Title: Conserved crosstalk between histone deacetylation and H3K79 methylation generates DOT1L‐dose dependency in HDAC1‐deficient thymic lymphoma
Abstract: Article17 June 2019Open Access Source DataTransparent process Conserved crosstalk between histone deacetylation and H3K79 methylation generates DOT1L-dose dependency in HDAC1-deficient thymic lymphoma Hanneke Vlaming Hanneke Vlaming orcid.org/0000-0003-1743-6428 Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Chelsea M McLean Chelsea M McLean Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Tessy Korthout Tessy Korthout Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Mir Farshid Alemdehy Mir Farshid Alemdehy Division of Tumor Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sjoerd Hendriks Sjoerd Hendriks Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Cesare Lancini Cesare Lancini Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sander Palit Sander Palit Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sjoerd Klarenbeek Sjoerd Klarenbeek Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Eliza Mari Kwesi-Maliepaard Eliza Mari Kwesi-Maliepaard Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Thom M Molenaar Thom M Molenaar Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Liesbeth Hoekman Liesbeth Hoekman Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Thierry T Schmidlin Thierry T Schmidlin Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands Search for more papers by this author AF Maarten Altelaar AF Maarten Altelaar orcid.org/0000-0001-5093-5945 Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands Proteomics Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Tibor van Welsem Tibor van Welsem Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Jan-Hermen Dannenberg Jan-Hermen Dannenberg Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Heinz Jacobs Corresponding Author Heinz Jacobs [email protected] orcid.org/0000-0001-6227-9850 Division of Tumor Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Fred van Leeuwen Corresponding Author Fred van Leeuwen [email protected] orcid.org/0000-0002-7267-7251 Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Amsterdam UMC location UvA, Amsterdam, The NetherlandsCorrection added on 28 August 2019, after first online publication: the author affiliations have been updated. Search for more papers by this author Hanneke Vlaming Hanneke Vlaming orcid.org/0000-0003-1743-6428 Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Chelsea M McLean Chelsea M McLean Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Tessy Korthout Tessy Korthout Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Mir Farshid Alemdehy Mir Farshid Alemdehy Division of Tumor Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sjoerd Hendriks Sjoerd Hendriks Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Cesare Lancini Cesare Lancini Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sander Palit Sander Palit Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Sjoerd Klarenbeek Sjoerd Klarenbeek Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Eliza Mari Kwesi-Maliepaard Eliza Mari Kwesi-Maliepaard Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Thom M Molenaar Thom M Molenaar Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Liesbeth Hoekman Liesbeth Hoekman Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Thierry T Schmidlin Thierry T Schmidlin Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands Search for more papers by this author AF Maarten Altelaar AF Maarten Altelaar orcid.org/0000-0001-5093-5945 Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands Proteomics Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Tibor van Welsem Tibor van Welsem Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Jan-Hermen Dannenberg Jan-Hermen Dannenberg Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Heinz Jacobs Corresponding Author Heinz Jacobs [email protected] orcid.org/0000-0001-6227-9850 Division of Tumor Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands Search for more papers by this author Fred van Leeuwen Corresponding Author Fred van Leeuwen [email protected] orcid.org/0000-0002-7267-7251 Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands Amsterdam UMC location UvA, Amsterdam, The NetherlandsCorrection added on 28 August 2019, after first online publication: the author affiliations have been updated. Search for more papers by this author Author Information Hanneke Vlaming1,7,‡, Chelsea M McLean1,‡, Tessy Korthout1, Mir Farshid Alemdehy2, Sjoerd Hendriks1, Cesare Lancini1, Sander Palit1, Sjoerd Klarenbeek3, Eliza Mari Kwesi-Maliepaard1, Thom M Molenaar1, Liesbeth Hoekman3, Thierry T Schmidlin4, AF Maarten Altelaar4,5, Tibor Welsem1, Jan-Hermen Dannenberg1,8, Heinz Jacobs *,2,‡ and Fred Leeuwen *,1,6,‡ 1Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands 2Division of Tumor Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands 3Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands 4Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre, Utrecht, The Netherlands 5Proteomics Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands 6Amsterdam UMC location UvA, Amsterdam, The Netherlands 7Present address: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA 8Present address: Genmab B.V., Antibody Sciences, Utrecht, The Netherlands ‡These authors contributed equally to this work. *Corresponding author. Tel: +31 20 5122065; E-mail: [email protected] *Corresponding author. Tel: +31 20 5121973; E-mail: [email protected] The EMBO Journal (2019)38:e101564https://doi.org/10.15252/embj.2019101564 Correction added on 28 August 2019, after first online publication: the author affiliations have been updated. PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract DOT1L methylates histone H3K79 and is aberrantly regulated in MLL-rearranged leukemia. Inhibitors have been developed to target DOT1L activity in leukemia, but cellular mechanisms that regulate DOT1L are still poorly understood. We have identified the histone deacetylase Rpd3 as a negative regulator of budding yeast Dot1. At its target genes, the transcriptional repressor Rpd3 restricts H3K79 methylation, explaining the absence of H3K79me3 at a subset of genes in the yeast genome. Similar to the crosstalk in yeast, inactivation of the murine Rpd3 homolog HDAC1 in thymocytes led to an increase in H3K79 methylation. Thymic lymphomas that arise upon genetic deletion of Hdac1 retained the increased H3K79 methylation and were sensitive to reduced DOT1L dosage. Furthermore, cell lines derived from Hdac1Δ/Δ thymic lymphomas were sensitive to a DOT1L inhibitor, which induced apoptosis. In summary, we identified an evolutionarily conserved crosstalk between HDAC1 and DOT1L with impact in murine thymic lymphoma development. Synopsis Identification of a conserved chromatin crosstalk, in which histone deacetylases Rpd3/HDAC1 restrict H3K79 methylation, uncovers a DOT1L-dose dependency of thymic lymphoma in HDAC1-deficient mice. Yeast histone deacetylase and transcriptional repressor Rpd3 restricts Dot1-mediated histone H3K79 methylation at its target genes. The murine Rpd3 homolog HDAC1 negatively regulates DOT1L-mediated H3K79 methylation in thymocytes. Murine thymic lymphomas caused by the loss of HDAC1 depend on proper DOT1L dosage. Introduction Aberrant histone modification patterns have been observed in many diseases, and this deregulation of chromatin can play a causative role in disease. Since epigenetic alterations are, in principle, reversible in nature, histone (de)modifiers are attractive therapeutic targets (Brien et al, 2016; Jones et al, 2016; Shortt et al, 2017). Several epigenetic drugs are currently in the clinic or in clinical trials, but for many of the drug targets, we are only beginning to understand their cellular regulation. The histone H3K79 methyltransferase DOT1L (KMT4; Dot1 in yeast) is an epigenetic enzyme for which inhibitors are in clinical development for the treatment of MLL-rearranged (MLL-r) leukemia (Stein & Tallman, 2016). In MLL-r leukemia, DOT1L recruitment to MLL target genes, such as the HoxA cluster, leads to aberrant H3K79 methylation and increased transcription (reviewed in Vlaming & Van Leeuwen, 2016). Although the DOT1L inhibitor Pinometostat (EPZ-5676) has shown promising results in the laboratory and is currently in clinical development (Bernt et al, 2011; Daigle et al, 2013; Waters et al, 2015; Stein & Tallman, 2016; Stein et al, 2018), the cellular mechanisms and consequences of DOT1L deregulation are only just being uncovered (Vlaming & Van Leeuwen, 2016). An important mechanism of regulation is the trans-histone crosstalk between monoubiquitination of the C-terminus of histone H2B (H2Bub) at lysine 120 (123 in yeast) and methylation of histone H3K79 (Zhang et al, 2015). The addition of a ubiquitin peptide to the nucleosome at this position occurs in a co-transcriptional manner and promotes the activity of Dot1/DOT1L, possibly by activation of DOT1L or coaching it toward H3K79 and thereby increasing the chance of a productive encounter (Vlaming et al, 2014; Zhou et al, 2016). Another mechanism of regulation is mediated by the direct interactions of DOT1L with central transcription elongation proteins (reviewed in Vlaming & Van Leeuwen, 2016). These interactions target DOT1L to transcribed chromatin and provide an explanation for the aberrant recruitment of DOT1L by oncogenic MLL fusion proteins (Deshpande et al, 2014; Li et al, 2014; Chen et al, 2015; Kuntimaddi et al, 2015; Wood et al, 2018). Further characterizing the regulatory network of DOT1L could lead to the identification of alternative drug targets for diseases in which DOT1L is critical and provide alternative strategies in case of resistance to treatment with DOT1L inhibitors (Campbell et al, 2017). In a previous study, we presented a ChIP-barcode-seq screen (Epi-ID) identifying novel regulators of H3K79 methylation in yeast (Vlaming et al, 2016). The Rpd3-large (Rpd3L) complex was identified as an enriched complex among the candidate negative regulators of H3K79 methylation of a barcoded reporter gene. Rpd3 is a class I histone deacetylase (HDAC) that removes acetyl groups of histones, as well as numerous non-histone proteins, and is generally associated with transcriptional repression (Yang & Seto, 2008). Several inhibitors of mammalian HDACs have been approved for the treatment of cutaneous T-cell lymphoma and other hematologic malignancies, while others are currently being tested in clinical trials (West & Johnstone, 2014). HDAC1 and HDAC2, prominent members of the class I HDACs, are found in the repressive Sin3, NuRD, and CoREST complexes (Yang & Seto, 2008). Loss or inhibition of HDAC1/Rpd3 leads to increased histone acetylation, which in turn can lead to increased expression of target genes and cryptic transcripts (Carrozza et al, 2005; Joshi & Struhl, 2005; Li et al, 2007; Rando & Winston, 2012; Brocks et al, 2017; McDaniel & Strahl, 2017). Here, we demonstrate that Rpd3 restricts H3K79 methylation at its target genes. Most euchromatic genes in the yeast genome are marked by high levels of H3K79me3. We observed that a subset of the genes that do not follow this pattern has lower H3K79me3 levels due to the action of the Rpd3L complex, which deacetylates its targets and imposes strong transcriptional repression and absence of H2Bub1. Importantly, the Rpd3-Dot1 crosstalk is conserved in mammals: Genetic ablation of Hdac1 in murine thymocytes also leads to an increase in H3K79 methylation in vivo. High H3K79me is maintained in the lymphomas these mice develop, and a reduction in DOT1L activity by heterozygous deletion of Dot1L reduces tumor burden, an effect that was not observed upon homozygous deletion of Dot1L. Furthermore, DOT1L inhibitors induce apoptosis in Hdac1-deficient but not Hdac1-proficient thymic lymphoma cell lines, suggesting a DOT1L-dose dependence. Taken together, our studies reveal a new, evolutionarily conserved mechanism of H3K79me regulation by Rpd3/HDAC1 with relevance for cancer development. Results Identification of the Rpd3L complex as a negative regulator of H3K79 methylation We recently reported a systematic screening strategy called Epi-ID to identify regulators of H3K79 methylation (Vlaming et al, 2016). In that screen, relative H3K79 methylation (H3K79me) levels at two DNA barcodes (UpTag and DownTag) flanking a reporter gene were measured in a genome-wide library of barcoded deletion mutants, thus testing thousands of genes for H3K79me regulator activity at these loci (Fig 1A). Since higher Dot1 activity in yeast leads to a shift from lower (me1) to higher (me3) methylation states (Frederiks et al, 2008), the H3K79me3 over H3K79me1 ratio was used as a measure for Dot1 activity. A growth-corrected H3K79me score was calculated to account for the effect of growth on H3K79 methylation, and groups of positive and negative candidate regulators were identified (Vlaming et al, 2016). Components of the Rpd3L complex were enriched among candidate negative regulators (10-fold over-representation, P = 1.2E-4; Vlaming et al, 2016). The histone deacetylase Rpd3 is found in two complexes, the large Rpd3L complex and the small Rpd3S complex, which also share the subunits Sin3 and Ume1 (Carrozza et al, 2005; Keogh et al, 2005). A closer inspection of the Rpd3 complexes revealed that deletion of Rpd3L subunits resulted in an increase in H3K79 methylation on both the UpTag and the DownTag (promoter and terminator context, respectively; Fig 1B), with the exception of two accessory subunits that play peripheral roles (Lenstra et al, 2011). Deletion of the two Rpd3S-specific subunits did not lead to an increase in H3K79me (Fig 1B), which is consistent with Rpd3S binding and acting on coding sequences (Drouin et al, 2010) and thus away from the intergenic barcodes. To validate the effect on a global scale, we performed targeted mass spectrometry analysis to determine the relative levels of the different H3K79me states (me0 to me3) in rpd3Δ and sin3Δ strains. On bulk histones, these strains showed an increase in H3K79me (increase in H3K79me3 at the cost of lower methylation states; Fig 1C). The H3K79me increase was not caused by an increase in Dot1 protein (Fig EV1A) or mRNA expression (Kemmeren et al, 2014). Thus, although these regulators were identified using only two 20-base-pair barcodes to read out H3K79me levels at a reporter locus, their effects could be validated globally. Figure 1. Rpd3 and other members of the Rpd3L complex negatively regulate H3K79 methylation Schematic overview of the Epi-ID strategy. Epi-ID H3K79 methylation scores of the deletion mutants of members of the Rpd3L and Rpd3S complexes, calculated as described in Appendix Supplementary Methods, where 0 means a wild-type H3K79me level (log2 scale). The gray dots indicate accessory subunits. UpTag and DownTag are barcode reporters in a promoter and terminator context, respectively. Data were obtained on all Rpd3L/Rpd3S subunits except Sds3. Mass spectrometry analysis of H3K79 methylation in wild-type and mutant strains. Mean and individual data points of two biological replicates. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-way ANOVA, comparison to wild type. Heatmaps of H3K79me1, H3K79me3, H2Bub, and H3 in wild-type cells, aligned on the TSS. Genes were sorted based on the average H3K79me3/H3K79me1 ratio in the first 500 bp. Snapshot of depth-normalized ChIP-seq data tracks from wild-type and rpd3Δ strains showing 6 kb surrounding the DBP1 ORF, which is the top gene in the heatmap in panel (F). All tracks have the same y-axis (0–20 rpm). A snapshot of another top-regulated gene is shown in Fig EV1D. Heatmap of the H3K79me3/H3 change in rpd3Δ versus wild-type cells, aligned on the TSS. Genes were sorted based on the average ratio in the first 500 bp. Source data are available online for this figure. Source Data for Figure 1 [embj2019101564-sup-0003-SDataFig1.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Rpd3 and Sin3 negatively regulate H3K79 methylation A. Immunoblots show that deletion of RPD3 or SIN3 does not lead to a detectable increase in global H2Bub or Dot1 protein levels. B. Metagene plots of H3K79me1, H3K79me3, H2Bub, H3, and H2B in rpd3Δ and wild-type strains. C. Gene set enrichment analysis shows that subtelomeric genes (< 30 kb of telomeres) are enriched among genes with low H3K79 methylation (measured by the average H3K79me3/H3K79me1 ratio in the first 500 bp of the ORF). D–E. Snapshots of depth-normalized ChIP-seq data tracks from wild-type and rpd3Δ strains showing 7 kb surrounding meiotic gene ZIP1 (D) and subtelomeric genes AIF1 and COS10 (E). All tracks have the same y-axis (0–20 rpm), which, for comparison, is also the same scale as in Fig 1E. Source data are available online for this figure. Download figure Download PowerPoint Rpd3 represses H3K79 methylation at the 5′ ends of a subset of genes We next asked at which regions Rpd3 and Sin3 regulate H3K79 methylation in yeast, other than the barcoded reporter gene. To address this, we performed ChIP-seq analysis for H3K79me1, H3K79me3, and H3 in wild-type and rpd3Δ strains. In addition, we included ChIP-seq for H2B and H2Bub using a site-specific antibody that we recently developed (Van Welsem et al, 2018). First, we considered the patterns in the wild-type strain. Both the coverage at one representative locus and across all genes in a heatmap showed that H3K79me3 is predominantly present throughout coding sequences of most genes, where H2Bub is also high, as reported previously (Figs 1D and E, and EV1B; Schulze et al, 2009; Magraner-Pardo et al, 2014; Weiner et al, 2015; Sadeh et al, 2016). In contrast, H3K79me1 was found in transcribed as well as intergenic regions (Figs 1D and EV1B). This is consistent with published ChIP-seq data and our previous ChIP-qPCR results (Weiner et al, 2015; Vlaming et al, 2016). In agreement with the distributive mechanism of methylation of Dot1 (Frederiks et al, 2008; De Vos et al, 2011), H3K79me1 and H3K79me3 anti-correlated, and H3K79me1 over the gene body was found on the minority of genes that lacked H3K79me3 and H2Bub (Fig 1D). Among these low H3K79me3, high H3K79me1 genes were subtelomeric genes, where the SIR silencing complex competes with Dot1 for binding to nucleosomes and H2Bub levels are kept low by the deubiquitinating enzyme Ubp10 (Gardner et al, 2005; Emre et al, 2005; Gartenberg & Smith, 2016; Kueng et al, 2013; Fig EV1C and E). We then compared the patterns in wild-type versus rpd3Δ mutant strains. In metagene plots, the mutant showed a decrease in H3K79me1 and an increase in H3K79me3 just after the transcription start site (TSS; Fig EV1B), suggesting that in this region Rpd3 suppresses the transition from lower to higher H3K79me states. To assess whether the changes observed in the metagene plots were explained by a modest effect on H3K79me at all genes or a stronger effect at a subset of genes, we determined the H3-normalized H3K79me3 level in the first 500 bp of each gene and ranked the genes based on the change in H3K79me3 upon loss of Rpd3. A heatmap of H3K79me3 changes by this ranking showed that the absence of Rpd3 leads to an increase in H3K79me3 at a subset of genes (Fig 1F). Rpd3 represses H3K79me at its target genes To characterize the genes at which H3K79me is regulated, we calculated the levels of H3K79me1 and H3K79me3 per gene in the same 500-bp window and plotted values in the rank order of H3K79me3 changes described above, using locally weighted regression (Fig 2A; corresponding heatmaps can be found in Fig EV2A). Inspection of these plots revealed that the ORFs on which H3K79me3 was increased in the rpd3Δ mutant showed a simultaneous decrease in H3K79me1 (groups III–IV; Fig 2A). Strikingly, these Rpd3-regulated ORFs were on average marked with a relatively high level of H3K79me1 and low H3K79me3 in the wild-type strains but became more similar to the average yeast gene upon loss of Rpd3, consistent with the presence of a negative regulator of H3K79me acting on these ORFs. Next, we compared the genes with H3K79me changes with published data on Rpd3 binding and H4 acetylation (McKnight et al, 2015; Data ref: McKnight et al, 2015). The genes with the strongest increase in H3K79me3 upon Rpd3 loss had the highest Rpd3 occupancy, both at the promoter and in the 500-bp window downstream of the TSS (group IV; Fig 2A). Rpd3 was also found to be active at these genes, since they were devoid of H4 acetylation in wild-type cells and H4 acetylation was restored in the rpd3Δ mutant (Fig 2A). The role of the deacetylase activity of Rpd3 was confirmed by ChIP-qPCR analysis of two previously characterized mutants of Rpd3 that lack catalytic activity (Kadosh & Struhl, 1998; Sun & Hampsey, 1999). While re-expression of wild-type Rpd3 in the rpd3Δ strain restored low H3K79me3 levels at Rpd3 target genes, the Rpd3-H188A and Rpd3-H150A-H151A mutants did not rescue the loss of RPD3 (Fig 2B). Therefore, we conclude that the Rpd3 controls H3K79 methylation via its deacetylase activity. Finally, the top-regulated genes were also enriched for meiotic genes, which are known as Rpd3 targets, and binding sites of Ume6, the Rpd3L subunit known to recruit Rpd3 to early meiotic genes (Fig 2C and D) (Kadosh & Struhl, 1998; Rundlett et al, 1998; Carrozza et al, 2005; Lardenois et al, 2015). Together, our results suggest that the genes at which Rpd3 restricts the buildup of H3K79me are direct targets of Rpd3. Figure 2. Rpd3 represses transcription, H2B ubiquitination, and H3K79 methylation at its target sites A. ChIP-seq and RNA-seq data for genes ranked on H3K79me3/H3 in rpd3Δ/WT, smoothed using locally weighted regression. The gray band around the line shows the 95% confidence interval. Vertical dashed lines separate 4 groups with distinct changes upon RPD3 deletion. ChIP-seq data of H3K79me1, H3K79me3, and H2Bub were generated in this study (plotted is the average coverage in reads per genomic content, RPGC), Rpd3 binding, H4ac, and WT gene expression data were from McKnight et al (2015), and the relative expression in rpd3Δ/WT was from Kemmeren et al (2014). B. H3K79me3/H3 ChIP-qPCR efficiencies (relative to a non-transcribed region, which was unaffected by RPD3 deletion) in wild-type and in rpd3Δ cells harboring empty or RPD3-encoding CEN plasmids. The H188A and H150A-H151A mutations have previously been shown to abrogate catalytic activity (Kadosh & Struhl, 1998). Error bars indicate standard deviation of three biological replicates. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-way ANOVA, comparison to wild type. C–E. Gene set enrichment analysis on genes ranked on H3K79me3/H3 in rpd3Δ/WT; all genes have been ranked, and the ranks of the genes in the subsets are indicated by vertical lines. Meiotic (C) and Ume6-bound (D) genes are enriched among the genes at which Rpd3 represses H3K79 methylation, and subtelomeric genes (<30 kb of telomere) (E) are enriched among genes at which H3K79 methylation is decreased in rpd3Δ cells. Source data are available online for this figure. Source Data for Figure 2 [embj2019101564-sup-0004-SDataFig2.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV2. ChIP-seq and ChIP-qPCR data from WT vs rpd3Δ cells, and the relation between transcription and histone modifications Heatmaps of H3K79me1, H3K79me3, H2Bub, and H3 sorted on H3K79me3/H3 rpd3Δ/WT. H2Bub ChIP-qPCR (H2Bub/H2B) at 5′ ends of indicated genes in wild-type and rpd3Δ cells, with bre1Δ cells serving as a negative control. Error bars indicate standard deviation of three biological replicates. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-way ANOVA, comparison to wild type. H2Bub ChIP-qPCR (H2Bub/input) at the subtelomeric IRC7 gene, the promoter of the barcoded HO locus, and a non-transcribed region (NoORF) (Imbeault et al, 2008; Verzijlbergen et al, 2010). Error bars indicate standard deviation of three biological replicates. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-way ANOVA, comparison to wild type. ChIP-seq and RNA-seq data per gene (same data as in Fig 2A) ranked on gene expression level in wild-type cells, smoothed using locally weighted regression. The shaded band around the line shows the 95% confidence interval. Similar to panel (D), but using a ranking based on the level of antisense transcription per gene in wild-type cells as calculated by Brown et al (2018) using data from Churchman and Weissman (2011). H3K79me3 ChIP-seq data in gene promoters (−400 to TSS, where the Arp5 effect is maximal) in wild-type and arp5Δ cells (Xue et al, 2015), ranked by the effect Rpd3 has on H3K79me3 on each gene (same ranking as in Fig 2A). Source data are available online for this figure. Download figure Download PowerPoint Notably, a small subset of genes loses H3K79me3 in the absence of Rpd3 (Fig 1F, group I in Fig 2A). This group of genes already has low H3K79me3 and H2Bub levels in wild-type cells and is highly enriched for subtelomeric genes (Fig 2A and E). Loss of Rpd3 is known to enhance Sir-mediated silencing at subtelomeric regions (Ehrentraut et al, 2010, 2011; Gartenberg & Smith, 2016; Thurtle-Schmidt et al, 2016). Our findings show that the stronger transcriptional silencing occurs with a concomitant reduction in H3K79me3 and H2Bub in the coding regions of heterochromatic genes. Whether or not the loss of these modifications contributes to the stronger silencing in rpd3Δ/sin3Δ mutants or is a consequence of it remains to be determined. Strong repression of H3K79me by Rpd3 coincides with repression of H2Bub and transcription To understand the mechanistic basis for the crosstalk between Rpd3 and Dot1, we looked into other known functions of Rpd3 and other known regulators of H3K79me. Given the role of Rpd3 in repressing antisense transcription (Venkatesh et al, 2013; Castelnuovo et al, 2014; Murray et al, 2015), we compared our H3K79me data with data on antisense transcription in wild-type cells (as calculated by Brown et al (2018) using data from Churchman and Weissman (2011)). This analysis showed that Rpd3 does not specifically affect H3K79 methylation at genes with high or low antisense transcription, which agrees with the notion that Rpd3 represses antisense transcription via the Rpd3S complex (Venkatesh et al, 2013; Castelnuovo et al, 2014;