Introduction
Circadian clocks allow organisms from almost all branches of life to alter physiology in anticipation of diurnal changes in the environment. Circadian clocks are autonomous core oscillators that keep time even in the absence of environmental cues (Dunlap et al., 2004). Output pathways interpret timing information from the core oscillator to generate oscillating outputs, such as oscillations in the mRNA levels (expression) of genes and higher order behaviors (Dunlap et al., 2004; Wijnen and Young, 2006). Laboratory studies of the outputs of circadian clocks have been primarily performed under constant conditions to isolate circadian regulation from environmental responses. In nature, however, organisms with circadian clocks must also cope with unexpected fluctuations in the environment. Thus a major challenge in chronobiology is to understand circadian regulation in dynamic environments.
Previous studies suggest that circadian clock output pathways interact with responses to the environment to tailor physiology to both the time of day and the current state of the environment. For example, sleep/wake cycles in Drosophila melanogaster and photosynthesis in Arabidopsis thaliana are controlled by both the circadian clock and environmental variables like day length or light (Lamaze et al., 2017; Millar and Kay, 1996). Further, circadian clocks can modulate responses to the environment based on the time-of-day in a process called circadian gating (Hotta et al., 2007; Greenham and McClung, 2015). However, the complexity of higher organisms has prevented a detailed understanding of the interaction between circadian timing information and environmental responses. In contrast, the circadian clock in the cyanobacterium Synechococcus elongatus PCC7942, an obligate photoautotroph, has a simple architecture which controls gene expression oscillations (Figure 1A) to influence metabolism and growth. S. elongatus must carefully monitor its environment, as the sunlight required for photosynthesis fluctuates on the minute, day, and seasonal timescales (Figure 1B, [Petty and Weidner, 2017]). While it is well understood how the circadian clock in S. elongatus behaves under constant conditions, it is unclear how this system changes in natural, fluctuating light.
Figure 1.
The circadian and light response pathways in cyanobacteria.
(A) Schematic of gene expression output of the circadian clock under Constant Light conditions. Under Constant Light intensity (dashed navy blue line), dawn gene expression (dashed maroon line) and dusk gene expression (solid green line) display oscillatory patterns, peaking at subjective dawn and subjective dusk, respectively. The Kai post-translational oscillator generates oscillations in the levels of phosphorylated RpaA (RpaA
In S. elongatus grown under ‘Constant Light’ conditions (Figure 1A, dashed navy blue line), genes which show oscillatory expression (circadian genes) can be divided into two groups, the dawn and the dusk genes, which peak at subjective dawn and subjective dusk (Ito et al., 2009; Vijayan et al., 2009) (Figure 1A). Subjective dawn and subjective dusk refer to the times at which dark-to-light or light-to-dark transitions would occur in a 12 hr light-12 hr dark environmental cycle. The dawn genes consist of the core metabolic and growth genes for S. elongatus, including the photosystems, ATP synthase, carbon fixation/Calvin-Benson-Bassham cycle enzymes, and ribosomal proteins (Vijayan et al., 2009; Ito et al., 2009; Diamond et al., 2015). In the absence of regulation by the circadian clock under Constant Light, S. elongatus constantly expresses dawn genes (Markson et al., 2013). The clock primarily regulates the expression of dusk genes (Markson et al., 2013), which include the genes required to utilize glycogen as an energy source in the absence of sunlight, such as glycogen phosphorylase and cytochrome c oxidase. As such, the circadian clock serves a critical function in switching S. elongatus from a daytime state of photosynthesis to a nighttime state of carbon metabolism through glycogen breakdown (Diamond et al., 2015; Diamond et al., 2017; Pattanayak et al., 2014; Puszynska and O'Shea, 2017). In Constant Light conditions, the dusk and dawn genes show oscillatory expression with a 24 hr period, resulting in broad peaks of maximal expression (Figure 1A, solid green line and dashed maroon line) (Vijayan et al., 2009; Ito et al., 2009). Recent whole-cell modeling of metabolism, protein levels, and growth predict that this picture of circadian gene expression should change under the dynamic light conditions of a natural, clear day (Figure 1B, navy blue line) (Reimers et al., 2017). The modeling suggests that making and using glycogen is a major cost to cell growth and thus the expression of genes required to switch metabolism from photosynthesis to glycogen breakdown should be delayed until absolutely necessary (Reimers et al., 2017). However, gene expression in natural light conditions has not been measured in S. elongatus.
Consistent with predictions of light-dependent changes in circadian gene expression, current evidence suggests interaction between the circadian and light regulatory pathways. The cyanobacterial clock keeps track of the time of day using a core post-translational oscillator (PTO) that consists of three proteins, KaiA, KaiB, and KaiC, whose enzymatic activities result in 24 hr oscillations in the phosphorylation state of KaiC (Nakajima et al., 2005; Rust et al., 2007; Johnson et al., 2011). In vivo under Constant Light conditions the Kai PTO modulates circadian gene expression by controlling oscillations in phosphorylation state of the master OmpR-type transcription factor RpaA (Markson et al., 2013; Takai et al., 2006) to peak at subjective dusk (Figure 1A, dotted black line; Figure 1C) (Gutu and O'Shea, 2013; Takai et al., 2006). Phosphorylated RpaA (RpaA
Meanwhile the OmpR-type transcription factor RpaB binds to some circadian gene promoters (Hanaoka et al., 2012), and the phosphorylation state and DNA binding activity of this protein decreases in response to high light exposure (Figure 1C) (López-Redondo et al., 2010; Moronta-Barrios et al., 2012). However, it is not clear how natural light changes like sunset or shade pulses affect RpaB activity (Figure 1C). RpaB clearly plays some role in altering circadian gene expression in response to light (Espinosa et al., 2015), but it is unclear how (Figure 1C). While light likely exerts global, growth-rate-dependent regulation of protein levels (Scott et al., 2010; Du et al., 2016; Burnap, 2015), the interaction between circadian and light regulation to control the activities of RpaA and RpaB represents a particularly tractable scenario for dissecting the mechanisms underlying interaction between clock and environment to control circadian gene expression.
Here we measure and model circadian gene expression and several layers of regulation in cyanobacteria grown under the fluctuating light intensities typically experienced in nature. We find that fluctuations in light alter the expression patterns of almost all circadian genes. We identify key regulatory steps at which information about changes in light interact with clock output pathways to control gene expression, and reveal a complex regulatory network underlying circadian gene expression in natural conditions. Finally, we show that phenomenological models effectively describe the integration of the circadian clock with responses to environmental fluctuations.
Results
Sunlight on a clear day delays the timing of circadian gene expression relative to constant light conditions
To grow and assay cyanobacteria in natural light conditions, we custom-built a culturing setup with a light source that can be programmed to mimic natural fluctuations in sunlight. On a cloudless ‘Clear Day,’ light intensity varies in a parabolic manner due to the rotation of the Earth, ending with a gradual ramp down of light intensity prior to dusk (‘Sunset’, Figure 1B). Rapid changes in cloud cover cause abrupt increases (‘High Light pulse’) and decreases (‘Shade pulse’) in sunlight (Petty and Weidner, 2017) (Figure 1B). Using a set of programmable warm white LED arrays (Materials and methods, Construction of light apparatus and Calibrating light conditions) for illumination, in all experiments we grew cells for 12 hr in either a Clear Day condition that peaked at 600
Figure 2.
Natural clear day conditions sharpen the expression of dusk genes to peak just before expected darkness.
(A) Experimental setup for testing the effects of Clear Day conditions on circadian gene expression. The upper plot shows the light intensity profiles of Low Light (black) and Clear Day (magenta) conditions, in units of
Figure 2—figure supplement 1.
Pigment levels of cyanobacteria grown under Low Light or Clear Day conditions reveal adjustments in the photosynthetic apparatus to optimize growth in different light conditions.
(A) Estimation of phycocyanin and chlorophyll levels in cells grown under Low Light (black) or Clear Day (magenta) conditions for two days, measured at midday of the third light period. Phycocyanin and chlorophyll levels were estimated by measuring optical density of the culture at 620 nm or 680 nm, respectively, and normalizing to optical density at 750 nm to account for differences in cell density. Error bars show the standard deviation of three independent measurements. Cells grown under Clear Day conditions show lower levels of both phycocyanin and chlorophyll. (B) Image of cells harvested from cultures at midday on the third day (OD
Figure 2—figure supplement 2.
Gene expression dynamics of dusk and dawn circadian genes under Constant Light conditions (data from Markson et al., 2013).
(A) Gene expression dynamics of circadian genes over 24 hr in Constant Light conditions in wildtype cells (left heat map) and over 12 hr in OX-D53E cells (rpaA-, kaiBC-, Ptrc::rpaA(D53E)) (middle and right heat maps) as measured by RNA sequencing. The OX-D53E strain allows experimental control of RpaA activity via IPTG-inducible expression of the RpaA phosphomimetic RpaA-D53E in cells that lack wildtype RpaA (Markson et al., 2013). In the middle panel RpaA-D53E is not induced, and in the right panel IPTG was added to induce RpaA-D53E. Gene expression is quantified as the log
Figure 2—figure supplement 3.
Dawn gene expression increases during the early part of Clear Day relative to Low Light conditions.
(A) Gene expression dynamics of dawn genes (
Figure 2—figure supplement 4.
The gene expression dynamics of glycogen production and breakdown enzymes change in Clear Day conditions relative to Low Light conditions.
(A) Gene expression dynamics of the dusk gene glgP, encoding a key enzyme in glycogen breakdown, under Low Light (black) and Clear Day (magenta) conditions as measured by RNA sequencing (left y-axis). The light profile for each condition is plotted as dashed lines of the same color with values corresponding to the right y-axis. (B) Gene expression dynamics of the dusk gene glgX, a key enzyme in glycogen breakdown, measured and plotted as in (A). (C) Gene expression dynamics of the dusk gene glgC, a key enzyme in glycogen production, measured and plotted as in (A).
To determine whether a natural light profile affects circadian output, we compared genome-wide gene expression in Clear Day conditions versus Low Light conditions using RNA sequencing (Figure 2A, Setup, arrows indicate sampling). We acclimated cultures in their respective condition for 2 light/dark cycles, and sampled them (arrows) over the next (third) light period (Figure 2A, Setup). We focused our analysis on a set of high amplitude circadian genes that show oscillatory expression under Constant Light conditions (Figure 2—figure supplement 2; see Materials and methods, Definition of circadian genes). The Low Light condition (Figure 2B, upper panel) reproduces the expression profile previously observed under Constant Light conditions (Figure 2—figure supplement 2). However, in the Clear Day condition 159 of the 281 dusk genes were expressed at least two fold higher after midday compared to Low Light, demonstrating light-dependent expression. Dawn genes show the opposite behavior — they have higher expression at midday under Clear Day conditions, although this trend is less pronounced (Figure 2—figure supplement 3). Taken together, Clear Day conditions significantly influence the expression dynamics of almost all circadian genes, with the strongest effects on dusk genes.
To look more closely at how the Clear Day condition affects the dusk genes, which are the primary regulatory targets of the clock, we analyze the gene expression dynamics of the representative dusk gene Synpcc7942_1567. Under Low Light conditions, Synpcc7942_1567 exhibits an increase in expression from dawn to dusk, reaching a plateau by 8 hr after dawn (Figure 2C, solid black line). Under Clear Day conditions, however, the expression of this gene remains low through the midday peak of light intensity (Figure 2C, solid magenta line; 4–8 hr after dawn), and its expression sharply increases just prior to dusk as light intensity decreases, reaching maximal expression just as the dark period begins. This delayed pattern of gene expression can be seen in almost all dusk genes (Figure 2B; Synpcc7942_1567 indicated with arrows). Thus Clear Day conditions significantly alter the dynamics and amplitude of dusk gene expression to peak just before dusk.
The delay of dusk gene expression likely enables cyanobacteria to switch to glycogen breakdown only when absolutely necessary so that they can survive the extended darkness of night. The two glycogen breakdown genes, glgP and glgX, are both light-dependent dusk genes that strongly peak in Clear Day at dusk, while glgC, which codes for the rate limiting enzyme of glycogen synthesis, is a dawn gene whose expression is higher in Clear Day conditions compared to Low Light (Figure 2—figure supplement 4). These gene expression dynamics would favor both the maintenance of glycogen synthesis until the end of the day and a delay in the activation of glycogen breakdown until just before it is required at nighttime, in agreement with predictions from metabolic modeling during the same Clear Day conditions used here (Reimers et al., 2017). Thus, environmental conditions are integrated into the output of the circadian clock to potentially optimize resource allocation in naturally-relevant diurnal cycles, as recently suggested (Reimers et al., 2017).
Remarkably, though in both light conditions the cells experience 50
Changes in light intensity control the transcription of circadian genes
To test whether changes in light intensity are a key factor controlling the expression of circadian genes, we exposed cells to a High Light pulse or a Shade pulse and measured genome-wide gene expression using RNA sequencing. We grew cultures in either Low Light or Clear Day conditions for three days (Figure 3A–B, Setup). On the fourth day at 8 hr after dawn, when RpaA is most active, we exposed the cells to a High Light pulse (Figure 3A) or a Shade pulse (Figure 3B) for 1 hr before returning to the original condition. We sampled the cells before, during, and after the perturbation (Figure 3A–B, Setup, arrows). The expression of dusk genes rapidly changed in a direction opposite to the change in light intensity (Figure 3C, all dusk genes; Figure 3E, example dusk gene; Figure 3D, all dusk genes; Figure 3F, example dusk gene), as expected from the effects of the decrease in light intensity at Sunset of the Clear Day condition on circadian gene expression (Figure 2B–C). A large subset of dusk genes were affected by the light pulses, with 105/281 repressed by at least three fold by the High Light condition, and 136/281 induced by at least three fold by the Shade condition. Further, many genes responded rapidly and changed in expression at least three fold after just 15 min into the pulse (75/281 repressed by High Light, 79/281 induced by Shade). When cultures were restored to their original condition (High Light to Low Light, Figure 3C,E; Shade to Clear Day, Figure 3D,F), dusk gene expression quickly reverted to a level comparable to that before the pulse. Thus, light-induced changes in dusk gene expression are reversible and responsive to successive shifts in light availability. Dawn gene expression showed the opposite behavior of dusk genes, albeit with less dramatic changes (Figure 3—figure supplement 1). Hence, decreases in light intensity favor the expression of dusk genes (Sunset in Clear Day, Figure 2; Clear Day to Shade and High Light to Low Light, Figure 3), while increases in light favor the expression of dawn genes (midday peak in Clear Day, Figure 2—figure supplement 3; Shade to Clear Day and Low Light to High Light, Figure 3—figure supplement 1). Given the more substantial effects of light on dusk gene expression, we focus on these genes for the remainder of the manuscript.
Figure 3.
Rapid changes in light intensity modulate the recruitment of RNA polymerase to dusk genes to control dusk gene expression.
(A) Light intensity profiles of Low Light (black) and High Light pulse (orange) conditions, in units of
Figure 3—figure supplement 1.
Rapid changes in light intensity affect dawn gene expression in an opposite direction compared to dusk gene expression.
(A) Gene expression dynamics of dawn genes (
Figure 3—figure supplement 2.
Changes in RNAP enrichment and downstream dusk gene expression after rapid changes in light intensity.
(A) Changes in enrichment of RNAP upstream of dusk genes during High Light pulse conditions (left heat map) and corresponding changes in target dusk gene expression (right heat map) for the 82 dusk genes with RNAP peaks in their promoters. ChIP enrichment (left heat map) is quantified as the log
To cause these reversible changes in the mRNA levels of dusk genes, changes in light intensity must affect either the transcription and/or the degradation of dusk gene mRNAs. We reasoned that changes in transcription would manifest as differences in the amount of RNA polymerase (RNAP) localized to dusk genes. To determine whether changes in light intensity regulate the recruitment of RNAP to dusk gene promoters, we performed chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) of RNAP in cells immediately before the High Light or Shade pulse (8 hr after dawn in Low Light or Clear Day), and then again 15 or 60 min following the start of the pulse. Changes in RNAP enrichment upstream of dusk genes correlated with changes in downstream dusk gene expression (Figure 3G,H; Figure 3—figure supplement 2). Thus, changes in light affect RNAP recruitment to dusk gene promoters, suggesting that light conditions substantially affect the rates of transcription of dusk gene mRNAs. Because mRNAs in bacteria have very short steady state half lives (Chen et al., 2015; Hambraeus et al., 2003; Salem and van Waasbergen, 2004), we argue that changes in transcription rates of dusk gene mRNAs are sufficient to lead to the rapid changes in dusk gene mRNA levels given a fast basal degradation rate, though we cannot rule out that changes in light may affect the rates of degradation of some mRNAs. These results point to a potential interaction between sunlight and signaling pathways upstream of RNAP. We next explored how the observed changes in dusk gene expression in the presence of natural light fluctuations (Figures 2 and 3) could be achieved via gene regulatory mechanisms.
Regulation of dusk gene expression by RpaA and RpaB under dynamic light regimes
Given the strong dependence of dusk gene expression on RpaA
Figure 4.
Changes in environmental light intensity regulate RpaA
(A) Phosphorylation dynamics of RpaA under Low Light vs High Light pulse. Relative levels of phosphorylated RpaA were measured using Phos-tag Western blotting (left y-axis) in cells grown under Low Light conditions (black squares, see Figure 2A for Setup) or High Light pulse conditions (orange triangles, see Figure 3A for Setup). Each point represents the average of values measured in two independent Western blots, with error bars displaying the range of the measured values. See Materials and methods, Measurement of RpaA
Figure 4—figure supplement 1.
Representative Western blots used to quantify relative levels of RpaA
(A) Representative Western Blot used to quantify levels of RpaA
Figure 4—figure supplement 2.
Changes in RpaA enrichment and downstream dusk gene expression after rapid changes in light intensity.
(A) Changes in enrichment of RpaA upstream of dusk genes during High Light pulse conditions (left heat map) and corresponding changes in target dusk gene expression (right heat map) for the 56 dusk genes with RpaA peaks in their promoters. ChIP enrichment (left heat map) is expressed as the log
Figure 4—figure supplement 3.
Changes in RpaA and RNA polymerase enrichment upstream of dusk genes after rapid changes in light intensity.
(A) Changes in enrichment of RpaA upstream of dusk genes during High Light pulse conditions (left heat map) and corresponding changes in RNAP enrichment upstream of the same gene (right heat map) for the 33 dusk genes with RpaA and RNAP peaks in their promoters. ChIP enrichment is quantified as the log
Figure 4—figure supplement 4.
Multifactorial behavior of RpaA
(A)-(C) Normalized ChIP-seq signal of RpaA (red), RpaB (blue), RNAP (green) and mock IP (black) upstream of the (A) the representative dusk gene Synpcc7942_2267, (B) the kaiBC operon and (C) another representative dusk gene, digC, at 8 hr since dawn in Low Light. The chromosomal position of the gene is located on the plot with a gray bar with an arrow indicating directionality of the gene. The location of RpaA and RNAP peaks are indicated on top of the plot with red (RpaA) and green (RNAP) bars. No RpaB peaks were found upstream of these genes. No RNAP peak was found upstream of kaiB or digC. See Materials and methods, ChIP-seq analysis for more details. (D)-(F) Changes in enrichment of RpaA (red) and RNAP (green) and downstream gene expression (black) after exposure to the High Light pulse (triangles) or the Shade pulse (circles) for (D) Synpcc7942_2267, (E) the kaiBC operon, and (F) digC. See Materials and methods, ChIP-seq analysis for more details. (G)-(L) Gene expression dynamics of Synpcc7942_2267 (G,J) kaiB (H,K) and digC (I,L) under Low Light vs High Light pulse (G–I) and Clear Day vs Shade pulse (J–L) conditions. RpaA binding does not change upstream of the dusk genes Synpcc7942_2267 and kaiB after changes in light (D,E), and the expression of these genes does not change significantly in response to changes in light intensity (G,J;H,K). In contrast, RpaA binding changes significantly (F) upstream of the light-responsive dusk gene digC (I,L).
Interestingly, RpaA regulation at a small number (
Our analysis so far has established that the previous model for the regulation and expression of circadian genes in Constant Light conditions (Figure 1A) becomes more complex in natural environmental conditions, suggesting the involvement of other pathways. Thus, we next asked whether RpaB plays a role in controlling light-dependent expression of circadian genes. We observed that levels of RpaB
Figure 5.
Light-induced changes in RpaB
(A) Phosphorylation dynamics of RpaB under Low Light vs High Light pulse. Relative levels of phosphorylated RpaB were measured using Phos-tag Western blotting (left y-axis) in cells grown under Low Light conditions (black squares, see Figure 2A for Setup) or High Light pulse conditions (orange triangles, see Figure 3A for Setup). Each point represents the average of values measured in two independent Western blots, with error bars displaying the range of the measured values. See Materials and methods, Measurement of RpaA
Figure 5—figure supplement 1.
Representative Western blots used to quantify relative levels of RpaB
(A) Representative Western Blot used to quantify levels of RpaB
Figure 5—figure supplement 2.
Changes in RpaB enrichment and downstream dusk gene expression after rapid changes in light intensity.
(A) Changes in enrichment of RpaB upstream of dusk genes during High Light pulse conditions (left heat map) and corresponding changes in target dusk gene expression (right heat map) for the 42 dusk genes with RpaB peaks in their promoters. ChIP enrichment (left heat map) is quantified as the log
Figure 5—figure supplement 3.
Changes in RpaB and RNA polymerase enrichment upstream of dusk genes after rapid changes in light intensity.
(A) Changes in enrichment of RpaB upstream of dusk genes during High Light pulse conditions (left heat map) and corresponding changes in RNAP enrichment upstream of the same gene (right heat map) for the 27 dusk genes with RpaB and RNAP peaks in their promoters. ChIP enrichment is expressed as the log
Because RpaA and RpaB bind only a subset of light-responsive dusk genes (Figure 6A,B), additional factors must be involved in controlling light-responsive dusk gene expression. Sigma factors are sequence-specific RNAP subunits which regulate gene expression in bacteria (Gruber and Gross, 2003). Interestingly, RpaA, RpaB, and RNAP bind to the promoters of three sigma factor genes (Figure 6C; Figure 6—figure supplement 1A–C). The binding of RpaA, RpaB, and RNAP to these promoters shifts in conjunction after abrupt changes in light intensity, correlating with light-responsive changes in expression of these genes (Figure 6D; Figure 6—figure supplement 1D–F). These sigma factor genes show light-dependent dusk gene expression patterns (Figure 6—figure supplement 1G–L) that mirror those of the larger group of dusk genes (Figures 2 and 3), suggesting that these sigma factors could regulate the expression of other dusk genes. Thus, RpaA and RpaB may indirectly regulate the expression of non-target dusk genes by controlling the circadian and light-responsive expression of sigma factor genes (Hanaoka et al., 2012), similar to how RpaA drives all dusk gene expression in Constant Light conditions by binding to a subset of dusk genes (Markson et al., 2013). It is also possible that changes in light intensity affect dusk gene expression in a manner independent of RpaA
Figure 6.
Global regulation of dusk gene expression in response to light changes.
(A) Number of dusk gene targets of RpaA only (red), RpaB only (blue), RpaA and RpaB (yellow), or neither (black). Target genes of binding sites of RpaA and RpaB were determined using chromatin immunoprecipitation followed by sequencing under several different light conditions (see Materials and methods, ChIP-seq analysis, for more details. See Figure 4—source data 2 or Figure 5—source data 2 for full lists of RpaA and RpaB peaks associated with dusk genes). (B) Light-responsive changes in gene expression of dusk genes. For each dusk gene, we calculated the maximal log
Figure 6—figure supplement 1.
Regulation of dusk sigma factor gene expression by RpaA and RpaB.
(A)-(C) Normalized ChIP-seq signal of RpaA (red), RpaB (blue), RNAP (green) and mock IP (black) upstream of the sigma factor genes (A) rpoD6, (B) rpoD5, and (C) sigF2. The location of the gene is located on the plot with a gray bar with an arrow indicating directionality of the gene. The location of RpaA, RpaB, and RNAP peaks are indicated on top of the plot with red (RpaA), blue (RpaB), and green (RNAP) bars. See Materials and methods, ChIP-seq analysis for more details. (D)-(F) Changes in enrichment of RpaA (red), RpaB (blue), and RNAP (green) and downstream sigma factor gene expression (black) after exposure to the High Light pulse (triangles) or the Shade pulse (circles) upstream of rpoD6 (D), rpoD5 (E), and sigF2 (F). See Materials and methods, ChIP-seq analysis for more details. (G)-(L) Gene expression dynamics of rpoD6 (G,J), rpoD5 (H,K), and sigF2 (I,L) under Low Light vs High Light pulse (G)-(I) and Clear Day vs Shade pulse (J)-(L) conditions. RpaA and RpaB binding changes in a correlated manner upstream of these genes. RpaA and RpaB binding also correlates with changes in RNAP enrichment and sigma factor expression levels.
We have defined a regulatory picture in which changes in light intensity affect the activity of RpaA and RpaB to control the expression of dusk genes. However, light affects RpaA activity in complex and promoter-specific ways. Additionally, light-dependent regulation in addition to that mediated by RpaA and RpaB may control dusk gene expression in response to environmental perturbations. Still, despite the apparent complexity of regulation of dusk genes in response to light fluctuations, the expression of almost all dusk genes show strikingly regular dynamics (Figures 2 and 3). Furthermore, the activity of RpaA and RpaB at a subset of promoters (especially those of sigma factor genes) could lead to pervasive and coordinated changes in the expression of other dusk genes. Hence, we reasoned that mathematical models (Alon, 2006) of RpaA and RpaB activity might effectively describe the regulatory circuits underlying the dynamics of large groups of dusk genes. Such an approach would enable an understanding of the basic principles of interaction between circadian gene expression regulation with light-dependent regulation without needing to describe all underlying molecular mechanisms.
Phenomenological models suggest simple principles underlying the activation of clusters of light-responsive dusk genes
We find that dusk genes collectively display a small number of responses to changes in environmental light intensity. Using k-means clustering of the gene expression dynamics from our different light profiles (Figures 2 and 3), as well as from perturbations of RpaA (Figure 2—figure supplement 2 [Markson et al., 2013]), we identify three major groups of dusk genes (35–80 genes, see Figure 7—source data 1 for full lists) which show distinct and coordinated changes in gene expression over circadian time and in response to changes in light intensity (Figure 7; Figure 7—figure supplement 1). Under Constant Light conditions, all three clusters are activated by RpaA
Figure 7.
Dusk genes group into three major clusters that show distinct and coordinated responses to changes in light intensity.
(A) Average expression profiles of genes belonging to the Early dusk gene cluster under Clear Day (magenta) and Shade pulse (gray) conditions (left y-axis). Dusk genes were grouped using k-means clustering of their normalized expression dynamics in response to the four light conditions of this study and perturbations of RpaA activity in Constant Light conditions (Figure 7—figure supplement 1, [Markson et al., 2013]), and clusters were named based on their order of activation. See Materials and methods - K-means clustering for more details, and Figure 7—source data 1 for full lists of genes in each cluster. The number of genes within the cluster, as well as the number of genes with an RpaA or RpaB peak in their promoters (targets) is listed. The expression values of each gene across all four light conditions in this work were normalized to a range of 0 to 1, and the normalized expression values were averaged within each cluster (solid lines). The shaded region on the plot indicates the standard deviation of the normalized expression values within the cluster. The light intensity profile for each condition is plotted as dashed lines in the same color with values corresponding to the right y-axis. (B) Average expression profiles of genes belonging to the Middle dusk gene cluster under Clear Day (magenta) and Shade pulse (gray) conditions (left y-axis), presented as in (A). (C) Average expression profiles of genes belonging to the Late dusk gene cluster under Clear Day (magenta) and Shade pulse (gray) conditions (left y-axis), presented as in (A).
Figure 7—figure supplement 1.
Average expression profiles of the major dusk gene clusters under various conditions.
(A) Average expression profiles of the Early (left plot), Middle (middle plot), and Late (right plot) dusk gene clusters under Low Light (black) and High Light pulse (orange) conditions (left y-axis). The expression values of each gene across all four light conditions in this work were normalized to a range of 0 to 1, and the normalized expression values were averaged within each cluster. The shaded region of the plot indicates the standard deviation of the normalized expression values within the cluster. Lists of genes belonging to each cluster and the scaled expression values are available in Figure 7—source data 1. The light intensity profile for each condition is plotted as dashed lines in the same color with values corresponding to the right y-axis. (B) Average expression profiles of the Early (left plot), Middle (middle plot), and Late (right plot) dusk gene clusters in Constant Light conditions in wildtype and OX-D53E cells (rpaA-, kaiBC-, Ptrc::rpaA(D53E)) (data from [Markson et al., 2013]). The OX-D53E strain allows experimental control of RpaA activity via IPTG-inducible expression of the RpaA phosphomimetic RpaA-D53E in cells that lack wildtype RpaA. Plotted are average cluster expression in wildtype cells in Constant Light conditions (black squares), OX-D53E cells without inducer (RpaA phosphomimetic not induced, brown downward triangles), and OX-D53E cells with inducer (RpaA phosphomimetic induced, purple upward triangles). The expression values of each gene within each strain in Constant Light (wildtype or OX-D53E) were separately normalized to a range of 0 to 1, and the normalized expression values were averaged within each cluster. Lists of genes belonging to each cluster and the scaled expression values are available in Figure 7—source data 1. The shaded region on the plot indicates the standard deviation of the normalized expression values within the cluster.
The Shade pulse and Sunset in the Clear Day condition have differing effects on the expression of each of the major dusk gene clusters. Early dusk gene expression rapidly increases in response to Shade, but during Sunset plateaus at
At present there is no mechanistic model to explain the differential response of these clusters to circadian regulation and changes in sunlight. Given that there are unknown regulators involved in circadian gene expression (Figure 6A,B), and because it is not possible to exhaustively test all possible models of regulation of dusk gene expression, we sought to construct the simplest models that can describe the expression dynamics of these clusters using a phenomenological modeling approach. Such models can be used to highlight regulatory architectures that are sufficient to recapitulate the observed gene expression dynamics, as well as direct further mechanistic studies to reveal the underlying molecular details of regulation.
Given the clear roles for RpaA
We began by asking whether levels of RpaA
Figure 8.
Phenomenological modeling of the activation of clusters of light-responsive dusk genes.
(A) Normalized RpaA
Figure 8—figure supplement 1.
Best fit simulations of ‘RpaA-only’ and ‘RpaB-only’ models in which RpaA
(A) Normalized RpaA
Figure 8—figure supplement 2.
Models in which either the Middle or Late cluster feeds back to influence Early cluster expression.
(A) Feedback model in which the expression of the Early dusk cluster is an activation Hill function of Middle gene expression and an activation Hill function of both RpaA
Figure 8—figure supplement 3.
Models in which either the Early or Late cluster feeds back to influence Middle cluster expression.
(A) Feedback model in which the expression of the Middle dusk cluster is an activation Hill function of Early gene expression and an activation Hill function of both RpaA
Figure 8—figure supplement 4.
Models in which either the Early or Middle cluster feeds back to influence Late cluster expression.
(A) Feedback model in which the expression of the Late dusk cluster is an activation Hill function of Early gene expression and an activation Hill function of both RpaA
We reasoned that additional regulatory interactions downstream of RpaA and RpaB, or ‘network motifs’ (Alon, 2006), could account for the observed gating of the Early and Late clusters. Thus, we constructed models in which dusk cluster gene expression is positively or negatively dependent on the expression of another cluster alongside activation by RpaA
Our results highlight that the measured dynamics of RpaA
Discussion
Changes in light adjust circadian gene expression to optimize metabolism in response to shifting ambient light intensity
We show that natural fluctuations in light intensity significantly affect the dynamics of circadian gene expression (Figures 2 and 3). While previous studies have measured genome-wide gene expression in a single natural light condition (Waldbauer et al., 2012), here we compare genome-wide circadian gene expression in several physiologically-relevant conditions, including Clear Day, High Light pulse, Shade pulse, and Low Light, to carefully dissect the effects of light on clock output. Natural light changes most greatly affected a large fraction of the dusk genes (Figures 2B and 3C,D), possibly because most of the direct targets of RpaA are dusk genes (Markson et al., 2013). We speculate that the opposing trends we observe in dawn gene expression (Figure 2—figure supplement 3 and Figure 3—figure supplement 1) may in part be due to competition for RNAP between the dusk and dawn genes (Gruber and Gross, 2003; Mauri and Klumpp, 2014) or by growth-rate-dependent mechanisms (Scott et al., 2010), as this group of genes contains the primary growth genes. A systematic exploration of the effects of light on circadian genes will be necessary to fully elaborate the contributions of light, clock, and growth rate on circadian gene dynamics.
We find that large groups of light-responsive dusk genes are activated by diminished light conditions to different extents depending on the time of day the stimulus is applied. These differences in activation may serve to optimally change metabolism for a given light condition and time of day. The light-responsive dusk genes grouped into three clusters - Early, Middle, and Late - with different activation dynamics during Sunset at the end of the Clear Day versus the Shade pulse in the afternoon (Figure 7, see Figure 7—source data 1 for full lists of genes in each cluster). Glycogen breakdown genes and the central carbon metabolism genes glyceraldehyde-3-phosphate dehydrogenase and oxalate decarboxylase belong to the Middle dusk genes, which are activated to similar levels by Shade and Sunset (Figure 7B). This suggests that cyanobacteria delay the activation of glycogen breakdown pathways (Reimers et al., 2017) until just before dusk when grown under Clear Day conditions, but can transiently activate these genes in response to Shade to access alternate energy reserves if necessary. Interestingly, genes encoding pyridine nucleotide transhydrogenase, which reversibly converts NADH to the NADPH required for electron transport, belong to the Late cluster and are strongly activated only by Sunset and not afternoon Shade (Figure 7C). Such a response might delay the adjustment of the relative levels of NADH/NADPH until only when absolutely needed at night, when NADPH is potentially important for defense against reactive oxygen species (Diamond et al., 2017). The cytochrome c oxidase genes belong to the Early cluster, which respond more intensely to Shade than to Sunset (Figure 7A). This enzyme is essential for preventing photodamage in response to rapid changes in light intensity (Lea-Smith et al., 2013); such changes are not expected to occur during the night, where it serves solely as the terminal electron acceptor for respiration. More generally, the genome-wide gene expression dynamics measured here qualitatively agree with predictions from a whole-cell model of S. elongatus that assumed optimization of growth (Reimers et al., 2017). To resolve how the circadian and light-dependent transcriptional changes effect these metabolic changes, future studies must measure enzyme levels and metabolic fluxes under fluctuating light conditions.
Mechanistic principles underlying the activation of light-responsive dusk genes
While light does not alter the post-translational oscillator/transcription-translation feedback loop circadian circuit, it regulates the activation of dusk genes via RpaA
We define a clear role for the stress-responsive transcription factor RpaB as a transcriptional activator of a large subset of dusk genes (Figure 5E). Further, we demonstrate that decreases in light intensity like a Shade Pulse lead to increases in RpaB
Although complex molecular mechanisms underlie the light-responsive expression of dusk genes, we demonstrate that phenomenological models effectively describe the differential activation of large groups of dusk genes to afternoon Shade and Sunset. These models suggest that transcription factors with the dynamics of RpaA
Closing remarks
RpaB and its cognate upstream histidine kinase NblS (van Waasbergen et al., 2002) have been implicated in a variety of stress responses (Marin et al., 2003; Mikami et al., 2002; Shoumskaya et al., 2005), which suggests that the mechanisms and regulatory circuits defined here may apply to other environmental changes such as temperature or osmolarity. The requirement of RpaB for mediating the environmental response of circadian genes suggests that the circadian circuit coevolved with RpaB to optimize responses to predictable and unpredictable changes in the environment and motivates the further exploration of the interaction between light and circadian rhythms in S. elongatus. Resolution of this interaction and subsequent integration into whole cell models of cyanobacterial growth (Burnap, 2015; Westermark and Steuer, 2016) will help to explain the fitness benefits of the circadian clock (Johnson and Egli, 2014) and optimize synthetic biology efforts to engineer cyanobacteria to produce useful compounds (Ducat et al., 2011) from the constantly changing sunlight in nature.
Genomics data
All high throughput sequencing data is available from the Gene Expression Omnibus with the accession number GSE104204.
Materials and methods
The resources table includes the genetically modified organisms and strains, cell lines, reagents, and software that are essential to reproduce the results presented.
Key resources table
| Reagent type or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (Synechococcus elongatus) | PCC 7942 (wild-type) | ATCC | Cat. Num. 33912 | |
| Strain, strain background (Escherichia coli) | Tuner (DE3) | EMD Millipore | Cat. Num. 70263 | |
| Gene (S. elongatus) | RNA polymerase Beta’ subunit | N/A | Cyanobase: Synpcc7942_1524 | |
| Gene (S. elongatus) | rpaB | N/A | Cyanobase: Synpcc7942_1453 | |
| Recombinant DNA reagent | RNA polymerase beta prime subunit FLAG | This paper | Addgene: 102337 | Plasmid encoding C-terminal FLAG tag RNA polymerase Beta’ subunit (Synpcc7942_1524) with Kan selection marker, targeted to integrate at native gene locus |
| Recombinant DNA reagent | pET-48b(+) | EMD Millipore | Cat. Num. 71462 | |
| Renetic reagent (S. elongatus) | EOC 398 and EOC 399 | This paper | S. elongatus PCC7942 transformed with RNA polymerase beta prime subunit FLAG plasmid. Confirmed by PCR and Western blot. | |
| Antibody | anti-RpaB | This paper | Anti-RpaB serum was produced by Cocalico Biologicals. Anti-RpaB was affinity purified as described in this work. | |
| Antibody | anti-RpaA | This paper | Anti-RpaA serum was produced by Cocalico Biologicals as described in Markson et al., 2013. Anti-RpaA was affinity purified as described in this work. | |
| Antibody | FLAG M2 mouse monoclonal antibody | Sigma Aldrich | Cat. Num. F3165 | |
| Software, algorithm | Imagequant | GE Healthcare | ||
| Software, algorithm | Bowtie | PMID: 19261174 | ||
| Software, algorithm | Peak-Seq | PMID: 19122651 | ||
| Software, algorithm | MATLAB | MathWorks | ||
| Commercial assay or kit | RNeasy Mini kit | Qiagen | Cat. Num. 74104 | |
| Commercial assay or kit | Ribo-Zero bacteria rRNA removal kit | Illumina | Cat. Num. MRZMB126 | |
| Commercial assay or kit | Truseq Stranded mRNA sample prep kit | Illumina | Cat. Num. 20020594 | |
| Commercial assay or kit | NEBNext Ultra II DNA library prep kit | New England Biolabs | Cat. Num. E7645S | |
| Chemical compound, drug | Phos-tagAcrylamide AAL-107 | Wako Pure Chemical Industries | Cat. Num. 304–93521 |
Cyanobacterial strains
Most experiments were conducted in a pure wildtype background of Synechococcus elongatus PCC7942 (ATCC catalog number 33912, RRID:SCR_001672). For RNAP ChIP experiments, we used a strain in which the
Construction of light apparatus
To grow the cyanobacteria in different light profiles, we constructed an apparatus to control the intensity of four high powered LED arrays (parts list in Table 3, p. 2). ‘Warm white’ LED arrays (
Table 1.
Fitting bounds.
Bounds used for fitting the variables in our simple model of gene expression. H is the Hill coefficient,
| Variable | Lower bound | Upper bound |
|---|---|---|
| H | 0 | 7 |
| 0 | 80 | |
| 0 | 80 | |
| 0 | 10 | |
| 0 | 1 |
Table 2.
Fitting results.
The definitions of the variables are given in Equations 1-3, p. 1–3. The error is defined as the square root of the sum of the squared deviations between simulation and data.
| Model | Cluster | Figure | Error | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RpaA-only | Early | 7D | 0.71 | 37.54 | 72.71 | 0.71 | 6.76 | - | - | - | - | 0.85 |
| RpaB-only | Early | 7-Fig. Supp. 2C | 0.37 | 24.03 | 78.62 | - | - | 0.37 | 0.78 | - | - | 1.01 |
| RpaA and RpaB | Early | 7G | 0.35 | 51.28 | 37.76 | 0.35 | 4.19 | 0.8 | 2.5 | - | - | 0.41 |
| Feedback, M act. | Early | 7-Fig. Supp. 3A | 0.01 | 55.85 | 30.01 | 0.01 | 0.3 | 0.87 | 2.38 | 0.06 | 2.47 | 0.37 |
| Feedback, M rep. | Early | 7-Fig. Supp. 3B | 0.67 | 58.69 | 38.89 | 0.67 | 6.96 | 0.62 | 2.47 | 0.96 | 7 | 0.24 |
| Feedback, L act. | Early | 7-Fig. Supp. 3C | 0.2 | 35.87 | 19.03 | 0.2 | 4.43 | 0.98 | 3.35 | 0.05 | 6.15 | 0.38 |
| Feedback, L rep. | Early | 7I, 7-Fig. Supp. 3D | 0.75 | 69.34 | 42.68 | 0.75 | 6.22 | 0.59 | 3.53 | 0.71 | 2.39 | 0.21 |
| RpaA-only | Middle | 7D | 0.79 | 37.95 | 63 | 0.79 | 6.76 | - | - | - | - | 0.86 |
| RpaB-only | Middle | 7-Fig. Supp. 2C | 0.26 | 0.03 | - | - | - | 0.26 | 5.6 | - | - | 0.85 |
| RpaA and RpaB | Middle | 7G | 1 | 57.46 | 25.97 | 1 | 4.96 | 0.52 | 4.12 | - | - | 0.29 |
| Feedback, E act. | Middle | 7-Fig. Supp. 4A | 0.8 | 23.73 | 22.19 | 0.8 | 6.96 | 0.49 | 4.53 | 0.21 | 6.35 | 0.32 |
| Feedback, E rep. | Middle | 7-Fig. Supp. 4B | 0.73 | 71.08 | 39.24 | 0.73 | 5.14 | 0.53 | 6.58 | 0.74 | 0.88 | 0.35 |
| Feedback, L act. | Middle | 7I, 7-Fig. Supp. 4C | 0.18 | 78.63 | 76.5 | 0.18 | 6.09 | 0.33 | 2.64 | 0.16 | 1.55 | 0.16 |
| Feedback, L rep. | Middle | 7-Fig. Supp. 4D | 0.68 | 31.02 | 17.98 | 0.68 | 3.34 | 0.57 | 6.79 | 1 | 0 | 0.44 |
| RpaA-only | Late | 7D | 0.96 | 39.82 | 64.37 | 0.96 | 6.7 | - | - | - | - | 0.78 |
| RpaB-only | Late | 7-Fig. Supp. 2C | 0.05 | 0 | 0 | - | - | 0.05 | 0.68 | - | - | 0.79 |
| RpaA and RpaB | Late | 7G | 0.95 | 77.65 | 67.1 | 0.95 | 7 | 0.48 | 5.9 | - | - | 0.5 |
| Feedback, E act. | Late | 7-Fig. Supp. 5A | 0.99 | 23.93 | 20.01 | 0.99 | 5.8 | 0.4 | 6.95 | 0.18 | 6.77 | 0.53 |
| Feedback, E rep. | Late | 7-Fig. Supp. 5B | 0.76 | 59.81 | 18.43 | 0.76 | 6.22 | 0.69 | 6.13 | 0.47 | 3.12 | 0.29 |
| Feedback, M act. | Late | 7I, 7-Fig. Supp. 5C | 0.37 | 27.3 | 16.09 | 0.37 | 3.72 | 0.01 | 3.46 | 0.91 | 6.23 | 0.22 |
| Feedback, M rep. | Late | 7-Fig. Supp. 5D | 0.86 | 25.1 | 14.46 | 0.86 | 6.92 | 0.48 | 7 | 1 | 0 | 0.52 |
Table 3.
Parts for controllable light source.
The table includes the parts chosen for their specific properties. The remaining parts, such as wires, heat shrink tubing, thermal paste for mounting the LEDs on the heat sinks, proto-boards, and housing are quite general and specific brands are unnecessary.
| Part name | Digikey part number | Current price ($) | Quantity |
|---|---|---|---|
| PWR SUP MEDICAL 18V 8.3A 150W | EPS439-ND | 73.71 | 1 |
| CONN RCPT 8CONT DIN SLD PNL MNT | SC2007-ND | 5.64 | 1 |
| LEDDynamics Flexblock BUCK BOOST 48V, 700 mA | 788–1038-ND | 19.99 | 4 |
| AD7376 digital potentiometer | AD7376ARWZ10-ND | 8.66 | 4 |
| AC to DC power supply, 10VDC, 275 mA | 993–1233-ND | 4.68 | 2 |
| BXRA-30E1200-B-03, Bridgelux, Warm white, LED | Not sold at Digikey. | ||
| Need to order from: | 10.47 | 4 | |
| AMBIT ELECTRONICS, INC. | |||
| Aavid thermalloy Spotlight 47W heat sink | 1061–1092-ND | 9.50 | 4 |
| Arduino Uno Board Rev3 | 1050–1024-ND | 21.49 | 1 |
Table 4.
Wiring the FlexBlock LED driver.
The FlexBlock LED driver needs to be connected in a ’boost only’ configuration (see spec sheet for more details), with connections as shown.
| Line | Connection |
|---|---|
| DIM GND | GND of 10 V power supply/Arduino |
| DIM | Wipe of AD7376 potentiometer (Pin 16) |
| Vin+ | +of 18V power supply AND + of LED array |
| Vin- | GND of 18V power supply |
| LED+ | NC (not connected) |
| LED- | - of LED array |
Table 5.
Wiring the AD7376 potentiometer.
We used the SOIC-16 housing for the AD7376 potentiometer for ease of soldering to wires. The table indicates how each pin was connected. The length of the GND wire from the Arduino board to the shared ground needs to be kept short (
| Pin | Connection |
|---|---|
| 1 | +of 10 V power supply |
| 2 | GND (shared GND between that of 10V power supply and Arduino |
| 3 | GND |
| 4 | GND |
| 5 | pin 10 on Arduino (or any other pin designated as a Slave Select, such as 5, 6, or 9 |
| 6 | +5V of Arduino |
| 7 | pin 13 on Arduino (SCLK) |
| 8 | NC (not connected) |
| 9 | NC |
| 10 | NC |
| 11 | pin 11 on Arduino (MOSI) |
| 12 | +5V of Arduino |
| 13 | NC |
| 14 | +of 10V power supply |
| 15 | NC |
| 16 | DIM line of FlexBlock |
Calibrating light conditions
A single LED was mounted to shine perpendicular to the ground and isolated from other light sources. A single 750 mL cyanobacterial culture in a 150 cm
To define the Clear Day conditions, we used light intensity values measured by the Ground-based Atmospheric Monitoring Instrument Suite, Rooftop Instrument Group on March 23rd, 2013 (Figure 1B, dark blue line, [Petty and Weidner, 2017]). We used this light intensity profile to define the rate of change of light intensity in our Clear Day condition, with a maximal light intensity of 600
Purification of anti-RpaA and anti-RpaB antibodies
Recombinant RpaA was purified as previously described (Takai et al., 2006). To purify recombinant RpaB, we cloned the rpaB gene (Synpcc7942_1453, gene info available through Cyanobase, RRID:SCR_007615) into the pET48-b + plasmid (Novagen) and overexpressed Trx-His-tagged RpaB in Novagen Tuner (DE3) competent cells carrying this plasmid by adding 300
Anti-RpaB serum was generated by immunization of two rabbits with purified RpaB by Cocalico Biologicals (Reamstown, PA). RpaA- and RpaB-conjugated Affigel 10/15 resin (Bio-Rad) was prepared following manufacturer’s instructions as described previously (Gutu and O'Shea, 2013). Anti-RpaB serum was first passed over an RpaA-conjugated resin and the flowthrough collected to subtract cross-reacting antibodies. Anti-RpaB antibodies were then purified from the flowthrough using an RpaB-conjugated resin as described previously (Gutu and O'Shea, 2013). The same process was repeated to purify anti-RpaA antibodies using rabbit serum described previously (Markson et al., 2013), passing the serum over an RpaB-conjugated resin and purifying with an RpaA-conjugated resin. No cross reactivity of the purified anti-RpaA and anti-RpaB antibodies for the opposite regulator was detected via ELISA assay.
Measurement of RpaA
Ten mL of cyanobacterial culture with OD
RNA sequencing
Twenty-five mL of cyanobacterial culture with OD
Definition of circadian genes
We defined a subset of previously identified circadian genes on which to focus our analysis. We began with a list of 856 previously described reproducibly circadian genes (Markson et al., 2013; Vijayan et al., 2009). We next required that these genes have a Cosiner amplitude (Kucho et al., 2005) of greater that 0.15 under Constant Light conditions (Vijayan et al., 2009). We also required that the gene display expression of at least one read per nucleotide in at least one time point of the RNA sequencing experiments in this study. These filters produce a list of 450 high confidence circadian genes.
We noted that genes classified as dawn (class 2) and dusk (class 1) genes under Constant Light conditions (Vijayan et al., 2009) showed maximal expression at a different time of day under our Low Light conditions, while the relative ordering of genes by Cosiner phase (Kucho et al., 2005) from Constant Light conditions (Vijayan et al., 2009) was preserved. As such, we redefined dawn genes as those genes with a phase of 40
ChIP sequencing
One hundred and twenty mL of OD
Pellets were resuspended in 1 mL of BG-11M supplemented with 500 mM L-proline and 1 mg/mL lysozyme and incubated at 30
For a given pulldown, 800
Sequencing libraries were prepared from the purified ChIP DNA using the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs, Ipswich, MA). Libraries were sequenced on an Illumina HiSeq 2500 instrument by the by the Bauer Core Facility at the Harvard FAS Center for Systems Biology. We created sequencing libraries of ChIP experiments from two separate biological repeats of the time course experiment. Reads were aligned to the S. elongatus genome using Bowtie (RRID:SCR_005476) as described previously (Markson et al., 2013), resulting in an average of 3 million aligned reads for replicate 1, and 5 million aligned reads for replicate 2.
ChIP-seq analysis
The aligned read data per genomic position was smoothed with a Gaussian filter (window size = 400 base pairs, standard deviation = 50). Each data set was normalized to the Mock ChIP-seq experiment and peaks which were significantly enriched above the Mock were identified in each data set using a previously described (Markson et al., 2013) custom-coded form of the Peak-seq algorithm (Rozowsky et al., 2009). Within each replicate time course for a given protein, we compiled a list of peaks which were enriched at least 3.5 fold over the Mock experiment at the position of highest ChIP signal. Finally, we required that a peak be detected in both replicates for it to be considered. This analysis generated 114 RpaA peaks, 218 RpaB peaks, and 451 RNAP peaks. To calculate enrichment for a peak, we determined the ChIP signal at a given time point at the genomic position of the highest ChIP signal detected for that peak and divided this by the value of the Mock experiment at that position. The data plotted in this manuscript are from replicate 2, but all trends hold in replicate 1. We assigned a gene as a target of a peak if: (i) the start codon of the gene was within 500 bp of the position of maximal ChIP signal within a peak; (ii) the peak resided upstream of the gene; (iii) The gene was the closest gene to that peak on the same strand. Lists of RNAP, RpaA, and RpaB peaks and gene targets are found in Figure 3—source data 2, Figure 4—source data 2, and Figure 5—source data 2, respectively.
For Figures 3G, 4C and 5C, we identified all RNAP, RpaA, or RpaB peaks with dusk gene targets based on the above criteria, respectively. 82 dusk genes are targets of RNAP peaks, 56 dusk genes were targets of RpaA peaks, and 42 dusk genes are targets of RpaB peaks. Then, for each peak - dusk gene pair, we calculated the change in gene expression of the dusk gene after 60 min, and the change in ChIP enrichment of the upstream peak over the mock pulldown (described above) after 60 min in High light, each compared to their respective values at Low light at 8 hr since dawn. We plotted these data on the x- and y-axes, respectively, with orange triangles. We repeated this process, comparing gene expression and ChIP enrichment values after 60 min in Shade compared to 8 hr since dawn in Clear Day conditions, and plotted the data as gray circles. We calculated the correlation coefficient between the change in gene expression and the change in ChIP enrichment for all peak-gene pairs of the relevant factor in the High Light pulse, and then calculated the same correlation in Shade pulse conditions separately. We calculated the correlation coefficients comparing changes after 15 min in either the High Light or Shade pulse conditions, and list these values in the legends of Figure 3—figure supplement 2, Figure 4—figure supplement 2, and Figure 5—figure supplement 2. The data used for these plots for RNAP, RpaA, and RpaB are available in Figure 3—source data 2, Figure 4—source data 2, and Figure 5—source data 2, respectively. We plot data from replicate 2, and the trends are reproduced in replicate 1.
For Figure 3—figure supplement 2, Figure 4—figure supplement 2, and Figure 5—figure supplement 2, we took the lists of RNAP/RpaA/RpaB peaks with dusk gene targets from above. For each peak - gene pair, we calculated the log
For Figures 4D and 5D we identified all dusk genes that were targets of both RpaA and RNAP (for Figure 4D) or both RpaB and RNAP (for Figure 5D). 33 dusk genes are targets of both RpaA and RNAP peaks, and 27 dusk genes are targets of both RpaB and RNAP. Then, for each pair of RpaA/B - RNAP peaks, we calculated the change in ChIP enrichment of the RpaA/B peak after 60 min, and the change in ChIP enrichment of the RNAP peak upstream of the same dusk gene over the mock pulldown (described above) after 60 min in High light, each compared to their respective values at Low light at 8 hr since dawn. We plotted these data on the x- and y-axes, respectively, with orange triangles. We repeated this process, comparing RpaA/B ChIP enrichment and RNAP ChIP enrichment values after 60 min in Shade compared to 8 hr since dawn in Clear Day conditions, and plotted the data as gray circles. We calculated the correlation coefficient between the change in RpaA/B ChIP enrichment and the change in RNAP ChIP enrichment for all RpaA/B - RNAP peak pairs of the relevant factor in the High Light pulse, and then calculated the same correlation in Shade pulse conditions separately. We calculated the correlation coefficients comparing changes after 15 min in either the High Light or Shade pulse conditions, and list these values in the legends of Figure 4—figure supplement 3, and Figure 5—figure supplement 3. The RNAP, RpaA, and RpaB peaks associated with each dusk gene are listed in Figure 2—source data 1 and Figure 3—source data 1, and the enrichment values for these peaks are listed in Figure 3—source data 2, Figure 4—source data 2, and Figure 5—source data 2, respectively. The data plotted here are from replicate 2, and the trends are reproduced in replicate 1.
For Figure 4—figure supplement 3 and Figure 5—figure supplement 3, we took the lists of RpaA/RpaB - RNAP peaks pairs upstream of the same dusk gene from above. For each RpaA/B - RNAP peak, we calculated the log
For Figure 4—figure supplement 4D–F, Figure 6D, and Figure 6—figure supplement 1D–F, we identified all RpaA, RpaB, and RNAP peaks that targeted the specified gene, as described above. Then, we calculated the log
K-means clustering
We calculated normalized expression values of high confidence dusk genes under our dynamic light conditions, as well as in previously described RpaA perturbations in Constant Light (Markson et al., 2013). We separately normalized the data from set of dynamic light conditions (Low Light, Clear Day, High Light pulse, Shade pulse) and the Constant Light data (Wildtype, OX-D53E cells — rpaA-, kaiBC-, Ptrc::rpaA(D53E) — without inducer, OX-D53E with inducer, [Markson et al., 2013]) using z-score normalization, and used this data to separate the dusk genes into eight groups with k-means clustering in MATLAB (RRID:SCR_001622) using Pearson correlation as the distance metric. We focused our analysis on the three largest clusters which accounted for most of the dusk genes (187/281 genes). The lists of genes belonging the three major clusters are found in Figure 7—source data 1.
Mathematical modeling
We observed very regular and systematic changes in the expression of large clusters of dusk genes in natural light conditions (Figures 2, 3 and 7) that correlated with
Our model treats the activation or repression of the expression of a dusk gene cluster by
(1)
(2)
(3)
We determined the sufficiency of a model to describe the data by fitting the parameters using the range of values shown in Table 1. Time propagation of the differential Equation 1 was performed using the ode45 solver in MATLAB (RRID:SCR_001622), with
The Akaike Information Criterion (AIC) and the Chi-squared test are typically used to quantify whether a model with more parameters fits the data better than another with fewer parameters simply because it is more complex. However, both approaches are for statistical models in which little to no information is used to construct the model and are not strictly applicable to the model constructed here, which is based on our understanding of transcription. If we do use AIC to compare the models, the feedback models are predicted to be most probable.
In our model,
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Abstract
The circadian clock interacts with other regulatory pathways to tune physiology to predictable daily changes and unexpected environmental fluctuations. However, the complexity of circadian clocks in higher organisms has prevented a clear understanding of how natural environmental conditions affect circadian clocks and their physiological outputs. Here, we dissect the interaction between circadian regulation and responses to fluctuating light in the cyanobacterium Synechococcus elongatus. We demonstrate that natural changes in light intensity substantially affect the expression of hundreds of circadian-clock-controlled genes, many of which are involved in key steps of metabolism. These changes in expression arise from circadian and light-responsive control of RNA polymerase recruitment to promoters by a network of transcription factors including RpaA and RpaB. Using phenomenological modeling constrained by our data, we reveal simple principles that underlie the small number of stereotyped responses of dusk circadian genes to changes in light.
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer