HIF sustain a transcriptional regulatory circuit of EPAS1 expression in renal clear cell carcinoma

Domain: chemistry
Published: 2026-02-19
Editorial outcome: major_revision

Abstract
Initiation and sustainment of oncogenic signaling is a hallmark of cancer evolution and progression. In renal clear cell carcinoma, loss of von Hippel-Lindau protein causes stabilization of hypoxia-inducible transcription factors (HIF) evoking a pseudo-hypoxic response, perturbing epithelial homeostasis and leading to cancer development. Although genetic polymorphisms link the EPAS1 oncogene (coding for HIF-2α) to renal cancer and anti-HIF-2 compounds emerge as renal tumor therapies, little is known about transcriptional dysregulation of this factor in renal malignancies. We use genetic, epigenetic and transcriptomic data from large patient cohorts and cell models to dissect mechanisms of augmented EPAS1 transcription in clear cell renal cell carcinoma. We define an oncogenic enhancer of EPAS1 which operates depending on the presence of HIF and renal lineage-specific factors, thereby providing evidence for an auto-regulatory feed-forward circuit of HIF-2α regulation which promotes renal cancer growth.

Introduction
Biallelic mutations in the von Hippel-Lindau (VHL) tumor suppressor gene are truncale events leading to the development of clear cell renal cell carcinoma (ccRCC)1–4. ccRCC is the most common type of renal cancer, accounting for more than 70% of kidney tumors5. VHL encodes pVHL, a member of the E3 ubiquitin ligase complex, which functions as an adapter molecule for the complex to recognize hydroxylated α-subunits of hypoxia inducible transcription factors (HIF)6,7. Loss of pVHL induces stabilization of HIF-α protein independent of the presence of molecular oxygen and triggers its broad transcriptional activity.

In most tumors, activation of HIF-2 signaling either by hypoxia or genetic mutations promotes tumor growth and correlates with adverse outcomes8. With respect to ccRCC, HIF-2α overexpression led to accelerated cancer growth in xenograft models and the transcriptional output of HIF-2-as opposed to targets of HIF-1-was associated with a negative outcome in the KIRC (Kidney renal clear cell carcinoma) cohort in The Cancer Genome Atlas Program (TCGA)9–11. Furthermore, single nucleotide polymorphisms (SNP) in intronic regions of the Endothelial PAS domain-containing protein 1 (EPAS1) gene, which codes for HIF-2α, predispose to the development of renal cancer12. Thus, EPAS1 is regarded as an oncogene in ccRCC and has emerged as a promising therapeutic target13. HIF-2 inhibitors have been licensed in the United States and Europe for the treatment of VHL-associated cancers not requiring immediate surgery in patients with hereditary VHL-disease and of patients with progressed stages of sporadic RCC14–16. Interestingly, preclinical data suggested that higher levels of HIF-2α mRNA and protein within tumors render them more sensitive to anti-HIF-2 therapy17,18.

HIF-2α protein could not be detected in the non-diseased renal epithelium and mechanisms how HIF-2α expression was released in early tubular lesions upon VHL-loss have not been defined yet, but may involve transcriptional, posttranscriptional and posttranslational mechanisms19,20. The presence of non-coding RCC-associated SNPs at the EPAS1 locus indicates that transcriptional dysregulation may contribute significantly to this process. Genome-wide association studies (GWAS) and fine-mapping analyses have defined two independent genetic signals within intronic regions of the EPAS1 gene which are associated with the development of renal cancer12,21,22. Of note, for one signal comprising the variant rs12617313 a significant association with the occurrence of VHL alterations in the corresponding renal tumors was determined suggesting an interaction of common genetic variation with tumor mutations21. Although the SNPs within the EPAS1 gene generated one of the strongest genome-wide association signals in RCC, surprisingly little is known about the regulation of HIF-2α mRNA expression in general and specifically in the context of renal cancer12. In this respect, DNA demethylation via inhibition of DNA (cytosine-5)-methyltransferase 3A (DNMT3A) of the EPAS1 promoter has been shown to be involved in releasing HIF-2α expression in renal tumors23. However, genetic mutations of DNMT3A are rare in kidney cancer indicating that other mechanisms participate in forcing HIF-2α release.

Recently, renal lineage-specific transcription factors such as paired-box-protein 8 (PAX8) and hepatocyte nuclear factor 1-beta (HNF-1β) have been identified as significant drivers of oncogenic signaling in kidney cancer explaining some of the tissue specificity of the tumors24,25. For example, PAX8 occupied enhancers in ccRCC cells which control the expression of metabolic genes24. Moreover, pro-proliferative PAX8 signaling co-operated with HIF signaling at a variety of regulatory DNA elements including the renal cancer susceptibility locus at a CCND1 enhancer25. PAX8 and HNF-1β form core regulatory circuitries in renal cancer24. These may stem from regulatory loops preserved from the renal tubule progenitor cells or may be rewired within renal cancer cells incorporating other oncogenic signals such as HIF.

The presence of HIF-2α in early tumorous lesions in the kidney and its susceptibility to therapy together with the fact that in most cases renal cancer evolution takes decades opens a broad window of opportunity for interventions19,26. Taking advantage of this window requires a much better understanding of the transcriptional regulation of HIF-2α with regard to different genetic backgrounds as well as its transcriptional consequences and interactions.

In this work, we define transcriptional enhancers of EPAS1 expression in ccRCC. We explore their effects on HIF-2α mRNA levels in the context of genetic susceptibility to RCC, their interactions with lineage-specific transcription factors, and their downstream effects on ccRCC biology.

Results
ccRCC tumor cells overexpress HIF-2α mRNA
Stabilization of HIF-2α protein is a hallmark of VHL-defective ccRCC. In addition, genetic variation in non-protein coding regions at the EPAS1 locus is associated with the development of ccRCC indicating the involvement of mechanisms upstream of protein-regulation in conferring the cancer risk12. In order to screen for a HIF-2α mRNA dysregulation, we analyzed samples from the Erlangen RCC cohort, which comprises 141 tumors and corresponding normal kidney samples from renal cancer patients (n=114 clear cell, n=16 papillary, n=11 chromophobe). By qPCR, we measured significantly increased HIF-2α mRNA levels in ccRCC tissue when compared to corresponding normal tissue (Fig. 1a). In contrast, HIF-2α mRNA was reduced in papillary and in chromophobe RCCs. Similar results were observed in the published TCGA RNA-seq datasets for KIRC (Kidney renal clear cell carcinoma, n=72), KIRP (Kidney renal papillary cell carcinoma, n=31) and KICH (Kidney chromophobe, n=23) (Fig. 1b). Across all TCGA tumor entities, expression levels of HIF-2α mRNA were highest in the KIRC samples (Supplementary Fig. 1)27. These findings suggest that ccRCC-specific mechanisms operate on HIF-2α mRNA expression.

Due to the excessive production of angiogenic factors caused by HIF-stabilization, ccRCC tumors are highly vascularized. HIF-2α is abundantly expressed in endothelial cells28. Therefore, we wanted to explore whether the effect of increased HIF-2α mRNA levels in ccRCC tissue was generated from a relatively large number of endothelial cells present in the tumor specimens or directly from tumor cells. First, we performed RNAscope experiments for HIF-2α mRNA in ccRCC tissue from three patients and confirmed substantial levels of HIF-2α transcripts in both, endothelial and cancer cells within the tumor specimens (Fig. 1c, Supplementary Figs. 2 and 3). Second, in a recent single nucleus RNA-seq analysis of ccRCC and normal tissue29, tumor cells from many individuals exhibited higher levels of HIF-2α and HIF target gene transcripts (e.g., CCND1, NDRG1, VEGFA) compared to proximal tubule cells, which are considered as cells of origin for ccRCC (Fig. 1d). Finally, we resorted to freshly explanted ccRCC and corresponding normal tubule cells from 34 patients (Fig. 1e). In the tumor cells, we observed robust HIF protein and absent pVHL in immunoblot experiments (Fig. 1f, Supplementary Fig. 4a). By qPCR, we detected significantly increased levels of HIF-2α mRNA and HIF target gene mRNA (CCND1, VEGFA) in tumor cells when compared to corresponding tubule cells from the same individual (Fig. 1g, Supplementary Fig. 4b, c). This indicates that tumor cells contribute substantially to the higher HIF-2α mRNA levels in ccRCC. It also implies that HIF-2α mRNA is subjected to upregulation in ccRCC and that the mechanisms engaged in this regulation are different compared to other tumors, especially other renal tumor entities.

Epigenetics defines a ccRCC-activated EPAS1-enhancer
To gain insight into the mechanisms causing increased HIF-2α mRNA levels in renal tumor cells, we explored the EPAS1 locus for epigenetic differences between ccRCC and normal tissue or other (renal) tumor entities, respectively. First, we examined isolated primary VHL-defective ccRCC tumor cells and corresponding tubule cells from three individuals (Fig. 1f, Supplementary Fig. 4a). We generated ATAC-seq and H3K27ac ChIP-seq as well as RNA-seq data from these cells. Differential expression analysis of the RNA-seq data confirmed that HIF-2α mRNA was increased in tumor compared to tubule cells (Supplementary Fig. 5a, Supplementary Data 1). Gene set enrichment analysis using a Meta-HIF ChIP-seq dataset integrating previously defined HIF-binding sites in ccRCC confirmed that the HIF-pathway is activated in the tumor cells (Supplementary Fig. 5b, Supplementary Data 2). Moreover, analysis of chromatin accessibility followed by motif discovery revealed enrichment of the HIF-binding motif within differentially accessible and active regions in the tumor cells indicating that genomic regions relevant for the HIF-response are preferentially active in tumor cells (Supplementary Fig. 5c, Supplementary Data 3). We expanded our analyses by using the activity-by-contact-model (ABC) of enhancer-promoter regulation in order to identify shared or differentially active enhancer-promoter interactions between tubule and tumor cells on a genome-wide level30. This analysis integrated the multi-omics data mentioned above and revealed approximately 65,000 predicted enhancer-promoter interactions per primary tubule cell culture and approximately 63,000 predicted contacts in the corresponding tumor cells (Supplementary Data 4). Between 65–70% interactions overlapped between corresponding tubule and tumor cells. We measured an average of 4.8 enhancer-promoter interactions per transcriptional start site.

Next, we inspected the EPAS1 locus for differential enhancer activity and enhancer-promoter interactions in the ABC data set (Fig. 2a and Supplementary Fig. 6a, Supplementary Data 4). Strikingly, this uncovered a putative regulatory loop from the EPAS1 promoter to an enhancer region approximately 70 kb upstream of the promoter. This loop was present in every individual-specific ABC analysis of the primary tumor cells, but absent in those from corresponding tubule cells (Fig. 2a and Supplementary Fig. 6a). The interaction was also detectable in an ABC-analysis performed with published data generated from the 786-0 ccRCC cell line (Supplementary Fig. 6b)31–33. To expand the epigenetic analyses of this region, we re-analyzed and inspected published data from renal tumor cells and tumor tissue. First, an intragenic region of the EPAS1 gene close to the promoter displayed moderate interaction signals with the putative enhancer in Capture-C experiments from a recent study exploring physical interactions of regulatory elements connected to RCC GWAS-associated polymorphisms in 786-0 cells (Fig. 2a)34. Second, the putative enhancer region exhibited significantly higher levels of the histone activity marker H3K27ac in the ten ccRCC tumors analyzed compared to normal tissue in a study from Yao et al. (Fig. 2a, b and Supplementary Fig. 7a, b)32. In these tumor samples, activity of this region defined by the H3K27ac signal correlated positively with EPAS1 expression, but this correlation missed significance (R=0.62, p=0.06; Supplementary Fig. 7c). Third, the ccRCC-activated enhancer was defined as a renal cancer-specific accessible region in a recent single-cell ATAC-seq analysis (Fig. 2a)35. Fourth, a CpG (cg09567048) within the ccRCC-activated enhancer region was significantly hypomethylated in the analyzed KIRC cohort compared to normal tissue (Fig. 2a)36. Fifth, regulatory elements within the ABC-associated ccRCC-activated enhancer were defined as being predominantly open in the KIRC data set in the comprehensive catalog of accessible chromatin in tumors from the TCGA cohorts37. This region comprises the four regulatory elements KIRC_10767 to KIRC_10770 (Fig. 2a, c). When inspecting the ccRCC-activated enhancer in the combined tracks of the different TCGA tumors, we observed variable accessibility across tumors at KIRC_10767 to KIRC_10769, whereas at position KIRC_10770 open chromatin signals were almost exclusively restricted to KIRC tumors, albeit with some heterogeneity within the individual KIRC samples (Fig. 2c and Supplementary Fig. 8). In addition to KIRC tumors, glioblastoma (GBM) and low-grade glioblastoma (LGG) but no other tumor exhibited accessibility to KIRC_10770 (Fig. 2c). Furthermore, we compared accessibility at KIRC_10770 from the available KIRC ATAC-seq data37 with corresponding HIF-2α mRNA expression levels (Fig. 2d). This revealed a positive correlation of accessibility of KIRC_10770 with HIF-2α mRNA expression in these tumors. Thus, higher levels of HIF-2α expression in ccRCC may be explained by an intergenic regulatory element approximately 70 kb upstream of EPAS1 gene that is preferentially open and active in ccRCC and interacts with the EPAS1 promoter.

HIF-2α mRNA expression depends on pVHL and HIF
In a next step, we examined whether regulation and accessibility are dependent on the presence of pVHL. First, we reassured that augmented HIF-2α mRNA levels in the TCGA KIRC data did not correlate with copy number gains of EPAS1, but were associated with copy number loss of VHL (Fig. 3a, Supplementary Fig. 9a). Conversely, re-expression of pVHL in the VHL-defective ccRCC cell line RCC4 reduced HIF-2α mRNA levels (Fig. 3b). We also employed the RCC4/i.VHL-HA cell line which contains a doxycycline inducible VHL expression DNA element. Treatment of the cells with doxycycline effectively re-installed pVHL and led to clearance of HIF-α protein over time (Fig. 3c). In line with a role of pVHL in regulating EPAS1 expression, we measured diminished HIF-2α mRNA levels in the doxycycline treated cells (Fig. 3d). This indicates that HIF-2α mRNA expression is dynamic and sensitive to the presence of pVHL in tumor cells. The best characterized function of pVHL is the recognition of hydroxylated HIF-α leading to proteasomal degradation of these HIF-subunits6,38. To explore a direct role of HIF in regulating EPAS1 expression, we performed a series of experiments in ccRCC cells. Knocking-out HIF-1β, the HIF-α dimerization partner, in RCC4 or 786-0 cells led to reduced HIF-2α mRNA and protein levels (Fig. 3e-h). In RCC4, we also noticed an increase of HIF-1α protein upon loss of HIF-1β (Fig. 3g). This is in line with previous reports indicating an involvement of HIF in negatively regulating HIF-1α mRNA expression, possibly via induction of an antisense-RNA at the HIF1A locus by HIF10,39. In addition, we used RCC4 cells constitutively re-expressing pVHL (RCC4/VHL+) and treated these cells with dimethyloxalylglycine (DMOG) to stabilize HIF-α. Confirming a HIF-dependent regulation, this treatment resulted in HIF-2α mRNA induction (Supplementary Fig. 10a–c).

To complement our analyses with epigenetic data, we performed ATAC-seq and H3K27ac CUT&Tag experiments in the different cell models. We noticed a moderate increase in chromatin accessibility and activity in RCC4/VHL+ cells at KIRC_10770 upon HIF-stabilization (Supplementary Fig. 10d). The same analysis in RCC4/i.VHL-HA cells treated with or without doxycycline for 18h as well as in HIF-1β depleted or control treated RCC4 or 786-0 cells revealed that accessibility to the ccRCC-activated enhancer and especially to KIRC-10770 was strongly reduced by VHL re-expression or HIF-1β depletion, respectively (Fig. 3i). Similarly, H3K27ac levels were reduced at this site in all three cell models (Fig. 3j). From this data, we conclude that HIF regulates EPAS1 expression in ccRCC and that KIRC_10770 is a HIF-sensitive enhancer.

HIF interacts with the ccRCC-activated enhancer
We were interested whether the regulation of EPAS1 expression by HIF was specific to VHL-defective cells. Thus, we proceeded to test the potential of non-transformed renal tubule cells with intact pVHL to induce HIF-2α mRNA upon HIF stabilization. For this, we used mRNA from isolated renal tubule cells derived from non-diseased tissue of nephrectomies of 105 patients and exposed these cells to the HIF-stabilizer DMOG or vehicle (Supplementary Fig. 11a). In addition to the expected increase of mRNA of the common HIF-target genes EGLN3 and NDRG1, we measured a small but significant increase in HIF-2α mRNA when HIF was stabilized by DMOG (Fig. 4a, Supplementary Fig. 11b, c). In a subset of the primary tubule cells, we knocked-down HIF-1α, the main regulatory α-subunit in these cells, or HIF-1β using siRNA which abolished HIF-2α and EGLN3 mRNA induction (Fig. 4b, Supplementary Fig. 12). This result suggests that HIF regulates EPAS1 expression in isolated renal tubule cells, albeit to a lesser extent than in ccRCC cells.

Thus, in order to explore a direct involvement of HIF via DNA-interactions in transactivating EPAS1 expression, we re-analyzed available ChIP-seq and generated additional ChIP-seq and CUT&Tag-seq data for the different HIF-subunits from renal tubule cells and ccRCC tumor cells. In accordance with a direct role of HIF in regulating EPAS1 expression, we discovered two sites of robust HIF-DNA interactions upstream of the EPAS1 gene (Fig. 4c). One site overlapped an intragenic region of the PRKCE gene (EPAS1-enhancer E1) which also coincided with a hypomethylated CpG (cg08900316) in the KIRC data (Fig. 4c). At this position, HIF signals were detectable for HIF-1α and HIF-1β in primary tubule cells in which HIF-α was stabilized with DMOG, and for all three HIF-subunits in ccRCC cell lines (Fig. 4c). The second region that interacted with HIF was located within the ccRCC-activated enhancer (EPAS1-enhancer E2) identified by the ABC-analysis and overlapped with KIRC_10770 (Figs. 2a, 4c). Importantly, HIF signals at EPAS1-enhancer 2 were restricted to renal tumor cells including primary ccRCC cells when compared to primary tubule cells (Fig. 4c, Supplementary Fig. 13). We used the Cicero algorithm for testing co-accessibility of DNA-elements in single nucleus ATAC datasets to test whether EPAS1-enhancer E1 and E2 are linked within ccRCC tumor cells35,40. This revealed a positive co-accessibility score for these two elements (co-accessibility score: 0.13 between KIRC_10759 and KIRC_10770) indicating that both sites can be accessible simultaneously in the same ccRCC cell. Besides, following HIF-stabilization we also detected HIF-signals at EPAS1-enhancer E1 (2 out of 7 cell lines) or at E2 (2 out of 7 cell lines) in non-renal tumor cells as well as in immortalized HKC-8 renal tubule cells and VHL-competent ccRCC Caki-1 cells in data from published and newly generated sequencing experiments. This suggests that some other tumor cells may regulate EPAS1 expression from these sites (Supplementary Fig. 14a). However, confirming the kidney cancer preference of this site DNAse hypersensitivity evaluated in the 733 biosamples data set from Meuleman et al41. was present at KIRC_10770 within EPAS1-enhancer E2 in samples from the renal/cancer cluster, but not at comparable levels in other clusters (Supplementary Fig. 14b). We corroborated HIF-responsive enhancer activity for EPAS1-enhancer 1 and the element KIRC_10770 within EPAS1-enhancer 2 in reporter gene assays performed in Kelly cells exposed to DMOG or control conditions (Supplementary Fig. 14c, d, e). In order to test whether the DNA methylation status of normal or tumor tissue explains some of the binding differences for HIF at EPAS1-enhancer 1 and 2 between tumor and tubule cells, we resorted to TCGA KIRC DNA methylation data36. Two of the three differentially methylated CpGs defined in the KIRC TCGA data set within the PRKCE-EPAS1 locus localized in the genomic vicinity of the HIF-binding EPAS1-enhancers E1 and E2, respectively, further indicating their relevance for RCC biology (Supplementary Fig. 15a). However, basal methylation status in normal kidney tissue was comparable between the two CpGs and methylation was reduced at both sites in the tumors with a more pronounced effect at the CpG near EPAS1-enhancer 1 (Supplementary Fig. 15b). Hence, these methylation data do not explain the differences in HIF-binding signals between tubule and tumor cells.

As mentioned above, we detected chromatin accessibility to the ccRCC-specific HIF-binding element KIRC_10770 in the GBM and LGG TCGA ATAC-seq data (Fig. 2c)37. From this, we speculated that ccRCC and glioblastoma cells might regulate HIF-2α expression through a similar mechanism from a shared cancer-activated regulatory element. To explore this further, we performed ATAC-seq, HIF-1β ChIP-seq and RNA-seq experiments in U87 glioblastoma cells exposed to DMOG to stabilize HIF (Supplementary Fig. 16a). HIF-2α mRNA levels increased significantly in U87 cells after treatment with the HIF-stabilizer DMOG when measured by qPCR or RNA-seq (Supplementary Fig. 16b–d, Supplementary Data 5). Furthermore, the ccRCC-activated enhancer was accessible and interacted with HIF-1β in U87 cells indicating that glioblastoma cells share this regulatory element of HIF-2α expression with ccRCC (Supplementary Fig. 16e). Finally, similar to the results from the KIRC cohort (Fig. 2d) we measured a positive correlation of HIF-2α expression with accessibility to KIRC_10770 in the TCGA LGG data (Supplementary Fig. 16f). So far, our results indicate that the overexpression of HIF-2α mRNA in ccRCC is generated from two HIF-binding enhancers upstream of the EPAS1 gene. EPAS1-enhancer 2 also displays affinity to HIF in glioblastoma.

The EPAS1-enhancers interact with genetic predisposition for renal cancer
HIF-binding sites frequently coincide with renal cancer susceptibility loci indicating that genetic predisposition modulates the HIF-response in this disease34,42,43. In this regard, an overlap of the ccRCC-activated EPAS1-enhancer 2 with a HIF-2α eQTL signal for 13 polymorphisms in the Pancan QTL catalog was observed, which was specific for the KIRC cohort and further highlights the relevance of this HIF-binding region for ccRCC biology (Supplementary Fig. 15a)44. Previous GWAS and fine-mapping analyses have revealed two independent genetic signals within intronic regions of the EPAS1 gene associated with renal cancer development12,21,22. Of note, for one of these signals including rs12617313 a significant association with VHL mutations within the corresponding tumors was described for risk allele A21. Moreover, putative regulatory elements close to this SNP have been shown to interact with sequences within the enhancers E1 and E2 in 786-0 cells in a Capture C study (Supplementary Fig. 17A)34. Thus, we next explored for an interaction of HIF-dependent EPAS1 activation and the underlying genotype for the ccRCC risk signals within EPAS1. To do this, we first resorted to available genotype and RNA-seq data from the KIRC cohort. We did not observe a genotype-expression correlation for SNP rs11894252 with EPAS1 expression. rs11894252 was defined as an RCC risk SNP in the initial GWAS and was available for analysis on the genotyping array used by TCGA (Supplemental Fig. 17b)12. However, rs12617313, the SNP with the strongest association with VHL-mutations in tumors in the fine-mapping analysis, displayed a significant eQTL for EPAS1 expression in tumor tissue, but not in corresponding normal tissue (Fig. 4d). Of note, EPAS1 expression levels were highest in tumors from patients homozygous for the risk allele A which is associated with VHL-mutations in ccRCC. We did not observe a similar genotype-expression trend for rs12617313 in the HIF-2α mRNA in tumors from the KICH and KIRP cohorts (Supplementary Fig. 17 c,d). In order to evaluate any effect of the genotype on EPAS1 expression in primary tubule cells, we genotyped DNA from the 105 donors of primary tubule cells for rs12617313 and stratified values for basal and HIF-induced HIF-2α mRNA levels accordingly. Strikingly, similar to results from the KIRC tumors we observed strongest HIF-2α induction by DMOG in cells homozygous for the A-allele, but no difference in expression between the genotypes under control conditions (Fig. 4e). This suggests that the intronic haplotype which is associated with RCC development and VHL-mutations in tumors operates in a HIF-dependent manner to modulate HIF-2α mRNA expression.

Activity of EPAS1-enhancer E2 depends on lineage-specific factors
De novo recruitment of HIF to enhancers in renal cancer was exemplified by comprehensive studies on a distal enhancer of CCND1 expression32,45. Interestingly, HIF action at the CCND1 enhancer was shown to be dependent on co-recruitment of the lineage-specific factor PAX825. Thus, we wondered whether the renal lineage-specific factors HNF-1β and PAX8 might operate at the ccRCC-activated EPAS1-enhancer 2. Both transcription factors are essential for renal tumor growth24. We performed HNF-1β CUT&Tag-seq in 786-0 cells and inspected publicly available PAX8 ChIP-seq data from a 786-0 derived cell-line25. At the EPAS1 locus, we observed DNA interactions of both factors within the HIF-interacting ccRCC-activated enhancer region, specifically KIRC_10767, suggesting that these factors are involved in the transactivation of EPAS1 expression (Fig. 5a). In line with this hypothesis, knock-out of PAX8 or HNF-1β in 786-0 cells by CRISPR/Cas9 technology diminished HIF-2α mRNA and protein levels significantly (Fig. 5b-d). We also performed ATAC-seq and H3K27ac CUT&Tag-seq in clones of 786-0 cells with knock-out of HNF-1β or PAX8, respectively. Motifs for both transcription factors were enriched in regions with reduced accessibility when comparing knock-out to control cells confirming an epigenetic effect at binding-sites for these transcription factors (Supplementary Fig. 18, Supplementary Data 6 and 7). At the EPAS1-enhancer E2, both accessibility to and activity of the enhancer were substantially reduced in these knock-out clones of 786-0 cells validating the prominent role for lineage-specific transcription factors for this site (Fig. 5e). Moreover, we used one sgRNA which targeted the HNF-1β motif within KIRC_10767 as well as two sgRNA which target the DNA-sequence in the center of the PAX8 ChIP-seq peak, which did not contain a consensus PAX8 motif, in 786-0 cells (Fig. 5f, Supplementary Fig. 19). Both maneuvers introduced mutations at these sites and reduced HIF-2α mRNA as well as protein levels significantly corroborating the relevance of the specific binding-sites for EPAS1 expression (Fig. 5g–h, Supplementary Fig. 20). We conducted HIF-1β CUT&Tag-seq in HNF-1β and PAX8 knock-out clones of cells as well as in the respective binding-site knock-out pools of cells (Supplementary Fig. 21). In all settings, we detected robust HIF-1β signals at KIRC_10770 indicating that HIF-binding to this element is not entirely depending on the presence of PAX8 or HNF-1β in the cell or at the enhancer. Taken together, the function of EPAS1-enhancer E2 is governed by the interaction with renal lineage-specific transcription factors PAX8 and HNF-1β.

Contribution of the two HIF-binding EPAS1-enhancers to HIF-2α expression
Next, we wanted to confirm the function and dissect the extent of contribution of the HIF-binding elements within EPAS1-enhancers 1 and 2 to HIF-2α mRNA expression and to more downstream effects in renal cancer cells. To do this, we employed CRISPR/Cas9 technology targeting the enhancers in primary renal cancer cells as well as the ccRCC cell lines 786-O and RCC4. We used 2 sgRNA per enhancer region covering HIF-motifs present within the HIF-binding sites in KIRC_10759 and KIRC_10770 (Fig. 6a, Supplementary Fig. 19). First, primary cancer cells were transfected with a pool of these sgRNAs and mRNA was isolated. In line with the transactivation potential of the elements for EPAS1 expression, we measured reduced HIF-2α mRNA levels in enhancer-knock out pools of cells validating the function of the HIF-binding elements in these explanted primary ccRCC cells (Fig. 6b). Similar results were obtained in 786-0 and RCC4 cells (Fig. 6c, d). By exclusively targeting HREs in EPAS1-enhancer E1 effects on HIF-2α mRNA were more pronounced in 786-0 cells than in RCC4 cells (Fig. 6c, d). Of note, manipulating HREs within KIRC_10770 (E2_HIF) within the ccRCC-activated EPAS1-enhancer E2 alone led to a striking reduction of HIF-2α mRNA by approximately 30-40% in the pools of 786-0 and RCC4 cells when compared to cells transfected with control sgRNAs (Fig. 6c, d). We validated efficient knock-out of the sequences using the ICE (Interference of CRISPR Edits) tool (Supplementary Figs. 22, 23). We also cloned a selection of sequences with mutations generated by CRISPR/Cas9 at both enhancers into DNA vectors and performed luciferase reporter assays. Confirming a functional role for the HREs in binding HIF and activating EPAS1 transcription, sequences with mutations led to a substantial reduction of luciferase activity in DMOG-stimulated Kelly cells when compared to the wild-type sequence (Supplementary Fig. 24). Importantly, reduction of HIF-2α mRNA also translated into reduced expression of HIF-2α protein in 786-0 and RCC4 cells underlining the relevance of the enhancers for the HIF-2 signaling pathway (Fig. 6e, f). We focused on the ccRCC-activated EPAS1-enhancer 2 and performed RNA-seq from lysates of EPAS1-enhancer 2 HRE-defective (E2_HIF) and control 786-0 and RCC4 cell populations. Analysis for differentially expressed genes revealed 18 significantly upregulated and 32 downregulated genes for 786-0 and 39 upregulated and 64 downregulated genes for RCC4 cells (log fold change: >0.5/<−0.5, adjusted p<0.05, Supplementary Data 8 and 9). Pathway analysis using the MsigDB “Hallmark” gene set confirmed significant enrichment of the hypoxia pathway in mRNA transcripts which were downregulated in the enhancer knock-out 786-0 or RCC4 cell pools, respectively, when compared to control cells (Fig. 6g, h). Analyzing the overlap of significantly regulated genes in EPAS1-enhancer E2 knock-out 786-0 and RCC4 cells revealed a concordance in gene regulation between the two cell lines including reduced expression of CCND1, NDRG1 and VEGFA transcripts (Fig. 6i).

Relevance of the ccRCC-activated EPAS1-enhancer 2 for downstream targets and tumor biology
In the following step, we generated EPAS1-enhancer 2 HRE-defective (E2_HIF) clones of 786-0 cells to explore the effect size to which this enhancer contributes to HIF-2 dependent tumor biology and transcriptional regulation. E2_HIF mutated clones of cells had reduced HIF-2α protein levels compared to control clones of cells (Fig. 7a). Importantly, inactivation of EPAS1-enhancer 2 (E2_HIF) prevented tumor formation of 786-0 cells in a tumor xenograft mouse model when compared to control cells, indicating an essential effect of this enhancer for ccRCC growth (Fig. 7b, c, Supplementary Fig. 25a). Our results are in line with a recent CRISPR inhibitor screen examining the function of HIF-associated enhancers in ccRCC which described reduction of tumor growth by targeting the ccRCC-activated EPAS1-enhancer 2 with sgRNAs also employed in our study (Supplementary Fig. 25b)25.

To follow the effects from the tumor model up, we evaluated the downstream transcriptional effects of the EPAS1-enhancer 2 (E2_HIF). We performed RNA-seq from lysates of two control (nt) and two EPAS1-enhancer 2 HRE-defective (E2_HIF) clones of 786-0 cells. In a differential expression analysis, downregulation of the HIF-2 target genes NDRG1, VEGFA, and CCND1 in EPAS1-enhancer 2 HRE-defective (E2_HIF) clones of 786-0 cells confirmed the critical function of this enhancer for downstream transcriptional effects of HIF-2α (Fig. 7d, Supplementary Data 10).

In order to define the direct HIF-2 component of the response, we intersected the RNA-seq results with available HIF ChIP-seq from 786-0 cells using gene set enrichment analysis45. This confirmed a significant enrichment of 786-0 HIF-binding sites amongst genes being downregulated in EPAS1-enhancer 2 HRE-defective (E2_HIF) cells (Fig. 7e). We further applied CUT&Tag technology for HIF-1β to interrogate disturbances of HIF-DNA interactions at the genome level in EPAS1-enhancer 2 HRE-defective (E2_HIF) clones of 786-0 cells. As expected, we detected absent HIF-1β protein at KIRC_10770 in the EPAS1-enhancer 2 HRE-defective (E2_HIF) cells, but reasonably preserved interactions at a positive control site within a known EGLN3 HIF-binding enhancer (Fig. 7f). In the knock-out clones of cells, ATAC-seq and H3K27ac CUT&Tag-seq experiments revealed reduced accessibility and activity at KIRC_10770 (Fig. 7f). Averaging CUT&Tag signals for HIF-1β and H3K27ac from EPAS1-enhancer 2 HRE-defective (E2_HIF) clones of 786-0 cells across the set of ChIP-seq defined HIF-binding sites centered on the respective H3K27ac signal revealed reduced HIF-1β and H3K27ac signals compared to control cells (Fig. 7g). Signals for HIF-1β and H3K27ac at non-HIF-binding enhancers were comparable between the two cell populations. This shows that downstream HIF-DNA interactions at and activity of HIF-binding enhancers depend on an intact EPAS1-enhancer E2.

Supporting the HIF-2α-specific effect and highlighting the relevance of the enhancer for renal cancer biology, the transcriptional response upon targeting the EPAS1-enhancer 2 overlapped strikingly with effects generated by treatment of 786-0 cells with the specific HIF-2 inhibitor PT238546 (Fig. 7h).

In summary, distal enhancers of EPAS1-expression interact directly with HIF in ccRCC. Together with lineage-specific factors HNF-1β and PAX8, they form a transcriptional autoregulatory circuitry of the HIF-2 signaling pathway activation in these tumors which is modulated by RCC-associated genetic variation and is indispensable for tumor growth (Fig. 7i).

Methods
Healthy kidney and tumor tissue samples
Tumor and healthy human kidney cortical tissue from anonymized donors undergoing tumor nephrectomy was provided by the Comprehensive Cancer Center Erlangen-EMN (CCC) at the Uniklinikum Erlangen. Each patient gave written informed consent and the local ethics committee at the Universität Erlangen-Nürnberg approved use of the tissue (329_16B; 542_20Bc). We did not conduct sex- or gender-specific analyses, as this was not the focus of the study. Specimens were collected in accordance with the World Medical Association Declaration of Helsinki. Tumor and normal kidney samples were examined and diagnosed by an expert pathologist. From our Erlangen RCC cohort we used tumor tissue and corresponding normal kidney tissue from 114 clear cell, 16 papillary and 11 chromophobe renal cancer patients for RNA analysis.

In situ hybridization using RNAscope
In situ hybridization experiments were performed on formalin-fixed paraffin-embedded kidney tissue using the RNAscope 2.5 HD Assay-BROWN (322300, RNAscope 2.5 HD Reagent Kit, Advanced Cell Diagnostics, Bio-Techne GmbH Wiesbaden, Germany) according to the manufacturer’s instructions. In short, paraffin blocks were cut in 5 µm sections using a microtome and mounted on Superfrost plus slides. After air drying slides overnight, slides were baked for 1 h at 65 °C and deparaffinized using xylene and 100% ethanol. Further pretreatment included incubation with target retrieval reagents, hydrogen peroxide and protease plus reagents. HIF-2α target mRNA was hybridized with the RNAscope Probe Hs-EPAS1 (410591, Advanced Cell Diagnostics, Bio-Techne GmbH Wiesbaden, Germany) which has been described earlier51. Six amplification steps were performed before signal detection using DAB. Slices were counterstained with 50% Hematoxylin and mounted with BioCare EcoMount (320409, Biocare Medical, Pacheco, CA, USA). Positive and negative control probes were routinely included to ensure interpretable results.

Co-staining for CA9 (polyclonal rabbit, NB100-417, Novus Biologicals, Centennial, CO, USA; dilution 1:500) and CD31 (monoclonal mouse, #3528, Cell Signaling, Danvers, MA, USA; dilution: 1:200) was performed on RNAscope-preanalyzed sections, which were cooked for 12 min in 1X Target Retrieval Solution (pH 6.1, S169984-2, Agilent Dako, Santa Clara, CA, USA), washed with PBS, and incubated in blocking solution (5% horse serum in PBS) for 1 h. Subsequently, sections were washed in PBS and incubated with primary antibodies overnight followed by thorough washing with PBS. As secondary antibodies goat anti-mouse IgG (Alexa Fluor 555, A-21422, Thermo Scientific, Waltham, MA, USA; dilution: 1:500) or donkey anti-rabbit (Alexa Fluor 488, A-21206, Thermo Scientific, Waltham, MA, USA; dilution: 1:500) were used. After incubation in secondary antibody solution for 60 min, sections were washed in PBS and mounted. Staining was visualized with a Leica DM6000B microscope (Leica, Wetzlar, Germany).

Cell culture
786-0 and Caki-1 cells were purchased from ATCC. Kelly cells were a gift from C. Warnecke and U-87 cells were a gift from F. Müller, Erlangen, Germany. RCC4 and RCC4/i-VHL-HA cells were from Sir P. Ratcliffe, Oxford, UK. Cell lines were regularly tested for mycoplasma infection and authenticated before being used. 786-0, RCC4, and U-87 cell lines were grown in Dulbecco’s modified Eagle’s Medium supplemented with 100 U/ml penicillin, 100 μg/ml streptomycin and 10% fetal bovine serum (Sigma Aldrich, St. Louis, MO, USA). Kelly cells were maintained in RPMI 1640 medium supplemented with 100 U/ml penicillin, 100 μg/ml streptomycin and 10% fetal bovine serum. Caki-1 cells were grown in McCoy´s 5A Medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 100 U/ml penicillin, 100 μg/ml streptomycin and 10% fetal bovine serum (Sigma Aldrich, St. Louis, MO, USA). Primary cell isolation from healthy human kidney cortical tissue or tumor tissue from patients undergoing tumor nephrectomy was performed as described previously52. Samples were minced, digested with collagenase II (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), and sieved through 100 µm and subsequently 70 µm filters. Primary human tubule cell cultures were maintained in Dulbecco’s modified Eagle’s medium/Ham’s F-12 supplemented with 2.5% (day 1) or 0% (day 2 onwards) fetal calf serum, 2 mM L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin, 5 μg/ml insulin, 5 μg/ml transferrin, and 5 ng/ml selenium (Sigma Aldrich, St. Louis, MO, USA), 10 ng/ml triiodothyronine (T3, Sigma Aldrich, St. Louis, MO, USA), 100 ng/ml hydrocortisone and 100 μg/ml epidermal growth factor (Peprotech, Hamburg, Germany). Epithelial origin was confirmed by immunocytochemistry for N- and E-Cadherin. Sub-confluent cell cultures were exposed to 1 mM dimethyloxalylglycine (DMOG, Cayman Chemicals, Ann Arbor, MI, USA) as indicated to induce HIF-α stabilization. Primary ccRCC tumor cell cultures were maintained in Dulbecco’s modified Eagle’s Medium supplemented with 100 U/ml penicillin, 100 μg/ml streptomycin and 10% fetal bovine serum. RCC4/i-VHL-HA cells were treated with to 0.5 μg/ml doxycycline (D3447, Sigma Aldrich, St. Louis, MO, USA) for the indicated time to induce VHL expression.

siRNA transfection
siRNAs targeting HIF-1α and HIF-1β subunits and control non-targeting siRNA against drosophila HIF (nt) have been previously described53. siRNAs were transfected at a final concentration of 40 nM using Lipofectamine 3000 (Thermo Fisher Scientific, Waltham, MA, USA) transfection reagent according to the manufacturers’ instructions.

DNA isolation and genotyping
Genomic DNA was isolated by phenol/chloroform extraction from tubule cells. We used a TaqMan® assay (C_26434874_10, 4351379 and TaqMan Genotyping-Master Mix: 4371355, Thermo Fisher Scientific, Waltham, MA, USA) to genotype for rs12617313. Data were analyzed using the TaqMan® Genotyper Software V1.3 (Thermo Fisher Scientific, Waltham, MA, USA).

Genome editing
For genome editing of primary ccRCC tumor cells, 786-0 and RCC4 cells sgRNA directed against exonic regions of HIF-1β, HNF-1β, PAX8 or enhancer regions (HIF-1β, HNF-1β and PAX8 binding sites) at the PRKCE-EPAS1 locus were designed according to algorithms provided by the Zhang lab using CRISPOR information in the UCSC Genome Browser54,55 (Supplementary Data 11). The two sgRNA targeting HREs within EPAS1-enhancer-2 corresponded to two sgRNAs used by Patel et al. for the CRISPRi screen25. Knock-out of the region of interest was performed by transfecting TrueCut Cas9 Protein (Thermo Fisher Scientific, Waltham, MA, USA) together with targeting sgRNAs (A35533, Trueguide Synthetic sgRNA, Thermo Fisher Scientific, Waltham, MA, USA) or non-targeting control sgRNAs (A35526, TrueGuide sgRNA Negative Control, non-targeting, Thermo Fisher Scientific, Waltham, MA, USA) using electroporation. Single clones of cells were generated by dilution. For mutation screens, genomic DNA of each clone was isolated by phenol-chloroform extraction. The region targeted by CRISPR/Cas9 was amplified by PCR and analyzed by Sanger sequencing. Knock-out of enhancer regions in cell pools was evaluated using the “Inference of Crispr Editing” method (ICE v2 CRISPR Analysis Tool, Synthego, Menlo Park, CA, USA). Single clones of cells were evaluated for effective knock-out of regulatory sites by cloning the respective regions in a pGL3 promoter vector (Promega, Madison, WI, USA) and subsequently performing Sanger sequencing. For EPAS1 enhancer knock-out experiments off-target copy number alterations of the EPAS1 gene was excluded by copy number determination. For sequences of sgRNAs used in this work please see Supplementary Data 11.

RNA isolation and quantitative RT–PCR
RNA from cells or fresh frozen tissue was isolated using peqGold total RNA kit (VWR Peqlab, Erlangen, Germany) according to the manufacturer´s protocol and transcribed into cDNA using the high capacity cDNA reverse transcription kit (4368814, Thermo Fisher Scientific, Waltham, MA, USA). Quantitative RT–PCRs were performed using Maxima SYBR Green/ROX qRT-PCR Master Mix (Ko233, Thermo Fisher Scientific, Waltham, MA, USA) on a StepOnePlus Real-Time PCR cycler (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). Expression values were normalized to values for the housekeeping gene HPRT. For expression primers please see Supplementary Data 11.

Western blotting
Cells were lysed in UREA/SDS buffer. Proteins were resolved by SDS-PAGE and transferred onto PVDF membranes. Rabbit polyclonal anti-HIF-1α antibody (Cay10006421, rabbit polyclonal, Cayman Chemicals, Ann Arbor, MI, USA; dilution: 1:1,000) and monoclonal mouse anti-HIF-1α antibody (610959, BD Biosciences, New Jersey, USA; dilution: 1:500), goat polyclonal anti-HIF-2α antibody (AF2997, R&D Systems, Minneapolis, USA; dilution: 1:250) and rabbit monoclonal anti-HIF-2α antibody (A700-003, Bethyl, Waltham, USA; dilution: 1:1,000) as well as rabbit polyclonal anti-HIF-1β antibody (NB100-110, Novus Biologicals, Biotechne, Wiesbaden, Germany; dilution: 1:1,000), monoclonal rat anti-hemagglutinin (HA) antibody (11867423001, Roche Diagnostics GmbH, Mannheim, Germany; dilution: 1:1,000) and polyclonal rabbit anti-VHL antibody (68547, Cell Signaling Technology, Danvers, MA, USA; 1:1,000) were applied for protein detection as applicable. Furthermore, we used a polyclonal rabbit anti-PAX8 antibody (10336-1-AP, Proteintech, Planegg-Martinsried, Germany; dilution 1:5,000) and polyclonal rabbit anti-HNF-1β antibody (HPA002083, Atlas Antibodies, Sigma Aldrich, St. Louis, MO, USA; dilution 1:2,000). Horseradish peroxidase-conjugated anti-rabbit antibody (P0399, swine polyclonal, Agilent Technologies, Santa Clara, CA, USA; dilution 1:2,500), anti-goat antibody (P0449, rabbit polyclonal, Agilent Technologies, Santa Clara, CA, USA; dilution 1:2,500) and secondary anti-rat antibody (Jackson ImmunoResearch, Ely, UK) served as secondary antibodies. β-actin was detected by using a monoclonal anti–β-actin-peroxidase antibody (A3854, mouse monoclonal, dilution 1:60,000; Sigma-Aldrich, St. Louis, MO, USA). Signal detection was performed on an Amersham Imager 600 (GE Healthcare, Amersham, UK). Band intensity of HIF-2α and β-actin protein was quantified by densitometry using ImageJ2 software (version 2.35; National Institutes of Health). HIF-2α signal was normalized to respective β-actin loading control (please find results in Supplementary Data 12).

Chromatin immunoprecipitation (ChIP)
Primary tubule cells, primary ccRCC and 786-0 cells were cultured in standard cell culture conditions or treated with 1 mM DMOG for 4 h as indicated. 15 cm dishes of sub-confluent cells were used and Chromatin immunoprecipitation (ChIP) experiments were performed using the ChIP-IT high sensitivity kit (Active Motif, Carlsbad, CA, USA). For immunoprecipitations, 70 μg of chromatin and 3–6 μl of antibodies directed against HIF-1α (Cay10006421, Cayman Chemicals, Ann Arbor, MI, USA) or HIF-1β (NB100-110, Novus Biologicals, Littleton, CO, USA), H3K27ac (ab4729, Abcam, Cambridge, UK) were used. Non-immunized rabbit serum or purified normal rabbit IgG (12-370, Merck Millipore, Burlington, MA, USA) served as negative controls as appropriate. Antibody–chromatin complexes were pulled down by proteinase A agarose beads (Millipore, Burlington, MA, USA). After reversal of the crosslinking by heat DNA was isolated via DNA purification columns.

Assay for transposase accessible chromatin sequencing (ATAC-seq)
Primary renal tubule and cancer cells from three individuals as well as RCC4 and 786-0 cells were grown under standard culture conditions and harvested at 80% confluency. RCC4 cells re-expressing pVHL and U-87 cells were exposed to 1 mM DMOG for 16 h or standard culture conditions and harvested at 80% confluency. RCC4/i.VHL cells were exposed to 0.5 μg/ml doxycycline or DMSO for 18 h and harvested at 80% confluency. Cells were trypsinized, manually counted and 60,000 cells were directly subjected to the Omni-ATAC protocol as described by Corces et al.56. Libraries were prepared using the Illumina Tagment DNA TDE1 Enzyme and Buffer Kit (20034197, Illumina, San Diego, CA, USA) and purified using the DNA Clean and Concentrator-5 Kit (Zymo Research, Freiburg, Germany) according to the manufacturer’s recommendations. Following purification, library fragments were amplified by using the NEBNext High-Fidelity 2X PCR Master Mix (New England Biolabs, Ipswich, UK) and custom made indexed/barcoded Nextera PCR primers (Sigma Aldrich, St. Louis, MO, USA) as published by Corces et al.56. Libraries were amplified for a total of 8–12 cycles. AMPure XP beads (Beckman Coulter GmbH, Krefeld, Germany) were used for library clean-up and double-sided size selection according to the manufacturer’s protocol. DNA concentration of the libraries and fragment size distribution were determined by electrophoresis using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). Equimolar concentrations of barcoded libraries were pooled prior to sequencing.

Cleavage under targets and tagmentation (CUT&Tag)
CUT&Tag was performed as described in Kaya-Okur et al.57 with modifications based on the protocol published by Bartosovic et al.58. Depending on the cell line, we used 250,000 to 350,000 cells per condition and antibody. Cells were washed three times in 200 μl of antibody buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 2 mM EDTA, 0.5 mM spermidine, 0.05% digitonin, 0.01% NP-40, 1× protease inhibitors and 1% BSA). Cells were centrifuged for 5 min at 600 g, resuspended in 100 μl antibody buffer with 1:50 diluted primary antibody (monoclonal rabbit anti-HIF-1β antibody [5537S, Cell Signaling Technology, Danvers, MA, USA] or polyclonal rabbit anti-HIF-1β antibody [NB100-110, Novus Biologicals, Biotechne, Wiesbaden, Germany] or monoclonal rabbit anti-H3K27ac antibody [ab177178, Abcam, Cambridge, UK] or polyclonal rabbit anti-HIF-1α antibody [Cay10006421, Cayman Chemicals, Ann Arbor, MI, USA] or polyclonal rabbit anti-HNF-1β antibody [HPA002083, Atlas Antibodies, Sigma Aldrich, St. Louis, MO, USA]) and incubated overnight. The next day cells were centrifuged for 3 min at 600 g, washed once with 200 μl of Dig-wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM spermidine, 0.05% digitonin, 0.01% NP-40, 1× protease inhibitors, 1% BSA), resuspended in 200 μl of Dig-wash buffer with 1:50 diluted secondary antibody (ABIN101961, Guinea Pig anti-Rabbit IgG antibody, Antibodies-Online, Aachen, Germany) and incubated for 1 h at room temperature with slow rotation. Afterwards cells were centrifuged for 3 min at 600 g, washed three times with 200 μl of Dig-wash buffer, resuspended in 200 μl of Dig-300 buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine, 0.05% digitonin, 0.01% NP-40, 1× protease inhibitors, 1% BSA) with 1:100 diluted protein A–TN5 transposase (C01070002, Diagenode, Seraing, Belgium, loaded with mosaic adapters according to manufacturer’s instructions) and incubated for 1 h rotating at room temperature. Following pA-Tn5 loading cells were washed three times with Dig-300 buffer, resuspended in 200 μl of tagmentation buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine, 0.05% digitonin, 0.01% NP-40, 1× protease inhibitors, 10 mM MgCl2) without BSA and incubated for 1 h at 37 °C. Tagmentation was stopped by addition of 7 μl of 500 mM EDTA. Subsequently 4 µl SDS 10% and 2 µl Proteinase K were added and the sample was incubated for 1 h at 55 °C. DNA isolation and clean-up were performed using the ZYMO DNA Clean & Concentrator-5 kit with a 1:5 ratio of binding buffer and 25 µl of elution buffer (Zymo Research, Freiburg, Germany). Following purification, DNA fragments were amplified by using the NEBNext High-Fidelity 2X PCR Master Mix (New England Biolabs, Ipswich, UK) and custom made indexed/barcoded Nextera PCR primers (Sigma Aldrich, St. Louis, MO, USA) as published by Kaya-Okur et al.57. Libraries were amplified for a total of 15 cycles. SPRIselect bead-based reagent (B23317, Beckman Coulter GmbH, Krefeld, Germany) was used for library clean-up according to the manufacturer’s protocol. For H3K27ac CUT&Tag samples 5 to 40 M iomapped reads were generated. For CUT&Tag with antibodies directed against transcription factors 1.3 to 21.4 M iomapped reads were acquired.

Reporter assays
668 bp and 727 bp sequences covering the hypoxia responsive elements of EPAS1 enhancer 1 and EPAS1 enhancer 2, respectively (hg38; chr2:46073427-46074094 and chr2:46232652-46233378) were PCR amplified from genomic DNA of primary cells or 786-0 cells with intact or defective EPAS1 enhancer 1 or 2 sites and ligated into the pGL3 promoter vector (Promega, Madison, WI, USA) using KPNI and NHEI restriction sites. Sequences were verified using Sanger sequencing (Eurofins Genomics, Ebersberg, Germany). Transfections of plasmids and a control β-galactosidase reporter were performed in Kelly cells using X-tremeGENE® HP DNA transfection reagent (Roche Diagnostics, Basel, Switzerland) according to the manufacturer’s instructions. Transfected cells were stimulated with DMOG where applicable for 16 h before harvest. Luciferase activity in extracts was measured using the Steady-Glo luciferase reporter assay system (Promega, Madison, WI, USA). Values were normalized to β-galactosidase activity in the same cell lysates. Primer sequences are listed in Supplementary Data 11.

Xenograft tumor model
Animal experiments were conducted under license 55.2.2-2532-2-2150-19 approved by the State of Bavaria, Regierungspräsidium Unterfranken, Germany. NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NOD/SCID-gamma) mice were bred in-house in the animal facilities of the Friedrich-Alexander University of Erlangen-Nürnberg. Mice were provided with food and water ad libitum and kept at a 12:12 h light-dark cycle, 45-65% humidity, and at 20 to 24 °C. Age-matched littermates of both sexes were used for experiments and the order of injections of cells were randomized. Cells were harvested using StemPro Accutase (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) for 5 min. 5×106 cells were used per injection. Cells were resuspended in 50 µL PBS and 50 µL Matrigel (Basement Membrane Matrix, 354234, Corning, Bedford, MA, USA) and kept on ice. We used a 30G insulin syringe (BD, Franklin Lakes, NJ, USA) for subcutaneous injection in the right flank of 8–12-week-old NOD/SCID-gamma mice in a randomized, non-blinded manner. The tumor growth was monitored twice a week using caliper measurements by an investigator blinded for the different conditions. Tumor volume was calculated using this formula: volume = (length × width2)/2. Mice were euthanised by cervical dislocation and tumors dissected at week 10 of the observation period. No tumor reached a volume of 1 cm3, which was prespecified as the maximal burden.

RNA-seq analysis
RNA was isolated using the peqGold total RNA kit (VWR Peqlab, Erlangen, Germany). Adequate quality of isolated RNA was confirmed using the RNA Integrity Number (RIN, Supplementary Data 13) determined by a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). Samples were sequenced in technical duplicates by Novogene (Cambridge, UK) on an Illumina Novaseq 6000 or Illumina NovaSeq X plus platform to a 2×150 pair-end format with an average number of 20–30 million reads per sample (Supplementary Data 13). After quality control using FastQC (v0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) reads were aligned to the human reference genome (hg38) using the STAR alignment software (v2.7.10a)59. A count table of mapped reads was generated with the feature-counts software (v1.6.1)60. Transcripts with less than 10 reads were discarded. The DESeq2 package version 1.32.0 was used for logarithmic transformation of the data and for data exploration61. Differential expression analysis was performed by using the DESeq2 lfc Shrink approach. Adjusted p-values were calculated using the Benjamini–Hochberg method within DESeq2. Gene annotations were added to the result files using biomaRT (v2.48.3)62. Differentially expressed (DEG) genes were visualized with a Volcano plot.

ATAC-seq analysis
Barcoded amplicons were sequenced on a HiSeq 2500 (Illumina) or Illumina NovaSeq X plus platform to a 2×150 pair-ended format (Novogene, Cambridge, UK). Reads were quality filtered according to the standard Illumina pipeline and de-multiplexed. Fastq file were processed by trimming Illumina adapter sequences using TrimGalore (v0.6.6). Reads were aligned against human reference genome (hg38) using Bowtie2 (v2.3.4.1)63. Using SAMtools (v1.8) aligned reads with a Mapping Quality (MAPQ) below 30 and those mapped to ChrM were removed64. PCR-duplicates reads were excluded via Picard (http://broadinstitute.github.io/picard/) (v2.25.2). ENCODE hg38 blacklisted regions (access 20.10.2020) and chromosome Y were removed using GenomicRanges (v1.46.1) and filterChromosomes function from regioneR (v1.26.1)65,66. Quality metrics are listed in Supplementary Data 14. For analysis of differentially open regions (DOR) in ccRCC tumor cells versus primary tubule cells sequencing duplicates of each ATAC sample of three different patients were examined. For the detection of DOR between HNF-1β knock-out cells or PAX8 knock-out cells versus cells treated with non-targeting controls, sequencing duplicates of two single clones of cells were used per condition. Peaks were called using MACS2 (v2.2.7.1) with the following parameters “--shift -75 --extsize 150 --nomodel --call-summits --nolambda --keep-dup all -p 0.01“67. Reads in the peaks were normalized using the variance-stabilized transform (VST) function and batch effects were removed using the limma::removeBatchEffect function68. Two meta-files covering open regions in PTC or ccRCC cells, respectively, were generated using the summit approach as published by Corces et al.37. Differentially open regions were discovered using the DESeq2 lfc Shrink approach (DESeq2 package v1.28.1)61. Adjusted p-values were calculated using the Benjamini–Hochberg (BH) method. ATAC sites exhibiting a change of accessibility with a log fold change > 0.5 or < -0.5 with an adjusted p<0.05 were defined as DOR.

SAMtools allowed merging of technical and biological replicates64. Normalized bigWig tracks for visualization were generated using BamCoverage (v3.3.2)69.

Single bigWig tracks per tumor of ATAC-seq data from TCGA tumors were downloaded from the TCGA resource website https://gdc.cancer.gov/about-data/publications/ATACseq-AWG (access 28.05.2021), converted to bedGraph files, normalized using Wiggletools 1.2.11 and merged37,70. The bedGraphToBigWig tool was applied to acquire an integrated KIRC bigWig track for visualization71.

ChIP-seq analysis
Library preparation of immunoprecipitated DNA and ChIP-sequencing was performed by Novogene (Cambridge, UK). Amplicons from the ChIP-Library preparation were sequenced on an Illumina Novaseq 6000 platform to a 2×150 pair-ended format. Raw sequencing files in fastq format were generated via fastq-dump (v2.8.0) and adapter sequences were trimmed using TrimGalore (v0.6.6). Quality control was conducted with FastQC (v0.11.8). Subsequently, reads were mapped to the hg38 version of the human genome using Burrows-Wheeler Aligner (BWA-mem; v0.7.17-r1188)72. SAMtools (v1.8) removed aligned reads with a Mapping Quality (MAPQ) below 30 and those mapped to ChrM64. PCR-duplicates reads were excluded via Picard (http://broadinstitute.github.io/picard/) (v2.25.2). Technical replicates from the samples were merged using SAMtools. We excluded ENCODE blacklisted regions from merged files and generated normalized BigWigs for visualization using BamCoverage (v3.3.2)65,71. Peaks were identified applying MACS2 (v2.2.7.1) using the parameters “f BAMPE -n Inputfile -q 0.05” for detection of transcription factor binding sites and “f BAMPE -q 0.01 -B -n Inputfile -q 0.05” for analysis of histone marks67. Quality metrics are listed in Supplementary Data 15.

In order to generate a Meta-HIF ChIP-seq dataset, published raw sequencing data of HIF-1α, HIF-1β and HIF-2α ChIP experiments in RCC4, 786-0 and primary ccRCC cells were downloaded (see Data availability) and processed as described above9,32,73. Peaks were combined using the GenomicRanges’ “reduce” function66. Only HIF binding sites present in at least 4 out of 7 ChIP-seq experiments were kept for further analysis (Supplementary Data 2).

H3K27ac ChIP-seq data for differentially active enhancer and promoter regions in ccRCC tumors in comparison to normal kidney tissue were from Yao et al.32. Liftover of coordinates of altered enhancers and promoters was performed by the LiftOver tool within the UCSC Genome Browser to acquire an hg38 compatible bed file for visualization74. After filtering for chromosome 2, average log fold chance and -log adjusted p-values were visualized with a Volcano plot using the R package EnhancedVolcano (v1.22.0). To be considered overlapping, differentially active sites must overlap with KIRC-specific ATAC sites at at least one base.

Transcription factor motif enrichment analysis
DOR exhibiting an increased accessibility in ccRCC cells in comparison to PTC (log fold change > 0.5, adjusted p<0.05) were filtered for overlapping H3K27ac ChIP-seq signals. These sites underwent motif analysis using HOMER2 (v4.9.1)75. RNA-seq data of PTC and ccRCC cells were analysed for differentially expressed genes applying the DESeq2 lfc Shrink approach (DESeq2 package v1.28.1)61. Integration of motif discovery in DOR and differential expression of respective transcription factors allowed for calculation of the Motif enrichment score according to Xin et al.76. To determine transcription factor motifs in DOR with decreased accessibility (log fold change < -0.5, adjusted p<0.05) in single clones of 786-0 HNF1-ß knockout versus single clones of control (nt) cells and single clones of 786-0 PAX8 knockout versus single clones of control (nt) cells HOMER2 (v4.9.1) was applied. Results were plotted using ggpubr (v.0.4.0).

ABC-analysis
The activity by Contact (ABC) Model of Enhancer-Gene Specifity30 (v1.0) was applied to predict enhancers that regulate EPAS1 expression in primary tubule and ccRCC cells. Inputs for the individual-specific analysis were bam files for ATAC-seq and H3K27ac ChIP-seq as well as TPM-normalized RNA-seq data from corresponding PTC and ccRCC cells of 3 different patients. A similar analysis was performed on published data from 786-0 cells31–33. For quantification of the contact frequency publicly available average Hi-C profiles (https://github.com/charlesfulco/ABC-Enhancer-Gene-Prediction)30 were used as previously described by others77. Only enhancer predictions with Power-law scores ≥ 0.015 were kept.

Correlation analysis ATAC vs RNA-seq
Cancer type-specific normalized ATAC-seq counts matrices from individual KIRC and LGG samples were downloaded from https://gdc.cancer.gov/about-data/publications/ATACseq-AWG37 (access 14.06.2021). Normalized RNA-seq data (TPM) for KIRC and LGG were downloaded from the TCGA database using TCGAbiolinks (v.2.22.4)78 (access 13.06.2023). A Spearman’s rank correlation analysis for the indicated ATAC sites was performed and plotted using ggprubr (v0.4.0).

Quantification of the H3K27ac ChIP-seq signal in predefined genomic regions
ChIP-seq bigWig (bw) files were used to quantify the H3K27ac signal at predefined genomic regions. These regions included the EPAS1-enhancer 2 locus (original coordinates in hg19, liftover to hg38: chr2:46222961-46234261) and a HIF-binding NDRG1 enhancer (original coordinates in hg19, liftover to hg38: chr8:133337907-133395407) as predefined by Yao et al.32. BigWig signals for the respective regions were extracted using the import function of the rtracklayer package (v1.54.0). After extraction, an approximation of the integral using the Riemann sums method was calculated79. The resulting integral values (reads per genomic content, RPGC) were then used to compare the H3K27ac signal between control and tumor tissues. Additionally, the integral values from both control and tumor tissues were correlated with the respective HIF-2α mRNA expression (TPM) employing the Spearman’s rank method using the stat_cor function. Results were visualized with ggpubr (v0.4.0).

Gene set enrichment analysis
Gene set enrichment analysis (GSEA) used pre-ranked genes and applied 100,000 permutations. Pre-ranking of differentially expressed genes was based on the fold-difference as determined by DESeq261. Functional enrichment analysis was performed via fgsea (v1.20.0)80. The “Hallmark” gene sets were downloaded from MsigDB (v7.5.1)81. Adjusted p-values were calculated using the Benjamini–Hochberg method. Pathways with an adjusted p-value < 0.05 were chosen for visualization using ggplot2 (v3.3.6) or fgsea.

CUT&Tag-seq analysis
CUT&Tag libraries were sequenced by Novogene (Cambridge, UK) on an Illumina NovaSeq X Plus platform to a 2×150 pair-ended format. FastQC (v0.11.8) was applied for quality control and sequencing adapters were removed using TrimGalore (v0.3.3). Bowtie2 (v2.3.4.1) was used for read alignment against the human reference genome (hg38). Low quality reads (MAPQ < 30) and reads mapping to mitochondrial DNA or ENCODE blacklisted regions were eliminated using SAMtools (v.1.8)82. Picard (http://broadinstitute.github.io/picard) (v.2.25.2) removed PCR-duplicate reads. BamCoverage (3.3.2) was used to create normalized bigwig tracks for visualization. Peaks were called via MACS2 (v2.2.7.1) using the MACS2 callpeak function. We benchmarked the results of the HIF-1β CUT&Tag experiment against published HIF-1β ChIP-seq peaks in the same cell line45. ChIP-seq data for HIF-1β (GEO Accession: GSE34871) were downloaded from https://chip-atlas.org/ (SRX114494, q<1E-10). Overlap analysis of the 805 ChIP-seq HIF-1β peaks resulted in 543 of these sites with significant CUT&Tag signals. Top ranked CUT&Tag peaks showed a remarkable overlap with the ChIP-seq validated HIF-1β binding sites (Supplementary Fig. 26).

For comparison of H3K27ac levels under different conditions, samples from RCC4/i.VHL-HA cells were sequenced in technical duplicates and tracks were merged for visualization. Captured DNA from two single control (nt) or HIF-1β knock-out clones of cells from each cell line (RCC4 or 786-0 cells) were sequenced in technical duplicates. All tracks belonging to one condition were merged for visualization.

For comparison of HIF-1β and H3K27ac peaks signals in single clones of 786-0 cells with a defective or intact E2_HIF enhancer KIRC_10770 element, two metafiles (HIF-binding H3K27ac sites and Non-HIF binding H3K27ac files) were generated. Peak calling on H3K27ac CUT&Tag tracks of the aforementioned single clones of cells was performed using the MACS2 tool. DiffBind (v.3.4.11) was applied to analyze the H3K27ac peaks and to establish a consensus peak set (n=33,027)83. These consensus peaks were then aligned to pre-defined and published HIF-1β binding sites (GEO Accession: GSE34871, downloaded from ChIP-Atlas: SRX114494, q<1E-10)45 to identify regions overlapping HIF-1β binding. To quantify the HIF-1β or H3K27ac CUT&Tag signal at the center of the H3K27ac consensus peaks with a HIF-binding site (n=472) or without a HIF-binding site (non-HIF binding, H3K27ac consensus peaks, n=32,555), bigwig files for HIF-1β and H3K27ac CUT&Tag signals from biological replicates of each condition (nt vs E2_HIFko) were combined. Peak signal intensity was analyzed using the computeMatrix function and results were visualized with the plotProfile function from the deepTools suite (v.3.5.1).

Expression quantitative trait loci (eQTL) analysis
TCGA level 3 RNA-seq data for KIRC, KIRP or KICH tumors was coupled with level 2 genotyping data of the respective patients from the Affymetrix Genome-Wide Human SNP Array 6.0. SNP reference rs11894252 was mapped to SNP_A-2290064 and rs12617313 to SNP_A-4200988 in the Affymetrix array. Data from patients with low-confidence SNP calls (confidence < 0.01) were excluded. Hence, 387 patients were retained for rs11894252 and 403 patients for rs12617313 in the KIRC cohort. In the KIRP cohort 225 patients for rs12617313 were included. The KICH cohort comprised 23 patients for rs12617313. In order to evaluate the significance of association between the SNP genotype and expression of EPAS1, RNA-seq expression across the samples was fitted to a negative binomial generalized linear model against the genotype status. Hereafter, the likelihood ratio of this model versus a model that ignores genotype status was computed. Finally, a χ2-test was used to identify the significance of the genotype coefficients in stratifying the patients43.

Co-accessibility analysis using Cicero
Single nucleus ATAC-sequencing data and metadata were downloaded from GEO (GSE240822)35. For each snATAC tumor sample, cells, which were not listed in the provided metadata, were removed. Peaks were called using MACS2 (version 2.2.7.1) by applying the function „CallPeaks“ of the R package Signac (version 1.12.0)84. Peaks overlapping genomic blacklist regions were excluded using Signac’s list „blacklist_hg38_unified“ and the function „subsetByOverlaps“ (invert = TRUE). We obtained a common peak set by merging intersecting peaks across samples („reduce“ from R package „GenomicRanges“, version 1.50.2) and removed resulting peaks that had a width greater than 10,000 or smaller than 20 bp. Finally, datasets were transformed into peak-count matrices using the function „FeatureMatrix“. Datasets were merged into one object for Signac downstream analysis, i.e., term frequency-inverse document frequency (TF-IDF) normalization („RunTFIDF“) and selection of most frequently observed features („FindTopFeatures“) for dimension reduction via singular value decomposition („RunSVD“). We used latent semantic indexing (LSI) components 2:30 to compute the Uniform Manifold Approximation and Projection (UMAP) with function “RunUMAP” of R package „Seurat“ (version 5.0.1)85. The dataset was further subset to cells that were annotated as cell type „Tumor“ by the provided metadata and co-accessibility analysis restricted on chromosome 2 was performed using function „run_cicero“ of the R Package Cicero (version 1.3.9)40.

CRISPRi screening data
Source data from Patel et al.25 were downloaded from https://zenodo.org/record/6335339 (access 06.02.2023). The provided R script was used to plot the individual enhancer sensitivity score for each construct. Shown are empirical one-sided p-values calculated for each target region by 10000 permutations of the normalized sgRNA depletion scores for each tumor as published by Patel et al.25.

Liftover of capture-C data
Liftover of published Capture-C data and ChIP-seq data from hg19 to hg38 was performed using CrossMap (v0.6.1)86.

Statistical analysis
All statistical analyses excluding sequencing data were performed using GraphPad Prism Version 9.0.2 (GraphPad Software Inc., San Diego, CA, USA). Results are presented as mean +/- SD if not indicated otherwise. Statistical tests applied to determine significance are provided in the figure legends. The two-tailed unpaired Student’s t-test was used for comparison of two conditions. One- or two-way ANOVA analysis followed by pairwise or multiple comparisons were performed for experimental setups comprising more than two conditions or two influencing factors. Adjusted p-values were determined using the Bonferroni correction unless otherwise indicated. The one-sample t-test comparing the mean with a hypothetical value of 1 was used if the values of the control condition were set to 1. Statistical testing of NGS datasets are described in the respective methods section.

Visualization
BigWig files were visualized using the Integrative Genomics Viewer (IGV_2.8.4). Adobe Photoshop Elements (Adobe Inc., San Jose, CA, USA) was used for creating figures. Schematics were created using Biorender.com: Naas, S. (2025) https://BioRender.com/c87s173 (Fig. 1e) and Naas, S. (2025) https://BioRender.com/w24j560 (Figs. 5f, 6a, 7i).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nSNPs in the intronic region of the gene EPAS1 (encoding for HIF2α), has been positively associated with the risk for renal\ncell carcinoma, yet how HIF2α is regulated to sustain ccRCC is poorly understood. In this manuscript Naas et al have used\nepigenomic profiling to identify an enhancer that controls the expression of EPAS1 specifically in ccRCC. The authors show\nthat this enhancer is crucial to the expression of EPAS1, and they suggest that the activity of this enhancer is regulated by\nHIF and other renal-specific transcription factors. Through this study, the authors have provided a molecular mechanism\nexplaining how the oncogene, HIF2α, is controlled in ccRCC to contribute to accelerated tumor formation/growth.\nMajor Concerns:\n• Figure1: The authors have shown a representative RNAscope image for EPAS1 in ccRCC tissue (1e). The authors should\ninclude images from the other three patients in supplementary figures as well. To also ensure that the signal is specific, the\nauthors should consider including a negative control (either normal tissue or control PTC cells). If the RNAscope probes\nhave been validated in a different study, the authors should include the reference. Could the authors also elaborate on how\ncancer cells were distinguished from noncancer cells in 1e? Why is the EPAS1 signal restricted only to some areas within\nthe tissue and is this seen across patients?\n• Figure 2: The authors have highlighted enh-1031503 as ccRCC-specific. However, this region is also active in PTC from\npatients 1 and 2, less so in patient 3. Could the authors comment on why this may be? Could it be cross-contamination or\nsome other reason? If the authors perform differential analysis on the K27ac peaks at this region, is there significant\ndifference? The same variability in H3K27ac signal is apparent in Supp Fig 3, when comparing normal and ccRCC tissue.\nCould the authors elaborate on this variability? Perhaps include the corresponding EPAS1 expression status?\nIn fig 2e, the authors should also include H3K27ac signal, wherever possible, to compare the degree of enhancer activity\nacross these tumor types.\n• Figure 3: When the authors knock-out HIF1β (3f, g), there is a modest decrease on HIF2α. Is this modest decrease in line\nwith what is expected? Is this true even at the EAPS1 transcript level? The authors have shown how ATAC signal changes\nat the ccRCC-enhancer after re-introducing VHL (Fig 3e). The authors should also depict how the enhancer activity is\nchanging as VHL is being re-expressed. Additionally, the authors show how EPAS1 RNA levels change upon addition of\nDMOG. Can the authors include how the EPAS1 enhancer status is changing in this case?\n• Figure 4: It is unclear if the HIF Chip-seq signal in 4a is from WT/untreated PTC cells or cells treated with DMOG. Can the\nauthors point out where exactly KIRC_10770 and KIRC_10767-69 lie? Are the HIF peaks specifically on KIRC_10770? Is\nthere some difference in motifs or methylation status at enhancer 1 versus enhancer 2 that could explain why enhancer 1\nshows stable HIF binding while enhancer 2 shows more dynamic binding? Based on Fig 4d, it appears as though PAX8 and\nHNF1  peaks do not coincide on KIRC_10770. What is the level of change in H3K27ac levels when these TFs are knocked-\nout in 786-O cells? Can the authors include this data to strengthen their claim that PAX8 and HNF1β regulate HIF2α,\nand\nthat enhancer 1 or 2 maybe involved?\n• Figure 5: The authors have deleted E1 or/and E2 to assay for the downstream effect on EPAS1 expression. However, there\nis insufficient data provided to show that the enhancers have been effectively abrogated. Can the authors include\ngenotyping and DNA sequencing data to show that the HIF motifs are deleted? Can they also include H3K27ac data to\nshow the extent to which the enhancer activity has been affected upon CRISPR-manipulation? It is curious that even when\nE1+E2 are deleted, HIF2α is still expressed. Could the authors speculate on why that might be? It is unclear to me, what the\nauthors mean by enhancer 2 activity in trans. Do the authors mean to assess the effect of deleting enhancer 2 on HIF-2α\ndownstream gene targets? Or did the authors only delete the enhancers on one of the alleles? Are 786-O cells diploid for the\nEPAS1 locus? If no, how many alleles for E1 and E2 were successfully deleted by the authors? Could the authors include\nthese details? Although it is mentioned in the methods, in the figure and figure legend, the authors state that the CUT&TAG\nwas performed to assess HIF1β binding.\n• Based on the ATAC-seq data, the element KIRC_10770 appears to be the most dynamic. What is the direct contribution of\nthis element in regulating EPAS1 expression levels? In the CRISPR deletions performed in Fig 5, it is unclear if this element\nwas abrogated or not. The authors should consider specifically deleting KIRC_10770 to assay for downstream effects. Also,\ndoes this element carry motifs for HIF? Or for Pax8/HNF1β? Surprisingly, contrary to the data from this manuscript,\nanalysis in Supp 6b shows that KIRC_10770 is very modest DNAse hypersensitivity in the renal/cancer category. Is it possible for the\nauthors regroup this data to separate out cancer v/s non-cancer samples?\nMinor Concerns:\n• The authors can include quantification for the western blots to depict the degree of change in HIF2α levels upon the\ndifferent perturbations throughout the manuscript.\n• Fid 1d is not necessarily adding to the figure and can be removed or moved to supplementary.", "reviewer_2": "HIF-2a is one of the most important driving factors in clear cell renal cell carcinoma.\nNaas et al. present a very interesting manuscript that identifies a feed-forward regulatory circuit controlling HIF-2a (EPAS1)\nmRNA expression in ccRCC. This involves an enhancer element that regulates HIF-2a mRNA expression.\nThe study makes excellent use of human cell resources from normal kidney tissue and from ccRCCs, of cultured ccRCC and\nother cancer cell lines, conducts different analyses of chromatin structure, status and protein binding (ATAC-seq, ChIP-seq,\nCUT&TAG), combined with RNA expression analyses and functional mutation of identified genomic sites as well as of the\ntranscription factors in the regulatory network. The study also integrates many different publicly available datasets from\nprevious studies to support the experimental work (however, many of the figures are in fact entirely or heavily based on\nalready published data). While the data presented in the manuscript suggest that the overall mechanism that the authors\nhave uncovered is very likely to be true, there are several aspects of the study that remain incomplete to fully support the\nproposed model and conclusions, as well as to address some important open questions that are central to the overall\nfindings. Most of these could be readily addressed using the tools that the authors describe in the manuscript.\nMajor issues\n1. A very interesting observation of the overall study is that the HIF-2a enhancer E2 appears to be relatively specific to\nccRCC (and also to glioblastoma) which might explain in part the sensitivity of the kidney to the oncogenic effects of HIF-2a.\nHowever, there are several limitations to the current study that prevent this conclusion being made. Fig 3b-e and Suppl Fig 8\nnicely show that VHL re-introduction into ccRCC cells suppresses HIF protein abundance, HIF-2a mRNA abundance and\nloss of ATAC-seq signal at the enhancer, showing that there is a dependency on VHL in established cancer cell lines.\nHowever, efforts to show the dependency of this activity on HIF fall short in their current form. Experiments in Fig 3f and g\nwould need to be complemented by HIF-2a mRNA measurements and crucially by ATAC-seq to investigate the status of the\nenhancer. Enhancer status assessment is also needed for experiments in Fig 4f and g which investigate the effects of\nmutation of PAX8 and HNF-1b. It remains unclear whether PAX8, HNF-1b or HIF factor binding is mutually dependent and\nwhat effect these factors have on the accessibility of the enhancer.\n2. The overall question which needs to be addressed is how the enhancer becomes accessible in ccRCC. It goes from a\nstate of inaccessibility in normal renal proximal tubule epithelial cells to being accessible in ccRCC cells. Based on the\npresented evidence it appears likely that this transition might be (at least partly) induced by loss of VHL and be HIF-\ndependent, however other genetic/epigenetic alterations that have arisen in these cancer cell lines might have also\ncooperated with VHL loss to induce this state (given the very long timeframe of ccRCC evolution this is plausible). This\nconcept should be investigated. Is VHL loss and HIF-a activation necessary and/or sufficient for enhancer activation?\nFor\nexample, it should be investigated if mutation of VHL or overexpression of non-degradable HIF-1a and HIF-2a mutants in\nnormal renal epithelial cells alters endogenous HIF-2a mRNA levels and enhancer status? It would also be highly\ninteresting to conduct hypoxia time course experiments in normal renal cells as hypoxia has previously been shown to\ninduce a transient HIF-1a increase, followed by a longer-term HIF-2a increase. It may be that the feed-forward enhancer\nmechanism identified here plays a role in this effect.\n3. Fig 3h and 3i lack evidence that DMOG was active in these cells (e.g. HIF-a western blots or activation of well\ncharacterised HIF-target genes) and proof of functional knockdowns is lacking.\n4. Fig. 3b, 3c. The argument presented around these figures is not convincing as it relies on the absence of an effect to prove\na hypothesis. There is such a massive spread of expression levels in TCGA data across each of the three genotypes that it\nappears very unconvincing that there is any relationship between these genotypes and HIF-2a mRNA.\n5. Fig 5. The effects of the sgRNAs at the target loci in whole populations and single cell clones should be verified by DNA\nsequencing.\n6. Supplementary Figure 6d is difficult to understand and currently not possible to interpret. A diagram of the vectors that are\nbeing tested would be helpful. It is not clear what normalized luciferase intensity is referring to as there is no mention of the\nempty luciferase vector. From this it appears that the normalization is simply a transfection normalization (beta-\ngalactosidase) but luciferase signals are not relative to an empty vector. It is therefore impossible to conclude whether\nDMOG treatment has any specific effects on the E1 and E2 elements or whether this is a non-specific effect on the luciferase\nplasmid in general. Cloning HRE mutants into the E1 and E2 elements would provide important additional evidence that the\nE1 and E2 elements are responsive to HIFs.\n7. Overall the study lacks in vivo evidence. Figure 6 essentially represents a series of reanalyses of published data (b,c,d)\nthat correlate with the model that is being proposed by the current study. 6e and f show a cell culture 3D growth assay\npresumably as a surrogate for a xenograft assay comparing control clones and E2 ko clones but there is no evidence that\nthe observed growth differences are dependent on the enhancer or on changed HIF-2a expression levels. A rescue\nexperiment with exogenous HIF-2a expression would be beneficial to support the dependency of this assay on HIF-2a\nlevels. It is currently unclear what the experiments shown in Fig 6 add compared to the published studies and don’t even\ndirectly validate the enhancer hits that were identified in the published screen from the Nature paper (reproduced in Fig 6d).\nMinor issues\n1. Some of the diagrams that are presented as figure panels are not necessary to understand these simple experiments. Figs\n1d, 1g, 2a, 5f could be removed from the figures.\n2. While many figures use data mining to interrogate datasets generated in the current study, quite a few figure panels are\nsimply re-representations of data generated in other studies so should not be shown again. e.g. the representation of TGCA\ndata shown in Fig 1b has been previously published by Nature Communications in another study\n(https://doi.org/10.1038/s41467-020-17873-3). Fig 6b and 6c are re-representations of published figures in Nature\n(doi:10.1038/nature19796) and Fig 6d replots data obtained in a recent Nature publication (https://doi.org/10.1038/s41586-\n022-04809-8).\n3. The axes of Fig 2d are not labelled in the figure.\n4. The western blot lanes in Fig 3c should be labelled above the blots as it is not immediately clear that these are the same\nconditions as in 3d below.\n5. It would be helpful for the DNA nucleotide HRE sequences that are being targeted in Fig 5a to be shown in relation to the\nsgRNAs that target these sequences so that the reader can understand what type of mutations are being introduced\n6. Supplementary Fig 6. The authors state that a small fraction of non-ccRCC cell lines show signal at enhancer 2 as further\nevidence of the ccRCC specifity of enhancer binding of HIF-b. However, 3 of 8 cell lines show this signal so this statement is\nnot supported by evidence", "reviewer_3": "HIF has been known to be important in the development and progression of renal clear cell carcinoma though its mechanism\nof dysregulation in the cancer is largely unknown. In the article, Naas et al. used epigenome and transcriptome data to study\nthe mechanism of augmented EPAS1 expression in ccRCC. They found that HIF-2α is highly expressed in ccRCC\ncompared to normal samples. According to the enhancer-gene linkage inference, the ccRCC-specific enhancer enh-\n1031503 shows great potential to regulate gene EPAS1 expression through the binding of HIF, HNF, and PAX8\ntranscription factors. They have also verified that knocking out the enhancer can inhibit the development of ccRCC, which\nindicates a great potential for clinical application for the study. I also have a few comments:\nMajor comments\n1. I understand this is a new mechanism of HIF-2α regulation, could you also explain the impact and potential application of\nthe study? What is the advantage of blocking E2 instead of eliminating HIF-2α expression?\n2. In vivo and survival studies shall be considered to prove the impact of E2 regarding its impact on cancer development and\nprogression.\n3. It seems most conclusions are based on 786-0 cell lines (e.g. ChIP-seq data, KO experiment, and xenograft model), which\nmay be biased. Could you also provide the result of RCC4 to make the conclusion more solid? Also, a ccRCC cell line\nwithout VHL loss can be a control.\nMinor comments\n1. Is EPAS1 expression related to the survival of patients?\n2. When you have single-cell ATAC-seq data for ccRCC, could you also verify if E2 is co-accessible with other regulatory\nelements such as E1 and promoter of EPAS1?\n3. It also requires data from corresponding patient samples such as Figure 4a.\n4. It seems HIF, HNF, and PAX8 bind to different regulatory elements in 4d. Do you have any direct experiment evidence for\nthe co-binding / collaboration at E2? What if designing sgRNA to target PAX8 or HNF binding sites at E2?\n5. 5b lacks the impact of individual E1 and E2.\n6. Is the SNP rs10193766 a risk site of ccRCC?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "We thank the referees for both their positive impressions of our work, and their helpful comments\nwhich improved the manuscript and data presentation. Please find our responses in blue.\nREVIEWER COMMENTS\nReviewer #1, expertise in CUT&Tag and chromatin biology (Remarks to the Author):\nSummary:\nSNPs in the intronic region of the gene EPAS1 (encoding for HIF2α), has been positively associated with\nthe risk for renal cell carcinoma, yet how HIF2α is regulated to sustain ccRCC is poorly understood. In\nthis manuscript Naas et al have used epigenomic profiling to identify an enhancer that controls the\nexpression of EPAS1 specifically in ccRCC. The authors show that this enhancer is crucial to the\nexpression of EPAS1, and they suggest that the activity of this enhancer is regulated by HIF and other\nrenal-specific transcription factors. Through this study, the authors have provided a molecular\nmechanism explaining how the oncogene, HIF2α, is controlled in ccRCC to contribute to accelerated\ntumor formation/growth.\nMajor Concerns:\n• Figure1: The authors have shown a representative RNAscope image for EPAS1 in ccRCC tissue (1e).\nThe authors should include images from the other three patients in supplementary figures as well. To\nalso ensure that the signal is specific, the authors should consider including a negative control (either\nnormal tissue or control PTC cells). If the RNAscope probes have been validated in a different study,\nthe authors should include the reference. Could the authors also elaborate on how cancer cells were\ndistinguished from noncancer cells in 1e? Why is the EPAS1 signal restricted only to some areas within\nthe tissue and is this seen across patients?\nWe thank the reviewer for evaluating our manuscript. In the revised version, we have included images\nof RNAscope experiments from tissues of additional donors (Supplementary Fig. 2 and 3). We\nperformed RNAscope for HIF-2α on non-tumor kidney tissue and used a control probe as negative\ncontrol. The commercially available probe had been validated by an independent group and we have\nadded the citation to the method section (Varberg, K.M. et al. 2023, Nat Commun,\nhttps://doi.org/10.1038/s41467-023-40424-5).\nWe had used staining for CD31 or CA9 to identify endothelial or tumor cells, respectively, and have\nincluded this data in the new version of the manuscript (Supplementary Fig. 3). As expected from\nprevious analyses (Wiesener M.S., et al. 2002, FASEB Journal, doi.org/10.1096/fj.02-0445fje),\nendothelial cells displayed high levels of HIF-2α mRNA in RNAscope experiments. This may explain the\nsomewhat restricted pattern of HIF-2α mRNA signal observed in the tumor tissue as noted by the\nreviewer, which appears to be generated by endothelial cells in vessels. However, we also detected\nsubstantial HIF-2α mRNA signals in tumor cells. In these cells, lipids and glycogen are enriched due to\nunderlying metabolic activity of HIF (“clear cell phenotype”), which may lead to accumulation of the\ncytoplasm signals at cell borders contributing to the pattern of expression. In order to corroborate the\nfinding of increased HIF-2α mRNA in tumor cells and exclude an incorrect assignment of the HIF-signals,\nwe have analyzed scRNA-seq data and have screened isolated primary tumor cells for HIF-2α mRNA\nexpression (Fig. 1d and 1g). In both analyses, HIF-2α mRNA was increased in tumor cells when\ncompared to renal tubule cells.\n1\n• Figure 2: The authors have highlighted enh-1031503 as ccRCC-specific. However, this region is also\nactive in PTC from patients 1 and 2, less so in patient 3. Could the authors comment on why this may\nbe? Could it be cross-contamination or some other reason? If the authors perform differential analysis\non the K27ac peaks at this region, is there significant difference? The same variability in H3K27ac signal\nis apparent in Supp Fig 3, when comparing normal and ccRCC tissue. Could the authors elaborate on\nthis variability? Perhaps include the corresponding EPAS1 expression status?\nThe region of enh-1031503 has been described as a “ccRCC-gained enhancer” by Yao et al. integrating\nH3K27ac ChIP-seq signals from ten tumors and corresponding normal kidney tissue (Yao, X. et al. 2017,\nCancer Discov, doi: 10.1158/2159-8290.CD-17-0375). We prefer to use the term “ccRCC-activated”\nenhancer rather than ccRCC-specific, because the reviewer is right that some accessibility and activity\ncould be seen in tubule cells, especially within the upstream region which interacted with the lineage-\nspecific factors PAX8 and HNF-1β. However, activity of the whole region increased substantially in\nccRCC cells. We speculate that HNF-1β and PAX8 bound to this region in tubule cells explaining some\nof the “basal” activity. In contrast, the element that interacted with HIF (KIRC_10770) showed a much\nmore prominent increase in accessibility indicating that HIF contributed to some extent to the gained\nactivity. As outlined below and in the new version of the manuscript, we performed a great number of\nadditional epigenetic analyses using transcription factor knock-out or enhancer-defective cells which\nconfirm this hypothesis.\nWe performed analyses of differential activity for this region as requested by the reviewer. Although\nactivity was increased at this site in tumor cells of every of the three individuals compared to\ncorresponding tubule cells, the effect was not significant in the combined analysis probably because\nof the low number of samples (p=0.14, Point-by-point Fig. 1). In the H3K27ac data from Yao et al.,\nwhich comprised ten tumors and corresponding normal tissue, we detected a positive correlation of\nHIF-2α mRNA expression and H3K27ac ChIP-seq signal at the EPAS1-enhancer E2. This result just\nmissed significance in a Spearman’s rank correlation analysis (R=0.62; p=0.06). We provide this data\nnow in Supplementary Fig. 7c. Of note, in the KIRC ATAC-seq data the positive correlation of EPAS1\nexpression with accessibility of KIRC_10770 was significant (Fig. 2d).\nPoint-by-point Figure 1: Activity of the ccRCC-activated EPAS1-enhancer. a) Tracks of H3K27ac ChIP-seq experiments\nperformed on chromatin from primary tubule (PTC) and tumor (ccRCC) cells from patients #1, #2 and #3 at the EPAS1 locus.\nThe ccRCC-enhancer comprises a region with increased H3K27ac signal in tumor cells in comparison to tubule cells. Data is\nsimilar to Fig. 2a and Supplementary Fig. 6a. b) and c) Quantification of the H3K27ac signal at the ccRCC-gained enhancer\n(enh-1031503) and a positive control HIF-binding site (NDRG1 enhancer) for PTC or ccRCC cells from data shown in a). Signals\nwere normalized to reads per genomic content (RPGC) and the integral for the respective locus was calculated. Please see\nMethod section for details. Two-tailed unpaired t-test.\nWe can exclude cross-contamination of tubule cells with tumor cells for various reasons: a) we use\ntissue specimens for isolating PTC with great distance to the renal tumor; b) we routinely check cell\nmorphology in our cultures; c) we did not detect stabilization of HIF in untreated PTC in immunoblots\n(Fig. 1f and Supplementary Fig. 4a) or mRNA signals of HIF activation in RNA-seq experiments; d) we\nchecked RNA-seq data from the primary tubule cells which were generated in parallel to the epigenetic\ndata for reads with VHL-mutations corresponding to those in the respective tumor from the same\npatient. For patient #1, we detected a mutation within exon 2 in the tumor cells (c.343C>A) which was\nabsent in any of the reads of PTC RNA-seq from the same patient (Point-by-point Fig. 2). Thus, we can\nrule out cross-contamination of the signals from PTC with tumor cells. Regarding activity of this region\nin non-tumor cells, we would like to point out that H3K27ac signals at this site were also detected to\nsome extent in normal tissue in the data generated by Yao et al. (Yao, X. et al. 2017, Cancer Discov,\ndoi: 10.1158/2159-8290.CD-17-0375, Supplementary Fig. 7a) indicating some “basal” activity in non-\ntumor tissue as discussed above.\nPoint-by-point Figure 2: ccRCC cells depict specific VHL mutation not detectable in corresponding primary tubule cells. RNA-\nseq tracks (BAM format) from of lysates from primary tubule (PTC) and tumor (ccRCC) cells from patient #1 at exon 2 of the\nVHL gene. Technical duplicates (replicate #1 and #2, respectively) were sequenced. The mutation c.343C>A (green bar in\nccRCC RNA-seq) is present in all reads from the tumor cells, but not in PTC reads. Please also not the substantially decreased\nabsolute read number across this exon of VHL in ccRCC compared to PTC.\nIn Fig. 2e, the authors should also include H3K27ac signal, wherever possible, to compare the degree\nof enhancer activity across these tumor types.\nThe data presented in this figure (now Fig. 2c) was derived from a set of ATAC-seq experiments in a\nsubset of tumors from the TCGA collection (Corces, M.R. et al. 2018, Science, doi:\n10.1126/science.aav1898). Unfortunately, there is no corresponding data set available for all of these\ntumors for H3K27ac. However, we now provide a detailed analysis of the H3K27ac signals at this site\nfor the Yao et al. data including normal and tumor tissue (please see comment above, Fig. 2b,\nSupplemental Fig. 7, Yao, X. et al. 2017, Cancer Discov, doi: 10.1158/2159-8290.CD-17-0375).\n• Figure 3: When the authors knock-out HIF1β (3f, g), there is a modest decrease on HIF2α. Is this\nmodest decrease in line with what is expected? Is this true even at the EPAS1 transcript level? The\nauthors have shown how ATAC signal changes at the ccRCC-enhancer after re-introducing VHL (Fig 3e).\nThe authors should also depict how the enhancer activity is changing as VHL is being re-expressed.\nAdditionally, the authors show how EPAS1 RNA levels change upon addition of DMOG. Can the authors\ninclude how the EPAS1 enhancer status is changing in this case?\nThank you for these comments. Yes, we would expect only a modest decrease in target gene\nexpression when manipulating the activity of a distant HIF-binding enhancer. For example, in previous\nwork we reduced expression of MYC mRNA by approx. 30% when inactivating a distal HIF-binding\nenhancer element in ccRCC cells (Grampp, S. et al. 2016, Nat Commun, doi: 10.1038/ncomms13183).\nIn a recent study, authors inactivated the HIF-binding regulatory element which is involved in ccRCC-\nspecific CCND1 expression and thereby reduced CCND1 mRNA levels by approx. 60% in ccRCC cells\n(Patel, S.A. et al. 2022 Nature, https://doi.org/10.1038/s41586-022-04809-8). Thus, the size of the\neffects measured for the EPAS1-enhancers on EPAS1 transcription was in the same order of magnitude\nas determined for other important HIF-binding ccRCC enhancers. This is also in line with previous\nfindings that HIF-stabilization increased expression of already transcribed genes rather than leading to\nde novo expression (Xia, X. and Kung, A.L. 2009, Genom Biol, doi: 10.1186/gb-2009-10-10-r113).\nWe added mRNA expression data for HIF-2α to the HIF-1β ko experiments in 786-0 and RCC4 cells as\nrequested (Fig. 3e and 3f). HIF-2α mRNA was decreased upon HIF-1β ko in both cell lines.\nout HIF-1β (Fig. 3j). Levels of H3K27ac decreased at KIRC_10770 upon reduction of HIF-levels.\nIn line with absent HIF-binding under DMOG conditions (Fig. 4c), we did not observe increased\nchromatin activity at KIRC_10770 as measured by H3K27ac ChIP-seq in DMOG treated PTC. We provide\nthis data for the reviewer below (Point-by-point Fig. 3). This work is part of a different study on HIF-\nactivation in tubule cells for which we are in the process of preparing a manuscript.\nPoint-by-point Figure 3\nPoint-by-point Figure 3: Chromatin activity at the ccRCC-activated EPAS1-enhancer 2 is not changed in primary tubule cells\nfollowing to HIF-stabilization by DMOG. a) Tracks of H3K27ac ChIP-seq experiments from chromatin of primary tubule cells\n(PTC) exposed to 1mM DMOG for 4h or control conditions. The ccRCC-enhancer enh-1031503 is highlighted in yellow. b) and\nc) Quantification of the H3K27ac signal at the ccRCC-gained enhancer (enh-1031503) and the positive control locus NDRG1\nenhancer as a known HIF-binding site for PTC in samples shown in a). Signals were normalized to reads per genomic content\n(RPGC) and the integral for the respective locus was calculated. Two-tailed unpaired t-test.\n• Figure 4: It is unclear if the HIF Chip-seq signal in 4a is from WT/untreated PTC cells or cells treated\nwith DMOG. Can the authors point out where exactly KIRC_10770 and KIRC_10767-69 lie? Are the HIF\npeaks specifically on KIRC_10770? Is there some difference in motifs or methylation status at enhancer\n1 versus enhancer 2 that could explain why enhancer 1 shows stable HIF binding while enhancer 2\nshows more dynamic binding? Based on Fig 4d, it appears as though PAX8 and HNF1 peaks do not\ncoincide on KIRC_10770. What is the level of change in H3K27ac levels when these TFs are knocked-\nout in 786-O cells? Can the authors include this data to strengthen their claim that PAX8 and HNF1β\nregulate HIF2α, and that enhancer 1 or 2 maybe involved?\nThe ChIP-seq tracks for HIF-binding in PTC as presented in Figure 4a (now Fig. 4c) were generated from\nDMOG treated cells. We added this information to the figure legend and apologize for the lack of\ninformation in the legend in the initial manuscript. HIF signals were detectable at the EPAS1-enhancer\n1 confirming the presence of a HIF-stabilizing agent. We added detailed illustrations of the different\nKIRC elements at the EPAS1-enhancers to the revised manuscript as requested by the reviewer (e.g.\nFig. 2a, Fig. 5 f, Fig. 6a). Yes, HIF peaks within the EPAS1-enhancer E2 were specific to KIRC_10770.\nWe thank the reviewer for the question regarding the methylation status, which is indeed very\ninteresting, since methylation of HREs has been reported to avoid HIF-DNA interactions (D’Anna, F. et\nal. 2020, Genom Biol, doi: 10.1186/s13059-020-02087-z). To gain first insights into effects of\nmethylation on enhancer function, we resorted to available methylation data of the KIRC tumors and\ncorresponding normal kidney tissue from TCGA. This revealed that methylation of CpGs in the vicinity\nof both loci was significantly decreased in the ccRCC tumors when compared to normal kidney tissue.\nIn fact, the methylation status of other CpGs in this region was more or less unaffected except for one\n5\nother site. However, the methylation status in normal tissue was not different between the CpGs in\nvicinity of the two enhancers and was more reduced at EPAS1-enhancer E1 in the tumors\n(Supplemental Fig. 15). This may suggest that methylation status changed for both enhancers.\nHowever, it does not necessarily explain the more dynamic accessibility for HIF to EPAS1-enhancer E2.\nOne limitation of our analysis is that we did not test the methylation status of the specific HREs within\nthe enhancers.\nIn response to the question regarding the TFs, we performed additional experiments in 786-0 cells.\nFirst, we generated single clones of 786-0 cells with defective PAX8 or HNF-1β signalling by knocking\nout these TFs. We then profiled epigenetics of these cells by performing ATAC-seq and H3K27ac\nCUT&Tag-seq. In differentially accessible DNA-elements defined by ATAC-seq, we noticed a strong\nenrichment of the respective binding motifs of the two transcriptions factors (Supplementary Fig. 18).\nAt the EPAS1-enhancer E2, we detected substantially reduced accessibility and activity upon depletion\nof both factors. We added this data to Fig. 5e. Thus, together with the finding that knock-out of HNF-\n1β and PAX8 reduced HIF-2α mRNA (Fig. 5b) the newly generated epigenetic data fully confirmed that\nPAX8 and HNF-1β were crucially involved in the regulatory circuitry of EPAS1 expression via EPAS1-\nenhancer E2. Furthermore, we also confirmed the contribution of the specific binding sites of these\nTFs within EPAS1-enhancer 2 by targeting the binding sites using CRISP/Cas9 technology. This resulted\nin reduced HIF-2α mRNA and protein levels (please see also below and Fig. 5f-h).\n• Figure 5: The authors have deleted E1 or/and E2 to assay for the downstream effect on EPAS1\nexpression. However, there is insufficient data provided to show that the enhancers have been\neffectively abrogated. Can the authors include genotyping and DNA sequencing data to show that the\nHIF motifs are deleted? Can they also include H3K27ac data to show the extent to which the enhancer\nactivity has been affected upon CRISPR-manipulation? It is curious that even when E1+E2 are deleted,\nHIF2α is still expressed. Could the authors speculate on why that might be? It is unclear to me, what\nthe authors mean by enhancer 2 activity in trans. Do the authors mean to assess the effect of deleting\nenhancer 2 on HIF-2α downstream gene targets? Or did the authors only delete the enhancers on one\nof the alleles? Are 786-O cells diploid for the EPAS1 locus? If no, how many alleles for E1 and E2 were\nsuccessfully deleted by the authors? Could the authors include these details? Although it is mentioned\nin the methods, in the figure and figure legend, the authors state that the CUT&TAG was performed\nto assess HIF1β binding.\nWe now provide DNA sequencing data (ICE-analysis and sequencing of plasmids carrying the mutated\nsequences, Supplementary Fig. 20, 22, 23 and 24) to show that CRISPR/Cas9 technology introduced\nmutations at the targeted sites (e.g. HREs in the HIF binding site). We also performed new H3K27ac\nCUT&Tag-seq experiments in EPAS1-enhancers E2 (E2_HIF) ko cells to explore changes in enhancer\nactivity as requested (Fig. 7f). This revealed reduced activity at the KIRC_10770 element by preventing\nHIF-interactions.\nRegarding the effect size on HIF-2α mRNA reduction after knocking-out two enhancers we would like\nto refer to our statement above that the HIF-binding EPAS1-enhancers E1 and E2 rather modulate than\ninitiate HIF-2α expression of target genes similar to other HIF-binding enhancers.\nActivity of the enhancer in trans: the reviewer is right, in the initial manuscript we referred to effects\nof the EPAS1-enhancer E2 that were mediated by differential regulation of HIF-2α, i.e. downstream\nregulation of HIF-2 targets. To increase clarity and to avoid any confusion with allelic effects, we have\nchanged the wording from “effects in trans” to “effects on downstream HIF-2 targets”. We provide\nevidence for this effect in Fig. 7e-h. HIF-2 targets were downregulated in cells with a defective HIF-\nbinding site at EPAS1-enhancer 2 as measured by RNA-seq.\n6\n786-0 cells are hypertriploid. We confirmed CRISPR/Cas9 efficacy for the different sgRNAs by ICE\nanalysis and cloned DNA fragments from EPAS1-enhancer ko clones of cells. We detected 2-3 different\nmutations at the HIF binding sites per clone of cells suggesting that at least two alleles had been\naffected by CRISPR/Cas9 modulation. In reporter assays, we confirmed that the sequences with\ndeleted HREs had much lower luciferase activity compared to the wild-type sequence when\ntransfected Kelly cells were stimulated with DMOG. We have included this information in\nSupplementary Fig. 24. Please note, we did not detect residual HIF-binding to EPAS1-enhancer 2 by\nHIF-1β CUT&Tag-seq confirming that the mutations were sufficient to prevent HIF-binding (Fig. 7f).\nWe have indicated when HIF-1β binding was assessed by ChIP-seq or Cut&Tag, respectively,\nthroughout the manuscript.\n• Based on the ATAC-seq data, the element KIRC_10770 appears to be the most dynamic. What is the\ndirect contribution of this element in regulating EPAS1 expression levels? In the CRISPR deletions\nperformed in Fig 5, it is unclear if this element was abrogated or not. The authors should consider\nspecifically deleting KIRC_10770 to assay for downstream effects. Also, does this element carry motifs\nfor HIF? Or for Pax8/HNF1β? Surprisingly, contrary to the data from this manuscript, analysis in Supp\n6b shows that KIRC_10770 is very modest DNAse hypersensitivity in the renal/cancer category. Is it\npossible for the authors regroup this data to separate out cancer v/s non-cancer samples?\nThe element KIRC_10770 interacted with HIF in our ChIP-seq and CUT&Tag experiments. As pointed\nout by the reviewer, accessibility to this element was highest in VHL-defective renal cancer cells. This\nelement carries three HREs. We had targeted two of these HREs in the CRISP/Cas9 experiments (sgRNA\nE2_HIF in Fig. 6c-i and Fig. 7), which led to mutations abolishing HIF-binding (Fig. 7f) and led to\ndecreased HIF-2α mRNA levels in different cell lines (Fig. 6c, d and 7d). We provide data for the\nmutations introduced to this site and analysis of reporter activity of vectors harbouring the enhancer\nsequence with the different deletions in Supplementary Fig. 22-24.\nWe agree that DNAse hypersensitivity at this site in the renal/cancer category from the 733 biosample\ndata set was modest. However, it was the only category that displayed substantial accessibility across\nthe tissues and this finding corresponded well to the data presented for KIRC_10770 in the TCGA ATAC-\nseq analyses of KIRC and the other tumors (Fig. 2c). The different categories of accessibility in\nMeuleman et al. (Meuleman, W. et al. 2020, Nature, doi.org/10.1038/s41586-020-2559-3) were\nderived from a subset of the 733 biosamples. The renal/cancer category consists of 89 samples, most\nof them are from fetal renal tissue (74 samples) which did not show accessibility to KIRC_10770.\nHowever, we identified the selection of adult renal cancer as well as renal tubule derived samples and\nprovide the data below (Point-by-point Fig. 4). Albeit the number of samples is low, this analysis\nconfirmed increased accessibility in renal cancer samples. Thus, this provides further evidence for\ntissue specificity and functionality of KIRC_10770.\nPoint-by-point Figure 4: Chromatin accessibility at the ATAC element KIRC_10770 is increased in renal cancer cell samples\ncompared to tubule derived samples. ATAC site KIRC_10770 overlaps with the chromatin region chr2:46233000-46233180\ndefined by DNAse I hypersensitivity from the 733 biosamples set published by Meuleman et al. Only samples derived from\nadult tissue or cell lines in the compartment “renal/cancer” are shown.\nMinor Concerns:\n• The authors can include quantification for the western blots to depict the degree of change in HIF2α\nlevels upon the different perturbations throughout the manuscript.\nWe quantified western blots and provide data for this in a separate supplemental table\n(Supplementary Table 12). In general, effects caused by the different treatments on HIF-2α mRNA\nlevels were consistent with effects measured on HIF-2α protein in immunoblots (on average approx.\n50% reduction across all conditions tested). Evidence that these changes in protein levels were also\nfunctional came from the RNA-seq experiments in which direct downstream HIF-2 targets displayed\nreduced expression upon EPAS1-enhancer manipulation.\n• Fid 1d is not necessarily adding to the figure and can be removed or moved to supplementary.\nWe have removed this figure.\nHIF-2a is one of the most important driving factors in clear cell renal cell carcinoma.\nNaas et al. present a very interesting manuscript that identifies a feed-forward regulatory circuit\ncontrolling HIF-2a (EPAS1) mRNA expression in ccRCC. This involves an enhancer element that\nregulates HIF-2a mRNA expression.\nThe study makes excellent use of human cell resources from normal kidney tissue and from ccRCCs, of\ncultured ccRCC and other cancer cell lines, conducts different analyses of chromatin structure, status\nand protein binding (ATAC-seq, ChIP-seq, CUT&TAG), combined with RNA expression analyses and\nfunctional mutation of identified genomic sites as well as of the transcription factors in the regulatory\nnetwork. The study also integrates many different publicly available datasets from previous studies to\nsupport the experimental work (however, many of the figures are in fact entirely or heavily based on\nalready published data). While the data presented in the manuscript suggest that the overall\nmechanism that the authors have uncovered is very likely to be true, there are several aspects of the\nstudy that remain incomplete to fully support the proposed model and conclusions, as well as to\naddress some important open questions that are central to the overall findings. Most of these could\nbe readily addressed using the tools that the authors describe in the manuscript.\nMajor issues\n1. A very interesting observation of the overall study is that the HIF-2a enhancer E2 appears to be\nrelatively specific to ccRCC (and also to glioblastoma) which might explain in part the sensitivity of the\nkidney to the oncogenic effects of HIF-2a. However, there are several limitations to the current study\nthat prevent this conclusion being made. Fig 3b-e and Suppl Fig 8 nicely show that VHL re-introduction\ninto ccRCC cells suppresses HIF protein abundance, HIF-2a mRNA abundance and loss of ATAC-seq\nsignal at the enhancer, showing that there is a dependency on VHL in established cancer cell lines.\nHowever, efforts to show the dependency of this activity on HIF fall short in their current form.\nExperiments in Fig 3f and g would need to be complemented by HIF-2a mRNA measurements and\ncrucially by ATAC-seq to investigate the status of the enhancer. Enhancer status assessment is also\nneeded for experiments in Fig 4f and g which investigate the effects of mutation of PAX8 and HNF-1b.\nIt remains unclear whether PAX8, HNF-1b or HIF factor binding is mutually dependent and what effect\nthese factors have on the accessibility of the enhancer.\nWe thank the reviewer for evaluating our manuscript and for the constructive comments. In order to\ncomplement our data on enhancer function, activity and interactions, we performed a series of new\nexperiments along the suggestions of this referee and referees 1 and 3. In the revised version, we have\nincluded ATAC-seq, H3K27ac CUT&Tag-seq and mRNA data from cells with transcription factor knock-\nout (HIF-1β, HNF-1β, PAX8) or defective transcription factor-binding elements (Fig. 3, 5, 7). Confirming\na regulatory effect of these factors and the DNA-elements on HIF-2α expression, the CRISPR/Cas9\nengineered cells displayed reduced accessibility and activity of the corresponding enhancer elements.\nHIF-2α mRNA and protein were also significantly reduced when compared to control treated cells.\nRegarding the dependency of EPAS1-enhancer E2 activity and EPAS1 regulation on HIF, we now can\nprovide multiple lines of evidence. First, HIF-1β knock-out, which prevents HIF-α DNA interactions at\nHRE containing sequences, led to reduced HIF-2α mRNA and protein in 786-0 and RCC4 ccRCC cells\n(Figure 3 e-h). Second, exposure of pVHL re-expressing RCC4 cells to the HIF-stabilizer DMOG led to\nincreased HIF-2α mRNA levels (Supplementary Fig. 10). Third, in primary tubule cells siRNA mediated\n9\nknock-down of HIF-1α or HIF-1β prevented DMOG-induced increases of HIF-2α mRNA (Figure 4b).\nFourth, targeting HREs within EPAS1-enhancer 2 in ccRCC cells abolished HIF-binding to this site and\nled to diminished HIF-2α mRNA levels (Fig. 7d, f).\nIn the settings of HIF-1β knock-out, DMOG-exposure of VHL-re-expressing RCC4 cells as well as HRE-\ntargeting in ccRCC cells, we conducted new ATAC-seq and H3K27ac CUT&Tag-seq experiments and\nhave included this data in the revised manuscript (Fig. 3i, j, Fig. 7f, Supplementary Fig. 10). These\nexperiments confirmed reduced accessibility and activity at EPAS1-enhancer E2 upon the manoeuvres\nmentioned above.\nWe also explored dependency of HIF-binding to KIRC_10770 on the presence of the lineage-specific\nfactors PAX8 and HNF-1β. For this, we conducted HIF-1β CUT&Tag-seq experiments in 786-0 cells\ndepleted for PAX8 or HNF-1β or mutated at the binding sites of both factors at EPAS1-enhancer E2.\nIn all conditions, we detected reasonably preserved HIF-1β signals at the KIRC_10770 element\nirrespective of the presence of either of the two lineage-specific TF within the cell or at the enhancer.\nThis indicates that HIF-binding to KIRC_107770 was not entirely dependent on PAX8 or HNF-1β\n(Supplementary Fig. 21).\n2. The overall question which needs to be addressed is how the enhancer becomes accessible in ccRCC.\nIt goes from a state of inaccessibility in normal renal proximal tubule epithelial cells to being accessible\nin ccRCC cells. Based on the presented evidence it appears likely that this transition might be (at least\npartly) induced by loss of VHL and be HIF-dependent, however other genetic/epigenetic alterations\nthat have arisen in these cancer cell lines might have also cooperated with VHL loss to induce this state\n(given the very long timeframe of ccRCC evolution this is plausible). This concept should be\ninvestigated. Is VHL loss and HIF-a activation necessary and/or sufficient for enhancer activation? For\nexample, it should be investigated if mutation of VHL or overexpression of non-degradable HIF-1a and\nHIF-2a mutants in normal renal epithelial cells alters endogenous HIF-2a mRNA levels and enhancer\nstatus?\nIt would also be highly interesting to conduct hypoxia time course experiments in normal renal cells as\nhypoxia has previously been shown to induce a transient HIF-1a increase, followed by a longer-term\nHIF-2a increase. It may be that the feed-forward enhancer mechanism identified here plays a role in\nthis effect.\nWe fully agree with the reviewer that the mechanism(s) which change(s) enhancer accessibility and\nactivity are of outstanding interest to better understand malign transformation in ccRCC. Regarding\nHIF-binding enhancers, a very similar observation of differential activity between tubule and tumor\ncells has been made for the CCND1 enhancer, which appears to be specifically accessible in ccRCC cells\n(Schödel, J et al. 2012, Nat Genet, doi: 10.1038/ng.2204 and Patel, S.A. et al. 2022, Nature,\nhttps://doi.org/10.1038/s41586-022-04809-8). As pointed out by the reviewer, the process of\nchanging chromatin accessibility might take years or decades and might also involve additional\nmechanisms/mutations apart from VHL-inactivation. It is also possible that only a small cell population\nof the tubular system is susceptible to malign transformation and that this population is epigenetically\npredisposed to allow HIF-interactions at oncogenic enhancers as early as VHL is lost (e.g. CCND1,\nEPAS1). Identifying these mechanisms or the cell population is ongoing work, but we feel out of the\nscope for this manuscript. However, to begin to address this question we performed a series of\nexperiments in different cellular backgrounds and analysed existing data.\nFor example, we inactivated VHL in immortalized PTC derived from the urine of VHL-patients (huPTC)\nwho are especially predisposed to develop renal cancer and performed RNA-seq and ATAC-seq on\n10\nsingle clones of these cells after approx. 14 weeks of culture. Confirming the loss of functional pVHL,\nwe did detect stabilization of both HIF-α isoforms upon VHL-knock-out in immunoblot analyses in\nhuPTC (Point-by-point Fig. 5a). However, RNA-seq did not reveal increases of HIF-2α mRNA and\naccessibility to the HIF-binding element KIRC_10770 was not different between control and VHL-knock-\nout cells (Point-by-point Fig. 5 b, c).\nWe also identified an ATAC-seq data set published by Patel et al. (Patel, S.A. et al. 2022 Nature,\nhttps://doi.org/10.1038/s41586-022-04809-8) which was derived from the HK2 immortalized proximal\ntubule cell line overexpressing HIF-2α or empty vector. Patel et al. reported no change of accessibility\nat the CCND1 HIF-binding enhancer after 6 weeks of culture of these cells. Similarly, we did not detect\nchanges in accessibility to KIRC_10770 when inspecting this element in the published data (Point-by-\npoint Fig. 5 d). However, these cells are capable of inducing HIF-2α mRNA upon HIF-stimulation by\nDMOG probably via binding to EPAS1-enhancer E1 (Point-by-point Fig. 5 e, f). This indicates that similar\nto the CCND1-enhancer additional mechanisms must operate to render EPAS1-enhancer E2 more\naccessible in full-blown tumor cells.\nAs requested by the reviewer, we have performed long-time hypoxia exposure in primary tubule cells.\nThese cells tolerate hypoxia at 0.1% O for up to 48 hours. In ATAC experiments, we did not determine\n2\ndifferential accessibility to the KIRC_10770 element at EPAS1-enhancer (E2_HIF) in the hypoxic cells\ncompared to control conditions and negative control loci. Of note, accessibility to EPAS1-enhancer E1\nincreased substantially under hypoxic conditions comparable to the accessibility at a positive control\nlocus at the NDRG1 gene (Point-by-point Fig. 5 g-i). We show data from two PTC isolates and 786-0\ncells (cultured in normoxia) as a control. As mentioned above identifying factors that increase\naccessibility of KIRC_10770 is ongoing work.\nTaken together, VHL-knock out in huPTC from a VHL-patient, overexpression of HIF-2α in the\nimmortalized proximal tubule HK2 cell line or long-term hypoxia in primary tubule cells did not change\naccessibility to KIRC_10770. Thus, we speculate that additional mechanisms must become active to\nrelease HIF-2α regulation from this element. We are currently following this up in a separate study by\nperforming genetic manoeuvres in addition to VHL-knock out in the primary cells.\nPoint-by-point Figure 5: Accessibility to KIRC_10770 within EPAS1-enhancer E2 cannot be induced by loss of pVHL, HIF-2\noverexpression or hypoxia in primary tubule cells. a) Immunoblot analyses for HIF-2α, HIF-1α, pVHL and β-actin in lysates\nfrom single clones of human urine-derived primary tubule cells (huPTC). Cells were donated from a patient with VHL disease\n(het VHL loss). CRISPR/Cas-mediated complete knock-out of VHL (hom VHL loss) leads to stabilization of HIF isoforms. b)\nVolcano plot depicting differentially expressed genes (DEG) in RNA-seq experiments using single clones of huPTC shown in a).\nEach RNA sample was sequenced in technical duplicates. Yaxis indicates –log adjusted p-value as calculated by the\nhomozygous (hom) loss of VHL at the EPAS1 (left) and NDRG1 (right) locus. ATAC-element KIRC_10770 is highlighted in\norange. HIF-1β ChIP-seq track indicates HIF-β binding sites in 786-0 cells for comparison. d) ATAC-seq tracks from lysates of\nHK2 cells transfected with empty vector (EV) or VHL-resistant HIF-2α protein (HIF2A) at the EPAS1 (left) and NDRG1 (right)\nlocus. Data was published by Patel et al. (GEO: GSE163485) e) and f) Expression qPCR analyses for HIF-2α mRNA (e) or EGLN3\nmRNA (f) in lysates from HK2 exposed to 1mM DMOG or control conditions for 16h. **, p<0.01. Mean + SD. One sample t-\ntest with a hypothetical value of 1. n = 6 independent experiments. g) and h) ATAC-qPCR for E2_HIF (KIRC_10770) and\nnegative as well as positive control loci (E1 and NDRG1_HIF) in PTC from two different patients. Cells were exposed to hypoxia\n(0.1% O ) for 24h or 48h or cultured in normoxia. Values were normalized to a control site in proximity of EPAS1-enhancer 2\n2\ndepicting no relevant accessibility in ATAC-seq data of the respective cell type. i) ATAC-qPCR for E2_HIF and negative as well\nas positive control loci (E1 and NDRG2_HIF) in 786-0 cells.\n12\n3. Fig 3h and 3i lack evidence that DMOG was active in these cells (e.g. HIF-a western blots or activation\nof well characterised HIF-target genes) and proof of functional knockdowns is lacking.\nWe have included HIF-1α western blots as well as quantification of HIF target gene expression by qPCR\nfrom a selection of the primary cell cultures to give proof of HIF-stabilization by DMOG (Supplementary\nFig. 11 and 12). We also performed immunoblots for HIF and qPCR for EGLN3 expression using PTC\nupon HIF-knockdown experiments (Supplementary Fig. 12c, d).\n4. Fig. 3b, 3c. The argument presented around these figures is not convincing as it relies on the absence\nof an effect to prove a hypothesis. There is such a massive spread of expression levels in TCGA data\nacross each of the three genotypes that it appears very unconvincing that there is any relationship\nbetween these genotypes and HIF-2a mRNA.\nThank you for this critical comment. We had presented the eQTL data derived from PancanQTL\nanalyses (Gong, J. et al. 2018, Nucleic Acids Res, doi: 10.1093/nar/gkx861) in the first version of the\nmanuscript because it highlighted the region harboring the EPAS1-enhancer E2 as being linked to HIF-\n2α expression especially in the KIRC data compared to the other tumor entities. However, we agree\nthat the data presented for genotype-dependent HIF-2α expression in PTC did not prove the\npostulated hypothesis of differential action of the genotype when comparing tumor vs primary cells.\nThus, we have removed the genotype-expression data and the corresponding paragraph from the\nmanuscript and only kept the track with the published PancanQTL SNPs in new Figure 4c.\nIn the context of interactions of the EPAS1-enhancers E1 and E2 with genetic susceptibility to renal\ncancer and also in response to a comment from referee 3, we accessed the KIRC genotype and mRNA\nexpression data to test intronic variants which are associated with the development of renal cancer\nfor effects on HIF-2α expression. For SNP rs11894252 which was reported in the initial GWAS\n(Purdue, M. et al. 2010, Nat Gen doi: 10.1038/ng.723) and which was available for analysis on the\ngenotype array used by TCGA, we did not detect an eQTL for HIF-2α mRNA (Supplementary Fig. 17b),\nwhich is in line with a previous report (Lasker, R.S. et al. 2019, Eur J Hum Genet., doi.org/10.1038/s41431-019-\n0455-9). However, we also evaluated rs12617313, which had been detected as an independent signal\nin a follow-up fine-mapping study and for which the risk allele was strongly associated with the\npresence of VHL-mutations in tumors (Han, S.S. et al. 2012, Human Molecular Genetics,\ndoi.org/10.1093/hmg/ddr551). This SNP is a significant eQTL for HIF-2α mRNA expression in the TCGA\nKIRC tumor tissues, but not in non-diseased kidney tissue (Fig. 4d). We agree that there is a wide spread\nin this kind of data, but effects of common variants in non-protein coding regions on gene expression\nare small in general. We detected a clear genotype-dependent expression in this analysis. Moreover,\nusing our large cohort of PTC which we genotyped for this SNP, we observed a similar association of\nthe rs12617313 genotype with HIF-2α expression (Fig. 4e). Importantly, this association was only\ndetectable in cells in which HIF had been stabilized by DMOG, but not in untreated cells. A recent study\n(Schmid, V. et al., 2019, Sci Rep, doi: 10.1038/s41598-019-55098-7) also confirmed that this intronic\nregion physically interacts with the upstream EPAS1-enhancer E1 indicating the existence of a complex\ninterplay of enhancers with intragenic regions of EPAS1 in renal cancer. Thus, we can now link HIF-2α\nexpression with effects of ccRCC-associated germline variants and HIF-activated enhancers. This is\nthe first report that explains some of the genetic associations of the intronic genetic variants within EPAS1\nin VHL-defective ccRCC biology.\nSince the effect appears to be dependent on loss of VHL and/or HIF-activation, this puts this genetic\nlocus in line with or – since HIF-2 is one of the master upstream regulators – even ahead of other\nimportant RCC risk loci interacting with HIF such as CCND1 (Schödel, J. et al. 2012, Nat Genet, doi:\n13\n10.1038/ng.2204 and Patel, S.A. et al. 2022, Nature, https://doi.org/10.1038/s41586-022-04809-8) or\nMYC (Grampp, S. et al. 2016, Nat Commun, doi: 10.1038/ncomms13183). We have included these\nnovel findings in Fig. 4d, e and Supplementary Fig. 17.\n5. Fig 5. The effects of the sgRNAs at the target loci in whole populations and single cell clones should\nbe verified by DNA sequencing.\nWe routinely perform ICE-analysis from Sanger sequencing data as well as sequencing of plasmids\ncarrying the introduced mutations to verify effects of sgRNAs experiments. We provide representative\nexamples of this data for the CIRSP/Cas9 experiments conducted in this study in Supplementary Fig.\n20, 22-24.\n6. Supplementary Figure 6d is difficult to understand and currently not possible to interpret. A diagram\nof the vectors that are being tested would be helpful. It is not clear what normalized luciferase intensity\nis referring to as there is no mention of the empty luciferase vector. From this it appears that the\nnormalization is simply a transfection normalization (beta-galactosidase) but luciferase signals are not\nrelative to an empty vector. It is therefore impossible to conclude whether DMOG treatment has any\nspecific effects on the E1 and E2 elements or whether this is a non-specific effect on the luciferase\nplasmid in general. Cloning HRE mutants into the E1 and E2 elements would provide important\nadditional evidence that the E1 and E2 elements are responsive to HIFs.\nWe apologize for the lack of clarity regarding strategy and normalization of reporter assays. We have\nincluded a diagram of the vector used in new Supplementary Fig. 14e. In the revised figure, we have\nincluded the data from empty vector controls for experiments testing the HIF-binding sequences from\nEPAS1-enhancers E1 and E2 upon HIF-stabilization in reporter assays (Supplementary Fig. 14d). We\namended the figure legend to clarify how normalization was performed: “Values of luciferase activity\nwere first normalized to activity of co-transfected β-galactosidase and subsequently to the activity of\nthe respective control condition without HIF-stabilization”. As requested by the referee, we also\ngenerated reporter constructs with different HRE mutations at KIRC_10770 introduced by the sgRNAs\nand performed reporter assays using DMOG as HIF-stabilizing agent (Supplementary Fig. 24). This\nconfirmed reduced reporter gene activation by DMOG in cells transfected with HRE mutant vectors\nindicating functionality of the HREs.\n7. Overall the study lacks in vivo evidence. Figure 6 essentially represents a series of reanalyses of\npublished data (b,c,d) that correlate with the model that is being proposed by the current study. 6e\nand f show a cell culture 3D growth assay presumably as a surrogate for a xenograft assay comparing\ncontrol clones and E2 ko clones but there is no evidence that the observed growth differences are\ndependent on the enhancer or on changed HIF-2a expression levels. A rescue experiment with\nexogenous HIF-2a expression would be beneficial to support the dependency of this assay on HIF-2a\nlevels. It is currently unclear what the experiments shown in Fig 6 add compared to the published\nstudies and don’t even directly validate the enhancer hits that were identified in the published screen\nfrom the Nature paper (reproduced in Fig 6d).\nWe now have performed tumor xenograft experiments using the two different EPAS1-enhancers E2\nknock-out (E2_HIF2.1 and 2.2) and two control (nt 0.1 and 0.2) clones of 786-0 cells. Inactivating the\nHIF-binding site in EPAS1-enhancer 2 prevented tumor growth in NOD/SCID-gamma mice (Figure 7b,\nc, Supplementary Figure 25a). In order to generate these cells, we had used two HRE-targeting sgRNAs\n14\nthat corresponded exactly to two out of eight sgRNAs employed by Patel et al. in CRISPRi experiments\nin xenograft models (Patel, S.A. et al. 2022 Nature, https://doi.org/10.1038/s41586-022-04809-8). We\nhave clarified the concordance of the two sgRNA sequences from our study with two sgRNAs from\nPatel et al. in the revised manuscript. We characterized the EPAS1-enhancers E2 knock-out cells in this\nwork now extensively and provide evidence for clear HIF-2 driven downstream effects. We removed\nthe 3D growth assay from the manuscript. Thus, our work complements the wealth of existing data on\nthe role of HIF-2α in promoting renal tumor growth.\nRegarding presentation of published data, we have removed former Fig. 6 b and c from the manuscript.\nWe refer to these results showing higher sensitivity to HIF-2 inhibition in tumors with high levels of\nHIF-2α mRNA now in the introduction and the discussion section. We have moved former Fig. 6d to\nsupplement material (Supplementary Fig. 25b). Patel et al. used 8 different sgRNAs (two each in four\nconstructs) to target this region, but in their article they plotted values only for a combined analysis of\nthe two most depleted constructs per region. We had plotted the data from the CRISPRi screen in a\ndifferent way than Patel et al. so that the effects of all four sgRNAs combinations targeting this region\nbecame obvious independently.\nMinor issues\n1. Some of the diagrams that are presented as figure panels are not necessary to understand these\nsimple experiments. Figs 1d, 1g, 2a, 5f could be removed from the figures.\nWe removed most of the diagrams as requested except new figure 1e, which we would like to keep in\nthe manuscript, since it illustrates that tumor and tubule cells were derived from the same patient.\n2. While many figures use data mining to interrogate datasets generated in the current study, quite a\nfew figure panels are simply re-representations of data generated in other studies so should not be\nshown again. e.g. the representation of TGCA data shown in Fig 1b has been previously published by\nNature Communications in another study (https://doi.org/10.1038/s41467-020-17873-3). Fig 6b and\n6c are re-representations of published figures in Nature (doi:10.1038/nature19796) and Fig 6d\nreplots data obtained in a recent Nature publication (https://doi.org/10.1038/s41586-022-04809-8).\nAs requested, we either moved the data to supplemental material (former Fig. 1b and former Fig. 6d)\nor removed it from the manuscript (former Fig. 6b and c). We refer to the results showing higher\nsensitivity to HIF-2 inhibition in tumors with high levels of HIF-2α mRNA in the introduction and the\ndiscussion sections (Chen, W. et al. 2016, Nature, doi.org/10.1038/nature19796).\n3. The axes of Fig 2d are not labelled in the figure.\nThank you. We have labelled the axes in this figure in the new version.\n4. The western blot lanes in Fig 3c should be labelled above the blots as it is not immediately clear\nthat these are the same conditions as in 3d below.\nThank you. We have included separate labelling for immunoblot and mRNA data.\n5. It would be helpful for the DNA nucleotide HRE sequences that are being targeted in Fig 5a to be\n15\nshown in relation to the sgRNAs that target these sequences so that the reader can understand what\ntype of mutations are being introduced\nWe now provide a supplementary figure that displays sequence information of the HRE carrying\nelements and the sgRNA guides used for targeting these elements (Supplementary Fig. 19).\n6. Supplementary Fig 6. The authors state that a small fraction of non-ccRCC cell lines show signal at\nenhancer 2 as further evidence of the ccRCC specificity of enhancer binding of HIF-b. However, 3 of 8\ncell lines show this signal so this statement is not supported by evidence\nWe thank the reviewer and have amended the statement to: “Besides, following HIF-stabilization we\nalso detected HIF-signals at EPAS1-enhancer E1 (2 out of 7 cell lines) or at EPAS1-enhancer E2 (2 out\nof 7 cell lines) in non-renal tumor cells as well as in immortalized HKC-8 renal tubule cells and VHL-\ncompetent ccRCC Caki-1 cells in data from published and newly generated sequencing experiments.\nThis suggests that some other tumor cells might regulate EPAS1 expression from these sites\n(Supplementary Fig. 14a).”\nFor “2 out of 7 cell lines” we refer to non-renal cells.\nHIF has been known to be important in the development and progression of renal clear cell carcinoma\nthough its mechanism of dysregulation in the cancer is largely unknown. In the article, Naas et al. used\nepigenome and transcriptome data to study the mechanism of augmented EPAS1 expression in ccRCC.\nThey found that HIF-2α is highly expressed in ccRCC compared to normal samples. According to the\nenhancer-gene linkage inference, the ccRCC-specific enhancer enh-1031503 shows great potential to\nregulate gene EPAS1 expression through the binding of HIF, HNF, and PAX8 transcription factors. They\nhave also verified that knocking out the enhancer can inhibit the development of ccRCC, which\nindicates a great potential for clinical application for the study. I also have a few comments:\nMajor comments\n1. I understand this is a new mechanism of HIF-2α regulation, could you also explain the impact and\npotential application of the study? What is the advantage of blocking E2 instead of eliminating HIF-2α\nexpression?\nWe thank the reviewer for these fundamental questions. Accessibility to the EPAS1-enhancer E2\ncorrelated with HIF-2α expression. Thus, an epigenetic screening of tumors for activity of this enhancer\ncould support selection of patients for anti-HIF-2 therapy in the future.\nRegarding a potential advantage of blocking the EPAS1-enhancer E2 instead of targeting HIF-2 protein,\nwe would like to refer to observations that have been made in some of the tumorgraft ccRCC-models:\nunder treatment with a HIF-2 inhibitor, mutations in HIF-2α or HIF-1β were detected which allowed\ntumors to escape HIF-2 targeting therapy (Chen, W. et al. 2016, Nature,\ndoi.org/10.1038/nature19796). Thus, in principle blocking HIF-2α mRNA expression by targeting\nEPAS1-enhancer E2 could represent an attractive alternative therapeutic option to decrease HIF-2\nlevels in patients with resistance to therapy aiming at HIF-2 protein. Moreover, one could speculate\nthat compared to directly targeting HIF-2 protein adverse effects of this therapy might be not as\npronounced, because functionality of the ccRCC-activated element appears to be limited to cancer.\nFor example, reduction of erythropoietin expression and resulting anaemia is the most common\nadverse event caused by belzutifan therapy in RCC patients (Choueiri, T.K. et al. 2024, N Engl J Med,\nDOI: 10.1056/NEJMoa2313906). Anaemia occurs in more than 80% of patients. From our data, one\nwould not expect such drastic effects on erythropoietin expression in interstitial cells of the kidney by\nspecifically targeting the tumor DNA-element EPAS1-enhancer E2.\n2. In vivo and survival studies shall be considered to prove the impact of E2 regarding its impact on\ncancer development and progression.\nIn the light of your comment and a comment from referee 2, we have performed tumor xenograft\nexperiments using two different EPAS1-enhancers E2 knock-out (E2_HIF 2.1 and 2.2) and two control\n(nt 0.1 and 0.2) clones of 786-0 cells. Inactivating the HIF-binding site in EPAS1-enhancer 2 prevented\ntumor growth in NOD/SCID-gamma mice (Figure 7b, c) confirming its relevance in vivo. Thus, our work\ncomplements the wealth of existing data on the role of HIF-2α in promoting renal tumor cell growth.\nFor example, overexpression of HIF-2α in ccRCC cells led to increased tumor growth in xenograft\nmodels (Kondo, K. et al. 2002, Cancer Cell, doi: 10.1016/s1535-6108(02)00043-0; Raval, R. et al. 2005,\nMol Cell Biol, doi: 10.1128/MCB.25.13.5675-5686.2005). In line with this, knock-down of HIF-2α\nreduced tumor burden in a xenograft model (Kondo, K. et al. 2003, PLoS Biol, doi:\n17\n10.1371/journal.pbio.0000083). More recently, specific pharmacological inhibition of HIF-2 by PT2399\nor by systemic administration of anti-HIF-2α siRNA reduced xenograft growth (Chen, W. et al. 2016,\nNature, doi.org/10.1038/nature19796; Cho, H. et al. 2016, Nature, doi: 10.1038/nature19795; Ma, Y.\net al. 2022, Clin Cancer Res, doi.org/10.1158/1078-0432.CCR-22-0963).\nWe agree that future studies should focus on the effects of EPAS1-enhancer E2 activity on cancer\nprogression and survival in humans. To do this we currently consider epigenetic analyses of enhancer\nactivity in our large cohort of ccRCC patients in order to identify a prognostic value of this enhancer.\nThis will be an immense task, which we feel is outside of the scope of this initial work describing the\nexistence of the enhancer and its interaction which oncogenic signalling.\n3. It seems most conclusions are based on 786-0 cell lines (e.g. ChIP-seq data, KO experiment, and\nxenograft model), which may be biased. Could you also provide the result of RCC4 to make the\nconclusion more solid? Also, a ccRCC cell line without VHL loss can be a control.\nIn the new version of our manuscript, we provide comprehensive transcriptomic and epigenetic data\nfor 786-0 and RCC4 cells as requested by the reviewer. Knock-out of the HIF-binding site at EPAS1-\nenhancer E2 evoked remarkable similar transcriptomic changes in both cell lines including the HIF-2\nspecific target CCND1 (please see Fig. 6i).\nWe also provide epigenetic data from primary RCC cells with defective VHL-protein and have\nperformed additional experiments in the VHL-competent ccRCC cell line Caki1. In this cell line, we\ndetected HIF-DNA interactions at EPAS1-enhancers E1 and E2 upon pharmacological HIF stabilization\nsuggesting that in addition to VHL-loss, other mechanisms might be involved in creating access for HIF\nto the EPAS1-enhancer E2 under certain conditions (e.g. in glioblastoma cells or this VHL-competent\nccRCC cell line; Supplementary Fig. 14a).\nMinor comments\n1. Is EPAS1 expression related to the survival of patients?\nThis is a very interesting question. We have explored the TCGA KIRC cohort for prognostic effects of\nHIF-2α mRNA on patient survival using the GEPIA2 online tool (Tang, Z. et al. 2019, Nucl Acids Res,\ndoi.org/10.1093/nar/gkz430). The analysis revealed that increased HIF-2α mRNA levels in the tumors\ncorresponded to longer survival of the patients (Point-by-point Fig. 6a). Conformingly, in samples from\nprogressed tumor stages lower levels of HIF-2α mRNA were detected compared to samples from less\nadvanced tumor stages (Point-by-point Fig. 6c). Similar results were observed and have been described\nfor the ccRCC-specific HIF-2 target gene CCND1 (Point-by-point Fig. 6b, d and e.g. Wang, Q. et al. 2019,\nCancer Medicine, doi.org/10.1002/cam4.2313). Currently, it is unclear to us whether this effect is a\ntrue result of reduced HIF-2 regulation/expression in tumor cells in progressed stages or whether this\nassociation is caused by effects not directly provoked by the tumor cells (e.g. other cell types contribute\nto HIF-2 mRNA levels and composition of different cell types and their contribution to expression levels\nmight change during tumor progression). In this respect, it would be interesting to test whether\naccessibility/activity of the EPAS1-enhancer E2 in tumor cells is prognostic. As outlined above, we plan\nthis in future work.\nPoint-by-point Figure 6: EPAS1/HIF-2α mRNA expression is associated with overall survival and tumor stage in KIRC. a)\nSurvival analysis based on the expression of EPAS1 RNA in the TCGA-KIRC cohort. Groups were separated in “Low EPAS1” and\n“High EPAS1” according to the median. Hazards Ratio (HR) was calculated based on Cox PH Model. Dotted line indicates 95%\nconfidence interval. b) Same analysis as in a) for CCND1 expression. c) Box plot depicting the EPAS1 RNA expression in\ndifferent cancer stages for the TCGA-KIRC cohort. d) Same analysis as in c) for CCND1 expression. Data for a) – d) were\naccessed and analysed via GEPIA2.\n2. When you have single-cell ATAC-seq data for ccRCC, could you also verify if E2 is co-accessible with\nother regulatory elements such as E1 and promoter of EPAS1?\nThank you. In order to approach this question, we resorted to snATAC-seq data from renal tumors and\nnormal tissue published by Wu et al. (Wu, Y. et al. 2023, Nat Commun, doi.org/10.1038/s41467-023-\n37211-7). We have used the software Cicero (Pliner, H.A. et al. 2018, Mol Cell,\nDOI: 10.1016/j.molcel.2018.06.044) to interrogate co-accessible regions within the broader region\n(500 kb) of EPAS1. This analysis revealed a significant correlation of accessibility of EPAS1-enhancer E2\n(chr2:46232855-46233286) with EPAS1-enhancer E1 (chr2:46073018-46074032) in ccRCC cells (co-\naccessibility score: 0.13). This result indicates that both EPAS1-enhancers can be accessible in the same\ncell. We have included this finding in the result section. In line with our results from bulk-ATAC-seq,\nwe could not detect a signal for KIRC_10770 in normal epithelial cells. Thus, this prevented the\ncorrelation of accessibility of EPAS1-enhancer E2 with EPAS1-enhancer E1 under this condition. We\ncould not detect a positive correlation of enhancer accessibility with the EPAS1 promoter.\n3. It also requires data from corresponding patient samples such as Figure 4a.\nWe performed CUT&Tag experiments in primary ccRCC cells for HIF-1β and detected robust binding of\nHIF-1β to EPAS1-enhancers E1 and E2. We have included this data in Supplementary Fig. 13.\n19\n4. It seems HIF, HNF, and PAX8 bind to different regulatory elements in 4d. Do you have any direct\nexperiment evidence for the co-binding / collaboration at E2? What if designing sgRNA to target\nPAX8 or HNF binding sites at E2?\nThe reviewer is right. HNF-1β and PAX8 bind to a different regulatory element than HIF as defined by\nCorces et al. (KIRC_10767: PAX8, KIRC_10767: HNF-1β, KIRC_10770: HIF; Corces M.R. et al. 2028,\nScience, doi: 10.1126/science.aav189). We followed the suggestion of the reviewer and deleted the\nHNF-1β motif within KIRC_10767. We did not detect a PAX8 consensus motif within the PAX8 ChIP-seq\nsignal. However, we used two sgRNAs to target the DNA-sequence located in the centre of the PAX-8\nChIP-seq peak. Both manoeuvres effectively introduced mutations within the binding-sites and led to\nreduced HIF-2α mRNA which confirmed the functionality of these elements on HIF-2α mRNA\nexpression (Figure 5f-h, Supplementary Fig. 19, 20). We also explored whether HIF-binding to\nKIRC_10770 is dependent on the presence of the lineage-specific factors PAX8 and HNF-1β. For this,\nwe conducted HIF-1β CUT&Tag-seq experiments in 786-0 cells depleted for PAX8 or HNF-1β or\ndepleted for the binding sites of both factors at EPAS1-enhancer E2. Under all conditions, we detected\nreasonably preserved HIF-1β signals at the KIRC_10770 element irrespective of the presence of either\nof the two TF within the cell or at the enhancer. This indicates that HIF-binding to KIRC_107770 is not\nentirely dependent on PAX8 or HNF-1β (Supplementary Fig. 21).\n5. 5b lacks the impact of individual E1 and E2.\nThe reviewer is right. This experiment was conducted in primary ccRCC tumor cells which grew slow if\ngrowing at all and from which only a limited number of cells was available for experiments. This is why\nwe transfected both sgRNA combinations together into these cells. The results were in line with results\nderived from combining the sgRNAs in 786-0 and RCC4 cells, in which we were able to also test\nindividual sgRNAs. In the new version of the manuscript, we confirmed that both enhancers interact\nwith HIF by HIF-1β CUT&Tag-seq indicating that they were functional in these primary cells\n(Supplementary Fig. 13).\n6. Is the SNP rs10193766 a risk site of ccRCC?\nGWAS analyses have discovered a large number of RCC risk sites. However, rs10193766 or SNPs in high\nLD with this SNP have not been described as a risk site for RCC development. It was discovered to have\nan eQTL for HIF-2α expression in the KIRC (ccRCC) data by PancanQTL TCGA analyses (Gong, J. et al.\n2018, Nucleic Acids Res, doi: 10.1093/nar/gkx861). However, in response to a valid comment of referee\n2, we re-arranged the manuscript and removed the genotype-expression correlation for rs10193766.\nIn response to your comment and a comment from referee 2, we further evaluated the role of genetic\nsusceptibility to renal cancer in regulation of EPAS1 expression. To do this, we first accessed the TCGA-\nKIRC genotype and RNA expression data to test intronic variants which are associated with the\ndevelopment of renal cancer for effects on HIF-2α expression. For the SNP rs11894252 which was\nreported in the initial GWAS and which was available for analysis on the genotyping array used by\nTCGA (Purdue, M. et al. 2010, Nat Gen doi: 10.1038/ng.723), we did not detect an eQTL for HIF-2α\nmRNA (Supplementary Fig. 17b), which is in line with a previous report (Lasker, R.S. et al. 2019, Eur J\nHum Genet., doi.org/10.1038/s41431-019-0455-9). However, we also evaluated rs12617313, which\nhad been detected as an independent signal in a follow-up fine-mapping study and for which the risk\nallele was strongly associated with the presence of VHL-mutations in renal tumors (Han, S.S. et al.\n2012, Human Molecular Genetics, doi.org/10.1093/hmg/ddr551). This SNP is a significant eQTL for HIF-\n20\n2α mRNA expression in the TCGA KIRC tumor tissues, but not in non-diseased kidney tissue (Fig. 4d).\nMoreover, using our large cohort of PTC which we genotyped for this SNP, we detected a similar\nassociation of the rs12617313 genotype with HIF-2α expression (Fig. 4e). Importantly, this association\nwas only detectable in cells in which HIF was stabilized by DMOG, but not in untreated cells. A recent\nstudy also confirmed that this intronic region physically interacts with the upstream EPAS1-enhancer\nE1 indicating the existence of a complex interplay of enhancers and intragenic regions of EPAS1 in renal\ncancer (Schmid, V. et al., 2019, Sci Rep, doi: 10.1038/s41598-019-55098-7). Thus, we can now link HIF-\n2α expression with effects of ccRCC-associated germline variants and HIF-activated enhancers. This is\nthe first report that explains some of the associations and effects of the intronic genetic variation\nwithin EPAS1 in VHL-defective ccRCC biology. We have included these novel findings in Fig. 4d, e and\nSupplementary Fig. 17. We thank you for this important comment that has stimulated this new part of\nthe work.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank you very much for evaluating our manuscript as a co-reviewer.\n22"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Many thanks to the authors for conducting so many new experiments that were suggested by myself and the other reviewers.\nThe quality of the data and presentation of all of the figures is very high. I believe that these fully address all of the questions\nthat were raised during the review and that the new submission represents a complete story. The study identifies the\ntranscriptional regulatory mechanisms that underlie the tissue-specificity and oncogenicity of HIF-2a expression in ccRCC.\nI congratulate the authors on their work and believe that this represents a very important contribution to understanding the\nmechanisms that underlie the development and maintenance of ccRCC and provides an explanation for the tissue\nspecificity of the consequences of VHL mutation. This work makes an extremely important contribution to the research field\nof ccRCC.", "reviewer_3": "All my comments have been addressed.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}]

Decision letter