Nucleosome Engagement Regulates RORγt Structure and Dynamics

Nucleosome Engagement Regulates RORγt Structure and Dynamics | 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 Nucleosome Engagement Regulates RORγt Structure and Dynamics View ORCID Profile Timothy S. Strutzenberg , View ORCID Profile Matthew D. Mann , Xiandu Li , Hyejeong Shin , Jordan Kelsey , View ORCID Profile Sriram Aiyer , View ORCID Profile Jingting Yu , Gennavieve Gray , View ORCID Profile Zeyuan Zhang , View ORCID Profile Zelin Shan , Bo Zhou , View ORCID Profile Ye Zheng , View ORCID Profile Patrick R. Griffin , View ORCID Profile Dmitry Lyumkis doi: https://doi.org/10.1101/2025.03.10.642251 Timothy S. Strutzenberg 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy S. Strutzenberg Matthew D. Mann 2 The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology , Jupiter, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthew D. Mann Xiandu Li 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hyejeong Shin 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordan Kelsey 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sriram Aiyer 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sriram Aiyer Jingting Yu 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jingting Yu Gennavieve Gray 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zeyuan Zhang 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zeyuan Zhang Zelin Shan 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zelin Shan Bo Zhou 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ye Zheng 1 The Salk Institute for Biological Studies , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ye Zheng Patrick R. Griffin 2 The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology , Jupiter, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick R. Griffin Dmitry Lyumkis 1 The Salk Institute for Biological Studies , La Jolla, CA, USA 3 Graduate School for Biological Sciences, Section of Molecular Biology, University of California San Diego , La Jolla, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dmitry Lyumkis For correspondence: dlyumkis{at}salk.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT The retinoic acid-related orphan receptor gamma (RORγt) acts as the major transcriptional activator in Th17 cell development and function to mediate adaptive immune defenses against pathogenic infection. RORγt engages accessible DNA response elements in the genome and interplays with coactivator proteins and accessory transcription factors to drive gene expression. However, how the chromatin environment mediates RORγt structure, dynamics, and function remains unclear. Here, we profile how the nucleosome promotes or restricts access to the main RORγt DNA response elements found in native enhancers and promoters, revealing preferential binding in regions of free DNA and nucleosomal entry/exit sites, with single base-pair resolution. Solution phase measurements using hydrogen deuterium exchange coupled to mass spectrometry identify novel allosteric effects that influence RORγt binding and mediate chromatin dynamics. A high-resolution structure of RORγt bound to the nucleosome reveals how structured elements assemble to confer binding specificity and avidity to chromatin substrates. The observations suggest an activation model where RORγt binding to chromatinized DNA promotes coregulator recruitment and chromatin decompaction. INTRODUCTION The retinoic acid-related orphan receptor gamma (RORγ) is a central regulator of the adaptive immune system. RORγ isoform 1 (RORγ1) is found throughout the body but has high expression in skeletal muscle, liver, and kidney 1 , 2 . RORγ isoform 2 contains a 21-residue truncation at the N-terminus (RORγ2 or RORγt) and is expressed exclusively in lymphocytes. RORγt function has been most closely examined in the context of IL-17 expressing T helper (Th17) cells, where the protein has become known as the ‘master regulator’ of Th17 cellular development and function. Th17 cells play an important role in pathogen clearance in the gut 3 and exhibit a wide range of pathogenic and non-pathogenic phenotypes that are sensitive to extracellular signals 4 – 6 . RORγt regulates lymphoid tissue development and thymopoiesis 7 , supporting cell survival and T cell receptor recombination by upregulating anti-apoptotic factor Bcl-xL and RAG recombinases, respectively 7 , 8 . RORγt also supports the differentiation of lymphoid tissue inducer cells and the establishment of lymph nodes 9 . RORγt activity is augmented in Th17-mediated autoimmune disease models, such as experimental autoimmune encephalomyelitis 10 , colitis 11 , and type 1 diabetes 12 . More recently, RORγt expression was observed in several types of antigen presenting cell types 13 , underscoring its role in novel biology that is being actively investigated. Outside the immune system, RORγt supports survival and metastasis of several types of cancer including triple negative breast cancer 14 , pancreatic cancer 15 , and castration resistance prostate cancer 16 . Given its prominent roles in the adaptive immune response, RORγt has also been the subject of extensive efforts to develop therapeutic modulators for human diseases 17 . A better understanding of RORγt function thus has potential to yield novel therapeutics targeting both autoimmune disorders and cancers. RORγt belongs to the nuclear receptor (NR) superfamily. Like all NRs, RORγt regulates gene expression in response to small lipophilic molecules, which is accomplished via its modular domain organization 18 . These include the N-terminal activation function-1 (AF-1) domain, the DNA-binding domain (DBD), a hinge region, the ligand-binding domain (LBD), and (if present) the C-terminal domain. The DBD is highly conserved and is responsible for recognizing and binding to ROR response elements (ROREs) in genomic DNA, which are located in conserved noncoding regions of the IL17 enhancer and drive IL-17 expression 19 . The hinge region provides flexibility between the DBD and the LBD and can also influence protein-protein interactions. The LBD binds endogenous and synthetic ligands and modulates receptor activity. This domain also contains the activation function-2 (AF-2) region, which is crucial for coactivator binding and ligand-dependent transcriptional regulation. Most biochemical and structural studies of RORγt have relied on reconstituting the individual DBDs, LBDs, or variants thereof. The sole crystal structure of the RORγ DBD bound to a direct repeat RORE revealed the molecular underpinnings behind DBD:RORE recognition and suggested a purported dimerization mechanism 20 . However, the direct repeat RORE that was used in the study plays a minor role in IL-17 expression, where RORγt primarily acts as a monomer 19 . By contrast, the LBD has been the subject of substantially more structural studies. There are 59 crystal structures of the RORγt LBD, which have provided great insight into the ligand and coregulator binding functions of the LBD. Notably, such studies helped to explain how RORγt binds to hydroxy-cholesterol derivatives from the cholesterol and bile acid biosynthesis pathways 21 – 23 , yielding important insights into RORγt pharmacology. While such prior efforts have shed light onto the activities of individual domains, they fail to capture how the domains work together in the context of the full-length protein. RORγt acts as a transcriptional activator by working with steroid receptor coactivators (SRCs) and CBP/p300 to establish super enhancers 24 – 26 . Mechanistically, RORγt plays a unique role in Th17 differentiation not by driving chromatin remodeling but by activating accessible enhancers that regulate expression of lineage defining genes such as Il17a, Il1r1, and Il23r . This is accomplished in concert with additional transcription factors, including BATF and IRF4 27 . Both coregulators and accessory transcription factors must work together in the context of more complex chromatin environments. However, the logistics for how these processes work at a molecular level remain largely unknown. The absence of biochemical and structural data for how RORγt engages chromatin limits our fundamental understanding of RORγt function and biology. Here, we use interdisciplinary biochemical, biophysical, and genomic approaches to elucidate how RORγt engages chromatin, and its structural and functional implications. Using the nucleosome as the basic constituent form of chromatin, we first systematically profile all possible nucleosomal binding positions of RORγt, revealing preferential binding at exposed DNA regions with quantifiable differences in engagement between the two major ROREs found in the IL-17 enhancer 28 and the Androgen receptor promoter 29 , respectively. Using hydrogen deuterium exchange coupled to mass spectrometry (HDX-MS), we then demonstrate how RORγt binding to nucleosomes elicits intramolecular allostery on both RORγt and histones. A high-resolution structure of the RORγt:nucleosome binary complex solved using cryogenic electron microscopy (cryo-EM) reveals novel features that drive chromatin binding and transcriptional activation. Guided by the structural results, mutagenesis experiments show how interactions between a helical region adjacent to the DBD affects nucleosome binding, thereby contributing to Th17 differentiation but not to IL-17 enhancer localization. Overall, our work reveals a complex network of allostery between RORγt and nucleosomes and highlights how the chromatin environment influences RORγt structure, dynamics, and function. RESULTS RORγt preferentially engages nucleosomes at the entry and exit sites RORγt regulates gene expression by engaging specific RORE sequence motifs. Current evidence suggests that, like other NRs, RORγt preferentially binds genomic sites that are depleted of nucleosomes. However, RORγt binding to chromatinized DNA is also required to transition ‘closed’ chromatin to ‘open’ chromatin to establish the IL-17 enhancer, suggesting that a complex series of events must ensue to promote gene expression 26 . Whether and how the chromatin environment specifically influences RORγt binding remains unclear. To address this question, we sought to profile the RORγt–chromatin binding preferences using an assay called Selected Engagement of Nucleosomes couple with sequencing (SeEN-seq) 30 , shown in Fig.1A . We tiled a classic RORE (cRORE) based on a sequence from IL-17 enhancer element 28 and a variant RORE (vRORE) based on a sequence from the androgen receptor promotor 29 through the tiling region of W601 nucleosomal DNA, yielding a library size of 336 members. To optimize the SeEN-seq assay for RORγt binding, we made several modifications. First, we synthesized the DNA library in pool and appended Fluorescein (FAM) fluorophores to the 5′-ends of the DNAs. The FAM-labeled DNA library was then mixed with unlabeled 207bp W601 DNAs that did not contain priming sequences in a 1-to-1000 molar ratio. These two steps reduced the detection of non-specific RORγt binding and allowed us to more confidently identify specific RORγt binding to response elements tiled through nucleosome species. Second, we changed the selection strategy to allow for multiple rounds of selection. Further details are provided in the Materials and Methods section, as well as Sup.Fig.1 . We then performed two rounds of selection. Download figure Open in new tab Figure 1. SeEN-seq analysis reveals the chromatin binding preferences of RORγt. ( A ) Schematic overview of the SeEN-seq assay. ( B ) The results from two rounds of selection. Log2 enrichment ratios are normalized to read counts from the library after selection divided by normalized read counts before selection. The super helical location (SHL) indicates where the center of the response element is located on the nucleosome. The top and bottom panels show the results for tiling a classic RORE (cRORE) and variant RORE (vRORE) through the nucleosome positioning sequence. The cRORE and vRORE sequences are based on endogenous sequences found in the IL-17 enhancer 28 and the Androgen receptor promoter 29 . Motifs showing the sequence differences are inlaid on the plots. ( C ) The log2 enrichment ratios are painted onto a model of the nucleosome, which is generated using Alphafold3. The DNA bases are colored according to the color key legend at the bottom of the panel. Histones and primers are not covered and are colored accordingly. The SeEN-seq experiment revealed the overall nucleosome binding preferences of RORγt, shown in Fig.1B . We describe the observations as the ROREs are tiled across the entire nucleosome, moving from the entry site prior to super helical location (SHL) -7.0 to the exit site following SHL +7.0. There is strong initial binding signal at the entry extension, followed by a loss of signal as the ROREs approach the nucleosome at SHL -7.0. Subsequently, there are multiple productive binding sites centered at SHL -6.5. Binding is increasingly inhibited from SHL -5.5 to the nucleosomal dyad (defined as SHL 0), and the trend is reversed through SHL +5.5. There is some recovery in binding at SHL +6.5, a loss at SHL +7.0, and a strong recovery at the extensions, which largely mirrors the observations at the entry site. In brief, RORγt prefers to bind ROREs at the extensions, entry, and exit sites of the nucleosome, with a periodicity of ∼10.5 bp. Access to internal ROREs between SHL ±5.5 is inhibited by the nucleosome core particle (NCP). Mapping of the SeEN-seq signals onto the nucleosome ( FIG.1C ). These results show that RORγt binding is enriched in areas where the major groove is exposed, consistent with the known preference for major groove engagement 20 . When comparing the results for RORγt-mediated cRORE vs vRORE engagement, we observe a similar overall trend, but generally less enrichment for the vRORE in the entry/exit sites, up to and including SHL ±6.5. This is likely caused by lower RORγt affinity for vRORE than for cRORE, which is supported by previous experiments that show that RORγt is less stable when bound to vROREs compared to cROREs 31 . Overall, the results define, with single bp resolution, how RORγt can engage a minimal chromatin substrate and how distinct ROREs impact nucleosome binding. Reconstitution of RORγt engaged to SHL -6.5 of a nucleosome We next set out to establish experimental conditions for assembling a binary complex of RORγt engaged to a nucleosome substrate, which would be used for subsequent biophysical and biochemical studies. We installed the cRORE at SHL -6.5 and reconstituted nucleosomes for complex assembly with RORγt. The cRORE was chosen over the vRORE due to higher sequencing counts by SeEN-seq, indicative of higher RORγt binding affinity to this element. SHL -6.5 was chosen to maximize chances of observing RORγt-nucleosome interactions and obtaining high-resolution structures with nucleosomes, namely: cROREs engineered into free DNA that are far from the NCP (beyond SHL ±7) would be conformationally dynamic and thus poorly visible in downstream cryo-EM experiments, whereas cROREs engineered between SHL -5.5 and +5.5 would likely not be engaged by RORγt. For complex assembly, we empirically optimized stoichiometric ratios based on gel shift assays and size exclusion chromatography (SEC). A molar ratio of 3-to-1 effectively shifted the nucleosome band, such that the binary complex could be efficiently purified using SEC ( Sup. Fig . 2A ). Denaturing PAGE stained with Coomassie confirmed that RORγt and histones comigrated together ( Sup.Fig.2B ), whereas native PAGE stained for DNA confirmed that the complex was intact ( Sup.Fig.2C ). Download figure Open in new tab Figure 2. HDX-MS captures the conformational consequences of RORγt binding to nucleosomes. Results from HDX-MS analysis of RORγt +/- nucleosomes are shown in panels A-E, while the results from HDX-MS analysis of nucleosomes +/- RORγt are shown in panels F-J. ( A ) RORγt peptides that were quantified are shown as boxes below the primary structure of the protein. The color key indicates the average change in deuterium incorporation at all timepoints. Secondary structural elements are marked such that alpha helices are cylinders and beta sheets are arrows. ( B-E ) Selected deuterium build-up plots are shown for residues ( B ) 1-28 in the DBD, ( C ) 74-94 within the CTE, ( D ) 95-128 within the hinge region and ( E ) 476-503 within the coregulator binding interface. The percentage of incorporated deuterium is plotted as a function of time. ( F ) Peptides for histone proteins were quantified and shown as boxes below the primary structures of each protein. ( G-J ) Selected deuterium build-up plots are shown for residues ( G ) 28-47 in H3, ( H ) 48-60 in H3, ( I ) 74-82 in H3, and ( J ) 89-102 in H4. The percentage of incorporated deuterium is plotted as a function of time. RORγt-nucleosome interactions promote changes in structural dynamics We next asked how RORγt-mediated engagement of a nucleosome containing a cRORE at SHL -6.5 affects protein/DNA conformational dynamics. To address this, we employed HDX-MS, a sensitive protein profiling technique that relies on measurements of backbone amide solvent exchange dynamics with deuterium followed by MS to quantify transient protein unfolding and conformational changes 32 . Bound and/or protected protein regions will exhibit low deuterium exchange with solvent, whereas regions that are solvent-exposed (or deprotected due to substrate binding) will show stronger deuterium exchange, thus providing direct insights into solution conformational dynamics. Relevant to this work, prior HDX-MS characterization of RORγt conformational dynamics showed limited long range allostery between the DBD and the LBD upon DNA binding 31 . However, these analyses employed free DNAs as binding substrates for RORγt, which do not fully recapitulate complex chromatin environments. Accordingly, we wished to extend these analyses to the nucleosomal context. We first examined how nucleosome engagement affects RORγt conformational dynamics by comparing the differential uptake of deuterium within RORγt in the free vs. nucleosome-bound form. Digestion optimization led to the identification of 32 peptides spanning 52% of RORγt with adequate redundancy ( Fig.2A ). HDX-MS results revealed profound structural changes throughout RORγt. As expected, there is protection to the RORγt DBD both in the core of the domain and in the C-terminal extension (CTE) Fig.2B and 2C . This protection is consistent with previous HDX-MS experiments that measured RORγt response to cROREs 31 . There are also several unexpected observations. First, the hinge region between the DBD and LBD is deprotected, suggesting that this region becomes more dynamic when RORγt binds the nucleosome in comparison to its free-form Fig.2D . Second, the LBD is clearly protected at the AF2 coregulator interaction surface within the LBD, as evidenced by a reduction in deuterium exchanged at all time points tested for each peptide measured from the AF2 surface Fig.2E . The AF2 region is a conserved segment on all NRs that is essential for recruiting coactivator proteins to enhance gene transcription. The observation that nucleosome binding via the RORγt DBD affects the distal AF2 surface on the LBD is reminiscent of long-range functional allostery observed in other NRs such as PPARγ:RXR 33 , 34 , RAR:RXR 35 , VDR:RXR 36 – 39 , as well as monomeric receptor LRH1 40 . We next examined changes to histone dynamics within the nucleosome upon RORγt binding. Digestion optimization led to 85-95% coverage with high redundancy for each of the 4 histones ( Fig. 2F ). HDX-MS results showed unchanged deuterium uptake for histones H2A and H2B, but localized deprotections to histones H3 and H4, Fig.2F . We observe an increase in deuterium incorporation at the first and third α helices of histone H3, Fig.2G , 2H , 2I . The first α-helix serves as the interface between the disordered N-terminal tail and the ordered NCP. This region is where the entry and exit site DNA contact the histone H3/H4 tetramer. We also see deprotections to histone H4 C-terminal tail Fig.2J . This region is located at the core of the NCP and mediates important contacts with the histone H2A/H2B dimers. The majority of deprotections that were observed on the histones H3 and H4 occurred at later timepoints, suggesting that RORγt binding dynamically destabilized the histone octamer at these positions. Notably, flexibility within the H4 C-terminal tail contributes to efficient nucleosome remodeling 41 . Deprotection of the H4 tail may accordingly serve as one of several cues that promotes chromatin decompaction for transcriptional activation. Collectively, these results highlight how nucleosomes affect RORγt conformational dynamics, and vice versa. Both short- and long-range allostery may play roles in RORγt functional regulation, possibly serving to recruit downstream regulatory proteins and/or maintain open chromatin for transcription. Molecular architecture of the RORγt:nucleosome complex To gain insight into the architecture of the RORγt-nucleosome complex and define molecular interactions governing RORγt-nucleosome engagement, we proceeded to determine the structure of the complex using cryo-EM. We collected 9668 movies of frozen hydrated assemblies of RORγt bound to SHL -6.5 on the nucleosome, then optimized particle selection and developed a multi-step image classification and refinement procedure to isolate particles containing defined density for components of RORγt ( Sup.Fig.4 ). These procedures yielded a structure resolved to resolutions ranging from 2.6 Å within the NCP to ∼6-8 Å in the entry-site DNA containing the cRORE ( Sup.Fig.5 ). RORγt engages the nucleosome at two positions. Within the nucleosomal entry site that contains the cRORE, density for the RORγt DBD is clearly defined ( Fig.3A ). We could readily derive an atomic model for the DBD:cRORE interaction using an integrative modeling approach with AlphaFold3 42 and ISOLDE 43 . Additionally, there is density residing above the histone octamer, Fig.3B . This density lacks discernable features and generally exhibits low signal intensity, leading to its rapid disappearance at higher thresholds. Based on the shape and position, we interpret the density to belong to the RORγt LBD. The resolution is too limited for de novo model building, and an atomic model of the LBD cannot be unequivocally docked into this region. Notably, AlphaFold3 predictions recapitulate the preferential docking of the LBD onto the histone octamer face, but the predicted structures vary in the exact placement of the LBD ( Sup.Fig.6 ), consistent with the featureless shape of the cryo-EM density. We accordingly attribute this density to a loosely tethered LBD, which engages the histone octamer face through non-specific interactions that may increase the overall binding avidity. Download figure Open in new tab Figure 3. Molecular architecture of the RORγt engaging cRORE at nucleosome SHL -6.5. ( A-B ) The cryo-EM electron potential map is shown at two thresholds. Features at ( A ) low threshold (low intensity) demonstrate the presence of the LBD density, whereas features at ( B ) high threshold demonstrate specific DBD:cRORE engagement. ( C ) Atomic model of the binary RORγt:nucleosome complex. ( D ) Close-up showing the experimental cryo-EM map and the atomic model of the RORγt DBD bound to the cRORE. Both the maps and models have been annotated according to the color key at the bottom of the figure. ( E ) comparison of the DBD region from the cryo-EM model to a previously published crystal structure of RORγ (isoform 1) bound to a DR2 response element 20 . RORγt uses its DBD to directly and specifically bind the cRORE, which is located within the outward facing major groove at nucleosomal SHL -6.5 ( Fig.3C -D) . Helix 1 of the DBD inserts directly into the major groove, with both structure- and sequence-specific contacts being mediated by the first of two zinc finger domains in the DBD. The general fold of the DBD core is highly conserved amongst NRs and unique to the superfamily. Sequence specificity for recognizing ROREs is determined by Lys31, which contacts the two guanine bases of the cRORE (denoted by the arrow in the motif, NAANTAGτGTCA). Outside the DBD core, RORγt makes minor groove contacts through its ‘KFGR’ motif, consistent with previous structural analyses 20 , 44 . In the context of the nucleosome, this motif inserts directly into the minor groove at SHL +7.0. Downstream, a prominent α-helix corresponding to the RORγt CTE stretches away from the nucleosomal DNA. Notably, in the crystal structure of the RORγ1 (an isoform that contains an additional 21 bp at the N-terminus) DBD bound to the DR2 response element 20 , the CTE is mostly disordered, with only the first four residues (Lys108–Arg111, equivalent to Lys87–Arg90 in RORγt) adopting an α-helical configuration, with the remainder being disordered. By contrast, when RORγt is bound to the nucleosome, residues spanning Lys87–Val97 within the CTE and the hinge region are clearly helical, Fig.3E . Consistent with the cryo-EM structure, by HDX-MS we observe increasingly strong protection to solvent exchange between residues spanning 74-94 upon DNA recognition ( Fig.2C ). These observations suggest that (i) the nucleosomal contacts influence the configuration of the CTE, and (ii) the CTE helix likely nucleates upon binding to DNA. The cryo-EM structure reveals the basic molecular architecture for RORγt-nucleosome engagement. While the DBD:cRORE interaction builds upon the basic principles for NR-mediated response element recognition, the structure additionally reveals an unexpected nucleation of the α-helical CTE and an extensive LBD density positioned prominently above the histone octamer. The CTE and the LBD are connected by a linker spanning 147 residues, yet the distance between the experimental densities is only ∼51 Å, far less than the expected distance for an outstretched linker. The combination of the specific binding of the DBD to the RORE and nonspecific binding of the RORγ LBD to the octamer may promote long-range allostery and avidity to stabilize the receptor on chromatinized DNA. CTE-chromatin interactions contribute to Th17 differentiation but not IL-17 enhancer localization The identification of the CTE α-helix has important implications for RORγt biology. Previously, a genetic screen identified a double mutation S92A/L93A in RORγt CTE that confers a phenotype that distinguishes two separate biological functions mediated by RORγt 45 . RORγt S92A/L93A mutant mice were resistant to Th17-mediated autoimmunity in a model of experimental autoimmune encephalomyelitis (EAE). Importantly, RORγt S92A/L93A mice maintained largely normal thymocyte development and lymph node genesis. These mice also did not develop thymic lymphoma, which otherwise arises rapidly in RORγt -/- mice 7 . The underlying molecular reasons for the phenotype remain unclear. We first validated previous observations pointing to the specific involvement of S92A-L93A in Th17 differentiation. We used a retroviral vector expressing RORγt to drive the differentiation of naïve T cells into Th17 cells. Mouse naïve CD4 T cells were cultured in vitro in Th0 conditions and transduced with retroviral vectors carrying WT or mutant RORγt, or an empty retroviral vector MIGR1. We tested the S92A-L93A double mutant alongside the K31A mutant (defective in DNA binding). Three days after transduction, we measured the cytokine production of transduced T cells. As shown in Fig.4D-E , WT RORγt transduced naïve T cells, turned on IL-17A expression, and completed Th17 differentiation. By contrast, T cells transduced with K31A, S92A-L93A RORγt mutants, or the MIGR1 empty vector showed very little IL-17A expression, confirming that these RORγt mutants do not support normal Th17 differentiation. Download figure Open in new tab Figure 4. Implications of the CTE in chromatin binding and genome localization. ( A ) The model of the RORγt DBD containing residues S92 and L93 selected for mutagenesis and follow-up chromatin binding studies. ( B ) Representative EMSA results are shown. ( C ) The quantitation of 3 assay replicates and plotted with fractional occupancy on the y axis and protein concentration on the x axis. ( D ) Representative flow cytometry analysis of transduced CD4+ T cells. ( E ) The results of 3 replicates are shown as a bar chart. ( F ) ChIP analysis at the IL-17 loci is presented from publicly available dataset 45 . ( G-H ) DiffBind analysis results comparing WT and S92A-L93A are shown as ( G ) a volcano plot and ( H ) a correlogram. We next tested how the S92A-L93A mutant protein might engage chromatinized DNA. Since residues S92 and L93 are located in the CTE of the DBD, forming the complete DNA-binding interface (shown in Fig.4A ), we hypothesized that the S92A-L93A double mutation could directly impact RORγt binding to chromatin. We thus tested the binding of WT, S92A/L93A, and K31A (DNA binding dead) mutants to nucleosomes containing cRORE at SHL -6.5 using an electrophoretic mobility shift assay. The WT protein was capable of binding and shifting the nucleosome with an EC50 of ∼100 nM protein concentrations, while the S92A/L93A and K31A mutants were unable to fully shift the unbound nucleosomes in the tested concentration range and have an EC50 of >1 μM, Fig.4C and 4D . These data indicate that the S92A/L93A mutant confers a substantial loss of DNA binding to chromatinized DNA. Despite loss of DNA binding, the S92A/L93A mutant remains capable of localizing to the IL-17 enhancer. Based on previously published ChIP-seq experiments 45 , WT and S92A/L93A mutant RORγt share the overall trends in binding to genomic DNA, as shown in Fig.4F . However, the total read count signal is reduced in the S92A/L93A ChIP-seq experiments in comparison to the WT protein. Prior work suggested that this loss is, in part, due to the increase in ubiquitin/proteasome mediated degradation of the S92A/L93A RORγt protein 45 . Our data further suggest that the S92A/L93A RORγt protein is less capable of engaging chromatin. However, the overall trends of the ChIP-seq experiments are similar. Differential binding analysis shows that out of 15,865 peaks, the S92A/L93A mutation only causes a significant decrease in 723 peaks, Fig.4G . Further, the ChIP-seq datasets for RORγt S92A/L93A show 60% correlation with RORγt WT compared to a 30% correlation to empty vector, Fig.4H . These data suggest that chromatin binding alone does not the determine how RORγt localizes in the genome. DISCUSSION ROREs embedded within higher-order chromatin are the native targets for RORγt. However, the role of chromatin in regulating RORγt structure, dynamics, and function has largely been ignored. Here, we set out to address this limitation and interrogate the consequences of RORγt-chromatin interactions using a mononucleosome containing stretches of free DNA as the basic constituent of higher-order chromatin. Using a modified SeEN-seq assay, we found that RORγt preferentially binds to nucleosomal entry/exit sites, and that cROREs are engaged with higher affinity than vROREs. Solution phase HDX-MS measurements showed that RORγt-nucleosome binding promotes destabilization of the histone H3/H4 tetramer that exposes the H3 N-terminal tail and H4 C-terminal tail, as well as stabilization of the AF2 region within the RORγt LBD. These events may be linked and contribute to short- and long-range intramolecular allostery that leads to histone modification and maintenance of open chromatin. A cryo-EM structure of the binary complex between RORγt and the nucleosome contextualized the conformational changes observed by HDX-MS and revealed the molecular architecture of the binary complex. Unexpectedly, we observed density corresponding to the RORγt LBD above the histone octamer core. Since the interaction did not produce a stable resolvable density, it is likely that the LBD:octamer interaction is non-specific and promoted through avidity effects associated with the more specific DBD:RORE binding. Importantly, the RORγt hinge is a 147 bp unstructured linker between the DBD and LBD. In a more native chromatin environment, it is likely that the LBD could interact with other nucleosomes and/or chromatin associated proteins that reside in close proximity. In this regard, the concomitant stabilization of the coregulator recruitment surface called AF2 is noteworthy. The stability of this surface has been found to correlate with RORγt-mediated transcriptional activation. For example, small molecules that increase the stability of the AF2 surface promote higher binding affinity for coregulator peptides and generally had an increased ligand-dependent transcriptional activity in cell-based assays 46 . Based on our observations, it is thus possible that the nucleosomal context may promote RORγt-mediated recruitment of coregulators to higher-order chromatin. Our results have uncovered a newfound appreciation for the RORγt CTE. The CTEs of monomeric NR DBDs augment the sequence specificities, but it is becoming increasingly clear that the full consequences of these features are not fully appreciated. For example, recently, the ‘RGGR’ motif located in the CTE in NR5A2 (LRH-1) was suggested to play a role in pioneering functions, but not in DNA binding activities 47 . In the case of RORγt, the CTE seems to influence DNA and chromatin binding properties. Prior work demonstrated how the RORγt CTE dynamics change depending on the DNA sequence to which RORγt is bound 31 . Our SeEN-seq analysis show that RORγt engages distinct ROREs with varying affinities in the context of higher-order chromatin. Unexpectedly, our cryo-EM structure revealed an α-helical CTE, which was supported by all AlphaFold predictions. This helix is likely nucleated by the ‘KFGR’ motif interacting with the 5′ A-rich extension found in the cROREs but not vROREs. Presumably, the RORγt CTE would exhibit less helicity when RORγt engages a vRORE, and this would further shift binding preference to free DNA. Our structure also led to the identification of the position of residues S92 and L93 within the context of nucleosome engagement. Briefly, the S92A/L93A double mutant mouse does not suffer from loss-of-function of RORγt in thymocytes but does have a loss-of-function phenotype for RORγt in Th17-mediated autoimmunity 45 . Mutations S92A/L93A in the RORγt protein revealed a substantial loss of nucleosome binding and loss of IL-17 expression. In addition, previous observations indicate that the double mutation destabilizes RORγt to promote ubiquitin-mediated proteasomal degradation 45 , which is not mutually exclusive with loss of DNA binding. Despite these effects, RORγt S92A/L93A still shares high correlation with RORγt WT binding in trends but not in signal. Altogether, we have identified that the RORγt CTE is an important feature that allows RORγt to engage chromatin in a manner that supports RORγt functions in pro-inflammatory T cells, and that this activity is presumably dispensable for crucial RORγt functions in developing thymocytes. The classical model of RORγt activation dictates that RORγt must bind ROREs and then drive the localization of coregulators (such as steroid receptor coactivators and CBP/p300) to establish topologically associated domains that regulate target genes. Collectively, our data reveals a complex network of allostery and avidity between RORγt and nucleosomes and highlights how the chromatin environment influences RORγt structure, dynamics, and function. Strikingly, a key outcome of our studies is that the localization of RORγt to chromatin in the genome is only partially dependent on DNA binding activity through the CTE. RORγt, and transcription factors in general, do not work alone, but instead function cooperatively to enact a cell type specific gene program. Eventually, studies examining RORγt and other NRs need to reconcile the role of transcription factor cooperativity with coregulatory protein function and assembly of larger complexes. Importantly, understanding how these complexes assemble on chromatin offers great potential to build more complete activation models of RORγt function. MATERIALS AND METHODS Chemicals and reagents Unless stated otherwise, all chemicals were purchased from Sigma Aldrich. Lyophilized human histones (H2A, H2B, H3.1, H4) purchased from The Histone Source at Colorado State University. Tris-HCl and HEPES pH 7.5 purchased from Teknova. Guanidine hydrochloride purchased from BioVision Inc. Dithiothreitol (DTT) purchased from Biopioneer Inc. Ammonium sulfate and TEMED purchased from Biorad. Bovine serum albumin (BSA) purchased from New England Biolabs. Potassium chloride and Chloroform:isoamyl alcohol were purchased from VWR. Sodium acetate was purchased from MP Biomedicals. Bis-acrylamide and Phenol:chloroform:isoamyl alcohol were purchased from Fischer Scientific. Magnesium sulfate purchased from Macron Fine Chemicals. RORγt expression and purification We performed insertion PCR to introduce a SRC2 peptide on the C-terminus of the RORγt LBD in the pESUMO-RORγt construct (Addgene 170329) using the Q5 mutagenesis tools (NEB). This peptide artificially stabilizes the LBD in an active conformation in the absence of ligand 46 , 48 . This was done to reduce the number of variables such as ligand binding and coregulator binding. This modification also improved the expression yield from ∼6 to ∼15 mg of protein per liter of expression media. HisSUMO-RORγt-SRC2 was expressed and purified using previously described methods 31 with some modifications listed below. BL21 (DE3) transformed with the pESUMO-RORγt-SRC2 plasmid was cultured in terrific broth supplemented with 50 μg/mL carbenicillin and 30 μM zinc chloride. The culture was grown with orbital shaking at 150 RPM at 37 °C to optical density of 0.5 at 610 nm, the temperature was reduced to 16°C, and then IPTG was added to 250 μM for overnight expression. The cells were harvested and washed with NiB1 (50 mM HEPEs pH 7.9, 500 mM NaCl, 25 mM imidazole, 10% glycerol) supplemented with 1X SigmaFast protease inhibitor cocktail (Sigma), and snap frozen in liquid nitrogen and stored at -80°C. The pellets were thawed and resuspended in lysis buffer (NiB1 supplemented with 1X SigmaFast protease inhibitor cocktail and Benzonase Nuclease from Sigma). The cells were lysed using sonication operating at 30% amplitude with 6 seconds on and 6 seconds off for a total of 5 minutes per cycle. We repeated the sonication 3-4 times until the sample was homogeneous. The lysates were clarified with high speed centrifugation at 30,000 relative centrifugal force. The supernatants were used for NiNTA affinity chromatography and ion exchange chromatography as previously described. After elution from the ion exchange column, the protein products were concentrated with amicon ultra centrifugation columns (Sigma) to 48 μM concentration determined by absorbance at 280 nm using a NanoDrop nanospectrophotometer (Thermo Scientific). The protein was aliquoted, snap frozen, and stored at -80 °C. On the day of use, 50 μL HisSUMO-RORγt-SRC2 protein samples were thawed, mixed with 5 μL His-tagged SUMO protease (gifted by the Griffin Lab), and then size exclusion purified into reaction buffers appropriate for the analysis as described below. The S92A-L93A and K31 mutations were introduced to the pESUMO-RORgt-SRC2 plasmid using the Q5 mutagenesis tools (NEB). Mutant proteins were expressed and purified using the same procedure described above. Reconstitution and purification of human histone octamer Lyophilized human histones H2A, H2B, H3.1, and H4 (purchased from The Histone Source at Colorado State University) were mixed at a molar ratio of 1:1:0.9:0.9, respectively, in denaturing buffer (20 mM Tris-HCl pH 7.6, 6 M guanidine hydrocholoride, and 5 mM dithiothreitol). The protein mixture was dialyzed against refolding buffer (10 mM Tris-HCl pH 7.6, 2 M NaCl, and 1 mM dithiothreitol). The dialysate was concentrated and purified via size-exclusion chromatography (Superdex 200 Increase 10/300; Cytiva) equilibrated in refolding buffer. The resulting octamer was concentrated by centrifugal filtration (Amicon Ultra, Millipore) and stored at 4°C. DNA preparation and purification DNA for nucleosome assembly was generated by PCR amplification. Briefly, Pfu polymerase was expressed and purified using a previously described protocol (REF), and the standard Pfu PCR conditions Pfu buffer (20 mM Tris-HCl pH 8.8, 10 mM ammonium sulfate, 10 mM KCl, 0.1 mg/ml BSA, 0.1% (v/v) Triton-X-100, 2 mM magnesium sulfate) was used to generate DNA at milligram scale. The resulting DNA fragments were extracted with phenol:chloroform:isoamyl alcohol mixtures using the MaXtract High Density gel column (Qiagen), followed by a chloroform:isoamyl extraction. DNA was extracted from the aqueous solution using sodium acetate ethanol extraction. The resulting DNA fragment was purified by anion-exchange chromatography (MonoQ 5/50 GL; Cytiva) The DNA was eluted from the MonoQ 5/50 GL column following a gradient of increasing salt concentration up to 1 M NaCl (0% NaCl for 20 ml, 20% NaCl for 10 ml, 20-60% NaCl for 15 ml, 60-80% NaCl for 22.5 ml, 80-100% NaCl for 1.5 ml, 100% NaCl for 3.5 ml).DNA fragments were extracted overnight at -20°C using sodium acetate ethanol extraction. The resulting DNA fragments were resuspended in 10mM Tris-HCl pH 8.0, assayed for size and purity by TapeStation (Agilent), and stored at 4°C for immediate use or -20°C for long-term storage. General nucleosome assembly and purification Nucleosomes were reconstituted using a previously described method 49 . All steps were performed on ice or at 4° C unless otherwise noted below. Briefly, DNA and octamer were mixed in high salt reconstitution buffer (10 mM HEPES or 10 mM Tris-HCl pH 7.5, 2 M KCl, and 1 mM dithiothreitol). DNA was held at a constant concentration (typically 3 μM or 6 μM) and the ideal molar ratio of histone octamer was determined empirically by testing molar ratios between 0.8 and 1.2 times the concentration of the DNA. The DNA octamer mixture was initially dialyzed against high salt reconstitution buffer for 30 minutes in Slide-A-Lyzer MINI 3,500 MWCO dialysis button devices (Thermo Scientific). The KCl concentration was gradually reduced from 2 M to 0.25 M over a 16 hour gradient by pumping low salt reconstitution buffer (10 mM HEPES or Tris-HCl pH 7.5, 0.25 M KCl, and 1mM dithiothreitol) using two peristaltic pumps. The samples were dialyzed against fresh low salt reconstitution buffer for three hours, followed by dialysis against nucleosome storage buffer (10mM HEPES or Tris-HCl pH 7.5, 1 mM DTT) for three hours, and then dialyzed against fresh nucleosome storage buffer overnight. After dialysis, the quality of the nucleosomes was assessed by native polyacrylamide gel electrophoresis using 5-6% PAGE gels (59:1 acrylamide:bis-acrylamide). Native polyacrylamide gels were prepared by combining the PAGE gel solution described previously, 10% ammonium persulfate, and TEMED in a gel cassette (1.0 mm Novex), followed by overnight polymerization. Once the ideal ratio of DNA and octamer was determined, nucleosome assembly reactions were scaled up and dialyzed using the method described above. After dialysis, nucleosomes were incubated at 37 °C for thirty minutes before being purified by native polyacrylamide gel (7 %) electrophoresis using the Prep Cell 491 apparatus (Bio-Rad) in nucleosome storage buffer (10W, 2-7 hour run time). The eluted nucleosomes were concentrated with an Amicon Ultra centrifugal filter (Millipore) and were stored in nucleosome storage buffer on ice until use. Selected Engagement of Nucleosomes coupled with Sequencing (SeEN-seq) SeEN-seq Assay Development We adapted a protocol based on the originally described SeEN-seq protocol 30 , overview in Fig.1A . Briefly, we elected to use a nucleosome positioning sequence with 30 bp extensions to allow for RORγt to bind to free DNA. The 30 bp extensions also contained 15bp priming sites that allow for amplification and functionalization of the library. This created a 177 bp tiling region across the Widom W601 nucleosome positioning sequence 50 , as shown in Sup.Fig.1A . We tiled two known 11 bp ROREs through the tiling region yielding a library size of 336 members. A classic RORE based on a sequence from IL-17 enhancer element 28 and a variant RORE based on a sequence from the androgen promotor 29 . We amplified and sequenced the library to examine the distribution of distribution of reads across all library members, Sup.Fig.1B . The FAM-labeled library was mixed with an unlabeled 207bp W601 sequence that did not contain priming sequences in a 1-to-1000 molar ratio. We used the DNA mixture for standard nucleosome core particle assembly and purification using established methods 49 . We performed selections by reacting the nucleosome samples with RORγt or buffer and separated reaction products from reactants using electrophoretic mobility shift assay, shown in Sup.Fig.1C . Using fluorescence scanning, we cut out small sections of the gel containing the shifted band as well as the same region of the gel in the buffer treated negative control sample. We extracted DNA and performed quantitative PCR to ensure an enrichment of in the RORγt treated sample compared to the negative control, shown in Sup.Fig.1D . We amplified and sequenced the library after one round of selection to identify the sequences that had been enriched based on changes relative abundance Sup.Fig.1E . We normalized the relative read counts to the input library to show the fold change in enrichment in Sup.Fig.1F . We investigated performing a second round of selection by repeating the process and including additional selection replicates, shown in Sup.Fig.1G . The qPCR trace showed a similar signal-to-noise compared to the first round of selection, Sup.Fig.1H . We also included an additional buffer only negative control which showed the background signal in qPCR originating from the gel extraction process. After sequencing the library after the second selection, we observed that there was an increased signal for some of the library members and a decrease in signal for the non-binding sequences, Sup.Fig.1I . After normalizing the library abundances, the enrichment ratios for productive binding sequences increased while the non-binding sequences decreased, Sup.Fig.1H . Overall, the trends for one round and two rounds of selection are very similar. We conclude that a second round of selection may benefit signal-to-noise and produce more confident results although a single round of selection is likely satisfactory for simple cases. DNA synthesis and amplification SeEN-seq libraries were designed and synthesized using the Twist Bioscience Oligopool system. SeEN-seq libraries were PCR amplified using the KAPA HiFi polymerase kit (Roche) according to manufacturer’s instructions. Briefly, test reactions were first conducted using quantitative PCR by including EvaGreen dye (Biotium) and following fluorescence using QuantStudio 6 (ThermoFisher Scientific) to identify suitable reaction conditions that did not over cycle the library. We then performed a larger reaction to generate ∼3 μg of library DNA. To fluorescently label the library DNA, FAM-labeled primers were used to amplify library DNA using low cycle (4-8 cycles) PCR. PCR products were cleaned up using AMPure XP (Beckman Coulter), assayed for size and quality by TapeStation (Agilent), quantified using Qubit assay (Thermo Fisher), and stored at 4°C for immediate use or -20°C for long-term storage. Selection Assay The FAM-labeled library was mixed with an unlabeled 207bp W601 sequence that did not contain priming sequences in a 1-to-1000 molar ratio. We used the DNA mixture for standard nucleosome core particle assembly and purification as described above. We performed selections by reacting the 1 μM nucleosome samples with 500 nM RORγt or buffer only control at 25°C for 30 minutes. The reaction buffer contained 20 mM HEPES pH 7.9, 100 mM NaCl, 5% glycerol, 1 mM DTT. and separated reaction products from reactants using electrophoretic mobility shift assay. We found that 5% PAGE gels (59:1 acrylamide:bis-acrylamide) were able to separate RORγt bound nucleosomes from unbound nucleosomes. We performed fluorescence imaging with the Amersham Typhoon (Cytiva) to isolate signal from the SeEN-seq library DNA. We cut out the bands and performed crush soak gel extraction by incubating the bands overnight in 50 mM Tris pH 8.3, 300 mM NaCl, 0.05% Tween-20, 1 mM EDTA. The gel extract was syringe filtered at 0.22 um and then used for overnight sodium acetate ethanol extraction with GlycoBlue coprecipitant (Thermo Fisher). The precipitates were isolated with centrifugation, washed with freshly prepared 70% ethanol, dried, and then resuspended in storage buffer (5 mM Tris pH 8.5). The DNA was amplified using the methods described above. Next-generation sequencing and data analysis We sequenced the unlabeled SeEN-seq library DNA samples using the Amplicon-EZ service at Genewiz. Briefly, 500 ng of material was prepared for paired-end 250bp sequencing reaction through a proprietary Illumina-based sequencing platform (Azenta Life Science). Reads were aligned to the designed library sequences using Bowtie2 (version 2.4.4) with options --no-discordant --no-mixed --local --score-min L,0,2 --minins 205 –maxins 207 . We found it was important to include a stringent read scoring parameters improved the alignment results. The scoring scheme allows for a single mismatch or 2 terminal base truncation, and does not tolerate insertions. The alignments rates ranged from 20-40 % of total reads. Read counts per member of the library were extracted using idxstats (samtools version 1.13). Read count data was imported into Rstudio (version 2024.04.2+764) for normalization to total read counts (per sample) statistical analysis, comparison of selection rounds, and plotting. RORγt:Nucleosome Complex Assembly The sequence for 207bp W601 with cRORE at site 14 (superhelical location -6.5) was synthesized and cloned into the pUCIDT vector by Integrated DNA Technologies. The DNA was amplified using the SeEN-seq library primers as detailed in the general DNA amplification and purification protocol described above. We assembled and purified nucleosomes using the standard protocol described above. 50 μL aliquots of HisSUMO-RORγ-SRC2 (48 μM) was thawed and mixed with 5 μL HisSUMO protease (50μL) and incubated on ice for 30 minutes. We optimized stoichiometries of nucleosome to RORγt using EMSA. Briefly, nucleosomes were mixed with excess RORγt-SRC2 at different molar ratios, reacted at 25°C for 30 minutes in 20 mM HEPES pH 7.9, 100 mM NaCl, 5% glycerol, 1 mM DTT. The reactions were assayed using 5% PAGE. We also performed analytical size exclusion using a 3.2/300 Superose 6 (Cytiva) on our AKTA pure FPLC (Cytiva) with running buffer containing 10 mM HEPEs pH 7.5, 50 mM NaCl, 1 mM DTT, and 5% glycerol. The fractions with native 5% PAGE and denaturing 4-20% PAGE. Hydrogen-deuterium exchange mass spectrometry Sample Preparation HisSUMO-RORγ-SRC2 (500 μL, 48 μM) was mixed 1:10 with HisSUMO protease (50μL) and incubated on ice for 30 minutes. Cleaved protein was separated using a size-exclusion Superdex 200 Increase 10/300 GL column (Cytiva, 28990944) pre-equilibrated with 2x reaction buffer [20 mM HEPES, pH 8, 200 mM NaCl, 2 mM DTT] on an AKTA pure (Cytiva, Marlborough, MA). Fractions were collected and run on denaturing SDS-PAGE gel (Bio-RAD, 4569036) and stained with Coomassie blue to identify correct fractions. Fractions were pooled and concentrated to 30 μM using a 30 kD MWCO spin column (Sigma, UFC5030). Protein concentrations were estimated using nanodrop A280 values and molar absorptivity from ProtParam 51 . Ligand 25-hydroxycholesterol was added to protein mixture to a final concentration of 40 μM. Cleaved RORγ-SRC2 was mixed 1-to-1 with nucleosome to give 50 μL protein mixture, containing 15 μM RORγ-SRC2, 5 μM nucleosome, and 20 μM 25-hydroxycholesterol in a final buffer of 15 mM HEPES, pH 8.0, 100 mM NaCl, and 1 mM DTT. Peptide identification To generate a peptide set, peptides were acquired using MS/MS experiments performed on a QExactive (ThermoFisher Scientific, San Jose, CA) over a 70-min gradient. Product ion spectra were acquired in data-dependent mode, with the five most abundant ions selected for MS2. For peptide identification, the MS/MS *.raw data files were analyzed on Sequest (version 2.3, Matrix Science, London, UK). Mass tolerances were set to ± 0.6 Da for precursor ions and ± 10 ppm for fragment ions. Oxidation to methionine was set as a variable modification. Non-specific digestion was selected in the search parameters with 4 missed cleavages. Only peptides with an FDR<1% were used in the dataset, and all identified charge states were included for each peptide in the peptide set. Continuous Labeling Experiments were carried out on a fully automated system (CTC HTS PAL, LEAP Technologies, Carrboro, NC; housed inside a 4°C cabinet) as previously described 52 with the following modifications: RORγ-SRC2 and nucleosome were mixed as described in Sample Preparation then incubated at 25°C in a thermocycler for 30 minutes. The solutions were cooled to 4°C, then transferred to the automated system for analysis. The reactions (5 μL) were mixed with 20 μL deuterated buffer [15 mM HEPES, pD 8.3, 100 mM NaCl, and 1 mM DTT] then incubated at 4°C for 10, 30, 60, 300, 900, or 3600 s. A non-deuterated control was included as a baseline (t=0s). Following deuterium exchange, deuterated solutions were quenched with an acidic quench solution [5 M Urea, 50 mM TCEP, 1% TFA, pH 2.5] before on-line digestion and data acquisition. HDX-MS acquisition Samples were digested using an immobilized Fungal XIII/pepsin column (1-to-1 ratio, prepared in-house) at 50 μL/min flow rate [0.1% (v/v) TFA at 4°C]. Digested peptides were trapped and desalted using a 2x10mm C8 trap column (Hypersil Gold, ThermoFisher Scientific). Trapped peptides were gradient eluted [4 to 40% (v/v) CH3CN, 0.3% (v/v) formic acid] on a 2.1x50mm C18 analytical column (Hypersil Gold, ThermoFisher Scientific) over 5 minutes. All acquisition steps were performed at 4°C. Eluted peptides were directly ionized via electrospray ionization, coupled to a QExactive mass spectrometer (ThermoFisher Scientific). Data Rendering The intensity-weighted mean m/z centroid value of each peptide envelope was calculated and converted into a percentage of deuterium incorporation. This is calculated by determining the observed averages of the undeuterated (t=0 s) and fully deuterated spectra using the conventional formula described elsewhere 53 . The fully deuterated control, 100% deuterium incorporation, was calculated theoretically, and a 70% deuterium recovery was set to correct for back-exchange, accounting for the 80% final deuterium concentration in the sample (1:5 dilution in deuterated buffer). Statistical significance for the differential HDX data is determined by an unpaired t-test for each time point, a procedure that is integrated into the HDX Workbench software 54 . The HDX data from all overlapping peptides were consolidated to individual amino acid values using a residue averaging approach. For each residue, the deuterium incorporation values and peptide lengths from all overlapping peptides were assembled. A weighting function weights shorter peptides more than longer peptides. Weighted deuterium incorporation values were then averaged to produce a single value for each amino acid. The initial two residues of each peptide, as well as prolines, were omitted from the calculations. This approach is similar to one previously described 55 . Deuterium uptake for each peptide is calculated as the average of %D for all on-exchange time points, and the difference in average %D values between the unbound and bound samples is presented as a heatmap with a color code given at the bottom of each figure (warm colors for increased deuterium exchange [deprotection] and cool colors for decreased deuterium exchange [protection]). Peptides are colored by the software to display significant differences, determined either by a greater than 5% difference (less or more protection) in average deuterium uptake between the two states or by using the results of unpaired t-tests at each time point (P < 0.05 for any two time points or P < 0.01 for any single time point). Gray peptides represent nonsignificant changes in the differential experiment (i.e., within ± 5% differential deuterium incorporation). The exchange of the first two residues for any given peptide is not colored. Each peptide bar in the heatmap view displays the average Δ %D values, associated SD, and the charge state. In addition, overlapping peptides with a similar protection differential deuterium exchange trends covering the same region are used to rule out data ambiguity. Output from data rendering was transferred to ChimeraX (version 1.8) to create protein structure overlays. Cryogenic Electron Microscopy Graphene grid preparation We adapted a protocol for preparing monolayer graphene grids that has been described elsewhere 56 . Briefly, we purchased Trivial Transfer® poly methyl methylacrylate (PMMA)-coated graphene pads from ACS materials and UltrAufoil R1.2/1.3 300 mesh grids from Quantifoil. We washed the grids by sequentially soaking them in chloroform, acetone, and isopropanol in 20 minute periods on an orbital shaker. After drying the grids, we plasma cleaned the grids using a Solarus II Model 955 (Gatan) plasma cleaning instrument. The grids were immediately submerged into a water bath and PMMA-coated graphene was floated onto the grids using the manufacturer’s protocol. We recovered the PMMA-coated graphene grids, allowed them to dry for 20 minutes at room temperature, and then baked the grids at 300 °C for 60 minutes. After letting the grids cool to room temperature, we submerged them in acetone for 60 minutes. We transferred the grids to a clean acetone solution and repeated the acetone wash this time overnight. The next day, the acetone was exchanged for a third wash for 60 minutes, then a subsequent wash with isopropanol for 30 minutes. All washes of the grids were done in clean crystallization dishes covered in aluminum foil, on an orbital shaker in a fume hood at room temperature with gentle agitation. The graphene grids were dried at room temperature and then baked at 200 °C for 20 minutes. Grids were then either immediately used or stored in grid boxes and vacuum sealed. Sample preparation The complex assembly was done as described above with some minor modifications. We reacted 2 μM of nucleosomes with 6 μM of RORγt and SUMO protease in standard reaction buffer. After complex assembly reaction was completed, glutaraldehyde (Electron micrscopy sciences) was added to a final concentration of 0.01%. The complex was crosslinked for 10 minutes on ice, and then quenched by the addition of Tris pH 8.0 to a final concentration of 50 mM. The crosslinked sample was then purified with analytical size exclusion using a 3.2/300 Superose 6 (Cytiva) on our AKTA pure FPLC (Cytiva) with running buffer containing 10 mM HEPEs pH 7.5, 50 mM NaCl, 1 mM DTT. The material eluted at ∼300 nM based on absorbance at 260 nm which was found to be ideal for achieving good particle distribution on graphene grids. Vitrification Graphene grids were plasma cleaned using a Solarus II Model 955 (Gatan) plasma cleaning instrument. Grids were immediately loaded into a vitrobot mark IV (thermos fisher) operating at 4 °C and 100% humidity. 2 μL of ∼300 nM RORγt:nucleosome complex in size exclusion buffer was applied to the graphene surface of the grid, incubated for 30 seconds, and then 2 μL of size exclusion buffer + 0.02% DDM was added to the specimen. The sample was immediately blotted and plunge frozen in liquid ethane. The grids were clipped, screened, and ultimately used for data collection on a. Data Collection We collected data on an FEI Titan Krios (Thermo Fisher) equipped with a K3 direct electron detector (Gatan). The data collection was automated using serialEM 57 . We collected 9668 movies with a total dose of 49.23 electrons/Å 2 at a rate of 0.7 electrons/pixel/frame at a pixel size of 1.06 Å/pixel. We collected 72 frames per movie. We allowed for random defocus ranging from -0.7 to -2.5 um during collection. Data Processing The overview of our processing scheme is shown in Sup.Fig.3A . Briefly, CryoSPARC version 4.6.2 was used for preprocessing, 2D classification, and ab initio 3D reconstruction. We used Relion version 5.0b to perform 3D classifications, Sup.Fig.4A . Raw movies and the gain reference were loaded into CryoSPARC version 4.6.2 58 . The data was preprocessed using patch motion correction and patch CTF estimation, an example is shown in Sup.Fig.3B . We performed two rounds of particle picking starting with initial template matching and convolutional neural network approach with Topaz 59 in parallel. We pooled the particle picks, removed redundant picks from the two approaches, extracted and 2D classified. The initial 2D classes showed a lot of features consistent with free DNA, Sup.Fig.3C . The particles that resolved into clear nucleosome particles were used to retrain the convolutional neural network and a second round of picking with templates and Topaz. We again pooled the particle picks, removed redundant picks, extracted, 2D classified, and removed bad particle picks. Representative 2D classes after optimizing the particle picking procedure are shown in Sup.Fig.3D . In the end, there were ∼1.41 million particle picks that were used for ab initio reconstruction. We performed nonuniform refinement and imposed C2 symmetry to align particles to the pseudo C2 symmetry axis that runs through the dyad of the nucleosome. Afterwards, the particle stack was imported into Relion, symmetry expanded, and used for focused 3D classification using the masking strategy show in Sup.Fig.4A . Important parameters for this process were to disable alignment, use blush regularization 60 , and to adjust the regularization parameter T to 50. After rounds of 3D classification without alignment, any potentially duplicated particles were removed. The final particle stack was imported into CryoSPARC for final reconstruction, gold standard global Fourier shell correlation (FSC) analysis ( Sup.Fig.4B ), 3D FSC analysis ( Sup.Fig.4C ), and sampling compensation factor calculation 61 , 62 , Sup.Fig.4D . Integrative Modeling We used Alphafold3 42 to predict structures of RORγt bound to the nucleosome at SHL -6.5. Alphafold3 was able to predict the RORγt DBD:RORE interaction at the correct sequence in every prediction. Alphafold3 generally predicted that the LBD would interact with the nucleosome, but the position and orientation was strikingly different for each prediction and the predicted alignment error for the LBD was > 30 Å. We examined the different maps for agreement to the experimentally determined structure and HDX-MS observations. We then used ISOLDE 43 to relax the predicted structure into the experimental structure as implemented in ChimeraX version 1.8 63 . Electrophoretic Mobility Shift Assay FAM-labeled 207bp W601 with cRORE at site 14 (SHL -6.5) was amplified using FAM-labeled SeEN-seq library primers. The FAM-labeled DNA was mixed with an unlabeled 207bp W601 sequence that did not contain priming sequences in a 1-to-1000 molar ratio. We used the DNA mixture for standard nucleosome core particle assembly and purification as described above. We reacted 1 μM nucleosome samples with a range of RORγt-SRC2 concentrations and included a buffer only control at 25°C for 30 minutes. The reaction buffer contained 20 mM HEPES pH 7.9, 100 mM NaCl, 5% glycerol, 1 mM DTT. and separated reaction products from reactants using electrophoretic mobility shift assay. We found that 5% PAGE gels (59:1 acrylamide:bis-acrylamide) were able to separate RORγt bound nucleosomes from unbound nucleosomes. We performed fluorescence imaging with the Amersham Typhoon (Cytiva) to isolate signal from the SeEN-seq library DNA. Retroviral Transduction of RORγt in CD4 T cells CD4 T cell isolation and culture Naive CD4+ T cells were isolated from the spleen and lymph nodes of 8- to 10-week-old C57/BL6 wild-type mice by negative selection using BioLegend streptavidin beads with biotin-labeled antibodies (anti-B220 (clone: RA3-6B2), anti-CD11c (clone: N418), anti-CD11b (clone: M1/70), anti-CD8a (clone: 53-6.7), anti-Ter119 (clone: TER119), anti-MHCII (clone: M5/114.15.2), anti-CD49b (clone: DX5), anti-TCR γ/δ (clone: GL3), and anti-CD25(clone: PC61.5))in MACS buffer (PBS + 2% FBS + 1 mM EDTA) with 5% rat serum. These cells were seeded in 48-well plates pre-coated with rabbit anti-hamster antibodies and activated and cultured with 1μg/ml anti-CD3 (145-2C11; eBioscience), 1 μg/ml anti-CD28 (37.51; eBioscience), 2 μg/ml human IL-2, 2 μg/ml anti-IL-4 (11B11), 2 μg/ml anti-IL-12, and 2 μg/ml anti-IFNγ (XMG 1.2). Activation was performed in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS), 55 mM 2-mercaptoethanol, 1% Pen/Strep, 1X GlutaMax, 1 mM HEPES, 1 mM sodium pyruvate, and 1X non-essential amino acids at a density of 8 × 10⁵ cells/ml. Retroviral Production 293T cells were cultured in DMEM supplemented with 10% FBS and 1% Pen/Strep at 70% confluency and transfected with 1.2 μg of MigR1-RORγt and 0.8 μg of the packaging vector pCL-Eco (Addgene, #12371) using Lipofectamine 3000 (Thermo Fisher, #L3000008), following the manufacturer’s protocol. The cell culture medium was refreshed 16 hours post-transfection. Retroviral-containing supernatants were collected at 48 and 72 hours post-transfection. Spin-fection For retroviral transduction, T cells 17 hours post-activation were spin-infected with viral supernatants (1,258 × g for 90 minutes at 32°C) in the presence of 5 μg/mL Polybrene (Millipore #TR-1003-G). The viral supernatant was replaced with the original culture medium after spin infection. A second retroviral transduction was performed 32 hours post-activation to achieve a high transduction rate. Cytokine Stimulation At day 5 of CD4 T cells culturing, cells were treated with ionomycin (0.5 μg/ml) and PMA (0.05 μg/ml) stock concentrations at day 5 of activation for 1.5 hours. Subsequently, GolgiPlug (1 μg/ml) was added, and the cells were incubated for an additional 3.5 hours before harvest. Surface and Intracellular Cytokine Staining and FACS analysis Antibody solutions were prepared in MAC buffer using the following concentrations: CD4-PreCP5.5 at 1:400, CD8-AmCyan (BV510) at 1:400, and Ghost Red-APC Cy7 at 1:1000. Cells obtained with/ without cytokine stimulation were incubated with antibody for 40 minutes at 4°C. For intra-cellular staining, the cells were fixed in Fixation/Permeabilization solution (BD Cytofix/Cytoperm) for 15 minutes and intracellular cytokine staining IL-17A-PE-Cy7 at 1:400 and RORγt-BV421 at 1:400 in permeabilization buffer. Flow cytometric analysis and sorting were performed on LSR II (BD Bioscience) supported by the Flow Cytometry Core Facility of the Salk Institute (RRID:SCR_014839) or FACSAria II (BD Bioscience) flow cytometer and analyzed using FlowJo software (BD Bioscience). Chromatin Immunoprecipitation Exploratory Data Analysis Reads for wild type RORγt, S92A-L93A RORγt, and their empty vector negative control were downloaded from the sequencing read archive (Ascension code SRP104092) using SRA tool kit version 3.1.1. Adapter sequences were removed using TrimGalore version 0.6.10 and read quality was assessed before and after trimming using FastQC version 0.12.1. Trimmed reads were aligned to the mm10 mouse genome using bowtie version 1.3.1 with the following options --chunkmbs 512 -p 48 -k 1 -m1 -v 2 --best –strata . The resulting SAM files were filtered for junk alignments and converted to BAM files using samtools view (version 1.21) with the following options -F 1804 -q 30 . Replicate BAM files were merged with samtools, converted to bedGraph format using bedtools version 2.31.1 and then to BigWig format for bedGraphToBigWig version 2.10. BigWig files were visualized using the integrative genome viewer for jupyter notebooks igv-notebook version 0.6.2. We performed differential binding analysis using DiffBind 64 version 3.16.0 implemented in R version 4.4.2. View this table: View inline View popup Download powerpoint SUPPLEMENTARY TABLE S1 View this table: View inline View popup Download powerpoint SUPPLEMENTAL TABLE S2 SUPPLEMENTARY FIGURE LEGENDS Download figure Open in new tab Supplementary Figure 1. Development of SeEN-seq Assay. ( A ) Schematic illustration of the library design. ( B ) The normalized read counts of the library members prior to selection. The read counts for each member are normalized by dividing by the total number of aligned reads and plotted as a function of SHL. ( C ) A representative gel image from the selection assay is shown. We included both positive and negative control nucleosomes for initial experiments to ensure that the selection assay promoted specific binding to ROREs, but did not promote nonspecific binding to nucleosomes that did not contain the ROREs. The addition of the positive control nucleosome also helped identify where to cut the gel to extract the DNA (indicated by red boxes). ( D ) Quantitative PCR was performed on DNA extracted from the SeEN-seq library samples treated with RORγt and the buffer treated nucleosome. The DNA from the RORγt-treated samples was amplified and sent for sequencing. ( E ) The normalized read counts of the library members after one round of selection. ( F ) The log2 enrichment ratios of normalized read counts after one round of selection. ( G ) A representative gel image for the second round of selection. We performed the selection in triplicate for the second round and cut the bands as indicated by the red boxes. ( H ) Quantitative PCR of the extracted DNA. The DNA from the RORγt-treated was amplified and sent for sequencing. ( I-J ) As E-F, but after the 2 nd round of selection. Download figure Open in new tab Supplementary Figure 2. RORγt:nucleosome complex reconstitution. ( A ) RORγt and nucleosomes with cRORE installed at SHL -6.5 were used for complex assembly. A representative size exclusion chromatogram (SEC) of the complex purification. Selected fractions were assayed using ( B ) denaturing PAGE and ( C ) native PAGE. Download figure Open in new tab Supplementary Figure 3. Cryo-EM data processing scheme and representative results. ( A ) The overall cryo-EM processing scheme for the dataset of RORγt engaged to SHL -6.5 on the nucleosome. All tasks were performed in CryoSPARC Version 4.6.2, unless otherwise noted. ( B ) Representative micrograph and 2D contrast transfer function (CTF) fit for the data. ( C ) Results from an initial round of 2D classification. ( D ) Results from 2D classification after optimizing particle selection through Topaz. The scale bar in the 2D classification results is 110 Å. Download figure Open in new tab Supplementary Figure 4. 3D classification strategy and Final Map Quality Control. 3D classification strategy implemented in Relion version 5.0b is shown. Download figure Open in new tab Supplementary Figure 5. Final Map Quality Control. (A) Gold standard Fourier Shell Correlation (FSC) analysis for the final map. ( B ) 3D FSC plots are shown. ( D ) The Fourier sampling distribution and sampling compensation factor calculation is shown. Download figure Open in new tab Supplementary Figure 6. Representative AlphaFold3 Structure Predictions. Alphafold predictions for the RORγt nucleosome complex are shown with histone tails, RORγt hinge region, and the DNA extensions hidden for clarity. A representative position alignment error plot is shown in the bottom right panel. ACKNOWLEDGEMENTS We are grateful for funding support from the following sources: National Institutes of Health Fellowship F32GM148049 awarded to TSS, National Institutes of Health grant R01DK133587 awarded to PRG, National Institutes of Health grants U01 AI136680 and MCB-2048095 awarded to DL, National Institutes of Health grant U54 AI170855 to DL and PRG, and National Institutes of Health grant R01 AI151123 awarded to YZ. DL also acknowledges support from the Hearst Foundations and the Margaret T. Morris Foundation. 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