University of Groningen

SETD2 and PBRM1 inactivation in the development of clear cell renal cell carcinoma Li, Jun

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Download date: 29-09-2021 chapter 4

PBRM1 loss in Primary Tubular Epithelial Cells leads to aberrant expression of immune response

Jun Li1, Joost Kluiver2, Jan Osinga1, Helga Westers1, Anke van den Berg2, Rolf H. Sijmons1 and Klaas Kok1

1Department of Genetics, and 2Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, PO box 30.001, 9700 RB Groningen, the Netherlands

Manuscript in preparation Chapter 4

Abstract Clear cell Renal Cell Carcinoma (ccRCC) is characterized by loss of the short arm of 3 in more than 90% of the cases. The Polybromo-1 (PBRM1) maps to this region and is the second most frequently mutated gene in ccRCC. PBRM1 is a subunit of the PBAF complex, a subgroup of the SWI/SNF complexes that modify local accessibility of chromatin, and in that way contributes to the regulation of . With a mutation frequency of 30%, of which about 80% are presumably inactivating, it is clear that inactivation of PBRM1 is a major contributor to the development of ccRCC. However it is unclear how this event contributes to the early steps of ccRCC development. To study the role of PBRM1 in ccRCC initiation, we performed lentiviral-based shRNA knockdown of PBRM1 in kidney primary tubular epithelial cells (PTECs), the presumed normal counterparts of ccRCC. Interestingly, knockdown of PBRM1 did not give the PTECS an clear growth advantage, nor did it extend the proliferative capacity as compared to control PTECs. At the gene expression level, both the gene set enrichment analyses and the analysis pointed towards a significant effect of PBRM1-KD on the expression on immune responsive genes. Previous studies have already shown aberrant expression of IFN responsive genes in malignant cells with defective SWI/SNF complexes, but mostly without specifying the specific subgroup of these complexes. Based on our data we suggest that functional loss of the wild type PBAF complex could be one of the events triggering the development of ccRCC.

86 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Introduction Clear cell Renal Cell Carcinoma (ccRCC) is characterized by copy number loss of a large part of the short arm of (Kok et al., 1997; van den Berg et al., 1997), which occurs in more than 90% of the cases (Hakimi et al., 2013). This frequent allelic loss indicates the location of one or more tumor suppressor genes (TSGs) at this chromosome arm. Any of these genes might be bi-allelicly inactivated due to a mutation in the remaining allele following the model proposed by Knudson (Knudson, 1971). Linkage studies of von Hippel-Lindau cancer syndrome families paved the way for the identification of the Von Hippel–Lindau (VHL) gene, the first identified TSG located at 3p (Latif et al., 1993). In recent years, a series of next generation sequencing studies revealed three additional candidate tumor suppressor genes on 3p, i.e. PBRM1, SETD2 and BAP1 (Duns et al., 2010; Duns et al., 2012; Cancer Genome Atlas Research 2013; Sato et al., 2013). In ccRCC, PBRM1 is the second most frequently mutated gene after VHL. Importantly, more than 80% of the nonsynonymous mutations in PBRM1 are 4 inactivating mutations (COSMIC database). This high mutation frequency indicates that PBRM1 inactivation is a crucial event in the development of ccRCC tumors. PBRM1 encodes the BAF180 , a subunit of a specific group of SWI/SNF complexes (Xue et al., 2000; Roberts and Orkin, 2004). In general, SWI/SNF complexes are recruited to chromatin and function to mediate ATP-dependent chromatin remodeling processes. The human SWI/SNF complex consists of multiple subunits including one of two known ATPases (Roberts and Orkin, 2004; Kadoch and Crabtree, 2015). SWI/SNF complexes are divided into two different subtypes known as BAF (BRM-associated factors) and PBAF (polybromo-associated BAF) (Nie et al., 2003). The BCL11, BCL7, CRD9 and ARID1 subunits are specific for BAF complexes, whereas PBRM1 (also known as BAF180), BRD7, and ARID2 (also known as BAF200) are specific for PBAF complexes (Xue et al., 2000; Hohmann and Vakoc, 2014). BAF and PBAF target different genomic segments (Angus-Hill et al., 2001; Lemon et al., 2001). By virtue of its bromodomains, PBRM1 functions as a reader of acetylated Lysines at H3K4 and H3K9 and enables targeting of PBAF to these regions (Kupitz et al., 2008; Thompson, 2009). The specific epigenetic recognition mechanism of the BAF complex is still not clear (Kadoch and Crabtree, 2015).The inactivation of one or more subsets of the SWI/SNF complexes can promote the development of cancer (reviewed by Reisman et al., 2009). PBRM1 inactivation will result in loss of the PBAF complex, and this will lead to loss of its tumor suppressive function. Missense mutations in PBRM1 seem to occur more frequently in the 4th bromodomain than in the other functional domains. Since the bromodomains are crucial for the interaction of the PBAF complex with the chromatin, these missense mutations are potentially pathogenic. The consequence of PBRM1 loss in the ccRCC precursor cells, i.e. primary tubular epithelial cells of the kidney (PTECs) (Thoenes et al., 1986), is still unknown. To evaluate this, we generated PTECs stably transduced with viral short hairpin RNA overexpressing

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constructs. We monitored changes in their phenotype over a period of three to four weeks and determined changes in gene expression profiles at day 6 after transduction.

Ma tERial and methods PTECs isolation and cell culture Kidney primary tubular epithelial cells (PTECs) were isolated from healthy renal cortex segments as previously described (Li et al., 2016). Briefly, the tissue block was cut into small cubes and seed into T25 FCS-pre-coated and Collagen-1-coated T25 flasks (BD Biosciences, San Jose, CA, USA, BD BioCoat 25cm2, Cat#356484). The isolated cells were cultured in DMEM/F-12 GLUTMAX-1 supplemented with 1% ITS (5μg/ml insulin, 5μg/ml transferrin, 5ng/ml selenium ITS), 0.1% EGF (5ng/ml) and

1% P/S (100U/ml penicillin and 100μg/ml streptomycin), at 37°C, 5% CO2. When the cells reached 80%-90% confluence (day 5 to 7), they were split and frozen for use in the experiment as passage 1. At passage 3 the primary PTECs were characterized with the following markers: Cytokeratin 8 (CK8.18), epithelial membrane antigen (EMA), pan cytokeratin (CK AE1.3), C5α receptor (c5αR), and liver-type fatty acid-binding protein 1 (L-FABP). During the experiment the PTECs were maintained in DMEM/F- 12 GLUTMAX-1 containing 10% FBS, 1% ITS, 0.1% EGF and 1% P/S. All the reagents used for cell culturing are from Sigma-Aldrich (St. Louis, MO, USA). CcRCC cell lines RCC1, RCC4, RCC5, and RCC6 are a gift from Dr. C.D. Gerharz (Institute of Pathology, University Hospital, Düsseldorf, Germany), who established these cell lines. CcRCC cell lines RCC-ER, RCC-MF, RCC-JF, RCC-HS, RCC-GW, and RCC-FW were purchased from Cell Line Services, Eppenheim, Germany. The ccRCC cell lines were maintained in RPMI 1640 supplemented with 10% FBS, 1% ITS, and 1% P/S. All the

cells were maintained at 37°C in humidified air containing 5% CO2.

Construction of shRNA vectors and generation of lentiviral particles Oligonucleotides (Eurogentec, Liège, Belgium) were annealed and subcloned into the pGreenpuro shRNA cloning and expression lentivector (Systems Biosciences, Mountain View, CA, USA). The non-targeting shRNA lentiviral vector was obtained from Systems Biosciences (Mountain View, CA). The insert sequences were confirmed by Sanger sequencing (sh-PB1: 5’-GATCCAGCTAAATTTGCCGAGTTATTCAAGAGATAAC TCGGCAAATTTAGCTTTTTTG-3’; sh-PB2: 5’-GATCCGTTAGGAGTTGTCGGAA TATTCAAGAGATATTCCGACAACTCCTAACTTTTTG-3’). Lentiviral particles were produced by co-transfection of 7x105 HEK293T cells in a 6-well plate by the calcium

phosphate (CaPO4)-mediated method, with 2µg pGreenPuro shRNA expression lentivector (sh-PB1, sh-PB2 or non-targeting (NT)) in combination with a plasmid mix containing 1µg pCMV-VSV-G, 1µg pRSV.REV, and 1µg pMDL-gPRRE. Lentiviral particles were harvested 48 hours after transfection and passed through a 0.45µm pore PVDF Millex-HV filter (Millipore, Billerica, MA, USA).

88 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Cell transduction for expression studies and growth competition assay PTECs were transduced with a serial dilution of viral stocks in the presence of 4μg/ ml polybrene (Sigma-Aldrich, St. Louis, MO, USA). For expression studies, PTECs were transduced at high multiplicities of infection (MOI) resulting in more than 85% GFP positive (GFP+) cells. For the GFP growth competition assay, PTECs were transduced at low MOI aiming at approximately 20% GFP+ cells. The percentage of GFP+ cells in the mixed cultures was determined for 3 weeks by a FACS Calibur flow cytometer (BD Biosciences). FACS results were analyzed using Kaluza software (v1.3, Beckman Coulter, Brea, CA, USA). The relative change in the fraction of GFP+ cells in the cultures was calibrated to the percentage of GFP+ cells at the first measurement, carried out on day 2.

Senescence-associated beta-galactosidase (β-gal) staining Transduced and untransduced PTECs were cultured for 20 days (passage 5) and subjected to β-gal staining by using the senescence β-galactosidase (β-gal) Staining Kit 4 (Cell Signaling, Danvers, USA) according to the manufacturer’s instructions. Images were captured by a TissueFax (TissueGnostics, Vienna, Austria) equipped with a Zeiss objective LD Plan-Neofluar 20x/0.4 Corr Dry, Ph2 objectives.

RNA extraction and RT-qPCR Total RNA was extracted using the GeneJET RNA purification kit (Fermentas, St. Leon-Rot, Germany) according to the manufacturer’s instructions. RNA integrity and quantity were measured by using the HT RNA LabChip GX/GXII kit (Caliper GX, Life Sciences, Hopkinton, MA). Total RNA (1µg) was used for reverse transcription using the RevertAidTM H Minus First Strand cDNA Synthesis Kit with random primers (Fermentas, St. Leon-Roth, Germany). Quantitative PCR was performed in triplicate with equal amounts of cDNA mixed with the iTaqTM Universal SYBR® Green Supermix (BIO-RAD, Hercules, CA) and 5pmol of both forward and reverse primers. The sequences of primers (5’→3’) used in this study: RP II (F’: GGTTCAGGCAGAAGACTTTG; R’: TTGGGAGAAGCCATGTCATC), PBRM1 (F’: GGTTCAGGCAGAAGACTTTG; R’: TTGGGAGAAGCCATGTCATC). The quantification of transcript abundance was determined on the ABI 7900T Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Results were analyzed by SDS software (V1.3.0, Life Technologies, Foster City, CA, USA). The relative quantification of target genes was analyzed by using the 2–ΔCT method and presented as mean ± SD of triplicate experiments. RPII was used as the endogenous control.

Gene expression microarrays The microarray-based gene expression procedure was performed as described previously (Winkle et al., 2015). First 50-100ng total RNA was used for cDNA synthesis, amplification, and labeling with Cy5 dyes (Agilent Technologies, Santa Clara, CA, USA).

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Labelled RNA was purified using the RNeasy Mini Kit (Qiagen, Valencia, USA). The resulting cRNA concentration and dye incorporation was quantified by a NanoDrop 1000 UV-VIS spectrophotometer (Thermo Fisher Scientific, Rockford, IL, USA). All reagents and equipment used for the subsequent hybridization were purchased from Agilent (Agilent Technologies). Each Cy-5 sample was mixed with the same amount of a Cy3-labeled sample, which was non-relevant for this study. The samples were hybridized at 65°C overnight on Agilent SurePrint G3 Custom Human 8x60K Microarrays (ID- 050524). Next, the microarray array slides were washed and scanned on the Agilent DNA Microarray Scanner with Agilent Feature Extraction software v10.7.3 (Agilent Technologies). Data preprocessing and normalization was performed using GeneSpring GX 12.6 software (Agilent Technologies). The resulting data were subject to quantile normalization without baseline transformation. The 34,134 Agilent probes, specific for protein coding genes, were selected for further analyses. In the comparison of both PBRM1-KD PTECs vs PBRM1-WT PTECs the probes that are flagged present in all samples of one out of two conditions, and whose expression intensity falls within the 30-100th percentile were selected for statistical analysis. This filtering resulted in 11,579 (PBRM1-KD vs PBRM1-WT) probes, which were used for further analysis. Heatmaps of differentially expressed genes were generated by Genesis software (v 1.7.6). Unsupervised clustering of the samples and genes was calculated using Euclidian distance metric.

Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analysis Gene Set Enrichment Analysis (Broad Institute) was performed for the 50 hallmark gene sets from the MSigDB collection using the Java GSEA implementation (V2.2.0). An enrichment score (ES) is assessed by walking down the ranked list of genes, and normalized by gene set size and correlations between gene sets and the expression profile. Functional annotation of genes by Gene Ontology (GO) was done using the Database for Annotation, Visualization and Integrated Discovery (DAVID, v6.7). The GO analysis is performed by using official gene symbols on the tool available at the DAVID website (http://david.abcc.ncifcrf.gov/). Gene ontology option GOTERM_BP (biology process)_ALL was used for generating an enrichment chart for up-and down- regulated genes separately.

Statistical analysis For three-group comparisons in the RT-qPCR experiments to determine the PBRM1 expression and in the growth competition assay to evaluate the GFP changes, the significance of the PBRM1-KD and the change in GFP percentages was determined by one-way ANOVA comparing PBRM1-shPB1 and -shPB2 treated PTECs to NT-shRNA and untreated PTECs. The resulting P value was adjusted by Dunnett’s multiple testing correction. Significantly differentially expressed genes in the microarray data were determined by using moderated t-test with Benjamini-Hochberg correction. P-values <0.05 were considered to be significant.

90 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Rsesult PBRM1 loss neither conveys PTECs growth advantage, nor extends their proliferation capacity We tested the knockdown efficiency of the shRNA constructs in HEK293T cells and HKC8 cells by RT-qPCR. This revealed a more than 80% reduction of PBRM1 mRNA levels in both cell lines (Supplementary Figure S1). In three independent PTEC cultures the knockdown efficiency ranged from 60%-70% (Figure 1A). We were unable to measure the actual decrease in the amount of PBRM1 protein due to lack of reliable antibodies. In the growth competition assay, sh-PB1 transduced PTECs appear to proliferate slightly faster as compared to the NT-shRNA transduced PTECs; but the increase is significant only at day 10 (p = 0.019) and day 22 (p = 0.022). No significant changes were observed for sh-PB2 transduced PTECs in comparison to NT-shRNA transduced PTECs (Figure 1B). Thus loss of PBRM1 did not appear to promote the proliferation of PTECs. Analysis of the morphology of the cells during the growth competition assay 4 also revealed no changes upon PBRM1 knockdown. After 20 days of culturing, both PBRM1-WT and PBRM1-KD PTECs showed a flattened appearance and enlarged nuclei, which are the characteristics of senescent cells. β-galactosidase (β-gal) staining of the treated and untreated PTECs at day 22 indeed revealed a positive staining for the fast majority of the cells consistent with a senescence state (Figure 1C). In summary, PBRM1-KD PTECs neither showed proliferative advantage, nor prolonged proliferation capacity as compared to PBRM1-WT PTECs.

PTECs show changes in their expression profile after PBRM1-KD To further investigate the effect of PBRM1 loss on PTECs, we determined the gene expression changes upon PBRM1-KD in PTECs at day 6 after transduction. Principal component analysis (PCA) showed a good separation of PBRM1-KD and PBRM1-WT PTECs in the first component explaining 30.4% of all variation (Figure 2A). A moderated t-test with Benjamini-Hochberg multiple testing correction revealed significant changes for2,747 probes (1,475 up and 1,272 down in PBRM1-KD). A fold change in the signal intensity of more than 2 was observed for 301 of the significant probes corresponding to 285 genes. Of these, 136 probes corresponding to 130 genes were upregulated and 165 probes corresponding to 155 genes were downregulated in PBRM1-KD cells compared to PBRM1-WT PTECs (Supplementary Table S1). Unsupervised hierarchical clustering of the 301 probes revealed two distinct clusters with PBRM1-WT PTECs samples in the first and PBRM1-KD in the second cluster (Figure 2B). Using expression data of 10 different ccRCC-derived cell lines (see chapter 5) we analyzed the expression pattern of these 301 probes by unsupervised hierarchical clustering. This resulted in one cluster including PBRM1-WT PTECs and PBRM1-KD PTECs, and a second cluster including all ccRCC cell lines (Figure 3). Visual inspection of the heatmap indicated that the expression level of the downregulated genes in PBRM1-KD PTECs was even lower in the ccRCC cell lines. In contrast, the expression

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A B

C

Figure 1. Consequences of PBRM1 knockdown in PTECs. (A) Quantification of PBRM1 mRNA abundance by RT-qPCR 6 days after transduction at high MOI of 3 independent cultures of PTECs with PBRM1-targeting (sh-PB1 and sh-PB2) or non-targeting (NT) shRNAs. The results are presented as 2-∆Ct values with mean ± SD from 3 independent experiments, RPII serves as a reference gene. (B) Three independent cultures of PTECs were transduced at low MOI with the shRNA constructs sh-PB1, sh-PB2 and NT at passage 2 (day 0). The fraction of GFP+ cells was determined by FACS at each passage until day 22. The relative change of the fraction of GFP+ cells in the mixed cultures was compared to the first measurement (day 2), and is presented as fold changes (mean ± SD from 3 independent experiments). The significance of observed differences between PBRM1-sh-PB1 and sh-PB2 compared with NT (both in RT-qPCR and GFP competition assay), are calculated by one-way ANOVA with Dunnett’s multiple testing correction. *P < 0.05, ***P < 0.001. (C) PTECs were transduced with sh-constructs as described in panel (A), and processed for β-gal staining at day 20 (passage 5) to determine the senescence status. Images are representative for one of 3 independent experiments. The scale bar indicates 100µm. NT: non-targeting shRNA transduced PTECs, KD: sh-PB1 transduced PTECs.

levels of the genes upregulated upon PBRM1-KD in PTECS showed a mixed expression pattern in ccRCC cell lines (Figure 3).

PBRM1-KD induces changes in the basal expression of immune responsive genes To characterize the nature of the genes with altered expression levels upon PBRM1- KD in PTECs, we performed a gene set enrichment analysis (GSEA). Compared with PBRM1-WT PTECs, PBRM1-KD PTECs were significantly enriched in gene sets related to Immune Response, E2F-TARGETS, and MYC-TARGETS-V2 (FDR<0.005, Table 1). Interferon-α (IFN-α) and interferon-γ (IFN-γ) response gene sets were the top enriched gene sets in PBRM1-KD PTECs (Figure 4A).

92 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

A B

4

Figure 2. Expression features of PBRM1-KD PTECs. (A) PCA plot shows the distribution of PBRM1-WT (black, including both control and NT-shRNA treated) PTECs and PBRM1-KD (gray, including both sh-PB1 and sh-PB2 treated) PTECs. The plot was generated using the 11,579 probes that were present in at least 1 out of 2 conditions with a signal intensity between the 30-100 percentile in at least 1 of the conditions. (B) Heatmap including the 301 probes that are differentially expressed between PBRM1-KD and PBRM1-WT PTECs (moderated t-test with Benjamini-Hochberg multiple testing correction, P<0.05 and fold change>2). Unsupervised clustering of the samples and genes was calculated using Euclidian distance metric.

Gene ontology (GO) analysis of the significantly differentially expressed genes upon PBRM1-KD revealed enrichment of genes involved in the immune system process and the nucleoside metabolism in the upregulated genes and enrichment of genes implicated in cell response to stimulus and cell differentiation in the downregulated gene set (Figure 4B).

Diu sc ssion PBRM1 mutations are detected in more than 20 different tumor types, with by far the highest frequency in clear cell Renal Cell carcinoma (ccRCC) (COSMIC database). In ccRCC, PBRM1 is the second most frequently inactivated gene next to VHL. Based on the presence of PBRM1 inactivating mutations in the “ trunk”, Gerlinger et al (2014) concluded that PBRM1 inactivation is a driver event in ccRCC development. In the cancer genome atlas (TCGA), 44 out of 157 ccRCC patients with a PBRM1 mutation have wild-type VHL, SETD2 and BAP1 genes (Cancer Genome Atlas Research, 2013). These observations indicate that PBRM1 inactivation can initiate ccRCC development.

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Figure 3. Genes downregulated upon PBRM1-KD in PTECs are expressed at low levels in ccRCC cell lines. A heatmap was generated using the 301 probes described in Figure 2B keeping the same ordering of genes. Unsupervised clustering was performed on the samples using Euclidian distance metric.

Inactivation of PBRM1 in primary tubular epithelial cells of the kidney (PTECs) did not induce significant differences in the growth characteristics nor in the morphology of these cells. PBRM1-KD PTECs became senescent at approximately the same passage as wild type PTECs. Thus, PBRM1 loss does not interfere with the process of senescence in these cells. This is in contrast to the results of an shRNA screen in primary fibroblasts set up to identify genes that regulate replicative senescence (Burrows et al., 2010). In this particular screen, PBRM1 was identified as a protein whose inactivation delayed the process of senescence. We showed that inactivation of SETD2 resulted in an escape from senescence in PTEC cells (Li et al., 2016, chapter 3), while knockdown of SETD2 in bronchial epithelial cells did not (our own preliminary and unpublished results). Likewise, also the effect of PBRM1 inactivation may be cell type and/or tissue specific. The Gene Set Enrichment Analysis (GSEA) of our expression data showed a significant enrichment for the E2F-TARGETS gene set in PBRM1-KD cells as compared to PBRM1-WT PTECS (Table 1). In chapter 3 of this thesis, this gene set was enriched in the WT-PTECs (day 6) as compared to the SETD2-KD PTECs (day 25) (Supplementary Figure S2). Thus, with respect to the E2F target gene set there appears to be a reciprocal effect of the two knock-down experiments. To what extend these

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Table 1. Enriched gene sets in PBRM1-KD PTECs.

NAME SIZE NES FDR q-val

HALLMARK_INTERFERON_ALPHA_RESPONSE 65 -3.08 <.001 HALLMARK_INTERFERON_GAMMA_RESPONSE 119 -2.80 <.001 HALLMARK_E2F_TARGETS 131 -1.90 <.005 HALLMARK_MYC_TARGETS_V2 51 -1.81 <.005

PBRM1-WT PTECs were compared to PBRM1-KD PTECs using the hallmark gene sets retrieved from Broad institute (https://www.broadinstitute.org). No gene set was significantly enriched in PBRM1-WT PTECs. NES, normalized enrichment score, FDR, false discovery rate.

A 4

B

Figure 4. Functional interpretations of the expression features of PBRM1-KD PTECs. (A) Enrichment plots showing the hallmark gene sets of INTERFERON_ALPHA _RESPONSES (left) and INTERFERON_GAMMA _RESPONSES (right) in the comparison between PBRM1-WT PTECs and PBRM1-KD PTECs. The hallmark gene sets were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www.broadinstitute.org/gse). The false discovery rate (FDR) and normalized enrichment score (NES) for each gene set are indicated. (B) Gene ontology (GO) analysis using the 285 differentially expressed genes from Supplementary Table 1. A minimum enrichment score of 1.5 is presented. The left graph shows GO analysis using the 150 genes upregulated upon PBRM1-KD in PTECS and the right graph shows the GO analysis for the 135 genes downregulated upon PBRM1-KD.

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differences explain the phenotypical differences of PBRM1-KD vs SETD2-KD PTECs, including those in the process of senescence, is unclear. PBRM1 (BAF180) is a subunit specific for the PBAF type SWI/SNF complex (Xue et al., 2000). However, it has been suggested that BAF180-deficient PBAF complexes retain part of their functionality (Yan et al., 2005). PBRM1 targets the PBAF complex to specific genomic loci and in that way makes the promoter region available for transcription factors. Using an in vitro chromatin transcription assay Lemon et al. (2001) showed that PBAF was indispensable for an effective activation of transcription by nuclear hormone receptors. Wang et al. (2004) identified a set of genes whose expression level was significantly changed upon PBRM1 knock-out in a mouse model. For a subset of genes their expressions were upregulated, suggesting that a functional PBAF complex can induce a suppressive chromatin state. Vice versa, presence of a subset of downregulated genes indicates that PBAF also can induce an activated chromatin state. Consistent with these findings we indeed found significantly up- and downregulated genes. The different approaches and cell types used within studies precludes a meaningful comparison of the genes altered upon PBRM1-KD. Several studies have shown that a functional SWI/SNF complex is a prerequisite for an efficient and fast response to IFN-α stimulation, i.e. change of expression of IFN-α target genes. These effects have been shown to be dependent on the presence of the BRG1 and BAF47 components of the SWI/SNF complex. Expression of BRG1 in BRG1-deficient SW13 cells caused upregulation of a number of genes (Liu et al., 2001), and restored the quick response of IFN-α target genes to IFN-α (Liu et al., 2002). Knockdown of BAF47, another subunit of the SWI/SNF complex, in HeLa cells prevented the activation of a set of IFN-α responsive genes (Cui et al., 2004). This indicated that the SWI/SNF complex is responsible for the maintenance of an open chromatin configuration of the IFN-α responsive genes facilitating a quick response to IFN-α exposure. Huang et al. (2002) showed that the interaction of BRGI with STAT2 is responsible for at least some of the effects of the SWI/SNF complex. At the same time this study indicated that not all IFN-α responsive genes depend on presence of a functional SWI/SNF complex. As BAF47 and BRG1 are present in all human SWI/SNF complexes (Kadoch and Crabtree, 2015), these studies still did not identify the specific components that are responsible for regulating the expression of IFN-α responsive genes. Indeed, Yan et al. (2005) showed that the BAF and BPAF complexes regulate different IFN-α responsive genes. In our GSEA, INTERFERON_ALPHA_ RESPONSE was the most significant enriched gene set, indicating that the PBRM1-containing PBAF complex indeed is involved in regulating the expression of these genes. As we did not treat the cells with IFN-α, our observations at this moment mainly reflect the basal expression levels of these genes. This is consistent with the mode of action proposed by Kadoch and Crabtree (2015), i.e. that in general SWI/SNF complexes induce a local open or closed chromatin structure, and in this way regulate the basal expression level and the response time of this gene set upon IFN-α exposure.

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Another gene set that was significantly enriched in our study was the IFN-γ response gene set. This finding is not surprising, as 75% of the IFN-α gene set is also part of the IFN-γ gene set (Supplementary Figure S3). Of the 50 IFN-α leading edge genes 39 were also present in the IFN-γ leading edge gene set. Zhang et al. (2010) reported a BRG1- mediated interaction of the SWI/SNF complex with STAT1 in vitro. This resulted in the recruitment of SWI/SNF complex to the IFN-γ activated sequences and induction of IFN-γ responsive genes (Zhang et al., 2010). The effect of this interaction on gene expression may well depend on the presence of PBRM1. The ccRCC cell lines showed a more pronounced downregulation of the genes that were also downregulated upon PBRM1-KD. This suggests that loss of PBRM1 indeed pushes the cells towards malignant transformation of PTECs. GO analysis of the genes significantly downregulated upon PBRM1-KD revealed enrichment of genes involved in cell differentiation, synapse organization and cytoskeleton organization, processes known to be essential for tumor progression (Quail and Joyce, 2013; Fife et al., 2014) This pinpoints potential changes in cell-cell or cell-matrix contacts as possible changes 4 involved in transformation of PTECs. Our findings may add to our understanding of immunotherapy induced treatment resistance in ccRCC tumors. Wolf et al. (2012) showed IFN-α treatment resistance is neither caused by the defective IFN receptors, nor by suppression of cytokine signaling. Our data indicates that PBRM1 depletion disturbs the expression signature of IFN-α and IFN-γ responsive genes, maybe by disturbing the balance between different subtypes of SWI/SNF complex in PTECs. Thus it will be interesting to determine whether immunotherapy-induced treatment resistant tumors have changes in PBRM1 expression levels or mutation status. Our preliminary analysis did not give a clear-cut answer as to how loss of PBRM1 could contribute to, or even initiate the development of ccRCC. However, our data are a good basis to design further studies aiming at elucidating the role of PBRM1 loss in the pathogenesis of ccRCC.

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Rreefe nces Angus-Hill, M. L., A. Schlichter, D. Roberts, H. large gene lists using DAVID bioinformatics Erdjument-Bromage, P. Tempst and B. R. Cairns resources.” Nat Protoc. 4(1): 44-57. (2001). A Rsc3/Rsc30 zinc cluster dimer reveals Huang, M., F. Qian, Y. Hu, C. Ang, Z. Li and Z. Wen novel roles for the chromatin remodeler RSC in (2002). Chromatin-remodelling factor BRG1 gene expression and control. Mol Cell. selectively activates a subset of interferon- 7(4): 741-751. alpha-inducible genes. Nat Cell Biol. 4(10): Burrows, A. E., A. Smogorzewska and S. J. Elledge 774-781.Kadoch, C. and G. R. Crabtree (2015). (2010). Polybromo-associated BRG1- “Mammalian SWI/SNF chromatin remodeling associated factor components BRD7 and complexes and cancer: Mechanistic insights BAF180 are critical regulators of p53 required gained from human genomics.” Sci Adv. 1(5): for induction of replicative senescence. Proc e1500447. Natl Acad Sci U S A. 107(32): 14280-14285. Kadoch, C. and G. R. Crabtree (2015). “Mammalian Cancer Genome Atlas Research, N. (2013). SWI/SNF chromatin remodeling complexes Comprehensive molecular characterization and cancer: Mechanistic insights gained from of clear cell renal cell carcinoma. Nature. human genomics.” Sci Adv. 1(5): e1500447. 499(7456): 43-49. Knudson, A. G., Jr. (1971). “Mutation and cancer: Cui, K., P. Tailor, H. Liu, X. Chen, K. Ozato and K. Zhao statistical study of retinoblastoma.” Proc Natl (2004). The chromatin-remodeling BAF complex Acad Sci U S A. 68(4): 820-823. mediates cellular antiviral activities by promoter Kok, K., S. L. Naylor and C. H. C. M. Buys (1997). priming. Mol Cell Biol. 24(10): 4476-4486. “Deletions of the short arm of chromosome 3 Duns, G., R. M. W. Hofstra, J. G. Sietzema, H. in solid tumors and the search for suppressor Hollema, I. van Duivenbode, A. Kuik, C. genes.” Adv Cancer Res. 71: 27-92. Giezen, O. Jan, J. J. Bergsma and H. Bijnen Kupitz, C., R. Chandrasekaran and M. Thompson (2012). Targeted exome sequencing in clear cell (2008). “Kinetic analysis of acetylation- renal cell carcinoma tumors suggests aberrant dependent Pb1 bromodomain-histone chromatin regulation as a crucial step in ccRCC interactions.” Biophys Chem. 136(1): 7-12. development. Hum. mut. 33(7): 1059-1062. Latif, F., K. Tory, J. Gnarra, M. Yao, F. M. Duh, M. Duns, G., E. van den Berg, I. van Duivenbode, J. L. Orcutt, T. Stackhouse, I. Kuzmin, W. Modi Osinga, H. Hollema, R. M. W. Hofstra and K. and L. Geil (1993). “Identification of the von Kok (2010). Histone methyltransferase gene Hippel-Lindau disease .” SETD2 is a novel tumor suppressor gene in clear Science. 260(5112): 1317-1320. cell renal cell carcinoma. Cancer Res. 70(11): Lemon, B., C. Inouye, D. S. King and R. Tjian (2001). 4287-4291. Selectivity of chromatin-remodelling cofactors Fife, C. M., J. A. McCarroll and M. Kavallaris (2014). for ligand-activated transcription. Nature. Movers and shakers: cell cytoskeleton in cancer 414(6866): 924-928. metastasis. Br J Pharmacol. 171(24): 5507-5523. Li, J., Kluiver, J., Osinga, J., Westers, H., van Gerlinger, M., S. Horswell, J. Larkin, A. J. Rowan, M. Werkhoven, M. B., Seelen, M. A., Sijmons, R.H., P. Salm, I. Varela, R. Fisher, N. McGranahan, van den Berg, A. and Kok, K. (2016). Functional N. Matthews, C. R. Santos, P. Martinez, B. Studies on Primary Tubular Epithelial Cells Phillimore, S. Begum, A. Rabinowitz, B. Indicate a Tumor Suppressor Role of SETD2 Spencer-Dene, S. Gulati, P. A. Bates, G. Stamp, in Clear Cell Renal Cell Carcinoma. Neoplasia. L. Pickering, M. Gore, D. L. Nicol, S. Hazell, P. 18(6), 339-346. A. Futreal, A. Stewart and C. Swanton (2014). Liu, R., H. Liu, X. Chen, M. Kirby, P. O. Brown and Genomic architecture and evolution of clear cell K. Zhao (2001). Regulation of CSF1 promoter renal cell carcinomas defined by multiregion by the SWI/SNF-like BAF complex. Cell 106(3): sequencing. Nat Genet. 46(3): 225-233. 309-318. Hakimi, A. A., C. G. Pham and J. J. Hsieh (2013). Liu, H., H. Kang, R. Liu, X. Chen and K. Zhao “A clear picture of renal cell carcinoma.” Nat (2002). Maximal induction of a subset of Genet. 45(8): 849-850. interferon target genes requires the chromatin- Hohmann, A. F. and C. R. Vakoc (2014). “A rationale remodeling activity of the BAF complex. Mol to target the SWI/SNF complex for cancer Cell Biol. 22(18): 6471-6479. therapy.” Trends Genet. 30(8): 356-363. Nie, Z., Z. Yan, E. H. Chen, S. Sechi, C. Ling, S. Zhou, Y. Huang da, W., B. T. Sherman and R. A. Lempicki Xue, D. Yang, D. Murray, E. Kanakubo, M. L. Cleary (2009). “Systematic and integrative analysis of and W. Wang (2003). Novel SWI/SNF chromatin-

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Sumepple ntary figures and tables

Supplementary Figure S1. PBRM1-knockdown (KD) in cell lines HEK293T and HKC8. HEK293T and HKC8 cells were transduced with PBRM1 targeting shRNAs sh-PB1 and sh-PB2. A non-targeting (NT) shRNA was included as a control. Total RNA was isolated from sorted GFP positive cells for cDNA synthesis. The mRNA abundance of PBRM1 was determined by RT-qPCR. The results are presented as 2-∆Ct values (mean ± SD) from 3 independent experiments using HPRT as a reference gene. Statistical significance is determined by one-way ANOVA with Dunnett’s multiple testing correction. ***P < 0.001.

Supplementary Figure S2. Enrichment plots for the E2F_TARGETS gene set. Enrichment plots of the E2F_TARGETS gene set in the comparison between WT (day 6) vs PBRM1-KD (day 6) PTECs, and WT (day 6) vs SETD2-KD (day 25) PTECs (Chapter 3, this thesis) are shown. The hallmark gene sets were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www. broadinstitute.org/gse). The false discovery rate (FDR) and normalized enrichment score (NES) in each comparison are shown.

100 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Supplementary Figure S3. Overlap of the genes in the gene sets of IFN-α and IFN-γ responsive genes. The total gene lists of INTERFERON_ALPHA _RESPONSES and INTERFERON_ GAMMA _RESPONSES were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www.broadinstitute.org/gse). The leading edge gene lists were retrieved from the GSEA. 4 Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECs and PBRM1-KD PTECs.

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3393821 0.0138 up 8.58 C1R A_23_P167983 0.0012 up 6.58 HIST1H2AC A_32_P101031 0.0019 up 5.91 LYPD1 A_23_P152782 0.0364 up 5.51 IFI35 A_33_P3400578 0.0066 up 4.99 HLF A_23_P100711 0.0026 up 4.80 PMP22 A_33_P3284129 0.0032 up 4.67 LYPD1 A_24_P317762 0.0400 up 4.65 LY6E A_24_P119685 0.0001 up 3.85 OBSCN A_23_P82503 0.0049 up 3.71 PEG10 A_33_P3399208 0.0489 up 3.69 HLA-B A_23_P75741 0.0325 up 3.66 UBE2L6 A_23_P216655 0.0333 up 3.48 TRIM14 A_23_P145238 0.0005 up 3.33 HIST1H2BK A_23_P88626 0.0008 up 3.28 ANPEP A_33_P3397865 0.0021 up 3.28 TNNT1 A_23_P8240 0.0005 up 3.25 FAM50B A_24_P678104 0.0132 up 3.23 STMN3 A_23_P120002 0.0226 up 3.22 SP110 A_32_P69368 0.0044 up 3.19 ID2 A_23_P50096 0.0035 up 3.17 TYMS

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Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3311493 0.0003 up 3.15 LOC283392 A_23_P19673 0.0002 up 3.08 SGK1 A_23_P218646 0.0131 up 3.07 TNFRSF6B A_24_P252078 0.0048 up 3.05 BTN3A2 A_33_P3290403 0.0022 up 3.03 IMPA2 A_24_P408047 0.0483 up 2.99 PLEKHA4 A_23_P50146 0.0374 up 2.95 SIGLEC15 A_23_P209625 0.0015 up 2.91 CYP1B1 A_24_P99216 0.0002 up 2.81 LRP10 A_24_P416177 0.0015 up 2.81 ADCY7 A_23_P393620 0.0335 up 2.81 TFPI2 A_23_P139912 0.0479 up 2.80 IGFBP6 A_23_P37441 0.0449 up 2.73 B2M A_33_P3393836 0.0458 up 2.72 NT5C3 A_33_P3412016 0.0021 up 2.71 SEMA4B A_23_P214208 0.0007 up 2.70 CNR1 A_23_P114740 0.0091 up 2.69 CFH A_33_P3632937 0.0027 up 2.67 LOC100131262 A_23_P143143 0.0263 up 2.61 ID2 A_24_P346431 0.0001 up 2.58 TNS3 A_32_P120895 0.0044 up 2.58 LYSMD2 A_23_P95930 0.0004 up 2.57 HMGA2 A_33_P3249046 0.0314 up 2.57 CLDN2 A_23_P384044 0.0089 up 2.56 CNIH3 A_23_P43726 0.0013 up 2.56 NUP160 A_23_P119562 0.0021 up 2.55 CFD A_33_P3211520 0.0002 up 2.54 SNAP47 A_24_P354715 0.0008 up 2.54 NT5E A_23_P102364 0.0489 up 2.53 NGEF A_23_P151710 0.0000 up 2.51 PTGER2 A_24_P48057 0.0032 up 2.51 IRX5 A_23_P216630 0.0004 up 2.51 SLC44A1 A_33_P3290343 0.0005 up 2.48 CYP1B1 A_33_P3344204 0.0025 up 2.48 ZDHHC11 A_23_P212617 0.0019 up 2.47 TFRC

102 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_23_P64617 0.0489 up 2.46 FZD4 A_23_P251421 0.0052 up 2.44 CDCA7 A_23_P140256 0.0366 up 2.43 PNP A_33_P3228325 0.0378 up 2.43 SP100 A_23_P66715 0.0049 up 2.43 PIGS A_23_P62115 0.0022 up 2.42 TIMP1 A_23_P137035 0.0255 up 2.40 PIR A_23_P15357 0.0366 up 2.38 LGALS3BP A_33_P3331366 0.0317 up 2.37 TRIM25 A_33_P3345643 0.0131 up 2.36 ZDHHC11B A_33_P3318288 0.0068 up 2.36 CFH 4 A_24_P278126 0.0019 up 2.35 NBN A_23_P61050 0.0061 up 2.35 MLKL A_23_P211957 0.0143 up 2.35 TGFBR2 A_32_P171313 0.0017 up 2.35 GNB4 A_23_P86900 0.0001 up 2.33 B3GNT1 A_33_P3403117 0.0022 up 2.33 NR2F1 A_33_P3229083 0.0003 up 2.32 HIST1H2BK A_23_P136978 0.0066 up 2.31 SRPX2 A_23_P119478 0.0012 up 2.30 EBI3 A_23_P50426 0.0040 up 2.30 KANK2 A_23_P353717 0.0021 up 2.30 RMI2 A_33_P3280213 0.0003 up 2.29 CTSA A_23_P302787 0.0002 up 2.29 LOC375295 A_33_P3800734 0.0273 up 2.27 RYR3 A_33_P3336257 0.0019 up 2.26 IRX1 A_33_P3277110 0.0022 up 2.26 SLC5A3 A_24_P14260 0.0002 up 2.26 CARD8 A_23_P414273 0.0009 up 2.25 C5orf62 A_33_P3228305 0.0035 up 2.25 ARHGAP26 A_23_P165608 0.0018 up 2.24 SEMA4F A_32_P117170 0.0042 up 2.24 NAPEPLD A_33_P3397418 0.0117 up 2.24 ZC3HAV1 A_32_P41487 0.0026 up 2.24 HMGN2 A_24_P216313 0.0013 up 2.24 ERGIC3

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Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_23_P93180 0.0002 up 2.23 HIST1H2BC A_23_P250629 0.0147 up 2.22 PSMB8 A_33_P3347869 0.0397 up 2.21 C3 A_23_P80040 0.0173 up 2.20 PROCR A_23_P139704 0.0189 up 2.19 DUSP6 A_23_P203488 0.0037 up 2.17 SMPD1 A_23_P128613 0.0037 up 2.17 KDELC1 A_24_P379820 0.0148 up 2.16 ITM2C A_23_P38154 0.0012 up 2.16 FDXR A_24_P810290 0.0151 up 2.15 PPAPDC1A A_33_P3278941 0.0055 up 2.15 REC8 A_33_P3398448 0.0459 up 2.14 PARP10 A_23_P76914 0.0019 up 2.14 SIX1 A_23_P200030 0.0015 up 2.14 FPGT A_23_P95165 0.0015 up 2.13 SEMA4B A_24_P309317 0.0077 up 2.13 PSAP A_23_P208880 0.0046 up 2.12 UHRF1 A_24_P394246 0.0016 up 2.10 SHISA5 A_23_P111041 0.0010 up 2.10 HIST1H2BI A_23_P88589 0.0001 up 2.10 NR2F2 A_23_P391506 0.0031 up 2.10 IVNS1ABP A_23_P388433 0.0059 up 2.09 C4orf3 A_23_P138680 0.0215 up 2.08 IL15RA A_24_P90097 0.0005 up 2.08 ADD3 A_23_P58588 0.0323 up 2.08 SLIT3 A_33_P3227788 0.0076 up 2.08 PANK1 A_23_P153745 0.0358 up 2.07 IFI30 A_23_P416468 0.0400 up 2.07 PIF1 A_23_P13740 0.0099 up 2.06 NAV3 A_23_P152235 0.0479 up 2.06 IRX3 A_23_P390172 0.0020 up 2.05 RNASEL A_23_P66608 0.0021 up 2.05 KAT2A A_23_P210210 0.0017 up 2.04 EPAS1 A_24_P322474 0.0367 up 2.04 PDE4A A_23_P71513 0.0020 up 2.03 EFR3A

104 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_32_P112279 0.0005 up 2.02 CHTF8 A_32_P196142 0.0065 up 2.02 LOC100130938 A_32_P32413 0.0017 up 2.02 SETBP1 A_33_P3298062 0.0079 up 2.02 ABCC5 A_23_P43157 0.0164 up 2.02 MYBL1 A_24_P154037 0.0048 up 2.02 IRS2 A_24_P146211 0.0018 up 2.02 HIST1H2BD A_33_P3348239 0.0027 up 2.01 FBN1 A_24_P354689 0.0045 up 2.01 SPOCK1 A_23_P106562 0.0008 up 2.00 GALNS A_23_P137016 0.0037 down -2.00 SAT1 4 A_23_P334870 0.0014 down -2.01 TMEM217 A_33_P3403867 0.0030 down -2.01 PMEPA1 A_23_P154037 0.0153 down -2.01 AOX1 A_33_P3371727 0.0030 down -2.02 SAT1 A_33_P3229032 0.0164 down -2.02 CLEC11A A_33_P3288942 0.0157 down -2.02 FAM107B A_23_P68851 0.0014 down -2.02 KREMEN1 A_23_P162766 0.0024 down -2.02 DOCK9 A_33_P3294031 0.0204 down -2.02 KCNQ1OT1 A_33_P3289705 0.0010 down -2.02 GOLGB1 A_33_P3230658 0.0001 down -2.03 TSNAX A_23_P418199 0.0006 down -2.03 RP11-195F19.30 A_23_P203445 0.0000 down -2.04 UEVLD A_23_P113005 0.0105 down -2.05 EFNA1 A_33_P3383029 0.0003 down -2.05 MXI1 A_32_P113436 0.0035 down -2.05 HNRNPA1L2 A_33_P3397150 0.0050 down -2.05 FLJ22184 A_23_P36888 0.0024 down -2.06 FAM113B A_32_P104432 0.0056 down -2.06 NCRNA00087 A_33_P3353502 0.0002 down -2.06 PLCB4 A_33_P3381827 0.0018 down -2.07 OSBPL2 A_24_P358305 0.0129 down -2.07 GS1-44D20.1 A_23_P258002 0.0195 down -2.07 CDKN2AIP A_24_P82880 0.0077 down -2.08 TPM4

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Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3408054 0.0008 down -2.08 HSP90AB2P A_24_P102053 0.0104 down -2.08 OCLN A_33_P3398862 0.0015 down -2.08 RHOB A_23_P161727 0.0360 down -2.09 HSPB2 A_23_P213102 0.0243 down -2.09 PALLD A_33_P3306964 0.0003 down -2.10 PPP1R2 A_33_P3222380 0.0001 down -2.11 AHNAK2 A_33_P3347928 0.0068 down -2.11 CCNL1 A_23_P388168 0.0027 down -2.12 RAB3B A_33_P3371718 0.0080 down -2.13 SAT1 A_24_P703830 0.0014 down -2.13 NANOS3 A_24_P334130 0.0166 down -2.13 FN1 A_32_P49844 0.0009 down -2.13 RHOQ A_32_P116556 0.0114 down -2.13 ZNF469 A_23_P383422 0.0076 down -2.14 NFKBID A_33_P3343145 0.0008 down -2.14 MAP1B A_23_P52761 0.0091 down -2.14 MMP7 A_23_P157865 0.0042 down -2.14 TNC A_23_P316850 0.0358 down -2.15 ODF3L2 A_33_P3326312 0.0045 down -2.16 na A_24_P348925 0.0002 down -2.16 CCNK A_23_P132718 0.0297 down -2.17 SEMA3B A_23_P122216 0.0147 down -2.17 LOX A_24_P203502 0.0003 down -2.17 RSL24D1P11 A_33_P3337277 0.0037 down -2.17 LOC100129846 A_33_P3286621 0.0031 down -2.18 SCARNA16 A_24_P282309 0.0003 down -2.18 MYOF A_23_P39766 0.0046 down -2.18 GLS A_23_P151307 0.0039 down -2.19 RAPGEF3 A_24_P67681 0.0038 down -2.19 LOC100508670 A_33_P3429242 0.0346 down -2.19 LOC339988 A_33_P3332885 0.0006 down -2.20 BTN2A1 A_33_P3539345 0.0015 down -2.21 MYO6 A_33_P3375314 0.0022 down -2.21 ATP9A A_23_P162719 0.0049 down -2.22 DIAPH3

106 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3270863 0.0119 down -2.23 XDH A_33_P3285545 0.0094 down -2.23 CLDN4 A_33_P3399064 0.0063 down -2.24 RN5-8S1 A_33_P3232011 0.0178 down -2.24 RAB17 A_23_P421423 0.0213 down -2.24 TNFAIP2 A_23_P144465 0.0001 down -2.24 PAPSS1 A_33_P3332487 0.0022 down -2.24 FANK1 A_33_P3299754 0.0002 down -2.25 RAB18 A_33_P3224380 0.0001 down -2.26 DLG1 A_23_P39237 0.0070 down -2.26 ZFP36 A_33_P3212575 0.0008 down -2.26 NNAT 4 A_33_P3317815 0.0015 down -2.27 KRAS A_23_P373119 0.0019 down -2.27 HMGB3P1 A_32_P191895 0.0002 down -2.28 SDCBPP2 A_33_P3368188 0.0057 down -2.29 SEP9 A_23_P403335 0.0047 down -2.29 EXPH5 A_33_P3685216 0.0179 down -2.30 A1BG A_33_P3268304 0.0146 down -2.30 LIMS2 A_23_P155900 0.0005 down -2.31 NPFFR2 A_33_P3353692 0.0022 down -2.31 MYH9 A_23_P214080 0.0378 down -2.33 EGR1 A_23_P25674 0.0020 down -2.33 CKB A_33_P3260066 0.0011 down -2.35 BEAN1 A_33_P3315719 0.0039 down -2.36 PLEKHH2 A_32_P153388 0.0015 down -2.37 GULP1 A_23_P93269 0.0219 down -2.37 ZNF165 A_33_P3210099 0.0159 down -2.37 ALPK3 A_23_P34597 0.0464 down -2.37 CDA A_23_P748 0.0440 down -2.38 IRF6 A_33_P3320197 0.0084 down -2.41 FAM150B A_23_P88303 0.0121 down -2.41 HSPA2 A_23_P212608 0.0226 down -2.42 CLSTN2 A_23_P111395 0.0157 down -2.43 SLC22A2 A_33_P3221303 0.0014 down -2.44 CCR10 A_23_P376488 0.0019 down -2.45 TNF

107 Chapter 4

Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3388391 0.0157 down -2.46 GJB4 A_24_P295590 0.0084 down -2.47 RASSF4 A_23_P319583 0.0021 down -2.47 RIMS3 A_23_P81898 0.0099 down -2.47 UBD A_23_P16834 0.0037 down -2.48 FNDC4 A_33_P3389842 0.0043 down -2.48 PROM1 A_33_P3232798 0.0152 down -2.49 RAB11FIP1 A_24_P339944 0.0091 down -2.50 PDGFB A_23_P145397 0.0002 down -2.50 CCNC A_23_P53663 0.0004 down -2.51 PAWR A_33_P3245178 0.0045 down -2.51 BEX2 A_32_P150891 0.0024 down -2.52 DIAPH3 A_33_P3335042 0.0002 down -2.53 HSD17B12 A_23_P2181 0.0129 down -2.53 CYB5R2 A_23_P339119 0.0003 down -2.56 ACSS3 A_23_P8801 0.0120 down -2.60 CYP3A5 A_24_P4705 0.0096 down -2.61 PPME1 A_23_P19182 0.0466 down -2.62 REEP2 A_23_P12343 0.0181 down -2.63 GSTM3 A_23_P71328 0.0094 down -2.63 MATN2 A_33_P3252359 0.0029 down -2.68 BDH1 A_23_P115785 0.0003 down -2.69 FANK1 A_33_P3332492 0.0002 down -2.71 FANK1 A_32_P393316 0.0016 down -2.71 RAPGEF3 A_23_P133408 0.0123 down -2.72 CSF2 A_33_P3410279 0.0094 down -2.73 DOCK9 A_23_P8571 0.0219 down -2.73 SRCRB4D A_33_P3391005 0.0001 down -2.73 NEDD4L A_32_P180971 0.0000 down -2.73 LOC728323 A_23_P358917 0.0133 down -2.76 CYP3A7 A_24_P392110 0.0420 down -2.77 PSG8 A_24_P390060 0.0290 down -2.82 IQCD A_23_P429998 0.0198 down -2.82 FOSB A_32_P131031 0.0325 down -2.86 MACC1 A_23_P71946 0.0210 down -2.87 BSPRY

108 PBRM1-loss in PTECs leads to expression changes in IFNs response genes

Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECsSupplementary and PBRM1-KD table 1. PTECs.(continued)

Probe Name p (Corr) Regulation FC Gene Symbol

A_23_P4494 0.0010 down -2.89 DSC2 A_33_P3382271 0.0001 down -3.03 ATXN3 A_23_P119943 0.0015 down -3.13 IGFBP2 A_33_P3408913 0.0187 down -3.15 SAA2 A_33_P3293381 0.0043 down -3.26 RASSF4 A_33_P3274935 0.0136 down -3.32 C17orf28 A_32_P135336 0.0001 down -3.36 LOC388242 A_33_P3307013 0.0059 down -3.36 C17orf57 A_23_P154217 0.0002 down -3.41 ITGB6 A_23_P127565 0.0308 down -3.44 LAYN A_23_P331049 0.0012 down -3.52 DPYSL4 4 A_32_P703 0.0010 down -3.54 LOC646626 A_23_P76078 0.0039 down -3.60 IL23A A_32_P24376 0.0082 down -3.70 LOC730755 A_23_P15174 0.0374 down -3.80 MT1F A_23_P203540 0.0084 down -3.95 EHF A_33_P3214948 0.0059 down -3.99 SPOCK2 A_33_P3329088 0.0244 down -4.09 PRSS8 A_33_P3313055 0.0234 down -4.19 NOTCH3 A_23_P215720 0.0102 down -4.21 CFTR A_33_P3671291 0.0009 down -4.41 SNORA12 A_33_P3229107 0.0352 down -4.50 LOC642587 A_23_P94800 0.0005 down -4.53 S100A4 A_24_P33895 0.0388 down -4.59 ATF3 A_33_P3214105 0.0122 down -4.68 ATF3 A_23_P21363 0.0009 down -4.73 AHNAK A_23_P312150 0.0172 down -5.06 EDN2 A_23_P372834 0.0136 down -5.28 AQP1 A_23_P161218 0.0126 down -5.46 ANKRD1 A_23_P74778 0.0045 down -7.28 C1orf54 A_23_P15876 0.0004 down -7.43 ALPK2 A_33_P3243093 0.0013 down -7.82 RGS5 A_23_P125233 0.0043 down -9.06 CNN1 A_23_P17065 0.0035 down -9.10 CCL20 A_23_P46045 0.0023 down -9.58 RGS5

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