Pervasive transcription read-through promotes aberrant expression of oncogenes and RNA chimeras in renal carcinoma
Clear cell renal cell carcinoma ( ccRCC ) is the most common histological subtype of renal carcinoma . The genetics of ccRCC is dominated by either somatic or germline inactivating mutations in the VHL gene . Regarding the full spectrum of genomic alterations , ccRCC ranks amongst solid tumors with the lowest average number of point mutations , small indels ( Kandoth et al . , 2013 ) and somatic copy number alterations ( Zack et al . , 2013 ) . These findings suggest that epigenetic events make a significant contribution for the deregulation of the oncogenic and tumor suppressor gene expression programs that drive ccRCC development and progression . In fact , mutations in ccRCC are frequently observed in epigenetic factors such as the chromatin-remodeler PBRM1 and the histone modifying enzymes BAP1 and SETD2 , highlighting the central role of epigenetic regulation in this particular cancer ( Duns et al . , 2010; Varela et al . , 2011; Dalgliesh et al . , 2010; Creighton et al . , 2013 ) . Such mutations in genes that control the epigenome can strongly modulate the landscape of the tumor transcriptome via aberrant expression of global sets of genes . For instance , defects in transcription termination lead to read-through beyond the annotated 3’ gene boundary and have the potential to severely modify the transcriptome and to risk the integrity of vital gene expression programs ( Kuehner et al . , 2011 ) . Paradoxically , the prevalence and functional outcome of transcription read-through has not been thoroughly scrutinized in any cancer before . Here , we report an unprecedented transcriptional profiling of a cohort of 50 pairs of ccRCC tumor and normal matched samples from The Cancer Genome Atlas ( TCGA ) . We show that transcription read-through is prevalent in ccRCC and found that high levels of transcription read-through correlate with poor survival rates . Amongst the most frequently mutated genes in ccRCC , we identify SETD2 inactivation as a major driving force of impaired transcription termination and high levels of read-through . Moreover , we show that transcription read-through overruns and interferes with the expression of downstream genes . We identify the anti-apoptotic oncogene BCL2 as one of such interfered genes , thereby illustrating a new mechanistic basis for the transcriptional deregulation of oncogenes . In addition , our transcriptome analyses revealed recurrent RNA chimeras generated from read-through episodes in ccRCC . RNA chimeras are common features of cancer cells formerly thought to be produced solely by chromosomal translocations . We now know that many chimeric transcripts can originate from DNA-independent events such as trans-splicing , RNA recombination or transcription read-through ( Gingeras , 2009 ) . Our analyses revealed that read-through is a major source of RNA chimeras in ccRCC and identified a novel chimera , the CTSC-RAB38 , in 20% of ccRCC tumors , but not in any normal matched sample . Altogether , our data disclose the prognostic power of transcription read-through and emphasizes its role as a major source of transcriptome diversity in ccRCC , namely via aberrant expression of cancer genes and RNA chimeras .
To investigate the prevalence of transcription read-through in ccRCC , we analysed RNA-seq data from 50 pairs of tumor and normal matched samples from TCGA ( Cancer Genome Atlas Research N , 2013 ) . Compared to normal tissue , all tumor samples exhibited several genes with transcription termination defects revealed by a high number of reads mapping downstream the transcription termination site ( TTS ) ( Figure 1A , B and Supplementary file 1A ) . Such accumulation of reads results from transcription read-through beyond the TTS , a surrogate for deficient transcription termination ( Higgs et al . , 1983 ) . In agreement with a defect in transcription termination , we did not detect differences in read counts on any region upstream the TTS , contrasting with the significant increase in the intergenic region immediately downstream this site ( Figure 1C ) . 10 . 7554/eLife . 09214 . 003Figure 1 . Transcription read-through is prevalent in ccRCC . ( A ) Top graph depicts the number of genes with transcription read-through per ccRCC sample . The heatmap illustrates the genes with ( blue ) and without ( grey ) transcription read-through . The left graph indicates the percentage of samples on which read-through is observed for each individual gene . ( n = 50 tumor/matched normal ccRCC TCGA samples ) . ( B ) Heatmap representation of the RNA-seq profile distribution and fold change after the TTS region of genes with transcription read-through in one representative TCGA ccRCC sample ( patient barcode TCGA-CZ-5465 ) of a total of 50 tumor and matched pairs analysed . The gene body region was scaled to 60 equally sized bins and ± 4 Kb gene-flanking regions were averaged in 100-bp windows . The left panel shows the read counts ( log2 RPKMs ) of the matched normal tissue in all genes with read-through and the right panel shows the fold-change ( log2 ) of read counts between the tumor and the matched normal tissue . Genes are ordered according to the read-through length . Scales and colour keys for each panel are depicted in the bottom . ( C ) Metagene analysis of RNA-seq profiles for tumor and matched normal tissue from one ccRCC patient . *p<0 . 05 by Student’s T-test . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 003 We then examined whether global deregulation of gene expression at the level of transcription termination affects overall survival rates of ccRCC patients . For that we segregated the TCGA samples into two categories: ‘high read-through’ samples ( those with more than 200 genes with read-through ) and ‘low read-through’ samples ( less than 200 genes with read-through ) ( Figure 2A ) . We found that patients with a ‘high read-through’ phenotype died significantly earlier than patients with a ‘low read-through’ phenotype ( p = 0 . 008 , log-rank test; Figure 2B ) . 10 . 7554/eLife . 09214 . 004Figure 2 . Transcription read-through correlates with ccRCC survival rates . ( A ) The top graph indicates the number of genes with transcription read-through on each ccRCC patient sample . Samples were split in two groups according to the number of genes with transcription read-through ( low or high ) , using 200 genes as a cut-off . The heatmap represents the RNA-seq tumor/matched normal fold change 4 Kb after the TTS region of genes with transcription read-through . ( B ) Kaplan-Meier plot comparing the survival of patients separated into ‘high read-through’ and ‘low read-through’ subsets as defined in A . ( C ) Proportion of ccRCC patient samples with low and high transcription read-through . Results are shown for samples containing any of the most recurrently mutated genes in ccRCC: SETD2 , VHL , PBRM1 , BAP1 and MTOR . Proportions of high and low read-through were significantly different between samples carrying mutation in SETD2 and in any of the remaining genes ( Fisher’s Exact Test p<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 004 To estimate the contribution of the five most frequently mutated genes in ccRCC ( Cancer Genome Atlas Research N , 2013 ) to the observed transcription termination defects , we calculated the percentage of samples carrying any of these mutations that fall within each of the two subsets defined above . Samples with mutations in the histone methyltransferase SETD2 scored preferentially in the ‘high read-through’ category ( 58% ) ( Figure 2C ) . In contrast , samples carrying any of the four other frequently mutated genes in ccRCC segregated equally between both groups ( BAP1 ) or mainly in the ‘low read-through’ category ( VHL , PBRM1 , MTOR . Figure 2C ) . Mutations on other genes known to be required for transcription termination or pre-mRNA processing were rare and did not segregate specifically in any category ( Supplementary file 1B ) . These data suggest that widespread transcription read-through is a distinctive hallmark of ccRCC and identify SETD2 mutations as a putative contributing factor for this phenotype . To further investigate the correlation between SETD2 mutations and transcription read-through in ccRCC , we performed RNA sequencing of 2 SETD2 wild type ( wt ) and 4 SETD2 mutant ccRCC cell lines previously reported to have a marked reduction of H3K36me3 levels ( Duns et al . , 2010; Carvalho et al . , 2014 ) . The wt ccRCC cell line Caki2 showed the highest SETD2 expression levels , comparable with those of a non-cancer kidney epithelial cell line ( HEK293 ) ( Figure 3A ) . For this reason , we chose Caki2 as the reference dataset for the pairwise analyses of the remaining wt ( Caki1 ) and mutant ( MF , AB , ER , FG2 ) samples . These analyses revealed transcription read-through in hundreds of genes on all ccRCC cell lines , with a significantly higher incidence in all SETD2 mutant cells ( Figure 3B and Supplementary file 1C ) . A metagene analysis of genes with transcription termination defects in SETD2 mutant cells revealed that expression levels vary significantly downstream the TTS , but not within the gene body or at the promoter region ( Figure 3C ) . Moreover , heatmaps of the fold change of read counts between different cell lines depict a strong increase in the number of read-through reads in SETD2 mutant cells ( Figure 3D and Figure 3—figure supplement 1 ) . To rule out the contribution of different genetic backgrounds in these cell lines , we interrogated RNA-seq data from: SETD2 knockout ( KO ) ccRCC cells ( Ho et al . , 2015 ) ; SETD2-depleted human mesenchymal stem ( MS ) cells ( Luco et al . , 2010 ) ; and embryonic stem ( ES ) cells from Setd2 KO mice ( Zhang et al . , 2014 ) . Again , when compared to wt cells , SETD2-deficient cells displayed increased read counts specifically in the region immediately downstream the TTS consistent with transcription read-through events ( Figure 3E , F; Figure 3—figure supplement 2 and Supplementary file 1D , E , F ) . Similar results were obtained upon RT-qPCR measurements of transcription read-through in three distinct genes 48 hr after depletion of SETD2 from wt ccRCC cells by RNA interference ( Figure 3—figure supplement 3 ) 10 . 7554/eLife . 09214 . 005Figure 3 . SETD2 mutations promote transcription read-through in ccRCC . ( A ) SETD2 expression levels ( FPKMs ) in HEK293 and ccRCC cell lines . ( B ) Number of genes with transcription read-through up to 4 Kb downstream the TTS . *p<0 . 05 by Fisher’s Exact Test after comparing each SETD2 mutant cell line with the SETD2 wt cell line ( Caki1 ) ; ( C ) Metagene analysis of genes showing transcription read-through in SETD2 mutant and wt ccRCC cell lines . The gene body region was scaled to 60 equally sized bins and ± 4 Kb gene-flanking regions were averaged in 100-bp windows . *p<0 . 05 by Student’s T-test; ( D ) Heatmap representation of RNA-seq profile distribution and fold change after the TTS region of genes showing transcription read-through . Genes were scaled and averaged as in C . The left panel shows the read counts ( log2 RPKMs ) of the SETD2 wt cell line ( Caki2 ) in all genes with read-through . The two right panels show the fold-change ( log2 ) between each ccRCC cell line and the reference SETD2 wt ccRCC cell line ( Caki2 ) . Genes are ordered according to the read-through length . Scales and colour keys for each panel are depicted at the bottom of the panel . ( E ) Metagene analysis ( as detailed in C ) of genes showing transcription read-through in SETD2 KO and wt 786-O ccRCC cells . *p<0 . 05 by Student’s T-test . ( F ) Analysis as described in D of RNA-seq data from SETD2 KO and control 786-O ccRCC cell lines . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 00510 . 7554/eLife . 09214 . 006Figure 3—figure supplement 1 . SETD2 mutations in ccRCC cells promote transcription read-through . Heatmap and metagene of RNA-seq profiles for the following SETD2 mutant ccRCC cell lines: MF ( A ) , AB ( B ) and ER ( C ) . Heatmap representation of RNA-seq profile distribution and fold change after the TTS of genes with transcription read-through . Gene body was scaled to 60 equally sized bins and ± 4 Kb gene flanking regions were averaged in 100-bp window . Genes are ordered according to the length of the read-through . Left panel shows read counts ( log2 RPKMs ) of the SETD2 wt cell line ( Caki2 ) in all genes with read-through . The right panel shows the fold-change ( log2 ) for SETD2 mutant vs SETD2 wt ( Caki2 ) ccRCC cell lines . Metagene of RNA-seq profiles for SETD2 mutant and wt ccRCC cell line of genes showing transcription read-through . *p-value <0 . 05 by Student’s T-test . Scales and color keys for each panel are depicted at the bottom of the panel . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 00610 . 7554/eLife . 09214 . 007Figure 3—figure supplement 2 . SETD2-depletion impairs transcription termination . ( A , B ) Heatmap representation of RNA-seq profile distribution and fold change after the TTS region of genes showing transcription read-through . The left panel shows the read counts ( log2 RPKMs ) of all genes with read-through in control cells . The two right panels show the fold-change ( log2 ) between SETD2-depleted and control human MS cells ( A ) and Setd2 KO and wt mouse ES cells ( B ) . Genes are ordered according to the read-through length . Scales and colour keys for each panel are depicted at the bottom of the panel . ( C , D ) Metagene analyses of RNA-seq profiles of control and SETD2-depleted human MS cells ( C ) and of Setd2 KO and wt mouse ES cells ( D ) . *p-value <0 . 05 by Student’s T-test . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 00710 . 7554/eLife . 09214 . 008Figure 3—figure supplement 3 . SETD2-depletion impairs transcription termination in ccRCC cells . Caki2 cells were transfected with siRNA oligos against SETD2 or the firefly luciferase ( GL2 ) ( control ) 48 hr before RNA extraction . RT-qPCR was carried out with primers designed to detect transcription read-through in MRPL23 , SEL1L3 and FAM46A . The levels of SETD2 mRNA expression are also shown . The amount of PCR product estimated by RT-qPCR was normalized to the levels of U6 snRNA . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 008 We then investigated whether wt SETD2 can rescue the transcription termination defects observed in SETD2 mutant ccRCC cells . For that , we performed single-molecule RNA FISH after transient expression of GFP-tagged SETD2 in a SETD2 mutant ccRCC cell line ( Figure 4A ) . RNAs produced upon transcription read-through of MRPL23 and SEL1L3 were visualized as foci obtained with RNA FISH probes targeting a region downstream the termination site . These foci were present in significantly higher number in GFP-negative cells than in wt SETD2-GFP expressing cells ( Figure 4B ) . This result reveals that expression of wt SETD2 is sufficient to revert the transcription termination defects observed in mutant cells . Altogether , these data support the view that SETD2 is necessary to guide correct transcription termination genome-wide and that mutations in this histone modifier gene cause aberrant transcription patterns in ccRCC . 10 . 7554/eLife . 09214 . 009Figure 4 . SETD2 rescues the transcription termination defects of SETD2 mutant ccRCC cells . ( A ) RNA FISH experiments on a SETD2-mutant ccRCC cell line ( FG2 ) transiently expressing wt SETD2-GFP . Quasar570-labeled probes were designed against a region downstream the termination sites of MRPL23 and SEL1L3 . The arrows indicate single RNA transcripts generated by a transcription read-through event . ( B ) Quantification of the number of GFP-negative and GFP-positive cells containing more than 5 RNA FISH foci . Means and standard deviations from at least 50 cells from four individual experiments are shown . Scale bars: 10 μm . p<0 . 01 by Student’s T-test . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 009 One possible functional consequence of aberrant transcription read-through is the invasion of adjacent downstream genes altering their expression levels . This trans-acting transcriptional interference mechanism may play important roles in cancer development and progression by deregulating the expression of relevant oncogenes and tumor suppressors ( Proudfoot , 1986 ) . To test whether overrunning of neighbouring genes is a frequent outcome of transcription read-through in ccRCC , we analysed the expression levels of the entire intergenic region and of the gene located immediately downstream . In agreement with our prediction , there was a statistically significant increase in read counts along the intergenic region and within the body of the downstream gene of a tandem pair ( Figure 5A ) . This difference was still detected after the TTS of the downstream gene , but not within the upstream gene ( Figure 5B ) . Overall , reading-through RNA polymerase II ( RNAPII ) molecules invaded an average of 20% of genes located downstream ( Supplementary file 1A ) . 10 . 7554/eLife . 09214 . 010Figure 5 . Transcription read-through overruns and interferes with the expression of neighbouring genes . Heatmap ( A ) and metagene ( B ) profiles of the intergenic region and of the gene located downstream of a read-through event in one representative ccRCC TCGA sample ( patient barcode TCGA-CZ-5465 ) . Genes were scaled and averaged as in Figure 1 . *p<0 . 05 by Student’s T-test . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 010 We then asked if read-through levels correlate with the expression levels of the downstream gene across the TCGA dataset . Notably , expression of 52 out of 903 genes ( 6% ) exhibited a statistically significant correlation with read-through levels of the upstream gene ( Figure 6A , Supplementary file 1G ) . From these , 51 genes were positively correlated and only one gene showed a negative correlation . Amongst positively correlated genes we found the anti-apoptotic oncogene BCL2 . In support of its oncogenic role in kidney cancer , depletion of BCL2 with antisense oligonucleotides inhibits ccRCC tumor growth in vitro and in vivo ( Uchida et al . , 2001 ) . We further observed that expression of BCL2 is frequently increased in the tumor samples of the TCGA dataset when compared to the matched samples ( Figure 6B ) . Importantly , augmented levels of BCL2 mRNA and protein correlated positively with the levels of transcription read-through of the KDSR gene located immediately upstream ( Figure 6B , C ) . Altogether , these data suggest that transcription termination defects interfere with the expression of neighbouring genes and illustrate a new paradigm for the aberrant expression of cancer-related genes , which may explain the upregulation of the BCL2 oncogene in ccRCC . 10 . 7554/eLife . 09214 . 011Figure 6 . Transcription read-through of the KDSR gene correlates with the expression of the BCL2 oncogene . ( A ) Distribution of the correlation between the read-through and the expression levels of the downstream tandem genes . Significant correlation values ( Benjamini-Hochberg adjusted p<0 . 05 ) are represented in blue . ( B ) Correlation between the expression levels of BCL2 and the read-through of the upstream KDSR gene . The graph depicts the fold-change of read counts for each tumor and matched normal pair . ( C ) Correlation between BCL2 protein levels and the KDSR read-through . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 011 RNAPII elongation beyond the annotated gene boundaries and invasion of an adjacent gene as a result of impaired transcription termination may result in the formation of hybrid transcripts collectively called RNA chimeras ( Gingeras , 2009 ) . In addition to gene fusions ( formed upon chromosomal translocations ) RNA chimeras can originate from DNA-independent events such as trans-splicing , RNA recombination or transcriptional read-through ( Gingeras , 2009 ) . Our analysis of ccRCC RNA-seq data revealed a high number of reads mapping at two distinct tandem genes , which are indicative of RNA chimeras generated by intergenic splicing following a read-through event ( Figure 7A , Supplementary file 1H ) . Analysis of the splicing pattern of these chimeras showed that most events join the second-last exon of the upstream gene with the second exon of the downstream gene ( Figure 7B ) . This pattern is compatible with the exon definition model according to which transcription termination is necessary for the selection of the terminal 3’ splice site ( ss ) ( Niwa et al . , 1992; Dye and Proudfoot , 1999 ) . In the absence of transcription termination , the terminal 3’ss is evicted and the terminal 5’ss will splice together with the first 3’ss of the downstream gene , which emerges from the nascent transcript once RNAPII reaches the second exon ( Figure 7B ) . Interestingly , the number of RNA chimeras detected correlated positively with the levels of transcription read-through ( p = 0 . 003; R = 0 . 41 ) and was significantly higher in the sample group with a ‘high read-through’ phenotype defined above ( Figure 7C ) . 10 . 7554/eLife . 09214 . 012Figure 7 . Read-through RNA chimeras are prevalent in ccRCC . ( A ) Circos plots showing the location of genes forming each RNA chimera detected on human ccRCC cell lines and TCGA samples . Chimeras are represented by curves inside the Circos . ( B ) Number of read-through RNA chimeras formed by intergenic splicing between the represented exons . ( C ) Number of read-through RNA chimeras in the low and high read-through sample subsets defined in Figure 2A . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 012 A remarkable feature of these RNA chimeras is that some were recurrently detected in different tumor samples . One particular chimera , encoded by two consecutive genes - CTSC and RAB38 - was detected in 20% of the TCGA samples ( but not in any matched normal sample ) and in four of the six ccRCC cell lines that we sequenced de novo ( Supplementary file 1H ) . We validated this RNA chimera by RT-qPCR before and after transfection of the ccRCC cell lines with a small interfering RNA ( siRNA ) spanning the transcript break-point , which resulted in a robust depletion of CTSC-RAB38 ( Figure 8A , B ) . In contrast , siRNAs targeting either the last exon of CTSC or the first exon of RAB38 ( which are not included in the chimeric transcript ) significantly decreased the levels of CTSC and RAB38 , respectively , but not the levels of the CTSC-RAB38 chimera ( Figure 8B , C ) . Moreover , we measured the RNAPII occupancy throughout the intergenic region and within the body of the CTSC and RAB38 genes by chromatin immunoprecipitation ( ChIP ) . We detected a robust occupancy of RNAPII throughout the intergenic region ( Figure 8D ) , which further suggests that the CTSC-RAB38 chimera is generated following a read-through episode without any genomic deletion or translocation involved . 10 . 7554/eLife . 09214 . 013Figure 8 . The CTSC-RAB38 chimera is recurrently detected in ccRCC . ( A ) Schematic illustration of the CTSC-RAB38 locus depicting the position of the primers used to measure the transcripts levels by RT-qPCR shown in ( B ) and ( C ) and the position of the siRNAs targeting each of the three transcripts ( CTSC , RAB38 and the CTSC-RAB38 chimera ) . The dashed curve in the scheme illustrates the splicing pattern of the chimeric transcript . ( B ) Relative expression of the CTSC-RAB38 chimeric transcript after depletion of the indicated transcripts by RNAi in three distinct ccRCC cell lines ( FG2 , MF , ER ) . ( C ) Relative expression of CTSC and RAB38 upon depletion of the indicated transcripts by RNAi in FG2 cells . Similar results were obtained with the other ccRCC cell lines . *p<0 . 05 by Student’s T-test compared to controls . ( D ) RNAPII ChIP along the CTSC-RAB38 locus in FG2 cells . Means and standard deviations from five independent experiments are shown . *p<0 . 05 by Student’s T-test compared to the gene desert . DOI: http://dx . doi . org/10 . 7554/eLife . 09214 . 013
Our transcriptome analysis of a large dataset of tumor and normal matched samples revealed that transcription events extending beyond the annotated 3’ end of genes are frequent in ccRCC . Strikingly , our analysis further disclosed an unexpected prognostic power of transcription read-through in kidney cancer: higher number of genes with transcription read-through correlates significantly with poorer patient survival . Amongst the most frequently mutated genes in ccRCC , we identified SETD2 inactivation as a contributing factor for increased transcription read-through . In fact , ectopic expression of SETD2 was sufficient to rescue the transcription termination defects of SETD2 mutant ccRCC cells . Moreover , we report that the effects of impaired transcription termination are not confined to the affected gene . Instead , it also contaminates the expression of neighboring genes that are overran by reading-through RNAPII complexes . Importantly , amongst the genes whose expression directly correlates with the volume of transcription read-through on the upstream gene , we detected the anti-apoptotic oncogene BCL2 . This finding unveils a new source of aberrant expression of cancer-related genes and provides a plausible mechanistic basis for the upregulation of BCL2 frequently observed in ccRCC . Our present study further reveals recurrent RNA chimeras in ccRCC combining sequences from two tandem genes . Such chimeras are generated following extensions of RNAPII beyond the annotated gene boundaries and invasion of an adjacent gene as a result of impaired transcription termination . According to the exon definition model ( Niwa et al . , 1992; Dye and Proudfoot , 1999 ) , deficient transcription termination is expected to impair the splicing of the last exon . In agreement , most chimeras skip the last exon of the upstream gene and the prevailing splicing pattern joins the second-last exon of the upstream gene with the second exon of the downstream gene . The finding that several of these RNA chimeras are recurrently detected in two or more tumor samples , suggests that they were selected during cancer development and supports the exciting possibility that they may play relevant functional roles . Although our study primarily focused on the characterization of the transcription termination defects and their impact on the ccRCC transcriptome , our pioneer findings raise intriguing questions such as: which mechanism ( s ) drive such transcription defects ? ; how individual RNA chimeras are functionally involved in tumorigenesis ? ; do chimeric transcripts produce functional oncoproteins and/or can they directly affect the expression of other relevant cancer genes ? ; how can we intervene therapeutically to restore the canonical transcription pattern ? The widespread incidence of aberrant termination in ccRCC cells suggests that it may play important roles in expanding the transcriptome diversity that drives cancer development and progression . The impact on BCL2 expression illustrates a relevant functional outcome of transcription read-though . The generation of chimeric transcripts , namely those recurrently identified in several samples , such as CTSC-RAB38 , further discloses the contribution of impaired transcription termination for the expansion of RNA species that may favor cancer progression . Nevertheless , future studies should provide direct evidence that a chimeric transcript has oncogenic functions in order to support the physiological relevance of these RNAs . This is a challenging and very exciting topic and further efforts are required to fully elucidate the impact of impaired transcription termination on cancer in general and on ccRCC in particular .
ccRCC cells ( Caki-1 , Caki-2 , MF , ER , AB and FG2 , Cell Line Services Eppelheim , Germany ) were grown as monolayers in Dulbecco’s modified Eagle medium ( DMEM , Invitrogen , CA , USA ) , supplemented with 10% ( v/v ) FBS , 1% ( v/v ) nonessential amino acids , 1% ( v/v ) L-glutamine and 100U/ml penicillin-streptomycin , and maintained at 37°C in a humidified atmosphere with 5% CO2 . RNAi was achieved using synthetic siRNA duplexes ( Eurogentec , Belgium ) . The sequence of the siRNAs is shown in Supplementary file 1I . siRNAs targeting the firefly luciferase ( GL2 ) were used as controls . Cells were reverse transfected with 10 μM siRNAs using OptiMEM ( Invitrogen ) and Lipofectamine RNAiMAX ( Invitrogen ) , according to the manufacturer’s instructions . 24 hr after the first transfection , cells were re-transfected with the same siRNA duplexes and transfection reagents and harvested on the following day . Total RNA was extracted with TRIzol ( Invitrogen ) . cDNA was made using Superscript II Reverse Transcriptase ( Invitrogen ) . RT-qPCR was performed in the ViiA Real Time PCR ( Applied Biosystems , CA , USA ) , using SYBR Green PCR master mix ( Applied Biosystems ) . The relative RNA expression was estimated as follows: 2^ ( Ct reference – Ct sample ) , where Ct reference and Ct sample are mean threshold cycles of RT-qPCR done in duplicate on cDNA samples from U6 snRNA ( reference ) and the cDNA from the genes of interest ( sample ) . All primer sequences are presented in Supplementary file 1I . ChIP was performed as described ( de Almeida et al . , 2011 ) . The relative occupancy of RNAPII at each locus was estimated by RT-qPCR as follows: 2^ ( Ct Input – Ct IP ) , where Ct Input and Ct IP are mean threshold cycles of RT-qPCR done in duplicate on DNA samples from input and specific immunoprecipitations , respectively . RNAPII was precipitated with an antibody against its largest subunit ( N20 , sc-899; Santa Cruz , TX , USA ) . The sequences of gene-specific , intergenic regions and gene desert primer pairs are presented in Supplementary file 1I . FG2 ccRCC cells were transiently transfected with a wt SETD2-GFP expression plasmid ( Carvalho et al . , 2014 ) and cultured on glass coverslip for 24 hr before hybridization with RNA FISH probes ( Biosearch Technologies , CA , USA ) following the manufacturer’s protocol . Briefly , cells were washed with PBS , fixed for 10 min at room temperature , washed twice with PBS , and permeabilized at 4°C in 70% ( vol/vol ) EtOH . Probes diluted in hybridization buffer were added to permeabilized cells before overnight incubation in a dark chamber at 37°C . After washing , DAPI was added to stain the nuclei . Epi-fluorescence microscopy was performed using a Zeiss Z1 microscope equipped with Z-piezo ( Prior , MA , USA ) , a 63x 1 . 4 NA Plan-Apochromat objective and a sCMOS camera ( Hamamatsu Flash 4 . 0 ) . FISH probes were designed to target a segment of the RNA transcript encoded by the intergenic region downstream of the canonical termination sites of MRPL23 and SEL1L3 . The sequences of the probes are shown in Supplementary file 1J . Samples were barcoded and prepared for sequencing by the Centro Nacional de Análisis Genómico ( CNAG , Barcelona , Spain ) using Illumina protocols . PolyA+ RNA-seq libraries of ccRCC cell lines were sequenced as paired-end 75-bp sequence tags using the standard Illumina pipeline . ccRCC RNA-seq datasets ( with at least 40 million mapped reads on each tumor and matched normal samples – in a total of 50 paired-samples listed in Supplementary file 1A ) were obtained from TCGA . HEK293 RNA-seq data were from the Sequence Read Archive ( SRX876600 ) . RNA-seq data from SETD2 KO ccRCC cells ( 786-O ) , SETD2-depleted human MS cells and Setd2 KO mouse ES cells were obtained from the GEO ( http://www . ncbi . nlm . nih . gov/geo/ GSE66879 , GSE19373 and GSE54932 , respectively ) . Data quality was assessed with the FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) quality-control tool for high throughput sequence data . Sequence tags were then mapped to the reference human ( hg19 ) or mouse ( mm9 ) genomes with TopHat software using default parameters ( Kim et al . , 2013 ) . Reads from samples with multiple sequencing lanes were merged and only the best score alignment was accepted for each read . Gene annotations were obtained from UCSC knownGene and refGene tables ( Karolchik et al . , 2014 ) and merged into a single transcript model per gene using BedTools ( Quinlan and Hall , 2010 ) . Our analysis was restricted to transcriptionally active genes defined as those with expression levels higher than the 25th percentile . To identify genes with transcriptional read-through , we filtered out all genes for which there was another annotated gene in either strand within a region of 5 Kb downstream of their TTS . We also filtered out genes with an overall increase in expression level relative to the control sample . Reads were counted in 100 bp windows for the 4 Kb region downstream of the TTS and normalized for the total number of mapped reads on each sample ( RPKMs ) ( Mortazavi et al . , 2008 ) . We considered the occurrence of transcriptional read-through when more than six 100 bp windows showed increased ( at least 1 . 5 fold-change ) RPKMs relative to control . The control samples were: Caki2 for ccRCC cell lines; the matched normal tissue sample for each ccRCC patient; control 786-O ccRCC cells for SETD2 KO 786-O cells; human MS cells for SETD2-depleted human MS cells; and wt mouse ES cells for the Setd2 KO mouse . In the ccRCC cell lines dataset , all the analysis were also performed comparing both SETD2 wt ( Caki1 vs Caki2 ) , with this comparison working as a negative control . Statistical significance of the differences between proportions of genes showing read-through was assessed using the Fisher’s exact test . RNA chimeras were detected using the fusion-search option ( fusion-min-dist set to 100 bp ) in TopHat alignment ( Kim et al . , 2013 ) and TopHat-Fusion ( Kim and Salzberg , 2011 ) with default parameters . For downstream analysis we only considered RNA chimeras supported by at least two reads . A set of in-house scripts were written in bash and in the R environmental language ( http://www . R-project . org/ ) ( Team RDC , 2011 ) . Figures were produced using BedTools ( Quinlan and Hall , 2010 ) and default packages from the R environment . To produce heatmaps and metagene average profiles showing transcription read-through , genes were scaled to 60 equally sized bins so that all annotated TSSs and TTSs were aligned . Regions 4 Kb upstream of TSSs and 4 Kb downstream of TTSs were averaged in a 100-bp window . Individual gene profiles were produced by successions of 10-bp windows ( single gene ) or 100-bp windows ( two genes ) . All read counts were normalized by genomic region length and number of mapped reads ( RPKM ) , and RPKM values were log2 transformed when representing multiple genes . Protein levels assessed by reverse phase protein array ( RPPA ) were gathered from the TCGA portal . Circle plots showing RNA chimeras distribution were produced using Circos ( Krzywinski et al . , 2009 ) . The RNA-seq data for ccRCC cell lines have been deposited in Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/geo/ ) database under the accession number GSE64451 . ccRCC RNA-seq datasets were obtained from TCGA . HEK293 RNA-seq data were from the Sequence Read Archive ( SRX876600 ) . RNA-seq data from SETD2 KO 786-O ccRCC cells , human mesenchymal stem cells and mouse embryonic stem cells were obtained from the GEO ( GSE66879 , GSE19373 and GSE54932 , respectively ) .
Aberrant expression of cancer genes and non-canonical RNA species is a hallmark of cancer . However , the mechanisms driving such atypical gene expression programs are incompletely understood . Here , our transcriptional profiling of a cohort of 50 primary clear cell renal cell carcinoma ( ccRCC ) samples from The Cancer Genome Atlas ( TCGA ) reveals that transcription read-through beyond the termination site is a source of transcriptome diversity in cancer cells . Amongst the genes most frequently mutated in ccRCC , we identified SETD2 inactivation as a potent enhancer of transcription read-through . We further show that invasion of neighbouring genes and generation of RNA chimeras are functional outcomes of transcription read-through . We identified the BCL2 oncogene as one of such invaded genes and detected a novel chimera , the CTSC-RAB38 , in 20% of ccRCC samples . Collectively , our data highlight a novel link between transcription read-through and aberrant expression of oncogenes and chimeric transcripts that is prevalent in cancer .
Mutations in genes play important roles in many types of cancer . However , mutations alone cannot explain all the biological changes that occur to cancer cells . For example , very few mutations have been linked with a type of kidney cancer called clear cell renal cell carcinoma ( or ccRCC for short ) . Instead , scientists suspect that this cancer is largely caused by changes in the expression of particular genes so that certain cancer-promoting genes are more highly expressed , while other genes that would prevent tumor growth become less active . One of the few genes that is often mutated in ccRCC is called SETD2 . This gene is involved in processes that alter the structure of DNA , but do not alter the genes themselves . These “epigenetic” changes can alter how the instructions in genes are used to make proteins . The first step in making proteins is to use a section of DNA as a template to make molecules of messenger ribonucleic acid ( mRNA ) in a process called transcription . There are markers within a gene that show where transcription should start and stop to produce the mRNA required to make a particular protein . Epigenetic changes can mask these markers so that the cell produces longer mRNAs that incorporate instructions from neighboring genes . It was not known how often these stop signs are ignored in ccRCC cells . Here , Grosso et al . compared transcription in normal cells and in ccRCC tumor cells from 50 different patients . The experiments show that more stop signs were ignored in many of the cancer cells , especially in cells with mutations in SETD2 . This caused all or parts of neighboring genes to be transcribed along with the target gene and led to changes in the expression levels of these genes . For example , a cancer-promoting gene called BCL2 was more highly expressed in these cells . Furthermore , some of the mRNA molecules produced in these cancer cells may make “fusion” proteins that combine elements from several proteins . These fusion proteins may work differently to normal cell proteins and therefore might also promote the development of tumors . Grosso et al . ’s findings reveal a new link between epigenetic changes and cancer .