base pairs covered by any type of repeats. Comparisons of genome-wide H3K9me3 bound regions with CTCF binding sites and ATAC-seq open chromatin regions were performed using R package ChIPpeakAnno 137 and tracks were generated using Gviz 125 . Author contributions E.J.W. and S.L.B. conceived the project. A.E.B., Z.C., Z.Z., P.A.AG., E.J.W. and S.L.B. designed the experiments. A.E.B., Z.C., Z.Z., P.A.AG., P.S. and S.C. performed experiments. H.H. analyzed data and prepared figures with help from A.E.B., Z.Z., C.R.G and K.A.A., K.M.G., L.W., G.D., S.M., J.R.G. and J.S. consulted on data analysis. H.H., A.E.B., Z.Z., C.R.G, K.A.A., S.L.B. and E.J.W. wrote the manuscript. All authors reviewed the manuscript. Competing interests E.J.W. is a member of the Parker Institute for Cancer Immunotherapy. E.J.W. is an advisor for Arsenal Biosciences, Coherus, Danger Bio, IpiNovyx, New Limit, Marengo, Pluto Immunotherapeutics, Prox Bio, Related Sciences, Santa Ana Bio, and Synthekine. E.J.W. is a founder of Arsenal Biosciences, Danger Bio, Prox Bio and holds stock in Coherus. J.R.G. is a consultant for Arsenal Biosciences, Cellanome, GVM1, and Seismic Therapeutics. The remaining authors declare no competing interests. Ethical approval This study is reported in accordance with ARRIVE guidelines. Data availability RNA-seq and CUT&RUN data generated in this study will be deposited in the National Center for Biotechnology Information Gene Expression Omnibus and made available after publication. ATAC-seq data used in this study is from GSE86797. Acknowledgements Protein A-MNase (batch 6) and yeast heterologous spike-in DNA were kindly provided by Dr. Steve Henikoff. The authors also thank Terri D. Bryson from Henikoff Laboratory for sharing the pA-MNase purification protocol. This work was supported by NIH grants AI155577, AI115712, AI117950, AI108545, AI082630, AI149680, HL145754 (to E.J.W.) and funding from Celgene (to S.L.B. and E.J.W.), Parker Institute for Cancer Immunotherapy (to E.J.W.), and The Mark Foundation (to E.J.W.). J.R.G. was supported by a Cancer Research Institute - Mark Foundation Fellowship. S.L.B. is supported by NIH grant CA078831. C.R.G is supported by NIH grant CA232466 and is a Rob Kugler American Cancer Society Postdoctoral fellow. Funding National Institutes of Health , AI155577 , AI115712 , AI117950 , AI108545 , AI082630 , AI149680 National Institutes of Health , HL145754 Celgene (United States), https://ror.org/0527yg379 , Celgene Parker Institute for Cancer Immunotherapy The Mark Foundation for Cancer Research, https://ror.org/00v7th354 National Institutes of Health , CA078831 National Institutes of Health , CA232466 Footnotes ↵ * Lead contact REFERENCES 1. ↵ Turner , S. J. , Bennett , T. J. & Gruta , N. L. L . CD8+ T-Cell Memory: The Why, the When, and the How . Cold Spring Harb. Perspect. Biol . 13 , a038661 ( 2021 ). OpenUrl Abstract / FREE Full Text 2. ↵ Kaech , S. M. & Cui , W . Transcriptional control of effector and memory CD8+ T cell differentiation . Nat. Rev. Immunol . 12 , 749 – 761 ( 2012 ). OpenUrl CrossRef PubMed 3. ↵ McLane , L. M. , Abdel-Hakeem , M. S. & Wherry , E. J . CD8 T Cell Exhaustion During Chronic Viral Infection and Cancer . Annu. Rev. Immunol . 37 , 457 – 495 ( 2019 ). 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Glastad , Lu Wang , Gregory Donahue , Sasikanth Manne , Josephine R. Giles , Junwei Shi , Shelley L. Berger , E. John Wherry bioRxiv 2025.04.16.649198; doi: https://doi.org/10.1101/2025.04.16.649198 Share This Article: Copy Citation Tools Deciphering the role of histone modifications in memory and exhausted CD8 T cells Hua Huang , Amy E. Baxter , Zhen Zhang , Charly R. Good , Katherine A. Alexander , Zeyu Chen , Paula A. Agudelo Garcia , Parisa Samareh , Sierra M. Collins , Karl M. Glastad , Lu Wang , Gregory Donahue , Sasikanth Manne , Josephine R. Giles , Junwei Shi , Shelley L. Berger , E. 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