PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at  The authors reconstruct high fidelity networks of protein-protein interactions between promoters and enhancers in prostate cancer and demonstrate the potential of such an analytical framework to obtain actionable insights into the disease and potential therapeutic targets. Enhancer-promoter (E-P) interactions play a crucial role in orchestrating gene expression and ensuring the proper regulation of cellular processes. DNA-binding proteins (DBPs), including transcription factors (TFs), act as key players in this regulatory network by binding to enhancers and bridging additional protein interactions between enhancers and promoters. In this work we define Enhancers-Promoter protein-protein Interaction Network (EPIN) as the local interactome connecting a single promoter with all its interacting enhancers. EPIN interactions are facilitated by various types of intermediate proteins, such as co-activators , chromatin structural proteins , and noncoding RNA-binding proteins.2, the integration of chromatin architecture information, specifically through chromosome conformation capture (3C-like) techniques, with PPI analysis is still in its early stages. Joint investigations of chromatin loops and PPIs are crucial for prioritizing functional interactions3. However, it is important to note that many of these studies often lack the necessary biological context at various levels.While protein-protein interactions (PPIs) have been extensively studied4. Previous studies have highlighted the significance of disrupted E-P loops in several human disorders7. In cancer, enhancers are frequently subject to sequence and structural variations, leading to the dysregulation of TFs and chromatin modifiers, which contribute to oncogenesis8. Consequently, targeting these enhancer-driven mechanisms holds great promise for therapeutic interventions in cases such as Prostate Cancer (PrCa)9. In this context, advanced techniques such as HiC and its derivative HiChIP10, in combination with ChIP-seq, could enable the identification and characterization of specific chromatin interactions between enhancers and promoters. In particular, H3K27ac-HiChIP has emerged as a powerful tool designed to detect and amplify E-P interactions and has been successfully employed to uncover susceptibility genes associated with cancer, including PrCa11.As of today, the characterization of context specific intermediate PPIs involved in disease pathways and their association with DBPs remains largely unansweredTo characterize protein interactions that take place at the E-P contacts, we developed the Promoter-ENhancer-GUided Interaction Networks (PENGUIN) approach. For each promoter annotated in the genome and covered by at least one HiChIP interaction, PENGUIN builds an EPIN by integrating several sources of information: (1) high-resolution chromatin interaction maps enriched for a marker of active E-P activity (H3K27ac-HiChiP); (2) tissue-specific physical nuclear PPIs; (3) high-quality curated binding motifs of protein-DNA interactions; (4) tissue specific gene expression.12. Its distinct hormone-dependent nature is characterized by high expression and frequent genetic amplification of AR. AR is a regulator of homeostasis and proteases transcription, such as KLK3 encoding PSA (Prostate-Specific Antigen). AR gene is also a principal therapeutically targeted oncogene in PrCa13. Increased genetic instability resulting in chromosomal rearrangements and high frequency of mutations are deemed indicative of PrCa aggressiveness14 for which there is need of ad hoc treatments15. Recurrent mutations in FOXA1, involved in prostate organogenesis and regulator of AR transcription, have been observed in several populations17. Hundreds of PrCa-associated single nucleotide polymorphisms (SNPs) have been identified by genome-wide association studies (GWAS), including genomic regions within tumor suppressor genes and oncogenes, such as MYC18. However, the functional relationship between most of these SNPs and PrCa pathophysiology is unknown. This missing part of the picture, together with the growing evidence of abnormal transcriptional programs driven by genetic instability, led us to investigate the role of chromatin architecture in PrCa. In particular, we focused on the nuclear proteins potentially involved in transcriptional regulation through the interaction of promoters and non-coding regulatory elements, enhancers.To prove the usefulness of our PENGUIN approach, we applied it to uncover EPINs in a PrCA cell line, androgen-sensitive human prostate adenocarcinoma cells (LNCaP), and validate our findings in comparison to a benign prostate epithelial cell line (LHSAR). PrCa is the 2nd most common cancer in menhttps://penguin.life.bsc.es/.By clustering together promoters with similar EPIN structures with PENGUIN, we identified 273 promoters whose genes are enriched in PrCa fine-mapped SNPs, known PrCa oncogenes, and ChIP-Seq-validated binding sites of transcriptional repressor CTCF. The proteins that populate such EPINs constitute putative PrCa-related factors, some of which have not been previously described to be associated with PrCa SNPs or oncogenes. Moreover, the EPINs detected by PENGUIN enable the characterization of distinct molecular cascades enriched in PrCa SNPs at E-P contacts. These represent potential molecular targets in PrCa that cannot be identified through conventional analytical procedures, such as E-P contacts and GWAS overlap. To explore our results we made a dedicated server available at Our methodology, focusing at the specific EPIN resolution level, reveals a relation between 3D genome conformation and disease phenotype. This relation allows PENGUIN to propose new directions in the molecular characterization of chromatin interactions as well as in the definition of potential targets for molecular screening towards disease treatment.PENGUIN leverages multiple sources of information to build EPINs by grouping enhancers interacting with the same promoter Then, it populates these EPINs with intermediate PPIs and finally clusters the EPINs by structural similarity. The input datasets include 1) high-resolution chromatin interaction maps that capture active promoter-enhancer interactions, highlighting the dynamic nature of gene regulation; (2) tissue-specific physical nuclear protein-protein interactions (PPIs), enabling the exploration of molecular associations within the nucleus; (3) curated binding motifs of protein-DNA interactions, providing insights into the specific interactions between proteins and DNA and (4) gene expression levels, identifying active elements with the interaction networks  promoters (0.88 s.d.) are shared among enhancers, with a maximum of 15 promoters for the same enhancer. To identify communalities and differences among the 4,314 EPINs in LNCaP, we performed an unsupervised, hierarchical clustering based on edge composition , from the ENCODE21 project instead of predictions based on DNA-binding motifs (Methods). We found that the most significantly enriched  interactions with CTCF peaks concentrate in clusters 7 and 8, which we call CTCF+  across 137 PrCa-associated regions fine-mapped from the largest publicly available GWAS summary statistics .The result of the PrCa SNP enrichment is displayed in Fig. Beside CTCF enrichment we observed a significant enrichment in PrCa SNPs. This enrichment is most visible in cluster 8 Fig.  that we Interestingly the observed PrCa SNPs enrichment in the GWAS+ cluster is exclusively due to SNPs in enhancers Table . Our resFOXA1, ZFHX3, CDKN1B, KDM6A, BRCA2, CDH1, CCND1, NKX3-1, BAG4, MYC, GATA2 (Methods). It is important to emphasize that incorporating intermediate proteins into our reconstructed networks significantly improves the enrichment of functional annotations specifically related to PrCa. By incorporating these intermediate proteins, we were able to increase the number of oncogenes related to PrCa in the GWAS+ cluster from 6 to 11. This also led to a higher level of statistical significance in the enrichment analysis, thereby improving the specificity of our results  , WNT Signaling Pathway (KEGG:04310)  and Pathways in cancer (KEGG:05200) with genes such as BCL2L1, MYC, FOS   and intermediate nodes , in line with its greater number of enhancers per promoter , see Supplementary Fig. SWe identified the protein interactions that are enriched in each cluster and estimated the significance of overrepresentation of each edge in a cluster compared to all others (Methods). GWAS+ cluster exhibits the lowest number of promoters and distinctive network characteristics , but not with respect to the ensemble of all EPINs, which indicates that, despite the high number of shared pairwise interactions (67.5% of edges), the wiring of the cluster-specific EPINs are distinctive.We note that the EPINs of GWAS+ cluster have the lowest values of node-level centrality measures, namely betweenness and degree  and defined a region an ‘eQTL hotspot’ when associated to more than 3 genes (Methods). We observed an enrichment of eQTL hotspots across all clusters , but not specifically for cluster GWAS+ . As the selection of an exact number of clusters in a given tree could be considered an important variable in our analysis, we examined various cluster numbers . We investigated the presence of cluster enrichment in GWAS and CTCF (Supplementary Data Despite the high similarity in PPIs between LHSAR and LNCaP cells (Jaccard index of 0.85), their clustering based on H3K27Ac HiChIP data revealed distinct EPINs Fig. . This fiTo further investigate the significance of intermediate PPI networks, we conducted clustering analysis exclusively based on HiChIP interactions. Specifically, we utilized the list of enhancer IDs, denoted by their genomic coordinates, within each EPIN  , which annotates a total of 97 genes (Methods and Supplementary Data Prostate cancer pathway (KEGG:05215) appears to be highly enriched  together with other pathways related to tumors such as Colorectal cancer  Pancreatic cancer  and Breast cancer . KEGG pathway KEGG:04919  is an additional highly enriched pathway . Thyroid hormones have been previously described as modulators of prostate cancer risk28. Pathway KEGG:05200  appears as the fourth most enriched KEGG concept . Other classical tumorigenic pathways, such as Wnt signaling pathway  and TGF-beta signaling pathway  appear to be enriched. In this regard, recent studies analyzed the involvement of Wnt signaling in the proliferation of prostate cancer cells30, as well as the involvement and TGF-beta signaling32.Considering the genes encoding for 477 out of 751 intermediate proteins with annotations for KEGG pathways retrieved using g:ProfilerFurthermore, we examined the functional enrichment of significantly central proteins across all other clusters. This analysis was conducted to facilitate functional comparisons across different clusters . This analysis revealed no enrichments for clusters 1, 2, 4, 5, and 6 . This observation can be attributed to the higher number of central proteins in these clusters  compared to the other clusters . Despite having a similar number of significantly central proteins to cluster 8 (30 proteins), cluster 4 does not show any enrichment.prostate cancer pathway is enriched, adjusted p-value = 2.041e−2; Supplementary Fig. SMoreover, of the clusters presenting enrichments , only cluster 7 presents enrichments related to those observed in cluster 8 . In this analysis, a path in a network is a sequence of edges joining a sequence of nodes connecting the promoter and the enhancers of an EPIN Fig. . We dist34, our hypothesis is that SNPs in enhancers could disrupt the binding of proteins such as TFs having an impact on their interactome.We sought to detect SNPs located in the DNA binding motifs found in the enhancers of the EPINs. Based on previous evidenceGWAS+ cluster that had at least one PrCa SNP in their enhancers. Several of these EPINs have promoters of differentially expressed genes .In Supplementary Data https://depmap.org/, DepMap ID: ACH-000977). These screens provide essentiality scores, which quantify the relevance of specific gene networks to the proliferation of LNCaP cells. In our analysis, we retrieved essentiality scores for genes in prostate tissue from DepMap and compared three distinct gene sets: (1) the genes (EPIN promoters) prioritized in Supplementary Data p-value = 0.0017) for these EPIN promoters that harbor intermediate nodes with SNPs at their genomic location  scores36, to identify 8 SNP-Gene-Disease links with 4 genes  that were also considered in Supplementary Data Additionally, we used the SNP-Gene-Disease linking strategy cS2G from Gazal and colleaguesCASZ1, ID2, SEC11C), and 4 EPINs only differentially expressed in intermediates: MIIP, MRPL14, MYC, TMEM63B . We identify edges that are most enriched in our GWAS+ cluster which could be pointing to essential links between the gene encoding for the node and containing a PrCa predisposing SNP at a particular EPIN. For example, we identify the link between MDM4 containing SNP rs35946963  and TP5337 and between KDM2A containing SNP rs12790261  and BCL638 and ARNT continuing SNP rs139885151  and HIF1A39.We used the SNP paths to link 172 PrCa SNPs falling within the gene bodies of 26 genes of which 7 are known oncogenes . We identified 3 out of 26 promoter EPINs  with suggestive evidence  of association between the PrCa SNP with the PENGUIN-linked promoter EPIN, pointing to the cancer promoting mechanistic action of these variants: gene with SNPs in POU2F2 linked to the EPIN promoter of gene PHGDH ; gene with SNPs in TRIM26 and EPIN promoter of gene RRM2 ; gene with SNPs in MEIS1 and EPIN promoter of gene STOM .We integrated information from pQTL associations between the 172 PrCa SNPs and protein levels (Methods). Two intermediate proteins  were associated with PrCa SNPs falling within the gene encoding for them . This overlapping functionality further complicates the prediction of SNP effects on these intermediate nodes.MYC promoter (chr8:128747814-128748813) is in contact with 73 enhancer regions among which one holds the SNP rs10090154 . This SNP is located in the binding motif of the transcription factor FOXA1 on the MYC EPIN enhancer. The integration of PrCa SNPs information highlights paths in the EPIN of MYC that are particularly compelling in the context of the disease , all with p-values of association with PrCa below 1e-11 . Its EPIN presents up to 11 intermediates affected by PrCa related SNPs, namely TCF4, CTBP2, AR, ARNT, TCF7L2, CDKN2A, NEDD9, ANKRD17, MEIS1, MDM4 and CHD3. The role of GATA2 as mediator of AR signaling in AR-dependent prostate cancer, as well as its role as a potential target for treatment development45 has been previously described, as silencing of the gene is known to affect other relevant genes such as C-MYC and AURKA. Proteins bound to the promoter region include: ZBTB7A, ZBTB33, TCF3, SF1, NR2C2, KLF3, EGR1, E2F1 and CREB1, but most importantly, the EPIN presents AR bound to the enhancer region, which, as we pointed out with MYC EPIN, is the target of several PrCa treatments.We report two additional examples, the EPINs for the promoters of 46. We propose PENGUIN as a molecular approach to study variations in structural characteristics of chromatin loops, establishing a direct link to disease-related phenomena. By integrating the PPI network information, the method offers valuable insights into the underlying mechanisms driving these distinctive features and their relevance to disease progression.Here we introduced the PENGUIN approach that operates on the premise that the EPIN network structure connecting a promoter and its enhancers can serve as a distinctive signature associated with specific functional profiles and diseases. Our assumption is grounded in earlier research that has demonstrated the correlation between 3D loop topology and chromatin state or gene expression35. In this work, PENGUIN uses information on enhancer-promoter interactions using the HiCHIP experiment. There are other ways that this link could be identified. For example, Activity-By-Contact (ABC), a computational prediction method linking an enhancer region to its supposed target gene35, could be used instead of HiCHIP observations. We leave this for future explorations. As we note from Results, as well as for HiChIP interactions, PENGUIN is able to complete and extend the information given by solely using enhancer-promoter interactions. In a recent study, Dey et al.47 demonstrated the benefits of employing strategies that capture both distal and proximal gene regulation in prioritizing autoimmune-disease related genes. Similarly to our findings, the authors found that incorporating enhancer-gene links , and PPI networks are important to link SNPs-to-gene47.Previous computational approaches have linked enhancers to gene36) by combining information across different cell lines. Additionally, previous studies have incorporated PPI networks with GWAS hits to enhance their analysis48. Alternative methods have amalgamated information from 3D chromatin interactions and GWAS SNPs to establish connections between intergenic SNPs and gene regulation in cancer contexts50. These approaches have contributed to unraveling the relationship between genetic variations, chromatin organization, and disease. In contrast, our method takes a unique approach by being completely agnostic to the presence of SNPs. It combines information from PPI networks and enhancer-promoter interactions in a cell-specific context derived from H3K27ac-HiChIP data within a unified framework. This integrative approach allows us to leverage both the protein interaction landscape and the regulatory interactions between enhancers and promoters, leading to a comprehensive understanding of the molecular mechanisms underlying disease.Other previous computation approaches had the goal of linking SNP-Gene-Disease  that could be targeted by drugs, or cis-acting factors  whose DNA binding affinity could be modified through knock-outs via CRISPR for therapeutic intervention. Moreover, unlike traditional TF enrichment analysis which detects general enrichments of particular proteins, PENGUIN can help identify the specific protein cascade potentially disrupted at enhancer loci for the disease under study. Overall, our findings highlight the potential of PENGUIN in unveiling previously unknown gene networks and provide valuable insights into the identification and characterization of biomarkers in various diseases, including PrCa.By utilizing PPI networks, we were able to reveal a distinct set of genes associated with PrCa that would have remained undiscovered using other methods. Notably, the intermediate nodes within this PPI network possess intrinsic properties that can be leveraged for the classification and characterization of E-P chromatin loops. Thus, our study demonstrates the capability of PENGUIN to group genes based on their involvement in PrCa, even in the absence of any prior information. This breakthrough opens up an uncharted avenue towards comprehending and identifying unsuspected biological markers in disease. In particular, the genes identified within the cluster exhibiting the highest enrichment in SNPs associated with PrCa (the GWAS+ cluster) can be considered promising candidate oncogenes or potential partners of oncogenes. It is conceivable that these genes may share “onco-enhancers,” which are enhancers contributing to tumorigenic activity. For instance, PENGUIN can be used to identify To validate our findings, we have used cell-line specific datasets, androgen-sensitive human prostate adenocarcinoma cells (LNCaP) or a normal prostate epithelial cell-line (LHSAR). Each of the sources of information could be directly or indirectly related to the specific cell-lines used in this study: (1) H3K27ac-HiChiP in LNCaP and in LHSAR, (2) prostate-specific PPIs and (3) DNA binding motifs extracted from publicly available datasets but filtered by our cell-type specific interacting 1 kb promoter-enhancer regions and (4) gene expression on cell-line for filtering PPI networks. The comparison of the results in cancer cell-line (LNCaP) to the results in a benign cell line (LHSAR) support our PrCa cell-specific findings. In LHSAR we found a significant association between the obtained clusters and the presence of CTCF, pointing towards the correct classification of EPINs into biologically relevant categories. Strikingly, this same clustering in the benign LHSAR cell-line did not reveal any association to PrCA, neither at the level of PrCa-SNPs, nor at the level of specific oncogenes. Future analyses could explore the use of clustering E-P loops with PENGUIN using other methods and sources for each of these layers. For example, we have used as input enhancer-promoter loops cell-specific H3K27Ac HiChIP experiments , to maximize our true positives in the input data. The input for the PENGUIN clustering approach can also be constituted by enhancer-promoter links measured from other experimental methods aside from HiChIP or even using computational methods. We leave this for subsequent analyses.In this work, we use a targeted approach and use the information on association of SNPs from fine mapping as an annotation to our clusters. Specifically, we identify potential SNP paths from defined PrCa associated regions. SNP paths link genes in a network through a path that either starts from TF binding sites in enhancers or passes through proteins from the intermediate EPIN network that would have SNP in their gene bodies. This approach adds a new dimension in the contextualization of GWAS-associated SNPs using the EPIN looping realm.https://penguin.life.bsc.es/. This platform serves as a tool for convenient investigation into the pathways influenced by SNPs in the context of prostate cancer. It is also intriguing to observe that, while PENGUIN successfully identifies clusters of EPINs significantly associated with PrCA, the gene expression analysis did not reveal any significant trends. At first glance, this observation may appear contradictory to our definitions of EPIN clusters and the core concept of EPIN itself. However, considering the evidence presented by our analysis, we believe that PENGUIN enables the detection of cancer associations with heightened sensitivity compared to traditional differential expression analyses. The ability of PENGUIN to capture intricate associations between EPINs and cancer surpasses the limitations of relying solely on gene expression changes, offering a more comprehensive understanding of the underlying molecular mechanisms involved in cancer development and progression.It is important to mention our primary objective was to shed light on specific links that could be disrupted by PrCa-predisposing variants, such as CTCF bindings that connect promoters to their enhancers, or intermediate structural proteins that play a role in the E-P network. Further investigation is required to gain a comprehensive understanding of the biology and mechanisms underlying these crucial links. For this purpose, and to facilitate the exploration of SNP pathways associated with prostate cancer, we developed a user-friendly web interface accessible at Our analysis comes with some caveats to keep in mind. Firstly, we relied on data from the HiChIP technique for capturing enhancer-promoter (E-P) interactions, protein-DNA interactions from FIMO, and tissue-specific protein-protein interactions from the integrated interactions database (IID). The comprehensiveness of these datasets is inherently limited by the scope and constraints of the underlying databases and methodologies employed. Furthermore, our approach focuses on networks involving proteins with known edges, resulting in a consideration of only those proteins. Additionally, for the purpose of visualization, we have condensed the number of reported proteins and have presented only one intermediate protein (expanded one edge away). Moreover, it is worth mentioning that our study focuses on E-P interactions within a stable environment (LNCaP cells), representing a snapshot in time. While this field is still undergoing active research and further exploration, existing literature suggests that E-P interactions can exhibit minimal and quantitatively small changes in these conditions. Thus, while interpreting our findings, it is essential to consider the limitations of the utilized databases and methodologies, the specific protein selection, the condensed visualization approach, and the stable cellular context in which the E-P interactions were examined.In conclusion, the PENGUIN approach employed in this study to investigate PrCa in LNCaP cells has the potential to be applied to the study of other human diseases, given the availability of similar data. This approach can be extended to explore different scenarios, such as different cell types or combinations of GWAS data, offering a promising avenue for future investigations. For instance, utilizing E-P dataset from another prostate cancer cell line would allow the identification of target genes regulated by enhancers from diverse cell types. These target genes can be prioritized using a genome-wide collection of disease-specific risk SNPs. The networks generated by PENGUIN provide a molecular understanding of the associations involved in cancer-related chromatin dynamics, making them well-suited for training advanced machine learning models like graph neural networks (GNNs). We propose potential intermediates in PrCa that engage in E-P networks within cancer cells and present opportunities for therapeutic intervention. High-throughput functional studies could validate the impact of genetic perturbations on thousands of enhancers simultaneously. As shown in our analysis, leveraging CRISPR-Cas9 technology would enable precise editing of specific genomic regions, facilitating targeted investigations and further elucidating the functional consequences of these genetic perturbations.Trypsinized 10 million cells were fixed with 1% formaldehyde at room temperature for 10 min and quenched. Sample was lysed in HiChIP lysis buffer and digested with MboI (NEB) for 4 h. After 1 h of biotin incorporation with biotin dATP, the sample was ligated with T4 DNA ligase for 4 h and chipped with H3K27ac antibody  after chromatin. Reverse-crossed IP sample was pulled down with streptavidin C1 beads (Life Technologies) treated with Transposase (Illumina) and was amplified with reasonable cycle numbers based on the qPCR with 5-cycle pre-amplified library. Library was sequenced with 150-base pair end reads on the Illumina platform HiSeq® 2500 (Novogene).10 million cells were fixed with 1% formaldehyde at room temperature for 10 min and quenched. Cells were collected in lysis buffer .43 Chromatin was sonicated to 300–800 bp with Covaris E220 sonicator . H3K27ac antibody  was incubated with 40 μL of Dynabeads protein A/G (Invitrogen) for at least 6 h before immunoprecipitation with the sonicated chromatin overnight. Chromatin was washed with LiCl wash buffer  six times for 10 min each time. Eluted sample DNA was prepared as the sequencing libraries with the ThruPLEX-FD Prep Kit (Rubicon Genomics). Libraries were sequenced with 150-base pair end reads on the Illumina platform HiSeq® 2500 at Novogene.RNA from 5 million cells was extracted using the miRNeasy Micro Kit  coupled with on-column DNAse I treatment . RNA sample concentration and its trace were evaluated using a Bioanalyzer RNA 6000 Nano kit  to submit them to Novogene RNA seq dept which prepped RNA library. The library was for RNA seq sequenced to a target depth of 40 M reads on an Illumina platform HiSeq® 2500 with pair-end 150 bp reads. Read alignment, quality control and data analyses were performed using VIPER 57.11  was performed as previously described11. Briefly, HiC-Pro51 was used to map the HiCHiP trimmed reads and extract unique interactions; FitHiChIP52 was used to identify significant interactions with a predefined set of peaks from H3K27ac ChIP-seq in LNCaP to refine accurate anchor ranges. We used q-value < 0.01 and a 5 kb resolution and considered only interactions between 5 kb and 3 Mb as previously described11. In this analysis, we restricted to a stringent global background estimation to reduce as much as possible the number of false-positive interactions. The corresponding FitHiChIP specifications used were “IntType=3”  for the foreground, meaning at least one anchor to be in the H3K27 peak, and “UseP2PBackgrnd = 1” (the peak-to-peak (stringent)) for the global background estimation of expected counts and contact probabilities for each genomic distance for learning the background and spline fitting. We identified 49,565 significant interactions  for LNCaP, and 12,053 for LHSAR.We used Hi-C followed by chromatin immunoprecipitation (HiChIP) targeting H3K27Ac in LNCaP cells (androgen-sensitive prostatic carcinoma cell line) across 5 biological replicates including 1 billion reads as previously described11. Briefly, we first extended anchors by 5 kb on either side; we defined promoter regions around the TSS (+/−500 bases)53 using RefSeq hg19; we defined enhancer regions using regions from H3K27ac ChipSeq in the same cell. Specifically, these were 49,638 and 53,561 enhancer regions, respectively from H3K27ac LNCaP in regular media (union of narrow and broad peaks) and from H3K27ac LHSAR. We note that the enhancer anchors at this stage of the analysis are of length 15 kb, due to 5 kb resolution of the HiChIP data analysis and additional 5 kb padding added to anchors on either side. We labeled the promoters and enhancer regions that overlap either right or left anchors, and considered E-P if only one anchor overlaps a promoter and the other an enhancer region. For LNCaP, out of the 49,565 significant interactions, we considered 18,151 E-P interactions. For LHSAR, out of the 12,052 significant interactions, we considered 5435 E-P interactions. It is important to emphasize that our study relies solely on enhancers defined by our own HiChIP experiments, rather than relying on annotated enhancers or external definitions from ENCODE. We further prioritized E-P interactions to 1 kb regions and discarded from enhancers the 1 kb bins with fewer HiChIP interactions with the promoter (see E-P HiChIP prioritization section). We obtain 30,416 and 4,497 E-P interactions of 1 kb each for LNCaP and LHSAR respectively. The 15 kb original E-P interactions dataset contained a mean of 1.6 (1.3 s.d.) promoter anchors per enhancer anchor  promoters per enhancer). There were 11,127  enhancer anchors in total; 7341  enhancer anchors are contacted by one promoter anchor with a maximum of 21 promoter anchors (15 using prioritized enhancer regions) sharing the same enhancer.We categorized interactions by overlapping anchors with transcription start sites (TSS) and enhancers identified by H3K27ac ChIP-seq as previously described54, Vanilla, or KR55) that all biases  can be corrected together. For this normalization step, we assume that there is a specific bias per any 1 kb genomic loci  and the observed number of interactions (OXY between loci X and Y). In this representation we can define a system of 9 equations involving three 1 kb loci in the promoter (exactly from TSS −1 kb to TSS + 2 kb) and fifteen 1 kb loci on the enhancer side. This system of equations is then solved using Sequential Quadratic Programming (SQP)56. The procedure is repeated in an overlapping window manner along the 15 kb of the enhancer, always against the target 1 kb of the promoter and its two 1 kb neighboring loci. Before the normalization step, we observed a different interaction pattern for interactions below 10 kb . Because of the sparsity of the genomic matrix the normalization did not fully converge . Next we applied the following normalization to our loops dataset, with few modification in order to rescue as much signal as possible: 1- in the promoter site, as our definition of promoter is exact (TSS to TSS + 1 kb), we corrected using the average of the two bins spanning over this 1 kb region 2- on the enhancer site, as most of the 1 kb loci were excluded by the normalization filter we also averaged the ICE bias over the whole region. Even with these modifications, only half the original data was recovered. However, the correlation between genomic distance and number of interactions was significantly improved with respect to raw data. Overall, the correlation value observed with ICE was similar to the one measured for our normalization , with p-value ≤ 1e−4 and q-value ≤ 5e−2 cutoffs. JASPAR contains 810 DNA binding motifs of 640 proteins that overlap the E-P contacts identified with HiChIP.DNA binding motifs were retrieved from JASPAR , an open-access database of curated, non-redundant binding profiles of DBPs (a.k.a. motifs) stored as position frequency matrices (PFMs). To detect the binding motifs, we used FIMO from the MEME-suite software  values were calculated for 20,114 RefSEQ genes. Genes with expression levels above the threshold of 0.003 in both replicates were considered in the entire analysis  in the cell line LNCaP and LHSAR for two replicates using the VIPER pipeline as previously described58. To better contextualize the interactome information, we combined the annotations of the PPIs from IID database with the LNCaP gene expression data. As for the IID annotations, we applied the following selection criteria. First, we selected interactions annotated as “experimental” in the “evidence type” field and identified by at least two independent biological assays reported in the “methods” field. Then, we filtered only for interactions in the prostate or in prostate cancer cells and between nuclear proteins. Finally, we retain proteins whose gene expression levels were FPKM > 0.003 in both replicates (this cut-off removes ~30% of the genes). In total, 14,221 proteins from a pool of 20,111 human protein coding genes meet the gene expression criteria. The combination of the above filtering criteria  resulted in an unweighted network of 31,944 prostate-specific nuclear PPIs among 4295 proteins58.We obtained protein-protein interactions (PPIs) from the Integrated Interactions Database (IID)evidence type” field and identified by at least two independent biological assays reported in the “methods” field. Then, we filtered only for interactions in the prostate cells and between nuclear proteins. Finally, we retain PPIs between proteins whose LHSAR gene expression levels were FPKM > 0.003 in both replicates. In total 29,316 PPIs representing 4363 proteins were used for the EPIN reconstruction in the LHSAR cell line. Jaccard Index between the two resulting PPIs between LNCaP and LHSAR is 0.852.Similarly, for the comparison with the LHSAR cell line we reconstructed the PPI interaction networks with PPIs from the same database (IID) having the following annotation criteria: “experimental” in the “56 comes from the JASPAR database , and PPIs information comes from the IID database  further filtered using LNCaP RNA-seq data .We set up graph-based approach, called Promoter-ENhancer-GUided Interaction Networks (PENGUIN), to reconstruct individual networks of protein interactions that might occur between one promoter (P) and its contacting enhancers (E), that we call E-P protein-protein Interaction Networks (EPINs). To reconstruct the EPINs, PENGUIN integrates information about chromatin contacts, protein-DNA binding, and protein-protein interactions (PPIs). For the case under study in this work , chromatin contacts information comes from H3K27Ac HiChIP of LNCaP cells , protein-DNA binding informationThe reconstruction of EPINs follows these steps: for each E-P contact, (1) DNA binding motifs are detected in the corresponding sequences of promoter and enhancer regions; (2) a subnetwork of PPIs is selected containing all promoter-bound proteins, all enhancer-bound proteins, and all their intermediate interactors, with a maximum of 1 intermediate node between enhancer and promoter bound DNA binding proteins; (3) intermediate interactors are discarded if they only connect promoter-bound proteins or enhancer-bound proteins. Using the provided PrCa information, PENGUIN reconstructed 4314 EPINs consisting of a total of 9141 PPIs among 885 proteins of which 751 are intermediate proteins linking promoter-bound and enhancer-bound proteins.Betweenness is a measure of centrality in a graph based on shortest paths. For every pair of nodes in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through is minimized. The degree of a node in a network is the number of connections it has to other nodes; the degree distribution is the probability distribution of these degrees over the whole network.In several analyses we employed two measures of node centrality, namely betweenness and degree. SigClust2 MonteCarlo procedure59 on our clustering with the following parameters: n_min = 150, alpha = 0.05. We found that the first eight partitions of our hierarchical clustering were very robust with the following Normalized p-values (2-means cluster index): 0, 0, 8e−69, 6e−272, 7e−77, 1e−70, 2e−106 and 2e−77 for clusters 1 to 8 respectively . Then we computed the distance between EPINs by counting the number edges shared over the total number of edges in our predefined universe of edges. Finally, we performed clustering using this distance matrix from all possible combinations of EPIN pairs. The clustering was performed using Ward’s linkage method. Each leaf in the obtained cluster represents a promoter EPIN. In order to assess the robustness of this result we applied the We performed two-sided Fisher’s exact tests on every single branch of the dendrogram representing the obtained hierarchical clustering. We evaluated the enrichment of any feature  in the leaves under a branch of interest compared to those in the rest of the tree. For the enrichment in CTCF binding, we used CTCF peaks from an external dataset but in the same cell line (see CTCF ChiP-Seq peaks). We considered an EPIN to be CTCF-positive (CTCF+), if a CTCF peak was found in a 10 kb region around its promoter and around 10 kb of at least one of its enhancer regions.For the GWAS feature, we require the presence/overlap of a PrCa-associated SNP (see Genome-wide association data) in at least one of the enhancers of an EPIN. Two-sided Fisher’s exact tests were used to calculate the odds ratio (OR) and enrichment p-values for presence of PrCa annotations within the identified clusters.60. The use of each drug was obtained from the Therapeutic Target Database61. We annotated each protein node that is a target of drugs that are assigned as Approved or under Clinical Trials  or Investigable for Prostate Cancer, as PrCa druggable.We extracted information for target druggability from DrugBank21 project (https://www.encodeproject.org/) for the same Genome assembly, hg19 (GEO references: GSM2827202 and GSM2827203). Overlaps of the CTCF binding sites with enhancer and promoter anchors allowed a 10 kb gap between them. Since CTCF ChiP-seq peaks for LHSAR cell line were not available in ENCODE, we retrieved from ChIP Atlas (https://chip-atlas.org/) two distinct sets (GEO references: GSM2825573 and GSM2825574) of CTCF peaks (of same Genome assembly hg19) for prostate epithelial cells at a q-value of 1e−10 (Table PyRanges (see “E-P contacts” section). For both cases, LNCaP and LHSAR, the narrow peaks were considered as the CTCF binding sites.CTCF ChIP-seq peaks for LNCaP cell line were retrieved from ENCODE11 from fine-mapping 137 previously-associated PrCa regions using a Bayesian statistical method PAINTOR62 employing the largest PrCa genome-wide association studies (GWAS) 63. This set was composed of 5412 distinct SNPs (rsid). We will refer to these as PrCa SNPs. Note that this set also includes SNPs that do not reach genome-wide-filters of p-value significance. We illustrate the location of the associated PrCa regions and number of PrCa SNPs in Supplementary Fig. Sp-value = 0.06 and Pearson r = 0.1, p-value = 0.3 for Supplementary data To explore enrichment of SNPs associated to PrCa across the identified clusters, and to identify the SNP paths, we used the previously reported 95% credible set35. In this study, we compared the enhancer-promoter associations derived from H3K27ac-HiChIP in PENGUIN against the predictions made by the ABC method. Although a comprehensive comparison between the two was not feasible, we evaluated the ABC scores for enhancer-promoter links that were integrated into PENGUIN against those that were excluded. We obtained ABC scores for candidate element-gene pairs in the LNCaP cell line from https://osf.io/uhnb4/ (file LNCAP.AllPredictions.txt). Notably, one enhancer can map to multiple genes. Among 14,682 “TargetGene” entries linked to 151,071 genome regions and 8,640,476 enhancer-promoter links, 9567 matched PENGUIN links, while 190,175 did not. The matched ABC links had a higher mean ABC score . To assess support from ABC scores for our final SNP-gene links in Supplementary Data The Activity-By-Contact (ABC) model is a computational tool designed to associate an enhancer region with its potential target gene36. cS2G utilizes epigenetic data from various cell lines to link SNPs to diseases. We obtained the data linking 236 SNPs to prostate cancer (168 genes) from the NHGRI-EBI GWAS catalog . cS2G identified 8 SNP-Gene-Disease links, including 4 genes  matching those in our Supplementary Data We also used cS2G from Gazal et al. to complement our SNP-Gene-Disease linking resultsA path in a network is a sequence of edges joining a sequence of nodes. We detected PrCa SNPs located in the DNA binding motifs in the enhancers, and identified the corresponding SNP paths (linked edges and nodes) for each EPIN promoter. For SNP paths analyses and the web-browser, we used all PrCa SNPs in the 95% credible set. There were 36 PrCa SNPs falling in enhancer binding motifs across clusters 3, 4, 5, 6, 7, 8. To report the most interesting cases in the Tables and Results, we used the subset of those passing genome-wide significance of p-value for PrCa association <5e−8. There were 15 PrCa SNPs falling in enhancer binding motifs across clusters 3, 5, 6, 7, 8.We detected PrCa SNPs falling within genes that encode for intermediate nodes, and identified the corresponding SNP paths (linked edges and nodes) for each EPIN promoter. For SNP paths analyses and the web-browser, we used all PrCa SNPs in the 95% credible set.64. First, we retrieved GWAS data and filtered the traits according to their “umlsSemanticTypeName” as defined in DisGeNet database65 to one of the following: “Mental or Behavioral Dysfunction”, “Neoplastic Process”, “Disease or Syndrome”, “Congenital Abnormality; Disease or Syndrome”, “Disease or Syndrome; Congenital Abnormality”, “Disease or Syndrome; Anatomical Abnormality”. We considered only traits with at least 10 genome-wide-significant SNPs . We mapped the SNP location to prioritized enhancer anchor locations with a window of 10 kb. 104 diseases had SNPs overlaps and 17 of them have more than 10 SNP overlapping  explore whether we could replicate our finding and identify the GWAS enriched cluster using a different source for the PrCa associated SNPs using SNPs extracted from the GWAS catalog instead of fine-mapped SNPs; 2) to compare the GWAS signal for different diseases. We estimated enrichment of SNPs overlapping the enhancers in each of the identified clusters by exploring the NHGRI GWAS Catalog associations23. We grouped enhancers by collapsing when they were separated by less than 20 kb, thereby creating ‘enhancer clusters’. To qualify as a trans-eQTL hotspot, the enhancer clusters had to contain a SNP associated with at least 3 different genes. We quantified the normalized mutual information (NMI) between the hotspot-related enhancer clusters and our 8 EPIN clusters. In order to infer deviation from expected by chance and estimate an empirical p-value, we randomized 10 thousand times the association between each enhancer and its corresponding EPIN cluster and computed the NMI between each randomized EPIN clustering and the observed hotspot-related enhancer clustering. Additionally, we checked if a given cluster was significantly enriched in trans-eQTL hotspots. For this purpose we applied a Fisher test to our pool of enhancers comparing the two contingencies, inside/outside a given cluster, and inside/outside a trans-eQTL hotspot.We retrieved trans-eQTLs reported in the largest meta-analysis with up to 31,684 blood samples from 37 eQTLGen Consortium cohorts in whole blood inbedtools cluster.We defined enhancer hotspots as groups of enhancers separated by less than 15 kb, and identified 3752 enhancer hotspots using 11, the 122 oncogenes are a set of prostate cancer–genes curated from three large-scale PrCa studies that show evidence of somatically acquired mutations, at both localized and advanced prostate cancer, known and recurrently altered in localized prostate cancer and metastatic prostate cancer.We used a previously identified list of 122 Genes (“PrCa_GeneList_Used.csv”) known to be somatically mutated in PrCa oncogenesis (37 out of 4314 promoters considered). As previously describedp-values of the edges and nodes in the eight different clusters. Specifically, each edge or node was tested for presence/absence in a cluster compared to all others. Therefore, one edge or node can be enriched in one or more than one cluster, it cannot be enriched in all clusters.Two-sided Fisher’s exact tests were used to compute odds ratios and i) in a particular cluster (Cj), we define the specificity (S) as the ratio of the mean centrality (C) of Pi within the fraction of n networks belonging to Cj to the mean centrality of Pi within the fraction of m networks not belonging to Cj, NCjWe computed protein importance for each cluster in terms of two network centrality measures: betweenness and degree. For each protein we obtain both betweenness and degree specificity ratios in order to equitably quantify internal protein centrality differences between the clusters. For each of the found clusters we independently estimated the specificity of the observed protein centrality measures (“Betweenness” and “Degree”). For a given protein . Within each of those 1000 random clusters, we computed the corresponding protein specificity, with the p-value representing the probability of finding the protein specificity to be higher or equal to the value computed for the original cluster of interest.p-value < 0.01). EP300 was excluded from the enrichment test as the presence of that node was not significantly enriched . 22 proteins  were found to be significantly specific for both betweenness and degree ratios  and used as input for the functional gene set enrichment analysis presented as Supplementary Data https://github.com/bsc-life/penguin_software/tree/main/Protein_Significance_analysis].We also performed Fisher’s tests to assess enrichment for the presence of the node in the cluster  proteins of networks from GWAS+ cluster, the background is formed by the very limited set of 751 unique intermediate proteins forming the EPINs. We additionally provide, within the very same Jupyter Notebook, comparative dot plots presenting the functional enrichment analysis of significantly central proteins of each cluster under Supplementary Data We set alternative backgrounds for the gene set enrichment analysis, depending on the analysis. For the analysis presented as Supplementary Data p-values correspond to Benjamini-Hochberg correction for multiple testing, with adjusted p-values ≤ 0.05 considered to be significant. Gene set enrichment analysis results are provided for KEGG pathways, Reactome, Gene Ontology, Wikipathways, TRANSFAC, miRTarBase, Human Protein Atlas, CORUM and Human Phenotype Ontology. For the enrichment analysis of significantly specific proteins of the GWAS+ cluster, we provided as input the 22 previously described proteins. For the enrichment analysis of the GWAS+ cluster, we provided as input all genes associated with the EPIN promoters in cluster GWAS+.Reported adjusted http://gepia2.cancer-pku.cn/#degenes, which use the TCGA and GTEx projects databases to compare gene expression between tumor and normal tissues under Limma, both under and over expressed. We used the default thresholds of log2FC of 1 and qvalue cut-off of 0.01. These data covered 84 out of 885 genes encoding for intermediates in PENGUIN and 413 out of 4314 promoter EPINs. DE analysis of RNA-Seq on LHSAR  versus LNCaP was performed as previously described. Briefly, RNA-seq data were processed using the VIPER pipeline66. Reads were aligned to the hg19 human genome built with STAR. FPKM values were calculated with Cufflinks for 20,114 RefSEQ genes included in the VIPER repository. Differential expression analysis was performed with the DESeq2 R package67. 15,650 genes with DE data covered 884 of the 885 genes encoding for intermediates in PENGUIN and 3286 genes out of 4314 promoter EPINs.We integrated data from EPIN promoters with differential gene expression (DE) from two sources. DE analysis on prostate cancer tumor versus normal was downloaded from GEPIA: p-value ≤ 0.01. For the LNCAP/LHSAR dataset, we could compute a Fisher test of enrichment of differentially expressed genes encoding for intermediate proteins within each EPIN promoter versus within the SNP paths (we could not compute this for the GEPIA since we did not have the full dataset of covered genes). The genes that were not passing these filters were considered non-DE and the genes not covered by the two datasets were excluded from the enrichment analysis described next. For each EPIN we calculated the fraction of DE intermediates within the SNP paths and we estimated the enrichment of those compared to the fraction of DE intermediates in the full EPIN network.We annotated whether the EPIN promoters themselves and the genes encoding the intermediate proteins in our data were DE using either of the two databases. We considered as DE those genes passing |log2 fold change|>1 and adjusted To find the enrichment of DE genes in SNP paths (PrCa SNPs in intermediate proteins) compared to those in the entire EPIN, we computed as enrichment the ratio of Fraction1 / Fraction2, where:Fraction1 = (number of DE intermediates within SNP paths)/(number of covered intermediates within SNP paths), andFraction2 = (number of DE intermediates the EPIN)/(number of covered intermediates in the EPIN).We report the EPIN genes passing enrichment (“enrichment_DE_deseq_SNP.bs.DBP.path”) > 1.68 and annotated with pQTL association the SNPs we identified falling either in enhancer binding sites or in node genomic locations. The deCODE pQTL summary statistics data contained information on 4907 proteins and 186 . 808 out of the 4314 genes promoters (“EPIN_promoters”) and 278 out of the 885 gene intermediates  have information on associations with their respective coded proteins covered by the pQTL deCODE data.We downloaded summary statistics with genome-wide association between SNPs and 4907 proteins reported in the deCODE studyGene Effect and Gene Dependency metrics were downloaded from the DepMap portal . We used both the RNAi69 and CRISPR70 datasets.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1–12Reporting SummarySource Data**


Human nucleotide-binding oligomerization domain (NOD)-like receptors (NLR) include a large family of proteins that have important functions in basic physio-pathological processes like inflammation, cell death and regulation of transcription of key molecules for the homeostasis of the immune system. They are all characterized by a common backbone structure (the STAND ATPase module consisting in a nucleotide-binding domain (NBD), an helical domain 1 (HD1) and a winged helix domain (WHD), used by both prokaryotes and eukaryotes as defense mechanism. In this review, we will focus on the MHC class II transactivator (CIITA), the master regulator of MHC class II (MHC-II) gene expression and the founding member of NLR. Although a consistent part of the described NLR family components is often recalled as innate or intrinsic immune sensors, CIITA in fact occupies a special place as a unique example of regulator of both intrinsic and adaptive immunity. The description of the discovery of CIITA and the genetic and molecular characterization of its expression will be followed by the most recent studies that have unveiled this dual role of CIITA, key molecule in intrinsic immunity as restriction factor for human retroviruses and precious tool to induce the expression of MHC-II molecules in cancer cells, rendering them potent surrogate antigen presenting cells (APC) for their own tumor antigens. CIITA is a peculiar NLR member.•CIITA acts as master controller of adaptive immunity.•CIITA acts as crucial molecule of intrinsic immunity as restriction factor against human retroviruses. Human nucleotide-binding oligomerization domain (NOD)-like receptors (NLR) constitute a rather heterogenous family of proteins that have evolved both in eukaryotes and prokaryotes as basic elements of defense against aggressors . Most ofCIITA has a tripartite domain structure similar to that of other NLR proteins with an N-terminal caspase-like activation and recruiting domain (CARD)-like and an acidic activation domain (AD), a core signal transduction ATPase with numerous domains (STAND) and a leucin rich domain (LRR) . The CARCIITA expression is under the control of several promoters that show cell type specificity A 14]. L. L14]. LCIITA acts as a master regulator of not only polymorphic MHC-II genes but also accessory genes including invariant chain, HLA-DM and HLA-DO, involved in antigen processing and presentation ,24. CIITRegulation of CIITA gene expression has been extensively studied and CIITA promoter knock-out mice have significantly contributed to unveil the complexity of CIITA gene transcription. CIITA gene expression is regulated by four independent promoters, named promoter I, II, III and IV, that can be individually activated in a cell type- and stimulus-specific manner. Three of these promoters are highly conserved between the human and mouse gene . In contrast, pII has only been found in the human gene where it has been shown to display only very low transcriptional activity. The different promoters do not share sequence homology and are not co-regulated . Each ofEpigenetic mechanisms of CIITA silencing, particularly in tumor cells, may represent a way to avoid tumor antigen recognition by the immune system and thus elusion from anti-tumor immune response.Although CIITA is the major regulator of MHC class II gene expression other factors may influence the expression of these genes and their products. First, at transcriptional level, mutation in component of the RFX complex result in lack of expression of MHC class II genes . Second,During evolution, immunity against pathogens has progressed through a series of continuous adaptations of both the host versus the pathogen to counteract its virulence and of the pathogen versus the host to elude its mechanisms of defense. In most cases, particularly during virus infection, these mechanisms of mutual adaptation have allowed persistence of both species because prevalence of one versus the other would have had as result elimination of either the host or the pathogen . As far A third form of immunity, designed intrinsic immunity, acts in parallel to the two major forms of protection, is mostly focused against viruses and relies on intracellular molecules either constitutively expressed or induced by mediators of innate immunity, and defined as restriction factors (RFs). The function of RFs is to counteract distinct steps of virus life cycle ,51. AlthSimilarly to HIV-1, CIITA acts as restriction factor for the human T cell leukemia virus HTLV-1, the first discovered human retrovirus responsible of a still untreatable form of blood cancer designated Adult T cell Leukemia/Lymphoma (ATL) , and forThe finding that CIITA directly interacts with Tax-1 was the reason to investigate the other crucial aspect of HTLV-1-host interaction, that is the involvement of HTLV-1 in the oncogenic process leading to ATL ,61. It iM. Tuberculosis Ag85 protein to antigen-specific CD4+ T cell clones ,72M. Tubl clones . In deepl clones . Second,l clones . Third, l clones . All theor cells . These ror cells . Other cor cells . They alor cells ,79. Thisor cells B 80].Fi.FiM. TubThe discovery of MHC-II transactivator CIITA has marked a crucial advance in the understanding of the basic mechanisms controlling adaptive immunity. Its role on the expression of MHC-II genes has disclosed the complex mechanism through which CIITA acts as coordinator and integrator of expression at several levels including the assembly and binding of transcription factors to the MHC-II promoter, the remodeling of chromatin as well as the facilitation of the initiation of transcription and the elongation of primary transcripts. The fine regulation of its expression has helped to identify the existence of several promoters involved in its cell-specific and developmental-specific expression so crucial for the various functions of modulation of adaptive immunity. Its most recently discovered function as restriction factor of retrovirus replication and spreading has allowed to identify its unprecedented role as mediator of intrinsic immunity and thus as a rather unique player within the family of NLR. May be nature has selected this novel function of CIITA to increase the fitness of human species. Its usage as a tool to induce expression of MHC-II genes and molecule in tumor cells has opened new possibilities to increase recognition of tumor antigens, their characterization and the possibilities to envisage realistic strategies of anti-tumor vaccination. This in turn has challenged the dogma that professional APC such as dendritic cells and macrophages are the exclusive cells capable to prime Th cells in adaptive immunity.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This article comments on:Zhao W, Wang S, Li W, Shan X, Naeem M, Zhang L, Zhao L. 2023. The transcription factor EMB1444-like affects tomato fruit ripening by regulating YELLOW-FRUITED TOMATO 1, a core component of ethylene signaling transduction. Journal of Experimental Botany 74, 6563–6574. Red, orange, yellow, green, striped: many colourful and tasty tomatoes are currently on the market all over the world. These fascinating varieties are obtained by targeting specific units of a complex gene regulatory network (GRN) of transcription factors, which work in concert to regulate the downstream effectors of tomato fruit ripening. Through a multi-angle approach,unravel the regulatory role of the bHLH transcription factor EMB1444-like in the process of tomato fruit ripening, expanding the potential for developing elite tomato varieties that combine high-quality organoleptic and shelf-life characteristics.Solanum lycopersicum) are climacteric, meaning that their ripening occurs through a self-sustained process that relies heavily on the synthesis and perception of the gaseous phytohormone ethylene and is characterized by a high level of cellular respiration in the fruit , which encodes a homolog of the Arabidopsis EIN2, which stabilizes EIN3 and other EIN3-like proteins to sustain the ethylene cascade. In this issue, SlEIN2/YFT1.One of the central molecular players in tomato ethylene signalling is the gene YFT1 promoter. As a further step, an elegant experimental design based on yeast one-hybrid analysis and confirmed by electromobility shift assay was used to screen the promoter of YFT1 to pinpoint the exact motif to which EMB1444-like can bind. This motif turned out to be an E-box (CACTTG), which is known to be one of the preferred DNA sequences for bHLH binding. To verify whether EMB1444-like affects the expression of YFT1 in planta, the latter was measured by qPCR in EMB1444-like knockdown lines (named sledl lines). As expected, sledl lines showed differential expression of YFT1, which appeared to be down-regulated relative to wild-type tomato fruits. By contrast, the expression of EMB1444-like was unchanged in yft1 mutant lines. As a further piece of evidence supporting the hypothesis that EMB1444-like affects the downstream ethylene cascade, sledl lines showed decreased expression of ethylene biosynthesis genes such as ACC SYNTHASE 2/4 (ASC2/4) and ACC OXIDASE 1 (ACO1), and ethylene signalling genes including APETALA 2a (AP2a), ETHYLENE-INSENSITIVE3-like 3 (EIL3) and NEVER RIPE (NR). As a consequence, the fruits of sledl lines emitted less ethylene than wild-type fruits, confirming the role of EMB1444-like as a positive regulator of the ethylene cascade.EMB1444-like was selected as an important regulator of fruit ripening because of its expression profile and considering the results of an exploratory luciferase assay, which revealed that the presence of EMB1444-like was able to increase the expression of luciferase fused to the YFT1, the transcription factor WRKY32. EMB1444-like and WRKY32, both of which are positive regulators of YFT1, and consequently of tomato ripening, have an additive effect. The expression of EMB1444-like and WRKY32 is not affected in yft1 lines, which produce less ethylene, and therefore their expression appears to be unaffected by any ethylene-mediated feedback loop. This observation could be explained by considering EMB1444-like and WRKY32 as upstream regulators of the ethylene cascade. In fact, sledl lines, as well as WRKY32 knockdown lines (named slwr), produce less ethylene compared with their wild-type counterparts, suggesting that the ethylene cascade operates downstream of these transcription factors. These results differ from what has been observed for the MADS-box transcription factors SlFUL1 and SlFUL2. In that case, although displaying ripening phenotypes, FUL1 and FUL2 RNAi lines maintain an unaltered ethylene signalling cascade and ethylene production, and these transcription factors have been therefore identified as ethylene-independent regulators of fruit ripening  and confer the red colour of ripening tomato fruits. Accordingly, when EMB1444-like is silenced, the expression of carotenoid biosynthesis genes decreases. As a result, the fruits of sledl lines are green even 54 days after flower anthesis, a stage at which wild-type fruits are deep red and ripe.The downstream effect of EMB1444-like on fruit ripening is evident: per se a stepping stone towards building a complete picture of it, the phenotypes of fruit-ripening mutants can sometimes be misleading, and therefore any evaluation of the functional role of their causal transcription factors needs careful consideration. Spontaneous tomato mutants with a similar stunted fruit-ripening phenotype to those observed in sledl and slwr lines have served as the basis for the molecular study of tomato fruit ripening. Among these, ripening inhibitor (rin) and non-ripening (nor) mutants showed severe ripening alterations, and therefore the causal mutated transcription factors  have been long considered master regulators of fruit ripening (rin and nor lines, among others, are characterized by dominant negative mutations, which block the function of other transcription factors as well, usually when protein complexes are formed. Moreover, RNAi can trigger non-specific silencing, increasing the phenotype severity. On the other hand, the adoption of frameshift mutations obtained by using CRISPR-Cas can lead to masked phenotypes because of compensatory mechanisms that increase the transcription of other, partially redundant transcripts (Although the identification of new genes of the tomato fruit-ripening GRN is As is often the case, things are more complicated than they appear. Recent studies have contributed to unravelling the complexity of the tomato fruit-ripening GRN, a robust network of numerous transcription factors with partially redundant functions (


The KEAP1–NRF2 pathway plays a central role in the regulation of redox balance and cellular metabolism. Although NRF2 has been extensively investigated in various disease states, including cancer, there is a paucity of knowledge regarding the role of NRF2 during embryonic development. Here, we demonstrate that NRF2 activation induces lethality that is preceded by liver abnormalities and accumulation of lysosomes. Moreover, we find that NRF2 activates the master regulators of lysosomal biogenesis, TFEB/TFE3. These studies highlight a critical role for the maintenance of lysosomal homeostasis during embryonic development and, more broadly, suggest that aberrant lysosomal biogenesis may be a hallmark of NRF2-driven pathologies. The maintenance of redox and metabolic homeostasis is integral to embryonic development. Nuclear factor erythroid 2-related factor 2 (NRF2) is a stress-induced transcription factor that plays a central role in the regulation of redox balance and cellular metabolism. Under homeostatic conditions, NRF2 is repressed by Kelch-like ECH-associated protein 1 (KEAP1). Here, we demonstrate that Keap1 deficiency induces Nrf2 activation and postdevelopmental lethality. Loss of viability is preceded by severe liver abnormalities characterized by an accumulation of lysosomes. Mechanistically, we demonstrate that loss of Keap1 promotes aberrant activation of transcription factor EB (TFEB)/transcription factor binding to IGHM Enhancer 3 (TFE3)-dependent lysosomal biogenesis. Importantly, we find that NRF2-dependent regulation of lysosomal biogenesis is cell autonomous and evolutionarily conserved. These studies identify a role for the KEAP1–NRF2 pathway in the regulation of lysosomal biogenesis and suggest that maintenance of lysosomal homeostasis is required during embryonic development. Maintenance of redox and metabolic homeostasis during growth and differentiation is critical for embryonic development. Studies performed in the 1930s, by Joseph Needham, were among the first to identify that cellular metabolism and redox state are dynamically regulated during embryonic growth 1. This isNRF2 is a transcription factor that orchestrates the cellular response to redox imbalance . Under b12NFE2L2), and these patients manifest with a multisystem disorder that involves a failure to thrive, immunodeficiency, and neurological symptoms  family of transcription factors, which includes TFEB and TFE3. Importantly, these features were also observed in mammalian cells, which illustrates the cell autonomous and evolutionarily conserved nature of this NRF2-dependent TFEB/TFE3 pathway.SI Appendix, Fig. S1 A–C) (keap1a and keap1b) that act in a redundant manner with respect to regulation of Nrf2 (keap1a (crkeap1a), keap1b (crkeap1b), or keap1a/b (crkeap1a/b) were initially generated in a Nrf2 reporter zebrafish line [Tg(gstp1:EGFP)] in which EGFP expression is driven by the ARE-containing gstp1 promoter. No significant changes in Nrf2 reporter activity were observed following loss of keap1a or keap1b . Consistent with the lack of Nrf2 reporter activity, RNA-Seq analysis identified a limited number of differentially expressed genes (DEGs) in the single KOs . The molecular features associated with Keap1 deficiency preceded a marked loss in survival during postembryonic development  . Due to  of Nrf2 . Crispanr keap1b A. In contivation A. Nrf2 angle KOs B. In conb larvae B. Hypergb larvae C. Moreovb larvae B and D. b larvae E. Consiselopment G, which elopment . These dkeap1a/b zebrafish revealed Nrf2 reporter activity to be highest in the liver  liver, the crkeap1a/b liver appeared as a single droplet-shaped lobe (lf:NLS-mcherry)) was utilized to specifically examine Nrf2 activity in hepatocytes. While liver volume and hepatocyte number were unchanged in crkeap1a/b larvae, a dramatic increase in Nrf2 activity was observed in hepatocytes . Flow cytometry analysis confirmed elevated Nrf2 reporter activity in hepatocytes isolated from crkeap1a/b larvae . Multiphoton analysis of crkeap1a/b larvae on a combined Nrf2 and vascular reporter background [Tg] confirmed vasodilation . Importantly, Nrf2 activity was not observed in the vasculature . Collectively, these studies illustrate that hepatocytes are sensitive to loss of Keap1.Additional analysis of crhe liver A. In addped lobe A Next, ab larvae D. Histolkeap1a/b larvae was the KEGG lysosomal signature . To directly visualize lysosomes in the liver, a hepatocyte-specific lysosomal reporter [Tg] was generated. In this line, a dramatic increase in lysosome abundance was observed in response to loss of Keap1 . Moreover, loss of p53 did not impact ß-gal staining . Further analysis of the RNA-Seq datasets revealed an absence of senescence-associated secretory phenotype factors among the DEGs observed in crkeap1a/b larval livers and whole larvae . These data suggest that increased lysosomal biogenesis occurs as a consequence of Keap1 loss.Among the most enriched pathways observed by GSEA in crignature A. Indeedignature B. The lyignature C. Transmof Keap1 D and E. of Keap1 , crkeap1he liver F and G. he liver , it is ahe liver . Therefohe liver , 2522)..keap1a/bflcn (crflcn) on the hepatocyte-specific lysosomal reporter background phenocopied the increase in lysosome abundance and activity observed in crkeap1a/b larvae . Interestingly, loss of Flcn or Keap1 was associated with mortality at a similar developmental stage, suggesting that dysregulation of lysosomal biogenesis may contribute to postembryonic lethality .Expression of lysosomal genes, and regulation of lysosomal biogenesis, is controlled by members of the MiT/TFE family of transcription factors, which include TFEB and TFE3 . Loss ofkeap1a/b larvae  background . Loss of Bach1 had no effect on Nrf2 reporter activity in the WT background . ß-gal staining confirmed a significant increase in lysosomal biogenesis in Bach1 DKO; crkeap1a/b larvae  as one of the most enriched motifs in crb larvae C. BACH1 b larvae , 30. To ckground A. RNA-Seckground B. Howeveckground A, and thckground B and C. b larvae D and E. b larvae F. Collecnfe2l2fh318) . In addition, the increase in lysosomal gene expression observed in crkeap1a/b larvae was suppressed by loss of Nrf2 activity  , 31. Kea8 , 31. Kactivity A. Moreovckground B–D. ThesSI Appendix, Fig. S6A). Moreover, loss of KEAP1 was associated with a significant increase in ß-gal staining . To examine the role of TFEB/TFE3 in KEAP1 KO cells, RNA-Seq analysis was performed in the absence or presence of siRNA-mediated knockdown of TFEB and TFE3 . NRF2 target genes and lysosomal genes were up-regulated in KEAP1 KO HepG2 cells . Moreover, enrichment of the NRF2 pathway signature and the KEGG lysosome signature was revealed by GSEA . Consistent with increased lysosomal gene expression, elevated nuclear localization of TFEB/TFE3 was observed in KEAP1 KO cells . Collectively, these data indicate that KEAP1 loss induces a TFEB/TFE3-dependent lysosomal program in a cell-autonomous and evolutionarily conserved manner.To determine whether regulation of lysosomal biogenesis by KEAP1 occurs in a cell-autonomous and evolutionarily conserved manner, the impact of KEAP1 loss was also examined using mammalian cells. Given that KEAP1 loss primarily impacted the liver in vivo , a hepatstaining B and C.  by GSEA F. ImportKO cells G. AnalysThe NRF2 pathway is recognized as an important regulator of redox homeostasis and cellular metabolism. There is clear evidence that NRF2 can regulate both anabolic  and catabolic processes  –34, althLysosomes have traditionally been viewed as hubs of catabolic activity, harboring a plethora of proteases and hydrolases involved in degrading a range of macromolecules . More re393947G6PD, PGD, ME1, and IDH1) generate NAPDH. Elevated levels of reducing equivalents, such as GSH and NADPH, can promote reductive stress  in their promoter regions. Interestingly, TFEB and TFE3 have been shown to regulate the transcription of genes involved in FAO in vivo , 58. ThiBACH1 has historically been shown to act as a transcriptional repressor, suppressing expression of NRF2 target genes involved in the antioxidant response –63. HoweIn conclusion, this study has revealed that KEAP1 loss promotes lysosomal biogenesis. Mechanistically, we provide evidence that NRF2 activates a conserved TFEB/TFE3-dependent program. Given that KEAP1 and NRF2 are frequently mutated in cancer , it willgstp1:EGFP) (fabp10a:NLS-mCherry) (tp53M214K) (nfe2l2fh318) (kdrl:HsHRAS-mCherry)S916 (lf:Lamp1-mGreenLantern)uom302 and Tg(lf:mKate2-CAAX)uom303. Compound bach1a/bach1b mutants, herein referred to as Bach1 DKOuom301, were generated by crossing crbach1a  and crbach1b (S16*bach1b) mutants generated using the CRISPR/Cas9 method described below. For all experiments, clutch siblings were used as controls and all embryos and larvae were maintained at 28 °C throughout development. All zebrafish experiments were performed at the larval stage and therefore the sex of the organism was not yet determined. All other zebrafish experimental methods are provided in SI Appendix.Zebrafish were maintained according to institutional Animal Experimentation and Ethics Committee (AECC) guidelines . Previously described lines used in this study: Tg(p1:EGFP) , Tg(fabpmCherry) , p53 KO 53M214K) , Nrf2 KOl2fh318) , Tg(kdrlrry)S916 . Transge2. Cell lines were authenticated using short tandem repeat profiling and routinely assayed for mycoplasma contamination. All other cell culture experimental methods are provided in SI Appendix.HepG2 cells were purchased from CellBank Australia and maintained in MEM with NEAA  supplemented with 1 mM sodium pyruvate, 2 mM glutamine, and 10% fetal bovine serum. In addition, 293T cells were acquired from American Type Culture Collection and maintained in Dulbecco's Modified Eagle Medium  supplemented with 1 mM sodium pyruvate and 10% FBS. Cells were maintained a humidified incubator at 37 °C with 5% COt test. One-way ANOVA with the Holm–Sidak method for multiple comparisons was used for comparison of more than two groups. Survival curves were compared pairwise and the statistical significance was determined using the Gehan–Breslow–Wilcoxon method. All immunoblots are representative of results from at least three independent experiments. All other statistical details of experiments can be found in the figure legends.Statistical analyses were performed with Prism 9 software (GraphPad Software). All statistical analyses for data comparing two groups were performed with an unpaired Student’s Appendix 01 (PDF)Click here for additional data file.


The coarse partitioning of the genome into regions of active euchromatin and repressed heterochromatin is an important, and conserved, level of gene expression regulation in eukaryotes. Repressor Activator Protein (Rap1) is a transcription factor that promotes the activation of genes when recruited to promoters, and aids in the establishment of heterochromatin through interactions with silencer elements. Here, we investigate the role of Rap1 when bound to a promoter in silent chromatin and dissect the context-specific epigenetic cues that regulate the dual properties of this transcription factor. Together, our data highlight the importance of protein–protein interactions and local chromatin state on transcription factor function. Saccharomyces cerevisiae, heterochromatin is formed through interactions between site-specific DNA-binding factors, including the transcriptional activator Repressor Activator Protein (Rap1), and Sir proteins. Despite an understanding of the establishment and maintenance of Sir-silenced chromatin, the mechanism of gene silencing by Sir proteins has remained a mystery. Utilizing high-resolution chromatin immunoprecipitation, we found that Rap1, the native activator of the bidirectional HMLα promoter, bound its recognition sequence in silenced chromatin, and its binding was enhanced by the presence of Sir proteins. In contrast to prior results, various components of transcription machinery were not able to access HMLα in the silenced state. These findings disproved the long-standing model of indiscriminate steric occlusion by Sir proteins and led to investigation of the role of the transcriptional activator Rap1 in Sir-silenced chromatin. Using a highly sensitive assay that monitors loss-of-silencing events, we identified a role for promoter-bound Rap1 in the maintenance of silent chromatin through interactions with the Sir complex. We also found that promoter-bound Rap1 activated HMLα when in an expressed state, and aided in the transition from transcription initiation to elongation. Highlighting the importance of epigenetic context in transcription factor function, these results point toward a model in which the duality of Rap1 function was mediated by local chromatin environment rather than binding-site availability.In  Cellular identity can be defined by the array of expressed and repressed genes in a cell. Thus, two cells with identical genomes can exhibit vastly different phenotypes. Due to the wide variety of expression patterns needed for normal development and function, eukaryotic gene expression is controlled by many different processes ranging from gene-specific combinatorial effects of transcription factors to domain-wide compaction or accessibility of chromatin. Further, modifications to chromatin promote differential regulation via both the recruitment and restriction of transcriptional activators and repressors . The coaSaccharomyces cerevisiae, heterochromatin is controlled by the Silent Information Regulator (Sir) proteins, which assemble at the cryptic mating-type loci, HML and HMR, and the telomeres  acts as an early nucleation site of silencing  is the presence of a binding site for Repressor activator protein 1 (Rap1) (In elomeres –5. Studyelomeres . The canelomeres –10. The ilencing . Common 1 (Rap1) , 13.HML, HMR, and telomeres by binding to silencers and recruiting Sir proteins in combination with two other silencer-binding proteins, the Origin Recognition Complex (ORC) and the transcription factor ARS-binding factor 1 (Abf1)  –30. How Despite decades of research utilizing Sir-silenced chromatin as a model for heterochromatic gene repression, the fundamental question regarding the mechanism of Sir-based silencing has remained inadequately answered. In the most broad-scale model, Sir proteins form a macromolecular complex that blocks, wholesale, protein–DNA interactions in silent chromatin, including transcription factors accessing their cognate-binding sites. This model is supported by evidence of expression state–dependent cleavage and modification of enzyme recognition sites in silent or active chromatin , 32. A mMAT and the auxiliary HML allows a unique opportunity to study the role of Rap1 in both silent and expressed contexts, and how local chromatin state affects function. The promoters of MATα and HMLα are identical in sequence and, thus, each contains a Rap1-binding site. Rap1 binding at MAT is responsible for activation of α1 and α2  and wherein the α2 coding sequence at HML was replaced by the coding sequence for the Cre recombinase (hmlα2∆::Cre), thus avoiding confounding sequence identity. Similarly, to characterize enrichment at MATα, we performed experiments in strains lacking both HML and HMR (hml∆ hmr∆). In both cases, only one copy of the α1/α2 genes was present.Previous studies addressing the mechanism of silencing have yielded contradictory and sometimes paradoxical results, due in part to the low-resolution techniques used at the time –36, 39. the loci A. To ensHML and MAT . Utilizing Chromatin Immunoprecipitation followed by next-generation sequencing (ChIP-seq), we determined that Rap1 was in fact bound to the promoter of HML in silenced chromatin .Strong enrichment of Rap1 at the karyotes –36, 39. HML in silenced chromatin . As an internal positive control, we mapped enrichment of each protein at MATα, where the recruitment of each followed expected patterns; the initiation factor Taf1 was localized over the promoter, while the RNA Pol II subunit Rpb3 was enriched over the gene bodies . These data revealed that Sir-silenced chromatin was not entirely refractory to protein binding, but specifically to RNA pol II transcription machinery. In sum, we found robust recruitment of the endogenous activator to native Sir-silenced HML and narrowed the step at which silencing occurs to a point between recruitment of the activator, Rap1, and the formation of the preinitiation complex.TFIID is one of the first factors recruited to transcription initiation sites –42. A suhromatin C. It wase bodies C and D. HML promoter in silenced chromatin, but TFIID was not, we investigated the possibility that promoter-bound Rap1 contributed to silencing the locus. We generated a strain with a two base-pair mutation in the Rap1-binding site at the promoter known to strongly decrease expression of α1 αnd α2 . Introduction of this binding site mutation did not affect enrichment of Rap1 at other loci genome-wide .Given that Rap1 was enriched at the 1 αnd α2 , 38. ThiHML, we introduced the two base-pair mutation into a previously developed strain that monitors loss-of-silencing events  (SIR1 gene (sir1∆) in a strain with the rap1 bs mutation and the CRASH background. sir1∆ cells exist in a bimodal expression state at HML . We utilized flow cytometry to quantify changes to the silenced domain observed with the CRASH assay , indicating that promoter-bound Rap1 actively contributed to silencing at HML. Together, these contrasting results showed that Rap1 could perform opposing functions dependent on nuances in the local chromatin environment.To evaluate the impact of the Rap1-binding site mutation (rap1 bs mutant) on silencing at g events . This asig. S3B) . Silenciig. S3B) , 46. To e at HML . Recent he locus , therebyectoring B, BottomSH assay . Surprispromoter C. Additisir1∆ cells harboring the rap1 bs mutation, Sir3 occupancy was reduced in rap1 bs mutant cells, though not to the same degree as in sir4∆ cells . We also found a substantial decrease in Rap1 occupancy at the silencers HML-E, HML-I, and HMR-E, which each have a Rap1-binding site important for silencing, despite sequence-specific recruitment of Rap1 to these loci preceding Sir protein recruitment in canonical models for the establishment of silencing . To test whether diminished occupancy of Rap1 at HML in sir4∆ cells was due to a disruption of the interaction between Sir4 and the C-terminal domain of Rap1, we performed ChIP-seq in sir3∆ cells and found the results to be nearly identical . In support of this cooperativity, Rap1 enrichment at MATα was similar to that at unsilenced HML and less than the enrichment at HML in silenced chromatin . As expected, Rap1 binding at MATα did not vary based on the availability of the Sir complex . Collectively, these data implied that cooperative interactions existed between Sir proteins and Rap1 . Together these findings suggested a role for Rap1 in promoter escape at actively transcribed HML, though this finding may be unique to the promoter architecture of the locus.To understand which step of transcription Rap1 contributed to the most, we performed ChIP-seq of tagged transcription-associated proteins in pression A, enrichecreased C and D. ecreased C and D. ecreased . Furtherecreased B. TFIIB ecreased . HoweverSI Appendix, Fig. S5A)  . We characterized the fits and apparent residence times for 377 Rap1-bound loci across the genome, in replicate, at which Rap1 binding decayed over time . The residence time of Rap1 at the promoter in silent (HML) and active (MAT) chromatin was similar, although initial Rap1 enrichment was decreased at MAT as seen previously  . Whereasaset S2) . Due to  species , 55. NorQ method  and calceviously B. The dweviously C and D. HML did not appear to contribute to, or result from, differences in Rap1 apparent off-rate, we investigated which other context-specific cues contributed to this metric. Using cutoffs similar to those previously described . To assess the contribution of Rap1 apparent dwell-time to function in transcriptional regulation, we selected Rap1 enrichment peaks that were within 500 bp upstream of open reading frames and assigned peaks to these respective genes. This dataset was then subdivided into quartiles based on residence time: shortest (n = 95), short (n = 95), long (n = 93), longest (n = 94).As function in silencing or activation at escribed , we refir = 0.45, P-value < 2.2e-16), but much of the variation remained unaccounted for. Rap1 targets vary greatly in expression. Indeed, ribosomal protein genes whose transcripts make up nearly 60% of total mRNAs in the yeast cell , and a significant difference in Taf1 occupancy between the shorter and longer Rap1 dwell times . To investigate further the role of Rap1 binding dynamics in transcription regulation, we generated summary distribution plots  of nascent transcripts associated with the Rap1-bound loci genome-wide. Utilizing published datasets  and apparent dwell-time B. There ast cell C 61). P. Pr = 0.ast cell . Utilizidatasets , 58, we datasets E. These Many studies identify Rap1 as an important modulator of nucleosome-free regions through interactions with the chromatin remodeling complex RSC , 62, 63.This study explored the enigmatic ability of Rap1 protein to function in both gene activation and repression. Earlier work discounted the possibility that the function of Rap1 was determined by subtle differences in its recognition sequence . Other wHMLα1 and HMLα2, served opposing functions depending on the state of the surrounding chromatin. The HML promoter-binding site was robustly enriched for Rap1 in the presence of Sir proteins. In contrast, transcription initiation factors were undetectable . Taken together, we inferred that the enhanced binding of Rap1 in the presence of Sir proteins was consistent with an increase in the frequency of Rap1 binding, or on-rate, in this context. However, inferences regarding off-rate through the anchor-away method are limited by the method affecting primarily the loss of free Rap1, and any changes in on-rate are speculative. Nevertheless, our dwell-time data were consistent with those described by a related technique that reports on local competition between Rap1 molecules . Rap1 binding is necessary for downstream recruitment of the chromatin remodeler complex RSC to maintain a nucleosome-depleted region .Rap1 occupancy is an important determinant of the size and patterning of the nucleosome-depleted regions to which it binds, while removal of Rap1 may result in remodeling of the nearby chromatin , 70–72. 70d region . Withoutcription . Our finSI Appendix, Fig. S5G). Furthermore, nascent transcript abundance correlated with Rap1 dwell-time  using standard genetic techniques and CRISPR-Cas9 technology -Trp to minimize background fluorescence. Colonies were incubated for 5 to 7 d at 30 °C, then imaged as described in ref. SI Appendix, Supplementary Methods).This experiment was carried out as described in refs. SI Appendix, Supplementary Methods). Each reaction was performed in triplicate, with the matched nonreverse-transcribed sample run simultaneously. cDNA abundance was calculated using a standard curve and normalized to the reference gene ALG9. Oligonucleotides used for qPCR are listed in Dataset S2.RNA extraction and RT-qPCR was carried out as in ref. 8 cells were cross-linked in a final concentration of 2% formaldehyde at room temperature for 15 min. The formaldehyde was quenched using a final concentration of 1.5M of Tris for 5 min.For ChIP-seq experiments , cells wparadoxus cells by OD were spiked into each S. cerevisiae sample and processed according to the chromatin immunoprecipitation protocol.For anchor-away ChIP-seq experiments  and 5, cSI Appendix, Supplementary Methods. For all samples, ~850 μL soluble chromatin were collected for immunoprecipitation, and 50 μL was reserved for Input. For all ChIP samples, 50 µL DynaBeads Protein G magnetic beads (ThermoFisher Scientific) per sample were equilibrated by washing 5× in FA Lysis buffer. IP for 3xV5-Rap1 was performed using 5 μL mouse monoclonal V5 (ThermoFisher Scientific). For all 3xFLAG-tagged proteins, IP was performed using 5 μL mouse monoclonal anti-FLAG® M2 antibody (Millipore Sigma). Samples were eluted in 100 µL TE + 1% SDS. Beads and elution buffer were incubated at 65 °C overnight to reverse cross-linking, followed by treatment with RNaseA and Proteinase K. DNA was purified using a QIAquick PCR purification kit.Cell lysis and chromatin immunoprecipitation were performed as described in ref. SI Appendix, Table S2.For ChIP-qPCR, ChIP samples were diluted 10-fold, and input samples were diluted 100-fold in nuclease-free water. Reactions were set up in triplicate and run using the same reagents and parameters as for RT-qPCR above. Abundance was calculated using a standard curve for each primer set, and the ratio of IP/Input was plotted. Oligonucleotides used for this experiment can be found in Libraries were prepared for high-throughput sequencing according to the manufacturer’s recommendations using the Ultra II DNA Library Prep kit from New England Biolabs (NEB). Samples were multiplexed and paired-end sequencing was performed using either a MiniSeq or NovaSeq 6,000 (Illumina).SI Appendix, Supplementary Methods. Analysis was performed using custom scripts derived from ref. SI Appendix, Fig. S2A, peak summits were defined by MACS3 callpeak, using a cutoff of q < 0.01. The positional information and coverage for these peaks can be found in Dataset S3.Sequencing reads were aligned using Bowtie2, using options = “--local --soft-clipped-unmapped-tlen --no-unal --no-mixed --no-discordant”  to a refHML sequence, hmr∆) and the S. paradoxus genome CBS432 (genbank). Reads were aligned as above. S. paradoxus read count served as the normalization factor for each sample. Normalization data be found in Dataset S4.For anchor-away experiments, a custom, concatenated hybrid genome was generated using modified SacCer3  using a no-tag control sample as the input over which the program defined peaks. Summits were defined using the callpeak function and options “-f BAMPE -g 1.2e7 -q 0.01 --keep-dup=auto -B --call-summits”, identifying 1,118 Rap1-bound regions genome-wide. Peaks were defined as 150 bp on either side of the summit. We counted read coverage over each region in duplicate Rap1-depletion samples, and these values were normalized to the S. paradoxus read counts per sample as described above. A table containing positional information for these peaks, and the normalized count data, can be found in Dataset S5.We followed the framework for peak calling and filtering laid out in ref. p-value log(koff) < 0.05. The fits were done in R with the nls function using the formula: “nls” . The 377 peaks used in the analyses for SI Appendix, Figs. S5 and S6 represent peaks that fit the nonlinear regression model and were within 300 bp upstream of an ORF and/or located in the subtelomeric region (defined as 15 kb from the ends of chromosomes). A table containing the calculated fit of the decay curves, average residence time, and further classifications can be found in Dataset S6.Peaks at each locus were fit using the exponential decay model described in ref. H3 occupancy genome-wide for analysis of the relationship between Rap1 apparent dwell-time or Rap1 enrichment to nucleosome position was downloaded from GEO Accession GSE97290 .Transcript isoforms were defined using a TIF-seq dataset (GEO Accession GSE39128) . We thenAppendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.Dataset S02 (XLSX)Click here for additional data file.Dataset S03 (XLSX)Click here for additional data file.Dataset S04 (XLSX)Click here for additional data file.Dataset S05 (XLSX)Click here for additional data file.Dataset S06 (XLSX)Click here for additional data file.