Circadian Transcription Contributes to Core Period Determination in Drosophila

The Clock–Cycle (CLK–CYC) heterodimer constitutes a key circadian transcription complex in Drosophila. CYC has a DNA-binding domain but lacks an activation domain. Previous experiments also indicate that most of the transcriptional activity of CLK–CYC derives from the glutamine-rich region of its partner CLK. To address the role of transcription in core circadian timekeeping, we have analyzed the effects of a CYC–viral protein 16 (VP16) fusion protein in the Drosophila system. The addition of this potent and well-studied viral transcriptional activator (VP16) to CYC imparts to the CLK–CYC-VP16 complex strongly enhanced transcriptional activity relative to that of CLK–CYC. This increase is manifested in flies expressing CYC-VP16 as well as in S2 cells. These flies also have increased levels of CLK–CYC direct target gene mRNAs as well as a short period, implicating circadian transcription in period determination. A more detailed examination of reporter gene expression in CYC-VP16–expressing flies suggests that the short period is due at least in part to a more rapid transcriptional phase. Importantly, the behavioral effects require a period (per) promoter and are therefore unlikely to be merely a consequence of generally higher PER levels. This indicates that the CLK–CYC-VP16 behavioral effects are a consequence of increased per transcription. All of this also suggests that the timing of transcriptional activation and not the activation itself is the key event responsible for the behavioral effects observed in CYC-VP16-expressing flies. The results taken together indicate that circadian transcription contributes to core circadian function in Drosophila

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Regulation of circadian clock transcriptional output by CLOCK:BMAL1

<div><p>The mammalian circadian clock relies on the transcription factor CLOCK:BMAL1 to coordinate the rhythmic expression of 15% of the transcriptome and control the daily regulation of biological functions. The recent characterization of CLOCK:BMAL1 cistrome revealed that although CLOCK:BMAL1 binds synchronously to all of its target genes, its transcriptional output is highly heterogeneous. By performing a meta-analysis of several independent genome-wide datasets, we found that the binding of other transcription factors at CLOCK:BMAL1 enhancers likely contribute to the heterogeneity of CLOCK:BMAL1 transcriptional output. While CLOCK:BMAL1 rhythmic DNA binding promotes rhythmic nucleosome removal, it is not sufficient to generate transcriptionally active enhancers as assessed by H3K27ac signal, RNA Polymerase II recruitment, and eRNA expression. Instead, the transcriptional activity of CLOCK:BMAL1 enhancers appears to rely on the activity of ubiquitously expressed transcription factors, and not tissue-specific transcription factors, recruited at nearby binding sites. The contribution of other transcription factors is exemplified by how fasting, which effects several transcription factors but not CLOCK:BMAL1, either decreases or increases the amplitude of many rhythmically expressed CLOCK:BMAL1 target genes. Together, our analysis suggests that CLOCK:BMAL1 promotes a transcriptionally permissive chromatin landscape that primes its target genes for transcription activation rather than directly activating transcription, and provides a new framework to explain how environmental or pathological conditions can reprogram the rhythmic expression of clock-controlled genes.</p></div

Mouse liver CLOCK:BMAL1 transcriptional output is heterogeneous.

<p>A, B. Mouse liver BMAL1 (blue, A) and CLOCK (green, B) ChIP-Seq peaks from Koike et al., 2012 were mapped to their target genes and parsed based on their transcriptional output (Nascent-Seq from Menet et al., 2012, C). Each dot represents the phase of maximal DNA binding, and the ChIP-Seq signal is displayed using different shades of color to illustrate differences in binding intensity. C. Heatmap representation of the Nascent-Seq signal of direct CLOCK:BMAL1 target genes classified based on their transcriptional output in the mouse liver. Each lane represents the Nascent-Seq signal of a gene corresponding to the CLOCK and BMAL1 peaks in A and B. Nascent-Seq signal was ordered based on the phase of nascent RNA oscillations for the in-phase and out-of-phase transcriptional cyclers. Ordering of arrhythmically transcribed genes is based on the peak time of maximal expression; the lack of a distinctive 24-hr rhythm profile of nascent RNA expression over the 48-hr time-scale is indicative of arrhythmic transcription. No heatmap could be generated for the non-expressed genes because of the lack of nascent RNA expression. D. Nascent RNA expression, calculated as reads/bp, for each of the 4 CLOCK:BMAL1 transcriptional output group (Rinφ: in-phase transcriptional cyclers; Ro/φ: out-of-phase transcriptional cyclers; AR: arrhythmically transcribed target genes; NE: non-expressed target genes). Groups with different letters are significantly different (Kruskal-Wallis test; p < 0.05). E. Phase of maximal BMAL1 (left) and CLOCK (right) rhythmic DNA binding for each of the 4 CLOCK:BMAL1 transcriptional output categories. Groups with different letters are significantly different (Kruskal-Wallis test; p < 0.05). F. BMAL1 (top) and CLOCK (bottom) ChIP-Seq signal for each of the 4 CLOCK:BMAL1 transcriptional output groups. Signal is also displayed for the Rinφ and Ro/φ groups after removal of the ChIP-Seq signal at peaks targeting core clock genes. Groups with different letters are significantly different (Kruskal-Wallis test; p < 0.05). G. Location of CLOCK:BMAL1 peaks within gene loci for each of the 4 CLOCK:BMAL1 transcriptional output groups. TSS (transcriptional start site; +/- 1kb from annotated TSS); Gene body: + 1kb from TSS to + 1kb from transcription termination site; Extended promoter: - 10 kb to—1 kb from the annotated TSS. Numbers correspond to the percentage and numbers of peaks (outside and inside the pie chart, respectively) within each location for each group. * denotes a significant difference in the distribution of peaks between the AR and NE groups (chi square test; p < 0.05).</p

Tissue-specific and ubiquitous transcription factors are differentially recruited at CLOCK:BMAL1 enhancers.

<p>A-C. Enrichment for the DNA binding motif of CLOCK:BMAL1 (A), tissue-specific transcription factors (B) and ubiquitous transcription factors (C) at CLOCK:BMAL1 DNA binding sites for each of the four CLOCK:BMAL1 transcriptional output categories. Enrichment was calculated using HOMER and is reported as the ratio between the calculated enrichment over the calculated background. * q < 0.05 (Benjamini-Hochberg procedure). D-F. ChIP-Seq signal of tissue-specific transcription factors (D), ubiquitous transcription factors (E), and transcriptional co-activators / RNA Polymerase II at ZT6 (F) at CLOCK:BMAL1 DNA binding sites (peak center ± 250bp) for each of the transcriptional output categories. Groups with different letters are significantly different (Kruskal-Wallis test; p < 0.05). G. Transcription factor DNA binding variability index at CLOCK:BMAL1 DNA binding sites. The TF DNA binding variability index reflects differential TF DNA binding by calculating the variance of TF ChIP-Seq signal between the four CLOCK:BMAL1 transcriptional output groups (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#sec012" target="_blank">methods</a> for details). The variability index is displayed as a dot for each TF: CLOCK, BMAL1, PER1, PER2, CRY1, CRY2 (blue), seven ts-TFs (CEBPA, CEBPB, FOXA1, FOXA2, HNF1, HNF4A, and HNF6; red), thirteen u-TFs (REV-ERBα, REV-ERVβ, RORα, E4BP4, RXR, LXR, PPARα, GR-ZT12, E2F4, STAT5, BCL6, ERα, and GABPA; green), as well as for p300, CBP, and Pol II at seven time points (ZT02 to ZT26) (black). The horizontal lines represent the variability index median for the first 3 groups of TF. ChIP-Seq datasets used in this analysis are described in the method section. The variability index was calculated using all CLOCK:BMAL1 peaks analyzed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.g001" target="_blank">Fig 1</a> (left), or CLOCK:BMAL1 peaks that do not target a clock gene (removal of TF ChIP-Seq signal at peaks targeting <i>Per1</i>, <i>Per2</i>, <i>Cry2</i>, <i>Dbp</i>, <i>Rev-erbα</i>, and <i>Rev-erbβ</i>, <i>Tef</i>, <i>Hlf</i>, <i>Gm129</i>, and <i>Rorγ</i>; right).</p

REV-ERBα and REV-ERBβ ChIP-Seq signal is higher at CLOCK:BMAL1 DNA binding sites targeting genes transcribed at night.

<p>A-C <i>(Left)</i>. Average ChIP-Seq signal for REV-ERBα (A), REV-ERBβ (B) and E4BP4 (C) at CLOCK:BMAL1 DNA binding sites (center ± 1kb) for each of the 4 CLOCK:BMAL1 transcriptional output group. A-C (<i>Right)</i>. Distribution of REV-ERBα (A), REV-ERBβ (B) and E4BP4 (C) ChIP-Seq signal at CLOCK:BMAL1 peaks for each of the 4 CLOCK:BMAL1 transcriptional output groups (signal for each peak was averaged at CLOCK:BMAL1 peak center ± 250bp). Groups are labeled as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.g001" target="_blank">Fig 1</a>. Those with different letters are significantly different (Kruskal-Wallis test; p < 0.05). REV-ERBα and REV-ERBβ ChIP were performed from mice liver collected at ZT10, while E4BP4 ChIP was performed from mice liver collected at ZT22. ChIP-Seq datasets were retrieved from Cho et al., 2012 [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.ref035" target="_blank">35</a>], and E4BP4 ChIP-Seq datasets from Fang et al., 2014 [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.ref036" target="_blank">36</a>].</p

CLOCK:BMAL1 regulation of clock-controlled gene expression likely relies on the cooperation of CLOCK:BMAL1 with other transcription factors.

<p>A. Proposed model incorporating tissue-specific (ts-TFs) and ubiquitous (u-TFs) transcription factors into CLOCK:BMAL1 regulation of clock-controlled gene transcription. See text for details. B, C. Intron (B) and exon (C) signals of direct CLOCK:BMAL1 target genes classified based on their transcriptional output (Rinφ: in-phase transcriptional cyclers; Ro/φ: out-of-phase transcriptional cyclers; AR: arrhythmically transcribed target genes; NE: non-expressed target genes), in wild-type (left) and <i>Bmal1-/-</i> (right) mouse liver. Values correspond to the median RPKM for each transcriptional output group, and are displayed as the average ± s.e.m. of four (wild-type) or two (<i>Bmal1-/-</i>) independent samples for each time point. Data were retrieved from public RNA-Seq datasets [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.ref053" target="_blank">53</a>], and are double-plotted for better visualization. D-G. Rhythm of mRNA expression in the liver of mice fed <i>ad libitum</i> (blue) or fasted for at least 22 hours (orange). Data were retrieved from a public dataset [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007156#pgen.1007156.ref056" target="_blank">56</a>]. Mouse liver mRNA expression is displayed for <i>Clock</i> and <i>Bmal1</i> (D), as well as CLOCK:BMAL1 target genes that are rhythmically expressed (E), arrhythmically expressed (F), or not expressed (G) in the liver of mice fed <i>ad libitum</i>.</p

Public datasets used in this study.

<p>Public datasets used in this study.</p