Genome-Wide RNA Polymerase II Profiles and RNA Accumulation Reveal Kinetics of Transcription and Associated Epigenetic Changes During Diurnal Cycles

Abstract

Genome-wide rhythms in RNA polymerase II loading and dynamic chromatin remodeling underlie periodic gene expression during diurnal cycles in the mouse liver.

Mistimed sleep disrupts circadian regulation of the human transcriptome

“…Although the central circadian clock, as indexed by melatonin, remained largely unaffected, the temporal organization of expression of clock genes considered to be central to circadian rhythm generation was affected in the blood transcriptome, with only NR1D2 and CSNK1E remaining rhythmic when sleeping out of phase. Histone modification, and the control of transcription and translation (4)(5)(6)(7)(8)(9), is also considered central to circadian organization of the transcriptome and the time courses of transcripts associated with all of these processes were affected in this study (Fig. 3).…”

“…The circadian regulation of the mammalian transcriptome includes circadian transcriptional and translational regulation by proteins such as the positive transcription factors CLOCK and ARNTL (BMAL1), and the repressors PERIOD (PER) and CRYPTOCHROME (CRY) (4), chromatin modification by factors such as E1A binding protein P300 (EP300) and the methyltransferase MLL3 (4-6), RNA polymerase activity (4,7), and posttranscriptional events such as the regulation of ribosome biogenesis and translation (8,9). Furthermore, physiological factors such as body temperature (10) and endocrine rhythms such as cortisol (11) can modify and reinforce these regulatory processes.…”

Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization

“…Scaling to the total amount of sequence tags that can be aligned onto the genome is usually the first step (for examples, see Li et al 2011;Landt et al 2012;Le Martelot et al 2012). Quantile normalization is also broadly applied because it can reveal differ-ences at specific loci even in samples displaying relatively uniform global differences at all enriched loci (for examples, see Rahl et al 2010;Le Martelot et al 2012). Scaling to total tag amounts and quantile normalization can have very different effects, particularly in cases of global differences; for example, if all regions enriched in one sample are uniformly enriched to a higher or lower degree in another sample.…”

“…2) but otherwise were derived from the same batch of mouse chromatin and processed similarly (for numbers of tags aligned to mouse and human genomes, see Supplemental Table S2A,B) and can thus be considered technical replicates. We calculated Pol II scores in mouse regions extending from À250 to +250 bp around 11,217 annotated TSSs selected to be separated by at least 1000 bp from any other annotated TSS or polyadenylation site (see Le Martelot et al 2012). Figure 6A, left and right panels, show ECDF graphs of the samples after SPP score calculation, or after SPP score calculation and spike adjustment.…”

“…One tag sequence was worth one count, and fractional counts were attributed in the case of a partial overlap between tag and locus. For the Pol II samples, tag counts were attributed in the same manner to regions extended by 250 bp on each side of the 22,572 annotated RefSeq TSSs (human), and on 11,217 annotated TSSs selected to be separated by at least 1000 bp from any other annotated TSS or polyadenylation site (mouse) (Le Martelot et al 2012). The total numbers of tags, with and without redundancy, aligned onto the mouse and human genomes, as well as the numbers of tags falling in either mouse or human Pol III loci, are listed in Supplemental Tables S1, A and B, and S2, A and B.…”