Figure 1. AML containing the t(8:21) are extremely sensitive to CDK inhibitors. (A–C) Alamar blue assays were used to ... Figure 1. AML containing the.

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Figure 1. AML containing the t(8:21) are extremely sensitive to CDK inhibitors. (A–C) Alamar blue assays were used to ... Figure 1. AML containing the t(8:21) are extremely sensitive to CDK inhibitors. (A–C) Alamar blue assays were used to assess the cell proliferation of SKNO-1 cells to evaluate the dose response of CDK9 inhibitors flavopiridol (FVP), dinaciclib (Dina) and PHA767491 (PHA) were used in increasing concentrations. (D) Alamar blue assay for the CDK7 inhibitor THZ1 and (E) CDK4/6 inhibitor palbociclib (Palbo). (F) Combined growth curve for Kasumi-1 cells from Alamar blue assays. The graph represents the drugs used at the concentrations used in the final PROseq experiment (FVP (400 nM), PHA (5 uM) Dina (25 nM), THZ1 (400 nM) and Palbo (5 uM). For Alamar blue assay each experiment was performed at least three times (n = 3). Data are plotted as mean ± SEM. Error bars represent standard error of means (SEM) from three experiments. (G) Quantitation of Annexin V/PI staining for all the drugs used at the same concentration as in C and incubated for 6 hrs. Data are plotted as mean ± SEM. Error bars represent standard error of means (SEM). Asterisk (*) indicate P-values <0.05. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2. CDKi inhibits RNA Polymerase CTD Phosphorylation Figure 2. CDKi inhibits RNA Polymerase CTD Phosphorylation. (A) Co-IP showing AML-ETO (AE) fusion protein from Kasumi-1 ... Figure 2. CDKi inhibits RNA Polymerase CTD Phosphorylation. (A) Co-IP showing AML-ETO (AE) fusion protein from Kasumi-1 cells extracts was precipitated using anti-CDK9. Asterix (*) depicts non-specific bands. (B) CDKi treated Kasumi-1 cell extracts were tested for phospho-Ser2 RNA polymerase II levels by western blot. (C, D) THZ1 treated Kasumi-1 cell extracts from the indicated time-course experiment were tested for Ser5 and Ser7 phosphorylated RNA polymerase II levels by western blot. (E) Quantitation of Annexin V/PI staining analysis of Kasumi-1 cells treated with THZ1 at 400 nM for 30, 60 120 and 240 min. Asterisk (*) indicate P-values <0.05 Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3. CDKi affect promoter-proximal RNA polymerase pausing Figure 3. CDKi affect promoter-proximal RNA polymerase pausing. (A) log2 transformed fold change ... Figure 3. CDKi affect promoter-proximal RNA polymerase pausing. (A) log<sub>2</sub> transformed fold change (log<sub>2</sub>FC) values for the affected genes are ranked according to the pausing indices (log<sub>2</sub>FC). Then log2FC PROseq read counts in 200 bp bins ± 5 kb around the TSS for these genes from each indicated drug were depicted as heatmaps. The labels on the top indicated the drug used and the number of refseq genes in the heatmap. CDK9i represent the common genes affected by all the three CDK9 inhibitors (FVP, Dina and PHA). The labels on the side indicate the number of genes that gained or lost pausing of RNA polymerase II at promotor-proximal regions (P.Index). (B) Similar strategy as in (A) was employed in drawing the heatmaps but the genes were ranked according to the (log<sub>2</sub>FC) of gene body read counts from PROseq. The numbers on the side represent the number of the refseq genes that gained or lost polymerase in the gene body. (C) Time course of THZ1 treatment. log2FC of PROseq read counts in 200 bp bins ± 5 kb of the TSSs for the THZ1 affected genes from each indicated time-point of the time course experiment (15 min, 30 min, 1 h and 2 h with THZ1 treatment at 400 nM). Genes are ranked based log<sub>2</sub> transformed fold change values (log<sub>2</sub>FC) of pausing index. (D) heatmaps of genes ranked according to the log<sub>2</sub>FC of gene body read counts using similar strategy as in A. the log<sub>2</sub>FC of read counts in 200 bp bins ± 5 kb of the TSS for the genes that either increased gene body polymerase density (E) or decreased gene body polymerase density (F) at 1 h post treatment. These same genes were then plotted from each of the indicated time-point as heatmaps ± 5 kb from the TSS (up-regulated 4012 genes; down-regulated 502 genes, from D). Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 5. CDKi affects the transcription of eRNA from Enhancers and Super-enhancers. (A) Venn diagram showing the ... Figure 5. CDKi affects the transcription of eRNA from Enhancers and Super-enhancers. (A) Venn diagram showing the overlap between PRO-seq called enhancers and H3K27ac ChIP-exo peaks from Kasumi-1 cells indicates that the majority of the PRO-seq called enhancers were marked by H3K27ac. See also Supplemental Figure S5A (B, C) Meta-analysis plots for the PROseq signal around enhancer centers are shown for commonly overlapped enhancers from CDK9i (B). (C) Similar analysis as in (B) but using 124 enhancers with increased polymerase around the enhancer center (top panel) after THZ1 treatment or 71 enhancers with decreased polymerase around enhancer center (bottom panel). See also Supplemental Figure S5B for the genes associated with these enhancers. (D) Meta-analysis of long eRNA from THZ1 treatments that had either an increase (top panel) or a decrease in gene body (bottom panel) showed a concomitant increase in polymerase density around the start site of the long eRNA. See also Supplemental Figure S5C and S5D for the general distribution of these long eRNA. (E) Hockey stick plot of PROseq identified intergenic enhancers ranked according to the increasing amounts of H3K27ac signal. Those enhancers that had high H3K27ac signal and above the slope of 1 were termed ‘super-enhancers’ (SE) and the ones below the slope of 1 were considered typical enhancers (TE). (F) Metaplot of PROseq signal at the super enhancer regions after THZ1 treatment showed a slight accumulation of RNA polymerases at the enhancer center in comparison to DMSO. Metaplots were generated using HOMER. (G) Box plots of nearest neighbor genes associated with SE and TE against all genes (All) were plotted for the log<sub>2</sub>FC of the normalized PROseq read counts in the gene body. *P = 0.02, **P = 6.96e–05. Comparison testing was done by Kolmogorov–Smirnov (KS) test. (H) IGV browser screenshots of genome tracks for a ‘super-enhancer’ located downstream of SPI1 that was affected by THZ1 treatment. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 4. Unsupervised clustering analysis on genes affected by CDK Inhibitors. (A) Unsupervised clustering analysis on ... Figure 4. Unsupervised clustering analysis on genes affected by CDK Inhibitors. (A) Unsupervised clustering analysis on commonly overlapped genes that lost promoter proximal pausing after 1 h THZ1 treatment (Figure 3A) and 1 h time-point from the THZ1 time course experiment (Figure 3C) in comparison to the other CDK inhibitor treatments based on the pausing index change (PIndex change, left panel). Similar analysis was performed on genes that either increased polymerase density in the gene body or vice versa after THZ1 treatment. (B) Left panel: unsupervised clustering analysis of the commonly overlapped, differentially expressed genes from the RNAseq data for 1 h CDK9i (FVP, Dina and PHA) treated samples as compared to palbociclib or THZ1 treated samples processed at the same time as the PROseq samples. Right panel: unsupervised clustering analysis of the differentially expressed genes from the RNAseq data for 1 h THZ1 treated samples. The expression of these genes is then shown after a 1 h treatment with the other inhibitors. (C) log<sub>2</sub> transformed fold change values of pri-miRNA expression relative to DMSO are shown as heatmaps for the pri-miRNAs affected by the CDKi ranked according to the pausing indices (C) and pri-miRNA gene body (D). Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 6. THZ1 affects transcription at the 3′ end from the last exon Figure 6. THZ1 affects transcription at the 3′ end from the last exon. (A) PROseq IGV browser screenshot of genome ... Figure 6. THZ1 affects transcription at the 3′ end from the last exon. (A) PROseq IGV browser screenshot of genome tracks for MYB after 1 h of THZ1 treatment. Red dashed box highlights the region 3′ to the last exon and black bars indicate the region where primers were designed for the qRT-PCR. (B, C) Metaplots of normalized PRO-seq read signal for THZ1 treated samples at 1 h was plotted ±5 kb of the last exon divided into 20 bp bins for the genes with increased gene body density from Figure 3B, 944 genes (third panel), or genes with decreased gene body density from Figure 3B, 1070 genes (third panel). (D, E) Metaplots of normalized PolyA-seq read signal for THZ1 treated samples at 1 h was plotted ±1 kb from the last exondivided into 20 bp bins for the above mentioned genes in (C). (F) Graph of qRT-PCR of the effected region (n = 3) from MYB shows decreased read through transcript levels after 1 h treatment with THZ1. The representative gel image shows the qRT-PCR endpoint for technical replicates from one qRT-PCR experiment. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 7. THZ1 affects both 5′ and 3′ end polymerase pausing Figure 7. THZ1 affects both 5′ and 3′ end polymerase pausing. (A) Meta-analysis of short genes (less than 200 kb long) ... Figure 7. THZ1 affects both 5′ and 3′ end polymerase pausing. (A) Meta-analysis of short genes (less than 200 kb long) with more polymerase in the gene body at 1 h after THZ1 treatment plotted ± 5 kb from the 3′ end of the last exon at the times indicated after THZ1 treatment. (B) Same analysis as in (A) but using genes over 200 kb long. (C) IGV browser screenshots of genome tracks for a short gene SCRIB and a long gene CERS6. (D) Ser2-RNAPII ChIP-qPCR was performed after 1 h THZ1 treatment for SCRIB and MYB. The primers were designed at the regions just 3′ of the last exon. Two biological replicates of either DMSO or THZ1 treated Kasumi-1 cells were used. Error bars are mean ± SD of replicates. See Supplemental Figure S6 for additional examples. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Res, gkz127, https://doi.org/10.1093/nar/gkz127 The content of this slide may be subject to copyright: please see the slide notes for details.