Fig. 2

Orthogonal experimental analyses revealed the predominant role of Pol III in the regulation of Pol II transcription. A Genome browser track of Pol I ChIP-Seq signals at rRNA cDNA units in Pol II_degron cells under untreated conditions and after 1 h of IAA treatment to visualize the IGS regions (see “Methods” for details, top). Genome browser track of Pol II ChIP-Seq signals in Pol III_degron cells or Pol III ChIP-Seq signals in Pol II_degron cells in the 120,914,905–120,924,630 region on chromosome 7 under untreated conditions and after 1 h of IAA treatment (bottom). The y-axis shows the normalized read density in reads per genome coverage (RPGC). Note that the intergenic spacer (IGS) region is magnified at the bottom to show the details. Two biological replicates are shown. B Bar graphs showing the relative ChIP enrichment normalized to input (5%) at the loci indicated in Fig. 2A (Additional file 12: Table S11). Each sample was analyzed with two technical replicates per biological replicate and two biological replicates in total. Statistical significance was evaluated by Student’s t test (***: <0.001, **: <0.01, *: <0.05, NS: not significant). C MA plots showing differential enrichment of Pol I ChIP-Seq signals around Pol I-bound peaks in Pol II_degron (left) and Pol III_degron (right) cells under untreated and after 1 h of IAA treatment conditions (upper). Each dot represents one peak. Red indicates a significant change that meets both criteria of a false discovery rate (FDR) < 0.05 and fold change > 2. Similarly, Pol II ChIP-Seq performed in Pol I_degron (left) and Pol III_degron (right) cells (middle) and Pol III ChIP-Seq performed in Pol I_degron (left) and Pol II_degron (right) cells (bottom) are presented. D Horizontally stacked bar charts showing the genomic distribution of differential peaks identified by Pol II ChIP-Seq and evaluated by factor perturbation analysis (upper panel, data from Fig. 2C). These results were further confirmed by conducting a similar analysis at the transcriptional level using PRO-Seq data from factor perturbation experiments (bottom)