The cyanobacterial circadian clock follows midday in vivo and in vitro
 Cited 17
 Views 2,640
 Annotations
Abstract
Circadian rhythms are biological oscillations that schedule daily changes in physiology. Outside the laboratory, circadian clocks do not generally freerun but are driven by daily cues whose timing varies with the seasons. The principles that determine how circadian clocks align to these external cycles are not well understood. Here, we report experimental platforms for driving the cyanobacterial circadian clock both in vivo and in vitro. We find that the phase of the circadian rhythm follows a simple scaling law in lightdark cycles, tracking midday across conditions with variable day length. The core biochemical oscillator comprised of the Kai proteins behaves similarly when driven by metabolic pulses in vitro, indicating that such dynamics are intrinsic to these proteins. We develop a general mathematical framework based on instantaneous transformation of the clock cycle by external cues, which successfully predicts clock behavior under many cycling environments.
https://doi.org/10.7554/eLife.23539.001eLife digest
All life forms on the surface of our planet face an environment that cycles between day and night as the Earth rotates around its axis. To deal with these regular changes, organisms from bacteria to humans have internal rhythms, called circadian clocks, that coordinate different aspects of these organisms’ lives, from growth to the urge to sleep, with the daynight cycle.
The simplest of all known circadian clocks is found in bacteria called cyanobacteria, which live in bodies of water all around the world. Like plants, these microorganisms can harness the energy in sunlight to fuel their growth. The internal clock of cyanobacteria is remarkable because it can be rebuilt in the laboratory using just three components, proteins called KaiA, KaiB and KaiC. When mixed together in a test tube, these proteins spontaneously generate a 24hour cycle in which KaiC gets chemically modified by the addition and removal of a phosphate group. These chemical modifications on KaiC are like the hands of the clock that tell the time of day.
Much work on the internal clock of cyanobacteria has focused on how the clock proteins generate rhythms. What has been less clear is how this rhythm works in an environment that changes between day and night and from season to season. For example, away from the equator, the length of the day changes throughout the year. Changes in day length are important for cyanobacteria because they can grow only during the day and must rest at night. Yet, it was not understood how this microorganism's clock adjusts to days of different length.
To address this question, Leypunskiy et al. made a device to grow cyanobacteria in many different lightdark cycles while monitoring their circadian rhythm. The experiments revealed a fairly simple result: the internal clock time is always the same at the middle of the day, regardless of how long the day is. Next, Leypunskiy et al. developed a way to study how the clock proteins in the test tube respond to chemical signals mimicking day and night. Unexpectedly, this showed that the Kai proteins themselves have the ability to track the middle of the day. It turns out that these proteins are not only able to generate a 24hour rhythm, but also to correctly align it to the daynight cycle.
Together, these findings will guide other researchers to better understand the molecular origins of how circadian clocks align to the daynight cycle. Moreover, by showing that the effects of a lightdark cycle on a circadian clock can be mimicked in a test tube, these results open the doors to using the tools of biochemistry to understand how circadian clocks work in natural environments.
https://doi.org/10.7554/eLife.23539.002Introduction
Circadian clocks generate biological rhythms that temporally organize physiology to match the 24 hr diurnal cycle. Although these clocks continue to oscillate in constant laboratory conditions, in natural environments they are driven by the external cycle of day and night. Because of latitudedependent changes in the duration of day and night throughout the year, clock architectures must incorporate mechanisms that respond appropriately to environmental signals, such as dawn and dusk, whose schedule changes throughout the year. Data from several species indicate that circadian clocks adapt to different day lengths by modulating their phases relative to the lightdark cycle (de Montaigu et al., 2015; Rémi et al., 2010). In this way, circadian oscillators are able to coordinate physiological events relative to specific times of day and, in multicellular organisms, specialized mechanisms exist that allow oscillators in different cells to follow distinct times of day (Herzog, 2007; Daan et al., 2003). Because the biochemical circuits governing circadian rhythms and lightdark sensing in such organisms are complex, it has been difficult to identify which features of circadian systems are responsible for seasonal adaptation. More generally, it remains unclear what features oscillators must have to respond appropriately to varying lightdark cycles and how those features are implemented molecularly in specific systems.
Cyanobacteria present a unique opportunity to elucidate molecular mechanisms in circadian biology because the core oscillator responsible for driving genomewide transcriptional rhythms can be reconstituted using purified KaiABC proteins, and these proteins have been extensively studied biochemically in constant conditions (Nishiwaki et al., 2004; Rust et al., 2007). Cyanobacteria must contend with large seasonal variations in day length because their natural aquatic environments span a wide range of latitudes (Flombaum et al., 2013). Yet, comparatively little is known about how the cyanobacterial clock functions when driven by lightdark cycles that mimic days in different seasons.
To study how the circadian clock is affected by lightdark cycles with different day lengths, we developed multiplexed LED illumination devices to grow cyanobacteria in a wide range of lightdark conditions (Figure 1A). We used squarewave illumination patterns in our experiments, and used the times of lightson and lightsoff as experimental analogs of dawn and dusk, respectively. We defined the day length (τ) as the total time the lights are on each day (Figure 1A). We found that the circadian rhythm in the cyanobacterium Synechococcus elongatus PCC 7942 (S. elongatus) follows a simple rule: the phase of clockdriven gene expression scales linearly with day length and remains fixed relative to the middle of the day over a wide range of day lengths.
We biochemically simulated seasonal effects in the reconstituted KaiABC oscillator by driving the Kai proteins with pulses of nucleotides on a 24 hr cycle, simulating in vivo metabolic conditions during day and night. Remarkably, this in vitro system shows a similar linear scaling of clock phase with simulated day length, indicating that core mechanisms for seasonal adaptation are intrinsic to the Kai proteins. We developed a minimal mathematical model for oscillator entrainment based on rapid transitions between distinct day and night clock cycles. The model can account for the ability to track midday if the phase shifts caused by dawn and dusk depend linearly on clock time with appropriate slopes. By calibrating this model with independent measurements in vitro and in vivo, we were able to predict the results of phase response experiments, and the behavior of the clock in non24 hr lightdark cycles.
The essential feature of the model, the linear dependences of phase shifts on clock time, has a simple geometric interpretation in terms of the deformation of the clock orbit caused by a transition between light and dark. Thus, the entrained behavior of the circadian clock can be captured quantitatively by a mathematical framework with a small number of parameters. This framework in turn can be used to guide the design of experiments that probe the molecular origins of key mathematical features. The precisely defined nature of the cyanobacterial clock facilitates such experiments, but the model is general, so we expect it to be useful for other organisms as well.
Results
Cyanobacteria respond to seasonal changes in day length by aligning the phase of circadian gene expression relative to midday
S. elongatus is a photosynthetic bacterium whose physiology is closely tied to light and dark. In constant light, the circadian clock exerts pervasive control over gene expression: most transcripts cycle in abundance with ≈24 hr period, with the majority of transcripts peaking either near subjective morning (dawn genes), or nearly 12 hr later, at subjective nightfall (dusk genes) (Liu et al., 1995; Ito et al., 2009). In the dark, however, growth stops, and most gene expression is highly repressed (Hosokawa et al., 2011; Tomita et al., 2005; Pattanayak et al., 2015). Thus, portions of the circadian gene expression program that fall into nighttime hours are strongly attenuated.
Viewed in this way, the hours between dawn and dusk provide a limited window for gene expression, so that winter months at high latitude provide fewer daylight hours for this cyanobacterium to accomplish biosynthetic tasks relative to summer months. We surmised that an important function for the circadian clock in this organism might be to schedule gene expression appropriately during daylight hours, when biosynthetic resources are available. We thus expected that asymmetric lightdark cycles mimicking days in different seasons would realign the clock cycle.
To systematically study how circadian gene expression in S. elongatus adjusts to asymmetric lightdark schedules, we built a microcontrollerdriven LED array device and used it to grow cells in 24 hr daynight cycles with photoperiod (day length) varying from 4 to 20 hr (Figure 1A). This LED array was coupled to a plate reader that allowed us to monitor clock output—gene expression from clockdriven promoters—using strains engineered with luminescent reporters of representative dusk (kaiBC) and dawn (purF) genes.
In these experiments, the circadian clock stably entrained to lightdark cycles with a wide range of day lengths (τ = 8–16 hr) within roughly 3 days (Figure 1—figure supplements 1 and 2). We found that the phase of the oscillation, that is, when the peak clock output signal occurred relative to dawn, varied systematically as a function of day length. In general, longer days resulted in the peak reporter signal occurring later in the day (Figure 1B). By plotting the time of peak reporter signal versus day length, we found that the driven behavior of the clock follows a simple scaling law: the clock phase is proportional to day length (Figure 1C). The proportionality constant, m, falls in an intermediate range (Table 1) between the limits corresponding to either dawn (m = 0) or dusk (m = 1) fully resetting the clock. The linear relationship between clock phase and day length with slope m ≈ 0.5 implies that cyanobacteria set their clocks to reach the same internal time at the middle of the daylight hours, independent of day length. This entrainment behavior is not unique to the kaiBC promoter; a reporter for purF, a representative of the dawn class of genes, shows similar scaling behavior (Figure 1—figure supplement 3).
The fact that the core oscillator of the cyanobacterial circadian clock can be reconstituted in vitro from purified Kai proteins (Nakajima et al., 2005; Kageyama et al., 2006) naturally led to the question of whether the ability to track midday in vivo is intrinsic to the core oscillator, or if it requires additional factors present in the cell. We thus sought to extend the reconstituted system such that we could drive the biochemical oscillator with rhythmic input signals.
In vitro reconstitution of seasonal clock response
A purified mixture of the KaiA, KaiB, and KaiC proteins spontaneously generates a stable circadian rhythm in KaiC phosphorylation and in the formation of KaiBKaiC complexes (Nakajima et al., 2005; Kageyama et al., 2006). Although the Kai proteins are not lightsensitive, recent work has shown that they are sensitive to metabolite pools that shift in response to changes in photosynthetic activity in the cell: KaiA is sensitive to the redox state of quinones, and KaiC phosphorylation is sensitive to the ATP/ADP ratio (Rust et al., 2011; Kim et al., 2012; Phong et al., 2013). We thus developed a protocol to mimic repeated lightdark cycles in vitro by cycling the ATP/ADP ratio between physiologically relevant levels experienced during the day and night (Pattanayak et al., 2014), using ADP addition to simulate nightfall ([ATP]/([ATP]+[ADP]) ≈ 25%) followed by buffer exchange into an ATPonly buffer to simulate dawn ([ATP]/([ATP]+[ADP]) ≈ 100%) (Figure 2A).
We found that the KaiC phosphorylation rhythm readily responded to this metabolite pulsing protocol (Figure 2B). Stepping down to lower ATP/ADP conditions promoted dephosphorylation, in accord with previously published observations (Pattanayak et al., 2014). Conversely, stepping up to higher ATP/ADP conditions favored phosphorylation (Figure 2B). Pulsing the ATP/ADP ratio in this way caused the KaiC phosphorylation rhythm to synchronize to the external cycle. By the third cycle, the oscillator appeared to have stably entrained, and, similar to the clock behavior that we observed in live cells, longer times in daytime buffer led to later peak phosphorylation times. We estimated peak times for these oscillator reactions during the third entraining cycle and found that the phase of the in vitro rhythm scales linearly in proportion with simulated day length (Figure 2C), with KaiC phosphorylation rhythm peaking roughly 3 hr after the midpoint of simulated daytime, suggesting that the reconstituted Kai oscillator is capable of tracking the approximate midday point of an externally imposed metabolic rhythm.
To more accurately measure the scaling of entrained phase with day length in KaiABC oscillator reactions, we turned to a fluorescence polarization probe that enables automated measurement of oscillator state with high temporal resolution (Figure 2—figure supplement 1) (Chang et al., 2012; Heisler et al., 2017). We used this assay to determine clock phases in freerunning conditions after entraining the Kai proteins with three metabolic cycles, analogous to the design of the in vivo experiments. We again measured linear scaling of the entrained phase, albeit with a lower slope than the estimate from sparser gelbased measurements of KaiC phosphorylation (m = 0.38 ± 0.07 for polarization probe vs. m = 0.51 ± 0.04 for phosphorylation data, Figure 2C). Despite the variability in our estimates of m, these measurements suggest that the in vitro oscillator successfully captures the essential feature of seasonal entrainment we observed in vivo: linear scaling of entrained phase with an intermediate slope.
In the cell, the Kai proteins interact with histidine kinases and a network of other factors absent from the reconstituted system, ultimately leading to rhythms in transcription across the genome, including the kai genes themselves (Takai et al., 2006; Gutu et al., 2013; Markson et al., 2013). These additional factors may account for the differences we observe between proportionality constants in vitro and in vivo, and the time delay between KaiC phosphorylation and luminescent output of the kaiBC expression reporter is likely responsible for the offset in Figure 2C. While these considerations suggest that care must be taken in connecting the in vitro and in vivo results, they also underscore the significance of our observation that the Kai oscillator proteins by themselves are sufficient to yield the linear response of the system to altered day length. We therefore sought to use this in vitro model of seasonality to uncover the biochemical basis of the linear seasonal clock response.
Seasonal adaptation of the circadian oscillator can be decomposed into step responses to individual metabolic cues
In the purified system, the entraining cues are steps between high and low ATP/ADP that simulate dawn and dusk. We therefore sought to decompose the seasonal adaptation of the clock into step responses following each metabolic transition. This approach is related to the limit cycle theory of oscillators: if we assume that a unique stable orbit exists for both the day and night conditions, then a metabolic transition forces the system to adjust from its current cycle to one associated with the new condition. If relaxation to the new orbit occurs rapidly relative to the length of the day—that is, if the light and dark orbits are strongly attracting—then the state of the oscillator at each transition can be specified simply by a phase angle on that orbit. In this limit, the response of the system is fully determined by instantaneous phase shifts caused by the transitions. Mathematically, we describe this limit as an oscillator comprised of a single phase variable, $\theta $, that runs along a fixed limit cycle trajectory at constant speed (see Appendix 1). The responses to darktolight or lighttodark transitions are then represented by phase shifts that are specified by the functions $L\left(\theta \right)$ and $D\left(\theta \right)$, respectively.
To determine whether this fastrelaxation approximation can describe phase shifts in the core Kai oscillator, and to study their biochemical basis, we measured the $L\left(\theta \right)$ and $D\left(\theta \right)$ shift functions in our reconstituted system. To measure the $D\left(\theta \right)$ function, we incubated the Kai proteins in daytime buffer, then transferred them into nighttime buffer at various times throughout the circadian cycle and studied how the oscillation was affected. As above, we assayed oscillator response in separate experiments using either SDSPAGE to measure KaiC phosphorylation or the fluorescence polarization probe to achieve high temporal resolution. Example time series from the fluorescence polarization measurements are shown in Figure 3A–B.
We proceeded to calculate phase shifts from these measurements, taking into consideration that a switch to nighttime buffer conditions could affect the oscillator in two ways: the phase of the oscillation can shift, and the oscillator period may change, both of which can change the relative timing of peaks. To separate these effects, it is necessary to collect enough data to accurately determine the period, and then extrapolate backwards to infer the instantaneous phase shift at the time of buffer exchange. We detail our procedure for analyzing these data to extract phase shifts in Figure 3—figure supplement 1. We used an analogous design to measure the reverse steps from nighttime to daytime buffer (Figure 3B). Plotted together in Figure 3C–D, the extrapolated phase shifts measure the sensitivity of the clock to individual light and dark steps throughout the 24 hr day.
The measured $L\left(\theta \right)$ and $D\left(\theta \right)$ stepresponse functions show that the phase of the purified KaiABC oscillator is responsive to both stepup and stepdown metabolic changes. For example, a daytonight shift lengthens the period of the clock by about 2 hr, and, when it is applied at subjective morning, leads to an instantaneous phase delay of about 3 hr (Figure 3—figure supplement 1). On the other hand, a nighttoday transition in the middle of subjective night shortens the period and causes a phase advance of about 1.5 hr.
These measurements characterize how the oscillator responds to a metabolic stepchange throughout the entire clock cycle. However, when the clock is stably synchronized to a cycling environment, dawn and dusk fall only in a limited window of clock phases. To determine this window, we returned to our measurements in Figure 2C. We identified phases that correspond to dawn or dusk in entrained conditions mimicking LD 6:18 to LD 18:6 and marked these phases on the $L\left(\theta \right)$ and $D\left(\theta \right)$ functions (Figure 3C–D, colored arrows). These points fell on gradually changing, approximately linear, regions of both curves, spanning roughly 6 hr windows near subjective dawn (for $L\left(\theta \right)$) and subjective dusk (for $D\left(\theta \right)$). Because these regions of $L\left(\theta \right)$ and $D\left(\theta \right)$ represent phase shifts comparable in magnitude but opposite in sign, we hypothesized that their opposing forces could enable oscillator entrainment to lightdark cycles.
To check this hypothesis, we simulated a simple (phaseonly) oscillator that runs at constant frequencies in the light and dark and adjusts its phase in response to dawn and dusk according to the phase shift functions that we measured in Figure 3C–D (see Computational methods). This model maps the onedimensional phase variable from one cycle to the next according to $L\left(\theta \right)$ and $D\left(\theta \right)$. We analyzed the stability properties of this map and found that the oscillator could entrain with a unique phase to driving periods of within 4–5 hr of its natural circadian period. Outside this range, the oscillator failed to entrain or exhibited more complex dynamics (Figure 3—figure supplement 2).
We proceeded to test whether the measured $L\left(\theta \right)$ and $D\left(\theta \right)$ stepresponse functions could successfully reproduce the entrainment of the oscillator to repeated 24 hr metabolic cycles of varying day length, shown in Figure 2. When subjected to lightdark cycles in simulations, the oscillator model stably entrains to the diurnal schedule within twotofive cycles (Figure 4A–B, Figure 4—figure supplement 1A). Importantly, the simulated entrained phase scales linearly with day length with a slope similar to the experimental data, indicating that the driven clock response can indeed be decomposed into a series of step responses to environmental transitions (Figure 4C).
As described above, we also measured $L\left(\theta \right)$ and $D\left(\theta \right)$ stepresponse functions with a separate preparation of Kai proteins using a gelbased assay to read out the phase of the KaiC phosphorylation rhythm (Figure 3E–F). Although the absolute magnitude of the step responses in these measurements was larger, they still predict linear scaling of entrained phase in simulations for day lengths between 6 and 14 hr (Figure 4—figure supplement 2B). The discrepancy in magnitude between these measurements may point to differences in sensitivity to input cues between different preparations of Kai proteins, or to a slight perturbative effect of fluorescently labeled KaiB.
We found that simulated entrainment of the phase oscillator model was particularly sensitive to the regions surrounding subjective dusk (for $D\left(\theta \right)$) and subjective dawn (for $L\left(\theta \right))$ (Figure 3E–F, colored arrows). Because the fluorescence polarization approach allows us to measure many conditions in an automated way over many days, and thus to disentangle phase shifts from period differences (Figure 3C–D, Figure 3—figure supplement 1), these higher time resolution measurements better constrain the portions of the response functions critical for entrainment.
Given the observation that oscillator entrainment was sensitive to local shapes of $L\left(\theta \right)$ and $D\left(\theta \right)$, we wondered if midday tracking requires a specific form of these response functions or if certain generic features are sufficient. Based on our observation that $L\left(\theta \right)$ and $D\left(\theta \right)$ in the KaiABC system are approximately linear in the regions used during metabolic entrainment, we asked whether linear $L\left(\theta \right)$ and $D\left(\theta \right)$ would result in a linear dependence of clock phase on day length.
Linear regions of stepresponse functions underlie proportional tracking of day length
Consider a phase oscillator driven by lightdark cycles of period T and day length τ (Figure 7). When the oscillator is entrained (phaselocked) to the lightdark cycle, the oscillator returns to the same state by the end of a full cycle. Starting from an initial phase ${\theta}_{0}$, the clock accumulates phase ${\omega}_{L}\tau $ during the day, then experiences a phase shift of magnitude $D\left({\theta}_{\tau}\right)$ at dusk, accumulates phase ${\omega}_{D}(T\tau )$ at night, and finally responds to dawn with a phase shift $L\left({\theta}_{T}\right)$:
Here all angles are measured in units of cycles ($1\text{}\mathrm{c}\mathrm{y}\mathrm{c}=2\pi$), and $\omega}_{L$ and $\omega}_{D$ are the oscillator frequencies in the light and dark, respectively. For stable entrainment, the effects of $L\left(\theta \right)$ at dawn and $D\left(\theta \right)$ at dusk must balance the phase accumulated by the oscillator, so that the phase returns to the same point at the end of each cycle.
This expression for the entrained clock phase scales linearly with day length if its derivative with respect to τ is constant. The simplest way to achieve this condition is if $L\left(\theta \right)$ and $D\left(\theta \right)$ are themselves linear functions of $\theta $, such that $D\left(\theta \right)\approx d\left(\theta {\theta}_{D}\right)$ and $L\left(\theta \right)\approx l(\theta {\theta}_{L})$ over the relevant range of clock times. If oscillator frequency is the same in light and dark, as is approximately true for the Kai oscillator (${\omega}_{D}/{\omega}_{L}=$ 0.93 ± 0.01, see Computational methods), the peak time of the oscillation (measured in hours after dawn) can be expressed as ${t}_{pk}=m\tau +C$, with the slope $m(l,d)=d(1l)/(d+lld)$ determined by the slopes of the linear $L\left(\theta \right)$ and $D\left(\theta \right)$ functions. If the day and night oscillator frequencies differ, we still obtain a linear dependence on day length, but with an altered expression for the proportionality constant m (see Appendix 1). This expression for m imposes constraints on values of l and d required for an oscillator to track different portions of the daynight cycle. Figure 4—figure supplement 3 highlights the requirements on l and d for a middaytracking clock.
To determine whether $L\left(\theta \right)$ and $D\left(\theta \right)$ for the Kai oscillator are in line with these mathematical requirements, we examined the linear portions of the stepresponse functions in Figure 3. Indeed, the slopes of $L\left(\theta \right)$ and $D\left(\theta \right)$ from the fluorescence polarization assay (l = 0.34 ± 0.03, d = 0.38 ± 0.05) predict an m value consistent with our measurements of the entrained in vitro oscillator using the same method (m(l, d) = 0.34 ± 0.04, calculated in the linear model vs. m = 0.38 ± 0.07, measured) (Figure 4—figure supplement 3).
Because $L\left(\theta \right)$ and $D\left(\theta \right)$ are periodic functions on a circle, they cannot be linear everywhere with noninteger slope. However, maintaining the linear scaling of phase with day length only requires that both $L\left(\theta \right)$ and $D\left(\theta \right)$ be linear over the range of clock times when dawn and dusk occur, respectively, with the nonlinearities required to satisfy periodicity appearing at other times. The exact width of the linear region of $L\left(\theta \right)$ and $D\left(\theta \right)$ depends on m and the range of day lengths that the oscillator is required to track. For example, an oscillator capable of tracking midday (m = 0.5) over a 12 hr range of day lengths requires that $L\left(\theta \right)$ and $D\left(\theta \right)$ be linear over at least one quarter of the cycle. This criterion is met by the measured $L\left(\theta \right)$ and $D\left(\theta \right)$ (Figures 3CD).
To further test whether entrainment of the Kai system can be described by this framework, we returned to our phase oscillator simulations (Figure 4). When we replaced our experimentally measured $L\left(\theta \right)$ and $D\left(\theta \right)$functions with linear approximations, the simulated oscillator exhibited a similar scaling of entrained phase over a wide range of day lengths (Figure 4—figure supplement 2A–B). The linear approximations work because the regions where the step response functions deviate strongly from linearity are avoided in our entrainment simulations (Figure 4—figure supplement 2C).
Together, these results indicate that the driven behavior of the cyanobacterial circadian clock in 24 hr cycles can be approximated as a simple oscillator that shifts in response to lightdark transitions with a sensitivity that varies linearly with phase. To test whether this linear mathematical framework holds more generally and to understand how the cyanobacterial clock responds to a broader range of environments, we sought to measure clock response to a variety of external conditions with both single and repeated lightdark transitions.
The behavior of the driven circadian clock across diverse conditions can be collapsed to a simple mathematical representation
The phase oscillator model described above was inspired by the observation of linear scaling of clock phase in 24 hr days with various day lengths. The key model assumptions are (i) a unique clock cycle in the light and in the dark, (ii) rapid relaxation from one cycle to another when conditions change, and (iii) sensitivity to environmental changes that varies linearly with clock time. While these assumptions hold, the model should be capable of describing the behavior of a biological clock in arbitrary fluctuating environments. For example, the response to a single dark pulse can be decomposed into sequential stepdown and stepup responses.
To test the range of validity of this mathematical model, we used our LED array system (Figure 1A) to collect data on S. elongatus clock function in response to dark pulses administered at different clock times. This corresponds to a classical phase response curve (PRC) analysis, a commonly used tool in circadian biology for characterizing the response of a biological clock to perturbations. We also probed clock responses to dark pulses of varying lengths (a socalled ‘wedge’ analysis), and to repeated lightdark cycles with periods different from 24 hr. Our phase oscillator model predicts that the quantitative response to these various perturbations should all be related through the stepup and stepdown functions (see Appendix 1). One specific prediction of the model is that changing the time at which a dark pulse begins and the duration of a dark pulse should have separable linear effects on the oscillator, which would manifest as linear curves in phase response and wedge analyses. These experimental data and a fit of our linear model to the results are presented in Figure 5. The lines of best fit in Figure 5A–C were obtained from a global fit to all of the datasets, with two fitting parameters that fix the slopes of the regression lines across all conditions (Appendix 1). The overall agreement between the model and data suggest that limited data on clock response can be successfully extrapolated to other conditions using this approach.
To understand the implications of these results, consider the phase response curve (PRC) for a 12 hr dark pulse (Figure 5A). Although phase response analyses frequently separate PRCs into regions of low sensitivity to perturbation (‘dead zones’) followed by highly nonlinear regions with large phase shifts, our model suggests a different interpretation (Pattanayak et al., 2014; Johnson, 1999; Schmitz et al., 2000; Pfeuty et al., 2011). Clock response is weakest at times when nightfall is expected (t = 36 and 60 hr) and increases gradually with clock time. The slowly changing regions of the PRC are well described by line segments with the same slope, in agreement with our model based on linear stepresponse functions$L\left(\theta \right)$ and $D\left(\theta \right)$. The ‘breakpoint’ in the PRC near 50 hr is a consequence of the fact that these periodic stepresponse functions cannot be linear everywhere with noninteger slope, as discussed earlier.
In other words, a circadian clock capable of tracking midday over a wide range of day lengths is expected to have a PRC that is approximately linear over many hours except for a narrow region of rapid change that is required to satisfy periodicity. Indeed, in simulations where $L\left(\theta \right)$ and $D\left(\theta \right)$ are linear everywhere except for a discontinuous jump, the PRC for a long dark pulse appears as a straight line except for a single breakpoint (Figure 5—figure supplement 1). Near the breakpoint, small changes in the timing of darkness result in large differences in phase shifts, and we would expect to find this region of the PRC at a clock time when such a perturbation is least likely to occur naturally. In the darkpulse PRC for S. elongatus, the breakpoint near 50 hr occurs when prolonged darkness is improbable (Figure 5A). The maximal phase shift in a PRC can be a useful tool to characterize clock mutants, but our analysis highlights that these regions are unlikely to be experienced in natural conditions and may exist only as consequences of periodicity constraints.
A geometric interpretation of oscillator response to varying day length
Finally, we asked how step response functions with linearly increasing sensitivity are related to the mathematical structure of the oscillator and ultimately to the underlying molecular mechanism. Do step response functions with these properties arise generically, or do they require finetuned choices of parameters? To address these issues, we considered the simplest possible dynamical representation that allows for distinct day and night clock cycles. In this model, the clock cycle during the day is represented by a circular limit cycle with unit radius, and the clock time is defined by the angular coordinate of the oscillator on this limit cycle (Figure 6A). The effect of darkness is to deform the limit cycle, transforming the daytime orbit to a nighttime orbit. For simplicity, we assume this night cycle lies in the same plane as the day cycle and is also circular, but may be offset relative to the day cycle and have a distinct radius (Figure 6A).
After a transition from one condition to the other, the system state must evolve to the new limit cycle. We suppose that each cycle is very strongly radially attracting. That is, when the system is in a state off the limit cycle, it is rapidly pulled to the closest point on the cycle. Under these conditions, the step transitions from one cycle to another are determined purely by geometry (Figure 6A), and we can connect this picture with the phaseonly description we used to analyze the experimental data. Here, linearly increasing sensitivity of a step response has a simple geometric interpretation: when the two cycles are displaced from each other, a step transition maps an arc of one cycle onto an arc of the other cycle that subtends a smaller angle than the original (Figure 6B). Thus, step transitions compress or expand angular distance when mapping one circle onto another. The slopes of the step response functions are given by the compression factor in this mapping (Figure 6C–D, Figure 6—figure supplement 1; see Appendix 1).
To determine how the entrained clock phase depends on geometry in this model, we simulated step transitions and then calculated the slope m of clock phase versus day length (Figure 6E). This calculation indicates that midday tracking (m ≈ 0.5) requires that the separation between the light and dark cycles is comparable to the radius of the cycle (R ≈ X). This requirement can be understood intuitively by considering three cases where orbits have the same centertocenter separation (log X = 0.5) but different sizes (Figure 6—figure supplement 1). Dusk transitions are strongly resetting for this choice of X, but the strength of dawn resetting varies with the relative size of the orbits. Dawntracking entrainment results when the night orbit is much smaller than the day orbit (l ≈ 1) and dusktracking entrainment results when the night orbit is much larger (l ≈ 0, d ≈ 1), in accord with how m varies as a function of l and d (Figure 4—figure supplement 3). When the size of both orbits is similar to their center separation, the oscillator can track intermediate phases, such as midday.
Although this model makes a simple connection between attractor geometry and entrainment, it also makes a number of simplifying assumptions about oscillator dynamics that may not hold true for real biological clocks, such as instant transitions between cycles, perfectly circular orbits and constant angular frequencies along each cycle. To test the consequences of relaxing these assumptions, we used a dynamical model where the evolution of the system is described explicitly (see Computational methods). Our simulations in Figure 6—figure supplement 2 show that in these more complicated scenarios the geometric arrangement of day and night cycles remains a key determinant of the slope of entrained clock phase as a function of day length. Indeed, in all cases we studied, midday tracking was only possible for geometries where the centertocenter distance between the day and night cycles was comparable to the radius of the larger orbit (R ≈ X > 1).
This dynamical systems perspective allows us to reframe conditions on the underlying biochemical mechanisms that can produce the observed midday tracking behavior. Changes in the external environment caused by transitions between night and day should affect the oscillator in such a way that the period remains close to 24 hr, but that the limit cycle is shifted by an amount comparable to its radius. The relative geometry of the two limit cycles, which is determined by the mechanisms that couple the environment to the clock, must be finetuned to give a specific slope for the entrained phase. Indeed, when we plotted experimentally determined orbits of the purified KaiABC oscillator on axes showing the extent of phosphorylation of two key sites, we observed an arrangement similar to the expected geometry (R ≈ X) using nucleotide conditions that simulate either day or night (Figure 6F) (Rust et al., 2007; Phong et al., 2013).
Discussion
Although circadian clocks are defined in part by their ability to continue to cycle in constant environments, the defects associated with clock mutants are often most apparent when organisms are faced with fluctuating environments (Ouyang, 1998; Woelfle et al., 2004; Pittendrigh, 1972; Spoelstra et al., 2016; Pittendrigh and Minis, 1972). Thus, an important challenge is to understand how biological clocks respond to the cycling environments found in nature, and how they function to appropriately schedule gene expression and behavior.
For most organisms, there is an asymmetry between day and night, in terms of food availability, predation risk, etc., so that the need to carry out certain activities diurnally or nocturnally presents changing demands as the length of the day varies throughout the year. The situation is especially dramatic for cyanobacteria because there is an extreme metabolic contrast between day and night. We found that S. elongatus contends with these challenges using a clock that tracks the middle of the day.
The ability of circadian clocks to keep track of the phase of the lightdark cycle has been long recognized in plants, insects, rodents and higher mammals (de Montaigu et al., 2015; Daan et al., 2001; Edwards et al., 2010; Hut et al., 1999, Hut et al., 2013; Wehr, 2001). Although molecular mechanisms that give rise to these entrainment behaviors are still being uncovered, analysis of circadian clock models has found that the presence of multiple feedback loops in complex clocks determines the number of points in the driving cycle that the oscillator can track simultaneously, by allowing different internal phase relationships between the clock components (Rand et al., 2004). For example, the multifeedback loop clocks in plants are able to track phases of both dawn and dusk (Edwards et al., 2010).
Consistent with this picture, we find that the core circadian oscillator in cyanobacteria, which relies on a single posttranslational feedback loop, keeps track of a single phase—the midpoint of the day portion of the cycle, a property described in our mathematical framework as a linear scaling of entrained phase with day length with slope m ≈ 0.5. Why might keeping track of midday be useful for a photosynthetic organism with a simple clock? Clockcontrolled gene expression in S. elongatus tends to be bimodal, with most genes falling into subjective dawn or dusk classes. Because the biosynthetic capacity of S. elongatus is severely limited in darkness, the midday tracking effect we describe here could be a mechanism to ensure that biosynthetic resources are partitioned in a balanced way between the dawn and dusk genes, even as the day length changes with the seasons (Figure 6—figure supplement 3). In particular, clockdriven transcription in this organism has been shown to implement a switch between anabolic and catabolic carbon metabolism, suggesting that a role for the midday tracking we observe here is to ensure balanced growth by timing this switch appropriately in days of different length (Diamond et al., 2015).
The ability to reconstitute this effect in vitro by delivering metabolic pulses to the purified Kai proteins indicates that midday tracking is not necessarily achieved through additional feedback mechanisms in the cell, but appears to be a property of the clock proteins themselves. The purified clock responds to metabolic steps with phase shifts that are linear functions of the previous phase. The slopes of these response functions are presumably tuned to give an appropriately entrained clock phase. Notably, linear responses have also been observed for the Kai oscillator following temperature steps, suggesting that this is a general reaction of the system to inputs (Yoshida et al., 2009).
The mathematical framework that we describe here has deep similarities to the theory of nonparametric entrainment developed by Colin Pittendrigh (Pittendrigh and Daan, 1976; Daan, 2000; Pittendrigh and Minis, 1964). His work motivated a theory of entrainment to diurnal cycles mediated by instantaneous phase shifts at dawn and dusk, which can be summarized by a phase response curve. Daan and Pittendrigh suggested that the ability of the clock to track specific phases of the daynight cycle in different seasons depends on the shape of the phase response curve as well as the difference between the freerunning period of the clock and the period of the daynight cycle (Pittendrigh and Daan, 1976; Johnson et al., 2003). Our decomposition of driven behavior of the KaiABC oscillator into individual step responses is in the spirit of this classic paradigm.
We note that a mismatch between the freerunning clock period and the external cycle is not required for stable entrainment in our phase oscillator model (see Appendix 1). Instead, entrainment can be achieved from the opposing effects of the stepup and stepdown phase shifts, which occur at different clock times in days of different length. The stepresponse curves underlying entrainment in our model are nonlinear functions of clock phase, but they can be successfully approximated by linear functions over the interval of clock phases used during entrainment. Our simulations suggest that the slopes of these locally linear functions are key determinants of entrained phase, along with changes in clock period in daytime and nighttime conditions. Successful prediction of how the in vitro oscillator entrains to rhythmic environments is due in large part to the ability to map out the stepresponse curves with high temporal resolution. In this study, we achieve this by measuring freerunning oscillations in an automated way using a fluorescence polarization probe.
Even though there are likely other effects at play in natural environments, as long as the system can be described by fast relaxation back to distinct limit cycles in day and night, instantaneous stepresponse descriptions of the kind used here should be applicable. The data reduction achieved in our linear model holds the promise of predicting the behavior of the circadian rhythms in many timevarying environments from a minimal data set that characterizes oscillator response, and may be applicable to clocks in many organisms.
Although the biochemistry of even the simplest circadian clock is complex, our analysis suggests that a key determinant of entrained behavior is how the clock limit cycle is deformed by coupling to the environment. Viewed in this way, the entrainment properties of circadian oscillators arise from simple features of the geometry of the limit cycle attractor that could be measured in any organism. The concept that the phaseshifting of oscillators can be studied in terms of their geometric properties was initially developed by Winfree (Winfree, 1973). In this context, it would be informative to analyze the geometric arrangement of day and night limit cycles of clock gene transcripts in other species. An important goal for the future is to understand how the biochemical properties of the clock components in cyanobacteria and other organisms achieve the effect of shifting the limit cycle without changing the period, allowing us to use dynamical systems theory to bridge the gap between molecular detail and systemslevel clock phenotypes.
Materials and methods
Experimental methods
Cyanobacterial strains and culture conditions
Request a detailed protocolTwo strains of S. elongatus PCC 7942 were used for this study. AMC1300 is a wildtype derivative carrying a bacterial luciferase bioluminescent reporter of kaiBC expression. AMC1300 carries PkaiBC::luxAB at NS1 and PpsbAI::luxCDE at NS2, which enables the cells to produce the luciferase enzyme and the longchain aldehyde substrate for the luminescent reaction (Chen et al., 2009). AMC408 carries a purF reporter (PpurF::luxAB at NS2, PpsbAI::luxCDE at NS1) (Nair et al., 2002). Prior to experimental measurements, all cultures were grown in test tubes in BG11M liquid medium at 30°C with shaking under cool white fluorescent bulbs (≈60 μmol photons m^{−2} s^{−1}, Philips AltoII, Amsterdam, Netherlands).
Creating custom lightdark environments using LED arrays
Request a detailed protocolTo simulate different lightdark cycles, we used custombuilt red LED arrays in a 96well format (LEDs from superbrightleds.com, St. Louis, MO, cat. no. RL5R12008; 96well plates from Corning, Corning, NY, cat. no. 3916). The LEDs were mounted into a hollowedout 96well plate and the tails of the LEDs were soldered into a circuit board, where they were wired in parallel in groups of four to the analog inputs of an Arduino Mega 2560 microcontroller. Another hollow 96well plate was glued to the bottom of the LEDcarrying plate, beneath the LEDs, in order to create a light baffle and prevent light leakage between the wells. These devices were placed ≈2 mm above a black 96well plate containing cells growing on BG11agar, such that every well of the growth plate received illumination from a single LED (≈1.8 cm between LED and agar surface). The growing plate was sealed with transparent film, with holes punctured above each well to provide aeration. Custom Arduino scripts were written to administer appropriate lightdark schedules to cells in the growth plates. Every well received the same light intensity (1.8 V across each LED, ≈200 μmol photons m^{−2} s^{−1}) in the light portion of the day. The temperature of the agar was $31.6\pm 0.8$ °C underneath LEDs that were turned on and $28.9\pm 0.3$ °C under LEDs that were off (mean ± standard deviation of 6–10 wells).
Monitoring gene expression in vivo using bioluminescent reporters
Request a detailed protocolCells were grown in test tubes until OD 0.5–0.8, as described above, and diluted to OD 0.2 immediately prior to experiment. A black 96well plate was filled (200 μL/well) with BG11Magar (15 g/L agar) supplemented with sodium thiosulfate (1 mM) and HEPES (20 mM, pH 8.0). When the BG11Magar mixture cooled to room temperature, 35 μL of cells from growing culture at OD 0.2 were added to each well of the plate. The plate was sealed with transparent UniSeal (GE HealthCare Life Sciences, Pittsburgh, PA, cat no. 7704–0001), holes were punched above each well using a 26G½ needle (BD, Franklin Lakes, NJ, cat. no. 305111), and the plate was placed underneath an LED array. Bioluminescence from luciferase reporters was measured every 30 min using a TopCount scintillation counter (PerkinElmer, Boson, MA). Each well of a 96well plate was read for 1 s per measurement.
KaiABC in vitro reactions
Request a detailed protocolKaiA, KaiB, and KaiC were recombinantly expressed and purified as previously described (Lin et al., 2014), although the anion exchange chromatography purification step was omitted in the preparation of KaiC used for fluorescence polarization experiments. Protein concentration was measured via Bradford Assay Kit using bovine serum albumin (BSA) as a standard (BioRad, Hercules, CA). For experiments relying on SDSPAGE analysis of KaiC phosphorylation, KaiABC proteins were mixed in master reaction buffer (20 mM Tris [pH 8], 150 mM NaCl, 5 mM MgCl_{2}, 10% glycerol, 0.5 mM EDTA) supplemented with a mixture of ATP and ADP (day buffer: 2 mM ATP, night buffer: 2 mM ATP, 7.5 mM ADP). All reactions were incubated at 31°C. To mimic lighttodark transitions, ADP was added to appropriate reactions to 7.5 mM final [ADP]. To mimic darktolight transitions, the reactions were passed through Zeba desalting columns (7 MW kDa cutoff, ThermoFisher, Waltham, MA) equilibrated in day buffer. Because every buffer exchange step dilutes the proteins by about 10%, the reactions were prepared at 3× standard protein concentration (10.5 μM KaiB and KaiC, 4.5 μM KaiA). KaiC phosphorylation was assayed by SDSPAGE and quantified by densitometry, as previously described (Phong et al., 2013).
In the cases where oscillations were read out by monitoring fluorescence polarization, KaiABC proteins were mixed at 3× standard protein concentration in the master reaction buffer supplemented with a mixture of ATP and ADP (day buffer: 2.5 mM ATP, night buffer: 2.5 mM ATP, 7.5 mM ADP). All reactions were incubated at 31°C. Daytonight transitions were mimicked by addition of 7.5 mM ADP (final), and nighttoday transitions were mimicked by passing the reactions through Zeba desalting columns twice.
For stepup perturbations shown in blue in Figure 3D, buffer exchange steps were administered every 2 hr over a 24 hr interval, and phase shifts were measured relative to an unperturbed control reaction. For every other experiment in Figure 3C–D, buffer exchange or ADP addition steps were performed every 2 hr over a 12 hr interval on two outofphase reactions, prepared as follows. A master mix containing proteins and appropriate nucleotides was split into two tubes, which were flashfrozen in liquid nitrogen immediately after mixing and stored at −80°C. To prepare outofphase reactions, one of the two tubes was thawed in a 30°C water bath 12 hr later than the other.
After all buffer exchanges were completed, the reactions were supplemented with fluorescently labeled KaiB (0.2 μM final) and transferred to the plate reader.
Preparation and labeling of fluorescently tagged KaiB
Request a detailed protocolWe introduced a K25C mutation in KaiB using sitedirected mutagenesis of the pMR0019 plasmid carrying a 6xHisPSPKaiB^{WT} construct in the pET47b(+) backbone. KaiB^{K25C} was expressed in BL21 (DE3) E. coli by overnight induction with IPTG at 18°C and purified following the standard protocol (Lin et al., 2014). Labeling with 6iodoacetofluorescein (6IAF) was performed as previously described (Chang et al., 2012) with minor modifications. Briefly, 130 μL of KaiB^{K25C} stock (50–100 μM) was bufferexchanged into labeling buffer (20 mM Tris, 1 mM TCEP, pH 7.0–7.5) using a Zeba desalting column (7 MW kDa cutoff). A freshly prepared solution of 6IAF (Life Technologies Corp., Grand Island, NY) in DMSO was added to the protein solution in 10fold molar excess, and the mixture was incubated overnight at 4°C in the dark. The labeling reaction was quenched by addition of DTT in approximately 10fold molar excess relative to 6IAF. Unincorporated dye was removed through three rounds of fivefold dilution in reaction buffer and subsequent concentration using a centrifugal filter (10 kDa cutoff, Amicon, EMD Millipore, Billerica, MA). Concentration of final fluoresceinlabeled KaiB^{K25C} solution was determined by Bradford assay.
Monitoring fluorescence polarization rhythms using labeled KaiB
Request a detailed protocolOscillations in KaiABC reaction mixtures supplemented with fluorescently labeled KaiB^{K25C} (10.5 μM KaiB and KaiC, 4.5 μM KaiA, 0.2 μM KaiBfluorescein) were monitored on an Infinite F500 plate reader equipped with a fluorescence polarization module (Tecan Trading AG, Switzerland). At least 30 min prior to measurement, the builtin heating module was turned on to warm the instrument and a black 384 wellplate (Greiner BioOne, Monroe, NC, cat. no. 781900) was loaded into the plate reader. For the metabolic entrainment experiment shown in blue in Figure 2—figure supplement 1A and the stepup experiment shown in blue in Figure 3D, the plate reader temperature was 28°C; for all other experiments, the plate reader temperature was 31°C. Reactions were quickly transferred onto the prewarmed plate (20–35 μL/well) and one to three wells were filled with master reaction buffer. The plate was sealed with a polyethylene silicone plate sealer (Nunc, ThermoFisher cat. no. 235307) and returned to the instrument. Fluorescence polarization of wells of interest (exc. 485 nm, 20 nm bandpass; em. 535 nm, 25 nm bandpass; dichroic 510 nm.) was read out every 15 min using a script created in iControl software (v. 1.12, Tecan Trading AG). Wells containing reaction buffer only were used as blanks, and the G factor was calibrated such that a solution of free fluorescein in reaction buffer produced a reading of ≈20 mP.
Data analysis
Request a detailed protocolAll oscillating trajectories were fit to sinusoids using optimization routines written in MATLAB (Mathworks, Inc.) (RRID: SCR_001622). See Computational methods for detailed fitting procedures and descriptions of simulations. Computational pipelines used for analysis and simulations have been deposited to GitHub at https://github.com/euleip/Simulations_and_analysis_pipelines_github_repo (Leypunskiy, 2017). A copy is archived at https://github.com/elifesciencespublications/Simulations_and_analysis_pipelines_github_repo).
Computational methods
Optimization and curvefitting routines
Request a detailed protocolAll nonlinear fitting procedures were written in MATLAB using the lsqnonlin() or nlinfit() routines. Linear regressions were performed using the polyfit() function. Uncertainties around the bestfit slopes were evaluated using standard formulas for linear regression with known errors in dependent variables (Press et al., 1992). For in vivo experiments, these errors were computed as standard errors of the mean from replicate measurements; for in vitro measurements in Figure 2, the errors in fit phases were computed from the curvature of the cost function at the optimum using the nlpredci() function in MATLAB. Where indicated, 95% confidence intervals were also estimated by nlpredci().
Normalization of bioluminescent reporter traces
Request a detailed protocolPrior to fitting, bioluminescence trajectories were normalized to zero mean and unit standard deviation over the fitting interval, unless specified otherwise. In all analyses, we discarded data from the first 2.5 hr after lightson to avoid masking effects.
Estimation of clock phase in vivo in lightdark cycles
Request a detailed protocolThe bacterial luciferase reporter system exhibits transient masking effects following darktolight transitions. To overcome this issue in determining the phase of gene expression in lightdark cycles of varied day length, we employed a driveandrelease strategy. In this approach (illustrated for LD 8:16, LD 12:12, and LD 16:8 in Figure 1A), cells were first entrained to a given diurnal schedule and then released into constant light for several days to determine the peak times of the entrained rhythm.
We relied on two approaches to calculate the peak times of normalized bioluminescence trajectories recorded after release into constant light: (i) we fit sinusoids to 48 hr segments of the trajectories in constant light, and (ii) we locally fit parabolas near the maxima of the trajectories. The advantage of sinusoidal fitting is that it captures phase information for the entire waveform; on the other hand, local parabolic fitting allows for a precise determination of the time of an individual peak without influence from others. In practice, we found that the estimates of the scaling between entrained phase and day length derived from the two approaches were in good agreement with each other (Table 1, Figure 1—figure supplement 2). Fitting details are described in the following two subsections.
Error bars in Figure 1C, Figure 1—figure supplement 2A, and Figure 5 represent the standard deviation of peak times calculated from technical replicates (n = 4–8). Technical replicates refer to measurements obtained from sidebyside cultures subjected to the same lightdark conditions. In rare cases, cells in individual wells died or produced noisy bioluminescent signals or trajectories that fit poorly to sinusoids or parabolas. We rejected trajectories as outliers from our analysis if they produced fits with a squared error greater than 10 (0–13 outliers per 96 wells).
(i) Estimation of period and peak times from sinusoidal regression
Sinusoidal fits were performed by leastsquares minimization of the cost function:
where y_{i} and x_{i} represent, respectively, the normalized bioluminescence signal and the time after release into constant light for the ith timepoint. The period in the fits was constrained to 23 < T < 25 hr. The bestfit period was distributed within these bounds, independent of the day length of the preceding entraining cycle (Figure 1—figure supplement 2D), a conclusion we confirmed in a peaktopeak analysis described below.
We considered whether the quality of sinusoidal fits affected the estimated slope m. When we performed sinusoidal fits of the dataset in Figure 1C, the leastsquares errors, normalized by degrees of freedom, were between 0 and 1. If data from all the wells were included in the analysis, linear regression to this data yielded a slope of m = 0.55 ± 0.02 (Table 1). As the table below shows, imposing stricter cutoffs on leastsquare fitting error did not significantly impact the estimate of the slope, so an estimate of m based on all of the data is reported in Table 1.
Leastsquares error threshold  % wells satisfying  Slope m ± SD of estimate 

1  100%  0.55 ± 0.02 
0.5  90%  0.53 ± 0.01 
0.1  45%  0.51 ± 0.03 
0.05  17%  0.64 ± 0.03 
(ii) Estimation of period and peak times from local parabolic regression
We found that certain bioluminescent trajectories were fit poorly by sinusoids. As Figure 1—figure supplement 3 shows, purF reporter waveforms display (a) strong asymmetry, marked by faster rising and slower falling dynamics (e.g. second peak in τ = 18 hr curve), (b) wide peaks (e.g. first peak in τ = 8 hr curve), and (c) broad ‘shoulders’ after the peak that occasionally give rise to secondary peaks (e.g. first peak in τ = 18 hr curve). In some cases, kaiBC reporter trajectories also exhibited successive peaks with significantly different amplitudes. In Figure 1C and Figure 1—figure supplement 3, we fit parabolas to 6 hr intervals around the peaks of normalized bioluminescence trajectories. We then estimated phases from the first peak positions and periods from the average peaktopeak times (n = 4–8). We verified that this peakfitting procedure produced comparable results to sinusoidal fitting for the kaiBC datasets in Figure 1C (see Table 1 and Figure 1—figure supplement 2B–E).
Comparison of waveforms during lightdark cycling and in continuous light
Request a detailed protocolTo compare clock reporter dynamics during entrainment and in continuous light in Figure 1—figure supplement 1, we computed nonparametric correlations (Kendall’s τ coefficient) between the reporter signal (P_{kaiBC}::luxAB) measured during the ‘day’ windows in lightdark cycles (days 1–5) and during the corresponding time interval after release into continuous light (day 6). For example, in the case of LD 14:10, the normalized luminescence data recorded between 2.5 and 14 hr during each of the five entraining cycles were correlated with the luminescence dynamics measured between 2.5 and 14 hr after release into constant light. The fact that we observe Kendall’s τ > 0.8 after the second driving cycle suggests that cells are effectively entrained within three cycles and that rhythms observed during the lightson portion of a lightdark cycle can be thought of as a fragment of a freerunning rhythm.
Estimation of clock phase in vitro in metabolic cycles
Request a detailed protocolFor the %PKaiC measurements in Figure 2C, normalized KaiC phosphorylation time courses from day 3 of each reaction were fit independently to sinusoids. Bestfit parameters were obtained by minimizing the cost function:
with the period T fixed at 24 hr to match the period of the imposed metabolic cycles.
For the fluorescence polarization experiments in Figure 2C, fluorescence polarization dynamics were recorded for 48 hr after the last buffer exchange, normalized (to zero mean, unit variance) and fit to sinusoids according to the expression above, with fits to all reactions from the same experiment sharing a globally fit period T.
Fitting and error propagation analysis of stepresponse experiments
Request a detailed protocolStepresponse experiments described in this section were performed a total of three times: once using KaiC phosphorylation to read out clock phase and twice using the fluorescence polarization reporter of KaiBKaiC interaction. The phosphorylation measurements were made using an independent preparation of proteins from the fluorescence polarization experiments.
We performed stepresponse experiments in Figure 3 in order to determine whether the behavior of the clock in metabolic cycles could be decomposed into a sum of phase shifts due to individual transitions. To do so, we first needed (a) to extract L and D functions from step response measurements, and then (b) to use these L and D functions in numerical simulations of clock entrainment to lightdark cycles, as described in the main text and Appendix 1.
In our experiments, we directly observed how the dynamics of the fluorescence polarization reporter or KaiC phosphorylation changed as a function of lab time (e.g. Figure 3A–B), but for our downstream simulations and analysis (e.g. Figure 4) these values had to be converted to clock phase coordinates (e.g. Figure 3C–D) consistent with how $L(\theta )$ and $D(\theta )$ are defined. Specifically, we needed to (i) convert the lab time of each step to corresponding clock phase $\theta$ (or clock time, CT), and then (ii) determine the phase of each fluorescence polarization trace or KaiC phosphorylation trajectory after the perturbation in order to compute the phase shift ($L(\theta )$ and $D(\theta )$) relative to an unperturbed control.
Although these conversions are straightforward in principle, it is important to note that both (i) and (ii) rely on sinusoidal regression of phase from measured data, and that the bestfit phases in both cases are only estimates of the true values. The corresponding uncertainty in the bestfit parameters must be propagated through numerical simulations of entrainment.
Because the temporal resolutions of the KaiC phosphorylation and fluorescence polarization measurements are very different (3 hr vs. 15 min, respectively), we expected the sources of uncertainty in fitting these data to be different (see Figure 2—figure supplement 1), and we thus analyzed their errors in different ways. The sparse measurement of KaiC phosphorylation dynamics leads to relatively high uncertainty in bestfit phase and period, and we propagated the errors through our simulations using nonparametric bootstrapping of the datasets. In the case of the fluorescence polarization measurements, experimenttoexperiment variability is the major source of uncertainty. We therefore performed two replicate measurements of L and D (Figure 3C–D) and used the four possible combinations of L and D in entrainment simulations to assess the range of entrainment behavior constrained by our measurements of these stepresponse measurements. We describe each of these approaches in turn in the following two sections.
(i) Error propagation analysis of stepresponse experiments performed using the fluorescence polarization reporter of KaiBKaiC interaction
Request a detailed protocolPeriods and phases of reactions in each set were determined by global sinusoidal fitting: amplitude, offset and phase terms were fit independently for each reaction, but a single bestfit period was shared among all fits in a given stepup or stepdown set. To avoid transient effects due to a metabolic pulse, only those data points which were collected at least 16 hr after a stepup or stepdown perturbation were used for fitting. Mathematically, we used a nonlinear leastsquares optimization routine to minimize the cost functions:
and
where $y}_{r,i$ refers to the ith data point in the rth normalized fluorescence polarization trajectory, $t}_{r,i$ refers to the lab time when that data point was collected, and $N}_{r$ is the number of data points fit in the rth reaction. Here $A}_{r$, $\varphi}_{r$ and $C}_{r$ are the bestfit parameters for the rth reaction; $T}_{day$ and $T}_{night$ are bestfit periods for all reactions in the 100% ATP and 25% ATP datasets, respectively.
These bestfit parameters were used to determine the phase $\theta$ of each step perturbation and corresponding phase shifts $L(\theta )$ and $D(\theta )$, analogously to the way described for the KaiC phosphorylation datasets below. The stepresponse functions were interpolated linearly to generate smooth curves while enforcing 2πperiodicity.
Linearized stepresponse functions ${L}_{lin}(\theta )$ and ${D}_{lin}(\theta )$ were prepared by linear regression of $L(\theta )$ and $D(\theta )$ centered on the regions used during metabolic entrainment: D functions were linearized between 6 and 22 CT hr; $L$ functions were linearized between 18 and 34 CT hr (see Figure 3C–D). To satisfy periodicity, the stepresponse functions were assembled as piecewiselinear functions with the same slope everywhere but with offsets every 2π radians at ‘breakpoints,’ which were selected by manual inspection. See Figure 4—figure supplement 2C for an illustration of $L(\theta )$ and $D(\theta )$ and the corresponding ${L}_{lin}(\theta )$ and ${D}_{lin}(\theta )$.
Finally, step phases deduced from polarization trajectories were adjusted to match the phases of the KaiC phosphorylation rhythm. This conversion made use of measurements in Figure 2—figure supplement 1, which indicated that the phase of oscillation in KaiC phosphorylation lags behind the phase of the polarization reporter of KaiBKaiC binding by approximately $2\pi /3$.
By performing this analysis for the two stepup and two stepdown datasets in Figure 3C–D, we generated two sets of {$L$, $L}_{lin$, $T}_{day$, $T}_{night$} and two sets of {$D$, $D}_{lin$, $T}_{day$, $T}_{night$}. The phase oscillator simulations described below require using the $L$ and $D$ functions together in each simulation. To propagate the experimenttoexperiment variability in stepresponse measurements through our simulations, we combined the two measurements of $L$ and two measurements of $D$ into the four possible combinations of {$L$, $D$} pairs. This resulted in four sets of {$L$, $L}_{lin$, $D$, $D}_{lin$, $T}_{day$, $T}_{night$}, which were used in the simulations below.
The ratio of oscillator frequencies in dark and light, $\frac{{\omega}_{D}}{{\omega}_{L}}=\frac{{T}_{day}}{{T}_{night}}=0.93\pm 0.01$, was determined by averaging the values of T_{day} and T_{night} from the two stepup and two stepdown sets above. Best estimates for the slopes l and d in Figure 4—figure supplement 3 were computed via the following bootstrap analysis. For each L and D function in Figure 3C–D, we selected points from the regions used for linearization above (6–22 CT hr on D, 18–34 CT hr on L). We sampled these data points with replacement until we generated 500 samples containing at least three unique points. For each set of resampled points, we computed the slope of the bestfit line, for a total of 1000 samples of l and d. The crosshair in Figure 4—figure supplement 3 marks the average ± standard deviation of these values (l = 0.34 ± 0.03, d = 0.38 ± 0.05).
The phase oscillator model discussed in the main text and the Appendix 1 makes the prediction that the proportionality constant m between entrained phase and day length depends on l and d coefficients according to:
To check whether this prediction is in line with our experimental measurement of $m$, we used this formula to calculate $m$ for every pair of $l$ and $d$ samples generated in the bootstrapping procedure above, assuming ${\omega}_{D}/{\omega}_{L}=0.93$. According to this calculation, $m\left(l,d\right)=0.34\pm 0.04$ (mean ± standard deviation of the distribution), which is in agreement with the experimental measurement in Figure 2C ($m=0.38\pm 0.07$).
(ii) Nonparametric bootstrapping of stepresponse datasets collected using SDSPAGE analysis of KaiC phosphorylation
Request a detailed protocolRecall that measuring each stepresponse function requires (a) conversion of the lab time of each step to corresponding clock phase $\theta$ (or clock time, CT), and then (b) determination of the phase of each KaiC phosphorylation trajectory after the perturbation in order to compute the phase shift ($L(\theta )$ and $D(\theta )$) relative to an unperturbed control. In particular, uncertainties in (a) manifest as phase errors on $L$ and $D$ in Figure 3E–F; these errors are correlated across all points on the stepresponse function. Uncertainties in (b) manifest as phase shift errors on $L$ and $D$; these errors derive from the errors in the phase estimates of both the control and step reactions. To propagate both these sources of error, we used the following nonparametric bootstrapping strategy.
First, KaiC phosphorylation dynamics from all stepresponse measurements were assembled into a master dataset containing nine stepup trajectories, nine stepdown trajectories, as well as two control reactions. These measurements were performed once on an independent preparation of proteins from the batch used to generate data in Figure 3C–D. This master dataset was then trimmed to include only data collected at least 16 hr after a step transition, and every trajectory was normalized. To generate bootstrapped datasets, we sampled with replacement from the entire master dataset (as opposed to resampling reactions individually) 1000 times.
Next, we used each resampled dataset to compute phase shifts in KaiC phosphorylation due to stepup and stepdown transitions; in other words, each bootstrapped dataset was used to derive a bootstrapped pair of $L(\theta )$ and $D(\theta )$. For a given dataset, we globally fit all 100% ATP trajectories (nine stepup trajectories, plus the 100% ATP control reaction) such that all fits shared a bestfit period ($T}_{day$), but phase, amplitude and offset terms were fit independently for each reaction, as described above for polarization datasets. Likewise, we fit all 20% ATP trajectories (nine stepdown reactions, plus the 20% ATP control reaction) to obtain a globally bestfit period ($T}_{night$) and independently fit phases for every reaction.
$L(\theta )$ and $D(\theta )$ map the phase at which a metabolic step occurs to the resulting phase shift. The phase at which the metabolic step occurred for every reaction r was computed from the bestfit phase of the appropriate control reaction at step time $t}_{r,step$:
where $\varphi}_{con$ is the bestfit phase of the control reaction and $T}_{con$ is the globallyfit oscillator period in the appropriate control condition (i.e. $T}_{con$ = $T}_{day$ for stepdown reactions, $T}_{con$ = $T}_{night$ for stepup reactions).
Similarly, we computed the apparent phase of each perturbed reaction at the time of each step:
where $\varphi}_{r$ is the bestfit phase of the rth reaction and $T}_{pert$ is the globallyfit oscillator period in the perturbed condition ($T}_{pert$ = $T}_{night$ for stepdown reactions, $T}_{pert$ = $T}_{day$ for stepup reactions).
Finally, we defined the phase shift in response to each step perturbation as the difference in phase of the perturbed reaction and the appropriate unperturbed control.
To estimate phase shifts at other values of $\theta$, we linearly interpolated L and D between the measured values while enforcing 2πperiodicity.
For each set of L and D generated in this way, we also prepared linearized versions L_{lin} and D_{lin}. Linear fits to L and D were performed over the range of step times similar to the ones we selected for fluorescence polarizationbased stepresponse functions as discussed above (L between 18 and 33 CT hr, D between 6 and 23 CT hr). These regions of L and D were selected by visual inspection; they are centered on phases used by the KaiABC oscillator in diurnal cycles (i.e. see arrows in Figure 3E–F) but also contain enough points (57) to avoid biasing the slope estimate by a single poorly fit data point.
We extrapolated the linear approximations to L and D over the entire cycle, with a single breakpoint away from the linear region to satisfy 2πperiodicity. Breakpoints were selected by visual inspection. While interpolating between data points near the breakpoint, we assumed that L and D (or L_{lin} and D_{lin}) never generate phase shifts larger than one cycle (i.e. winding number of 0). We anticipate that this choice does not significantly affect entrained phase in most of our simulations because the regions of L and D near the breakpoint are rarely used by the oscillator during entrainment when τ <14 hr.
We repeated this procedure for each of the 1000 bootstrapped datasets, thereby generating 1000 sets of {$L$, $D$, $L}_{lin$ and $D}_{lin$, $T}_{day$, $T}_{night$} that were used for subsequent analysis. Figure 3E–F shows the mean ± standard deviation of the distribution of bootstrapped L and D generated this way.
Simulations of a phase oscillator driven by a lightdark cycle
Request a detailed protocolWe simulated entrainment to a steplike driving cycle for a phase oscillator governed by the four combinations of {$L$, $L}_{lin$, $D$, $D}_{lin$, $T}_{day$, $T}_{night$} determined from the fluorescence polarization assays and the 1000 bootstrapped sets of {$L$, $D$, $L}_{lin$, $D}_{lin$, $T}_{day$, $T}_{night$} derived from KaiC phosphorylation measurements. Electing to work in units of cycles (1 cycle = 2π rad), we defined a phase variable $\hat{\theta}=(\theta \pi /2)/(2\pi )$, such that $\hat{\theta}=0$ cycles and $\hat{\theta}=1$ cycles correspond to the trough of the KaiC phosphorylation trajectory and $\hat{\theta}=0.5$ cycles corresponds to the peak. Where circadian time (CT) is mentioned in the text, we have adopted the convention that CT = 0 and 24 hr refer to $\hat{\theta}=0$ and $1$ cycles, respectively; CT = 12 hr refers to $\hat{\theta}=0.5$ cycles. We also defined corresponding analogs of $L$ and $D$ in units of cycles:
See Figure 4—figure supplement 2C for examples of $\hat{L},\hat{D},{\hat{L}}_{lin}$ and $\hat{D}}_{lin$. For each set of $\text{}\left\{\hat{L},\text{}\hat{D},\text{}{\hat{L}}_{lin},\text{}{\hat{D}}_{lin},\text{}{T}_{day},\text{}{T}_{night}\right\}$ generated in this way, we modeled oscillator entrainment to a driving cycle. As shown schematically in Figure 4A and the Appendix 1, oscillator phase increases with constant angular velocity (given by ${\omega}_{L}=\frac{1}{{T}_{day}}$ during the day and $\omega}_{\text{D}}=\frac{1}{{T}_{night}$ during the night); at dawn and dusk, oscillator phase shifts instantaneously according to $\hat{L}$ and $\hat{D}$, respectively. Therefore, oscillator phases at dusk and dawn of the nth entraining cycle can be computed iteratively:
We simulated oscillator behavior in 24 hr lightdark cycles (driving period T_{dr} = 24) with day length τ lasting from 4 to 18 hr. In simulations relying on $\hat{L}$ and $\hat{D}$ determined from KaiC phosphorylation measurements, we set the initial condition $\hat{\theta}}_{0$ for a given value of τ based on our measurements of entrained phase of the KaiABC oscillator in corresponding metabolic cycles (i.e. based on values interpolated between black markers in Figure 2C). Simulations using stepresponse functions based on fluorescence polarization measurements were started from ${\hat{\theta}}_{0}=0$. We simulated 10 lightdark cycles and recorded oscillator phase after each dawn (immediately after $\hat{L}\left(\hat{\theta}\right)$ phase shift).
For each set of $\hat{L}$ and $\hat{D}$ we performed simulations for τ = 4–18 hr for both the phosphorylationbased step functions and fluorescence polarizationbased step functions. Figure 4—figure supplement 1 illustrates simulations governed by one such set of $\hat{L}$ and $\hat{D}$ (derived from the magenta and blue stepresponse measurements shown in Figure 3C–D). We also carried out entrainment simulations for the linearized stepresponse functions described above (Figure 4—figure supplement 2).
We judged that the oscillator entrained stably to a given diurnal cycle if the standard deviation of clock phases at the dawns of the last five driving cycles was less than 0.01 cycles. For each value of τ, we selected those simulations where the oscillator entrained stably to the lightdark cycle, and computed the mean phase at dawn and its standard deviation (σ) for that set of simulations. These values are represented in shaded areas in Figure 4C , Figure 4—figure supplement 2A (for fluorescence polarization data) and Figure 4—figure supplement 2B (for KaiC phosphorylation data).
We were also interested in how quickly the oscillator approached entrainment in simulations for 6 ≤ τ ≤18, the range we profiled experimentally in Figure 2. To make this determination for a given value of τ, we computed how much oscillator phase at dawn varied over three successive cycles in a sliding window:
We used the first value of n for which $E{C}_{n}\text{}\text{}0.01$ as a proxy for the speed of approach to entrainment.
The tables below display summary statistics for the entrainment simulations.
Simulations based on stepresponse functions measured via fluorescence polarization reporter
Request a detailed protocolStep fun. type  τ’s profiled  No. sim.  % entrained sim.  % entrained within three cycles (8 < τ < 16)  

4 < τ < 18  8 < τ < 16  all sim.  all sim. entrained within eight cycles  
$\hat{L}\text{}and\text{}\hat{D}$  4, 4.01, …, 18 hr  564  100%  100%  83%  83% 
$\hat{L}}_{lin}\text{}and\text{}{\hat{D}}_{lin$  4, 4.01, …, 18 hr  564  100%  100%  100%  100% 
Simulations based on stepresponse functions measured via SDSPAGE analysis of KaiC phosphorylation
Request a detailed protocolStep fun. type  τ’s profiled  No. sim.  % entrained sim.  % entrained within three cycles (8 < τ < 16)  

4 < τ < 18  8 < τ < 16  all sim.  all sim. entrained within eight cycles  
$\hat{L}\text{}and\text{}\hat{D}$  4, 4.25, …, 18 hr  57 000  76%  87%  86%  97% 
$\hat{L}}_{lin}\text{}and\text{}{\hat{D}}_{lin$  4, 4.25, …, 18 hr  57 000  98%  100%  99%  100% 
In the large majority of our simulations, we found that the phase oscillator entrained stably within three lightdark cycles. For simulations derived from KaiC phosphorylation datasets, we found that the oscillator either entrained to the driving cycle quickly or not at all. Indeed, when we restricted our analysis only to those simulations that were judged as entrained within eight cycles, over 96% entrained within three lightdark cycles. Generally, the oscillator entrained readily for day lengths shorter than 14 hr, but often failed to entrain for longer day lengths. We determined that this occurs because for τ >14 dawn phases often sample the $\widehat{L}$ function near the breakpoint of the curve (near 18 CT hr in Figure 3F), leading to erratic responses to driving cues (i.e. lack of entrainment) or disagreement between simulations and experiment (Figure 4—figure supplement 2B). Relatedly, we believe that the better agreement with experiment achieved in simulations using the step functions derived from the fluorescence polarization data than from the KaiC phosphorylation data reflects the better temporal resolution of the breakpoint (2 hr using the polarization approach vs. 4 hr for KaiC phosphorylation).
In simulations of entrainment to lightdark cycles of varying period in Figure 3—figure supplement 2, we relied on a single set of {$L$, $D$, $L}_{lin$, $D}_{lin$, $T}_{day$, $T}_{night$} based on the stepresponse measurements shown in blue and magenta in Figure 3C–D. We simulated entrainment to driving periods from 6 to 48 hr, in increments of 0.0025 hr. For each driving period T_{drive}, we subjected the phase oscillator to 1000 cycles with equal day and night durations (τ − T_{drive}/2) and plotted the phases attained by the oscillator at the end of nighttime (immediately preceding the action of L) at the last 950 cycles.
Simulations of phase resetting
Request a detailed protocolWe used $\hat{L}}_{lin}\text{}\text{and}\text{}{\hat{D}}_{lin$ derived from the same stepresponse measurements as in Figure 4—figure supplement 1 to simulate response of a phase oscillator to 12 hr dark pulses administered throughout the circadian cycle (Figure 5—figure supplement 1). Phase evolution of the oscillator was simulated explicitly for 120 hr using a timestep of dt = 0.01 hr:
Estimation of phase shifts in response to dark pulses in vivo
Request a detailed protocolFor phase resetting and wedge experiments (Figure 5), clock phase was estimated by sinusoidal regression of normalized bioluminescence data collected 36–48 hr after the end of the applied dark pulse. Phase shifts in response to dark pulses were computed as differences in average peak times between perturbed wells ($t}_{pk,DP$) and controls ($t}_{pk,LL$): $\mathrm{\Delta}{t}_{pk}={\overline{t}}_{pk,DP}\text{}{\overline{t}}_{pk,LL},$ where overbars indicate averages over replicate wells (n = 4–8). Clock phases at which the dark pulses were applied were determined from the average fit phase and period of the unperturbed (control) wells (${\overline{\varphi}}_{LL}$ and ${\overline{T}}_{LL})$) according to ${\hat{\theta}}_{t}=(t/{\overline{T}}_{LL})$$({\overline{\varphi}}_{LL}+0.5\pi )/(2\pi )$, where $\hat{\theta}}_{t$ is measured in cycles and $\hat{\theta}=0$ corresponds to the minimum of an oscillatory trajectory.
Global fit to phase response and seasonal entrainment datasets
Request a detailed protocolIn the Appendix 1, we show that in the regime where the circadian clock is wellapproximated by a phase oscillator governed by linear L and D stepresponse functions, the slopes of seasonal entrainment and phase resetting of the clock can be described by a model with two free parameters ${\beta}_{1}$ and ${\beta}_{2}$: for phase resetting, $\mathrm{\Delta}{t}_{pk}={\hat{\theta}}_{t}/({\omega}_{L}{\beta}_{2})+\text{}\delta \left(1+{\beta}_{1}/{\beta}_{2}\right)+{C}_{1}$ and for entrainment ${t}_{pk}=\tau \left(1{\beta}_{1}{\beta}_{2}\right)+T{\beta}_{1}+{C}_{2}$. Here, δ is dark pulse duration (in hours), τ is day length (in hours), $\hat{\theta}}_{t$ is the clock phase at time t (in cycles), ${\omega}_{L}$ is the clock frequency in the light (in units of cycles/hour), T is the driving period (in hours), and ${C}_{1}$ and ${C}_{2}$ are constants that do not depend on $\hat{\theta}}_{t$, $\delta $, $\tau $, or $T$. For phase resetting experiments, $\hat{\theta}}_{t$ and ${\omega}_{L}$ were estimated based on the average of the best sinusoidal fits to unperturbed wells in each experiment (${\overline{\varphi}}_{LL}$ and $\overline{T}}_{LL$). In particular, we set ${\hat{\theta}}_{t}={\omega}_{L}t$$({\overline{\varphi}}_{LL}+0.5\pi )/(2\pi )$. In the formula for $\mathrm{\Delta}{t}_{pk},$ the term ${\hat{\theta}}_{t}/({\omega}_{L}{\beta}_{2})$ thus simplifies to $\left(t/{\beta}_{2}\right)({\overline{\varphi}}_{LL}+0.5\pi )/(2\pi {\beta}_{2})$, and the term to the right of the minus sign was incorporated into the constant term C_{1}.
In the global fits of all datasets in Figure 5A–C, we varied ${\beta}_{1}$ and ${\beta}_{2}$ to simultaneously fit $\mathrm{\Delta}{t}_{pk}$ to our phaseresetting and wedge data and ${t}_{pk}$ to our seasonal entrainment data. The constant terms were allowed to vary as follows:
in Figure 5C, a single C_{2} term was fit for all curves, referred to as C_{entrainment} below;
in Figure 5B, a single C_{1} intercept was fit for both datasets (t_{DP} = 36 and t_{DP} = 39 hr), referred to as C_{wedge} below;
in Figure 5A, the points before and after the breakpoint were fit using the same slope, but varying constant (C_{1}) terms. The breakpoint was selected by visual inspection. Below, the intercepts to the left and right of the breakpoint are referred to as C_{PRC_left} and C_{PRC_right}, respectively.
In total, only two parameters (${\beta}_{1}$ and ${\beta}_{2})$) were used to determine the slopes of all curves, and six parameters were used for the entire global fit of nine linear segments, which minimized the cost function:
where each $\hat{y}}_{i$ represents the average measurement of phase shift or peak time and $\sigma}_{i$ represents the standard error of that measurement. The reduced chisquared value of the fit was ${\chi}_{\nu}^{2}=5.67$. The bestfit coefficients determined in the fit are summarized in the table below.
Parameter  Bestfit value  95% CI 

β_{1}  −1.31  [−1.46 –1.17] 
β_{2}  1.79  [1.64 1.95] 
C_{PRC_left}  −22.11  [−24.3 –19.9] 
C_{PRC_right}  −36.98  [−40.0 –34.0] 
C_{wedge}  −24.52  [−26.6 –22.5] 
C_{entrainment}  39.8  [36.3 43.3] 
Simulations of entrainment in the limit cycle geometry model
Request a detailed protocolTo simulate entrainment to lightdark cycles of different day length in the geometric resetting framework in Figure 6, we modeled an oscillator running along the daytime limit cycle (centered at 0, radius 1) in the light and the nighttime cycle (centered at X, radius R) in the dark. We set the angular frequency to be $\frac{2\pi \text{}\mathrm{r}\mathrm{a}\mathrm{d}}{24\text{}\mathrm{h}\mathrm{r}}$ in both light and dark. As described in the Appendix 1, dusk and dawn transitions were modeled as radial jumps from one cycle to the nearest point on the other cycle.
We considered values of R and X spanning six orders of magnitude and studied entrainment to 24 hr cycles with day length lasting from 6 to 18 hr. For each pair of R and X, we simulated oscillator dynamics in 30 lightdark cycles of a given day length. We judged that an oscillator failed to entrain to a given diurnal schedule if oscillator phases after 29 and 30 cycles were more than π/180 radians apart, or if simulations starting from different initial phases (θ_{0} = π/4 and 5π/4) reached phases over π/180 radians apart after 30 lightdark cycles.
For every simulation that passed the entrainment criteria above, we computed the best linear fit and slope m of oscillator phase dependence on day length τ. We assessed goodness of fit by computing the mean fit error, defined as the average absolute value of the deviations between the linear fit and simulation results. If the mean fit error was greater than 10% of the deviation between maximum and minimum phases to which the oscillator entrained for this range of τ, the phase dependence on day length was judged to be nonlinear.
Relaxing assumptions of the limit cycle geometry model
Request a detailed protocolThe results of the simulations described immediately above strongly suggest that the relative geometry of day and night orbits determines the scaling of entrained phase with day length. However, those simulations are based on idealized infinitelyattracting circular limit cycles with constant angular frequency, assumptions which are likely to be violated for biological clocks. For example, the KaiC phosphorylation limit cycles in Figure 6F are somewhat elliptical. We were therefore interested in understanding whether the relative geometry of the limit cycles would be the dominant determinant of oscillator entrainment if our assumptions were relaxed. To this end, we explored entrainment in limit cycle models where these features—(i) orbit attraction strength, (ii) orbit ellipticity, and (iii) variation in angular frequency throughout the cycle—could be treated explicitly.
(i) Orbit attraction strength
To consider the effect of orbit attraction strength (Figure 6—figure supplement 2A), we studied an oscillator with constant angular frequency ω orbiting a circular limit cycle of radius R_{orb} that is exponentially attracting:
where the polar coordinates (r, $\theta$) are defined relative to the center of the limit cycle. Here, the attraction strength a determines the halftime for relaxation to the orbit according to ${t}_{1/2}=\text{ln}\left(2\right)/a$ (hr). During the day, the equations of motion were integrated with respect to the daytime orbit of radius 1 centered at the origin. At night, the oscillator coordinates were computed with respect to the night limit cycle of radius R centered at (0, X). We considered geometries with R and X ranging over six orders of magnitude and a = 10, 1, and 0.1. For each set of R, X and a, we simulated entrainment to ten 24 hr lightdark cycles of day length τ between 6 and 18 hr starting from two outofphase initial conditions (θ_{0} = 0 and θ_{0} = π). After the end of the tenth entraining cycle, the oscillator was allowed to relax back to the daytime orbit. We then computed the ‘peak time’ ($t}_{pk$) of this oscillator as the additional time required to reach phase $\theta$ = π/2 after return to the orbit. For every simulation that passed the entrainment criteria defined above, we computed the slope m of the best linear fit of oscillator peak time dependence on day length τ. We assessed goodness of fit by computing the mean fit error, defined as the average absolute value of the deviations between the linear fit and simulation results. The phase dependence on day length was judged to be nonlinear if the mean fit error was greater than 0.5 hr and also greater than 10% of the deviation between maximum and minimum peak times to which the oscillator entrained for this range of τ.
(ii) Orbit ellipticity
To consider the effect of orbit ellipticity (Figure 6—figure supplement 2B), we studied an oscillator with constant angular frequency ω orbiting an elliptical limit cycle that is exponentially attracting. We only considered orbits with their minor axes (length R_{orb}) positioned along the x axis and major axes (length ρ×R_{orb}) lying parallel to the y axis. Mathematically, such an oscillator is defined by:
where the polar coordinates ($r$, $\theta$) are defined relative to the center of the limit cycle. Here ${d}_{ellipse}(r,\theta )$ is the distance from the current point to the nearest point on the ellipse, and ${\stackrel{.}{r}}_{ellipse}\left(\theta \right)$ measures how the radial coordinate changes as a function of the angle on an elliptical trajectory, assuming that $\stackrel{.}{\theta}$ is constant. ${d}_{ellipse}(r,\theta )$ was evaluated numerically at every integration time step using the fminbnd() routine in MATLAB. ${\stackrel{.}{r}}_{ellipse}\left(\theta \right)$ was computed explicitly based on the definition of the ellipse in polar coordinates:
For all simulations in Figure 6—figure supplement 2B, the day orbit was centered at (0, 0) with minor axis length 1 and major axis length ρ_{D}; the night orbit was centered at (0, X) with minor axis length R and major axis length ρ_{N}R. The attraction strength of both orbits was set to a = 1. Entrainment simulations were carried out for four lightdark cycles. Slope m was computed as in (i) above.
(iii) Varying angular velocity along the limit cycle
We also considered the case of circular orbits with nonconstant angular velocities (Figure 6—figure supplement 2C). To do so, we considered an oscillator defined by the following equations:
where $\omega =\frac{2\pi}{24}\text{}\frac{\mathrm{r}\mathrm{a}\mathrm{d}}{\mathrm{h}\mathrm{r}}$ is the natural oscillator frequency. As above, the polar coordinates ($r$, $\theta$) are defined relative to the center of the limit cycle. Such an oscillator completes one full cycle around the orbit within 24 hr, but the oscillator speed varies sinusoidally along the orbit. Here, the ratio $\epsilon /\omega$ defines the maximal deviation of the angular velocity from the natural frequency. In the most perturbative case we considered, $\frac{\epsilon}{\omega}=\frac{1}{2}$, the oscillator runs at 1.5 times the natural frequency at the peak of the oscillation and at 0.5 times natural frequency at the trough of the cycle. Simulations were carried out for ten lightdark cycles for orbits with attraction strength a = 1. Slope m was computed as in (i) above.
Simulations of daytime resource allocation in days of different length
Request a detailed protocolPhase of transcriptional dynamics of dawn and dusk genes in Figure 6—figure supplement 3 were derived from a sinusoidal fit to the transcriptional profiles of dawn and dusk genes identified in microarray time courses by Vijayan et al. (2009). Briefly, normalized time course data was downloaded from the GEO depository, and time series for transcripts annotated as dawn genes were averaged to obtain the average dawn gene transcriptional profile. The average dusk gene trajectory was obtained analogously. Average dawn and dusk waveforms were fit to sinusoids with 24 hr period, and the resulting phase estimates were used to define the phase shift between the orange and gray curves in Figure 6—figure supplement 3.
In Figure 6—figure supplement 3, nightfall in LD 12:12 for all values of m coincides with the 12 hr dark pulses administered during the initial synchronization in the Vijayan et al. experiment. In simulations of other day lengths (τ), we assumed that the only effect of diurnal cycling is to adjust the phase of the circadian transcriptional program relative to dawn and dusk, without affecting the shape or relative timing of dawn and dusk gene transcriptional waveforms. The bias in allocation of daytime resources between dawn and dusk genes was then computed according to the expression:
where $I\left(gene\right)$ is the integrated RNA signal for that gene over the daytime hours.
Appendix 1
Mathematical
Here we outline a simple and general mathematical framework for describing the interaction between a circadian clock and its environment. The essential idea is that we model only the phase of the oscillator and decompose its dynamics into a sequence of free runs interrupted by step responses to changes in the environment (Appendix 1—figure 1). We present the assumptions of the model and then derive key formulas for interpreting our measurements.
Modeling a circadian clock as a phase oscillator with step responses to lightdark cues
Circadian rhythms are characterized by the ability to maintain selfsustaining ≈24 hr oscillations in constant conditions and to adjust the phase of those oscillations in response to environmental signals (e.g. transitions between light and dark). To represent the first feature, we model a circadian clock as an oscillator that runs along a ‘light’ limit cycle with speed ${\omega}_{L}$ when lights are on, and along a ‘dark’ limit cycle with speed ${\omega}_{D}$ when lights are off (Appendix 1—figure 1A). Because the oscillator is always on one of the limit cycles, its state can be fully described by a single variable, the phase $\theta \left(t\right)$:
We define frequencies in units of cycles per hour ($1\text{}\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e}=2\pi \text{}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}\mathrm{s}$), so that the duration of one cycle in constant conditions is $T=1/\omega $ hours, and phase $\theta \left(t\right)$ is defined between 0 and 1.
We assume that the oscillator transitions between the two limit cycles immediately at darktolight (‘dawn’) and lighttodark (‘dusk’) transitions, with a change in phase. We define $L\left(\theta \right)$ and $D\left(\theta \right)$ as phaseshifts accompanying clock responses to lights on and lights off cues, respectively:
Though in principle $L\left(\theta \right)$ and $D\left(\theta \right)$ could be arbitrary periodic functions, our measurements in Figure 3 suggest that $L\left(\theta \right)$ and $D\left(\theta \right)$ are approximately linear over the range of clock phases when dawn and dusk occurred in our experiments. Henceforth we consequently assume that $L$ and $D$ are linear functions of $\theta$:
Given this framework, we can now determine the change in clock state over any interval simply by adding the changes in phase at the transitions and accumulated during the free runs. We now explicitly compute the change in clock state in response to a dark pulse and the state of stable entrainment for lightdark cycles of arbitrary period.
Modeling phase resetting
To probe the response of an oscillator, one can subject it to a perturbation of duration $\delta $ and compare the resulting phase to that of an unperturbed oscillator. Plotting the results of such an assay as a function of the phase ${\theta}_{t}$ at which the perturbation is delivered yields a phase response curve (PRC; Appendix 1—figure 1C, Figure 5A). Alternatively, varying the duration of the perturbation while delivering it at a fixed phase corresponds to a ‘wedge’ experiment (Figure 5B).
To determine the phase response, we need to determine the phase evolution as the system transitions to dark, free runs in the dark, and then transitions to light. Note that because this model treats the transitions between the light and dark cycles as instantaneous, we expect that it will only be valid for dark pulses that are long compared to the true relaxation time of the clock. Mathematically,
where the prime denotes the perturbed oscillator, and the + and – in the subscripts distinguish the phase after and before the transition from darktolight at $t+\delta $. That is,
Substituting the linear forms above for $D$ and $L$ and grouping like terms,
Subtracting the unperturbed phase ${\theta}_{t+\delta}={\theta}_{t}+\delta {\omega}_{L}$,
Thus we obtain a formula for the change in phase in response to a dark pulse, with linear dependences on the phase of the perturbation, ${\theta}_{t}$, and its duration, $\delta $.
Modeling entrainment to lightdark cycles
We can similarly describe the phase of a clock stably entrained to lightdark cycles of varying day length. To this end, consider a phase oscillator subjected to lightdark cycles of period $T$ and day length $\tau $ (Appendix 1—figure 1D). In this case, rather than a phase difference, we need to determine ${\theta}_{\tau}$ such that ${\theta}_{T+\tau}={\theta}_{\tau}+1$. If this condition is met, the oscillator is stably entrained to a cycle of period T. In other words, we enforce periodicity.
Proceeding analogously to above, we determine the evolution over a full driving period, starting at dusk. This comprises a transition to the dark at time $\tau $, free run in the dark for duration $T\tau $, a transition to the light at time $T$, and free run in the light for duration $\tau $. Mathematically,
Again, substituting the linear forms above for $D$ and $L$ and grouping like terms,
Setting ${\theta}_{T+\tau}={\theta}_{\tau}+1$ and solving for ${\theta}_{\tau}$,
Again, we obtain a formula with linear dependence, here on the day length $\tau $ and the driving period $T$.
Comparison with experiment
The expressions above describe phases, but we do not measure phase directly. Rather, we measure the time of peak expression of a reporter. Thus it is necessary to convert from phase to the time of peak expression. To this end, we arbitrarily assign the peak expression to occur at $\theta =0.5$ (midway through the cycle). Because phase advance and delay correspond to earlier and later peak times respectively, the time of the peak shifts with opposite sign to the phase. In each case above, we are interested when the peak (or $\theta =0.5$) occurs relative to the last dawn (i.e. immediately after the action of L). We can solve for this peak time:
where $x=t+\delta $ (end of dark pulse) in the case of phase resetting and $x=0$ (last dawn) in the case of entrainment. To solve for $t}_{pk$ in entrained conditions, we make use of the fact that ${\theta}_{0}={\theta}_{\tau}{\omega}_{L}\tau $, allowing us to substitute the expression above for ${\theta}_{\tau}$. For phase resetting, we must apply this formula to both the perturbed and unperturbed phase and then subtract:
Furthermore, we note that certain combinations of the frequencies ${\omega}_{L}$ and ${\omega}_{D}$ and the linear sensitivities $l$ and $d$ occur repeatedly. We thus define
Then, for phase resetting
and for entrainment
where ${C}_{1}$ and ${C}_{2}$ are constants that do not depend on ${\theta}_{t}$, $\delta $, $\tau $, or $T$.
In the global fits for all datasets in Figure 5A–C, we varied ${\beta}_{1}$ and ${\beta}_{2}$ to simultaneously fit $t}_{pk$ to our phaseresetting and wedge data and $t}_{pk$ to our seasonal entrainment data. Thus, the slopes of all the linear fits in Figure 5A–C were derived from the same values of ${\beta}_{1}$ and ${\beta}_{2}$. See Computational Methods for further details.
Geometric resetting
In our experimental measurements, we observed L and D stepresponse functions with linear regions with slopes < 1. Here we present a simple dynamical systems picture where a tunable, linear dependence of phase shift magnitude on oscillator phase arises from the geometry of how a strongly attracting limit cycle is deformed by a changing external input.
We suppose that the oscillator is described by a circular limit cycle in the plane with unit radius during the day. During the night, we assume that the limit cycle remains in the plane, but is offset a distance X from the daytime cycle and has an altered radius R (Figure 6A). At the moment of the lighttodark transition, the state of the system is on the daytime limit cycle—it is then attracted to the nighttime limit cycle. The stepresponse function $D(\theta )$ describes the shift between the new phase angle (measured relative to the center of the nighttime limit cycle) and the original phase $\theta$ (Figure 6A).
If we place the origin at the center of the nighttime limit cycle, the Cartesian coordinates of the system state immediately before the lighttodark transition, when the system is still on the daytime orbit, are:
If we assume that the fixed point giving rise to the nighttime orbit is very strongly attracting, then the system approaches the limit cycle much faster than any circulation occurs. In this limit, the lighttodark transition leads to a purely radial jump from the daytime orbit towards the center of the nighttime orbit (Figure 6A–B). Under these conditions, the new phase $\theta}_{night$ on the nighttime cycle is simply the angle measured relative to the center of the new limit cycle:
To develop intuition about how this mapping between phase on day and night orbits is affected by their geometric arrangement, we can perform a Taylor expansion for angles near $\theta$ = 0 at dusk, corresponding to conditions close to LD 12:12:
Likewise, it can be shown that for transitions from nighttime to daytime cycles, near θ = π , the phase immediately after transition to the daytime cycle at dawn can be given by:
These expressions show that, in the linear portion of the step response function, angles are compressed by a factor tunable by the relative geometry of the light and dark limit cycles. In this way, the geometric arrangement of the limit cycles sets the slopes of $L(\theta )$ and $D(\theta )$. Numerical simulations in Figure 6 and Figure 6—figure supplement 1 were carried out using trigonometric formulas as above to convert phases between the daytime and nighttime limit cycles. Details of simulations in Figure 6—figure supplement 2 are described in Computational Methods.
References
 1
 2

3
Assembling a clock for all seasons: are there M and E oscillators in the genes?Journal of Biological Rhythms 16:105–116.https://doi.org/10.1177/074873001129001809

4
Biological Rhythms (10th Sapporo Symposium)110–125, Biological Rhythms (10th Sapporo Symposium).

5
The Colin S. Pittendrigh Lecture. Colin Pittendrigh, Jürgen Aschoff, and the natural entrainment of circadian systemsJournal of Biological Rhythms 15:195–207.https://doi.org/10.1177/074873040001500301
 6
 7

8
Quantitative analysis of regulatory flexibility under changing environmental conditionsMolecular Systems Biology 6:424.https://doi.org/10.1038/msb.2010.81
 9
 10
 11

12
Neurons and networks in daily rhythmsNature Reviews Neuroscience 8:790–802.https://doi.org/10.1038/nrn2215
 13

14
Latitudinal clines: an evolutionary view on biological rhythmsProceedings of the Royal Society B: Biological Sciences 280:20130433.https://doi.org/10.1098/rspb.2013.0433

15
Natural entrainment without dawn and dusk: the case of the European ground squirrel (Spermophilus citellus)Journal of Biological Rhythms 14:290–299.https://doi.org/10.1177/074873099129000704
 16

17
Entrainment of circadian programsChronobiology International 20:741–774.https://doi.org/10.1081/CBI120024211

18
Forty years of PRCswhat have we learned?Chronobiology International 16:711–743.https://doi.org/10.3109/07420529909016940
 19
 20

21
Simulations_and_analysis_pipelines_github_repo, version da4be4342adee456b33be34b25985700a9cddc7bSimulations_and_analysis_pipelines_github_repo.
 22

23
Circadian orchestration of gene expression in cyanobacteriaGenes & Development 9:1469–1478.https://doi.org/10.1101/gad.9.12.1469
 24

25
Roles for sigma factors in global circadian regulation of the cyanobacterial genomeJournal of Bacteriology 184:3530–3538.https://doi.org/10.1128/JB.184.13.35303538.2002
 26
 27
 28
 29
 30

31
Robust entrainment of circadian oscillators requires specific phase response curvesBiophysical Journal 100:2557–2565.https://doi.org/10.1016/j.bpj.2011.04.043
 32

33
A functional analysis of circadian pacemakers in nocturnal rodents  IV. Entrainment: pacemaker as clockJournal of Comparative Physiology. A, Sensory, Neural, and Behavioral Physiology 106:291–331.https://doi.org/10.1007/BF01417857

34
The entrainment of circadian oscillations by light and their role as photoperiodic clocksThe American Naturalist 98:261.https://doi.org/10.1086/282327
 35
 36

37
Numerical Recipes in Fortran 77: The Art of Scientific ComputingCambridge University Press.

38
Design principles underlying circadian clocksJournal of the Royal Society, Interface 1:119–130.https://doi.org/10.1098/rsif.2004.0014

39
A circadian surface of entrainment: varying T, τ, and photoperiod in Neurospora crassaJournal of Biological Rhythms 25:318–328.https://doi.org/10.1177/0748730410379081
 40
 41
 42

43
Natural selection against a circadian clock gene mutation in micePNAS, 113, 10.1073/pnas.1516442113, 26715747.
 44
 45
 46

47
Photoperiodism in humans and other primates: evidence and implicationsJournal of Biological Rhythms 16:348–364.https://doi.org/10.1177/074873001129002060

48
Biological and Biochemical Oscillators (1st Edn)B Chance, A Ghosh, E. K Pye, editors. New York: Academic Press, Inc.

49
The adaptive value of circadian clocks: an experimental assessment in cyanobacteriaCurrent Biology : CB 14:1481–1486.https://doi.org/10.1016/j.cub.2004.08.023
 50
Decision letter

Naama BarkaiReviewing Editor; Weizmann Institute of Science, Israel
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your article "The cyanobacterial circadian clock follows midday in vivo and in vitro" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Naama Barkai as the Senior Editor and Reviewing Editor. The reviewers have opted to remain anonymous.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
Summary:
The authors examine how the cyanobacterial clock aligns under varying environmental cycles, revealing a simple scaling law to midday across various Light/Dark cycles. In a very nice follow up they then show that it is possible to recreate these results in vitro using metabolic pulses. It is an impressive strength of the paper that they are able to go from in vivo to in vitro and obtain similar results. They go on to develop a mathematical model, based on the immediate response of the clock to environmental changes, which they then successfully test. The model is elegant and simple and produces experimentally testable predictions that the authors follow up on.
Essential revisions:
1) Please downplay the optimality argument, as requested by the reviewer below. Both reviewers agreed that this argument is not well supported.
2) Most importantly, the experiments lack biological repeats. Please either justify why biological repeats are not needed, or (largely recommended) repeat the experiments to verify reproduciblity. This is important in particular for the in vitro experiments (Figure 2 and 3), where it is stated that the experiment was done once (subsection “Estimation of clock phase in vitro in metabolic cycles”, last paragraph and subsection “Nonparametric bootstrapping of stepresponse datasets and L and D functions”, first paragraph), but also for the other 96well based experiments.
Reviewer #1:
The authors study the phase of the cyanobacterial circadian oscillator under entrainment with different photoperiods (day length). A main finding is that the internal clock phase tracks midday under entrainment, both in vivo and in the test tube, and this can be explained by the measured phase shifts at dawn and dusk. Linearity properties of the phase shifting curves in a physiological range are sufficient to explain midday tracking, but this also generalizes to more complex perturbations. Overall, an elegant and general picture emerges, and a simple geometrical interpretation is proposed: that of the deformation of the clock orbit in light vs. dark conditions.
Would there be a more powerful way to exploit the purF reporter? I assume the authors do not have dual reporter strains? I would recommend putting the purF figure in the main, and to analyze the KaiC data in the same way (i.e. multiple peaks etc.).
An important test for the model would be to analyze period mutants. That, is assuming that the phase shifts do not depend on the mutations, it would be straightforward to predict if modelocking can still occur, and if so what the entrainment phase is. Looking at period mutants seems quite obvious, why did the authors not do this?
Moreover, it would have been informative to identify mutants with entrainment defects. Or perhaps some entrainment mutants are already reported, and could have been analyzed?
Also, from the dynamical systems points of view, 1d maps like the one proposed exhibit complex behavior (period doubling, chaos, etc.). It would have been very interesting to try and probe whether other regimes besides 1:1 can occur.
Reviewer #2:
The work appears to be of high quality, has very robust data, and should be interesting to the eLife audience. I do have the following concerns however.
1) Concepts developed in the paper. In the paper it is assumed that clocks in individual cells can only track one phase, and it is argued that tracking midday should be the optimal strategy, as it allows proportional expression of dawn and dusk genes. I think both these ideas require further thought and justification. It is well known in the plant clock field that the clock network can track dawn and dusk. For example (Edwards et al., 2010). There has also been theoretical studies on clocks (without assuming any multicellularity or coupling between cells) to examine what it takes to track more than one phase (Rand et al., J. Royal Society Interface, 2004). As the cyanobacterial clock is limited to one feedback loop this presumably means it is limited to tracking one phase, but this point should be discussed. If you are going to track one phase, it is not clear that tracking midday is an optimal decision. It could be that evening genes become more important under shorter days for example. It is not clear to me that equipartitioning of the resources into two classes is necessarily clearly the optimal strategy.
2) Figure 7F: The cycle in phosphorylation state for the 'Day cycle' in the invitro system is quite elliptical in shape. It is not clear how one would estimate X and R from this ellipse, and whether or not one would gain a linear phase shift relationship in $L(\theta )$ from the elliptical orbits. It would be useful if the authors could comment on this, and perhaps show how the experimental orbits would map to the calculated $D(\theta )$ and $L(\theta )$ from the geometrical model.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
Thank you for resubmitting your work entitled "The cyanobacterial circadian clock follows midday in vivo and in vitro" for further consideration at eLife. Your revised article has been favorably evaluated by Naama Barkai as the Senior Editor and Reviewing Editor, and two reviewers.
Please see comments below – the reviewers would like you to emphasize aspects related to the reproducibility, as was raised in the previous review. Specifically, please show all that data in the main figures (in Figure 2C, there's plenty of space to plot the two additional in vitro measurements). Also, please adapt your interpretation to take into the observed variability and discuss what the potential sources are.
Reviewer #1:
I believe the revised version addresses the general editorial comments adequately.
In addition, though the authors have partially addressed my comments, I regret that they did not consider my first major point on integrating the purF data in the main text, and also not analyze their KaiC data in the same way. I liked the purF analysis with the multiple peaks, which seemed more thorough than that presented for KaiC in the main. In particular this modification seemed not to represent a lot of extra work, so it is not intuitive why it was not done.
Reviewer #2:
In general I am happy with the revision, but I have the following concerns.
The new in vitro experiments slopes using a different method based on a fluorescent probe are less near to m = 0.5 than one would have expected (at 0.38). It isn't clear why the nonrepeated in vitro study based on% KAICP is used in Figure 2 (with a m value of 0.51), whilst the new method that is repeated (showing an answer that is less close to the in vivo values) is in the supplement. This is especially true as Figure 3 now uses the new data from the fluorescent probe. In Figure 2C the comparison is only made between the nonrepeated in vitro method based on %P KaiC, but the slopes based on the new method using an in situ fluorescent probe are not included. The authors should include the values from the new experiments in Figure 2C.
In Figure 3A it appears that phase shifts much larger than 4 hours are possible, even though phase shifts of <4 hours are claimed in the text and displayed in Figure 3C. Could the authors please explain the differences?
https://doi.org/10.7554/eLife.23539.043Author response
Essential revisions:
1) Please downplay the optimality argument, as requested by the reviewer below. Both reviewers agreed that this argument is not well supported.
We agree with the reviewers that the argument that biosynthetic resources need to be equally partitioned by the clock between morning and afternoon gene expression programs is not strongly supported by our data. Thus we have shortened the discussion of this hypothesis and moved it to the Discussion section of the paper. Likewise, we have moved the figure panels explaining the idea to Figure 7—figure supplement 3.
2) Most importantly, the experiments lack biological repeats. Please either justify why biological repeats are not needed, or (largely recommended) repeat the experiments to verify reproduciblity. This is important in particular for the invitro experiments (Figure 2 and 3), where it is stated that the experiment was done once (subsection “Estimation of clock phase in vitro in metabolic cycles”, last paragraph and subsection “Nonparametric bootstrapping of stepresponse datasets and L and D functions”, first paragraph), but also for the other 96well based experiments.
We have included additional, independent in vitro measurements in the paper and clarified the number of biological replicates for the in vivo results (3 independent measurements of entrainment scaling, as detailed in new Table 1).
The in vitro entrainment experiment in Figure 2 has been repeated two more times with a new batch of purified proteins and measured using a fluorescent reporter of oscillations which allows us to more accurately determine phase. These data, along with validation of the fluorescent reporter system, are now in Figure 2—figure supplements 1 and 2.
We measured the $L(\theta )$ and $D(\theta )$ step response functions in two more replicates, again using a new set of purified proteins and the fluorescent reporter system. These data are now shown in Figure 3. The original data from Figure 3 (KaiC phosphorylation measured by SDSPAGE) is now in Figure 4—figure supplement 2. The modeling in Figures 4,6 has also been updated to use these more accurately determined $L(\theta )$ and $D(\theta )$ step response functions for the new protein prep. While replicate measurements on the same protein prep are in close agreement (curves in Figure 3C and 3D) there is some preptoprep variability in these experiments; our new protein prep is less responsive to nucleotide shifts. For both preparations of clock proteins, the model works well in the sense that phase shift functions correctly predict linear scaling of entrainment (Figure 4C).
Reviewer #1:
[…] Would there be a more powerful way to exploit the purF reporter? I assume the authors do not have dual reporter strains? I would recommend putting the purF figure in the main, and to analyze the KaiC data in the same way (i.e. multiple peaks etc.).
We decided to keep the purF reporter data in the supplement as in the original submission. As the reviewer suggests, the best comparison would use a dual reporter approach, which is not currently feasible using our bioluminescence detection system.
An important test for the model would be to analyze period mutants. That, is assuming that the phase shifts do not depend on the mutations, it would be straightforward to predict if modelocking can still occur, and if so what the entrainment phase is. Looking at period mutants seems quite obvious, why did the authors not do this?
We agree that varying the natural frequency of the clock vs. the driving cycle is an important test for the model. Our preliminary experiments with period mutants suggested that many known point mutants that alter the period of oscillations also alter their amplitude. In light of our geometric model, where amplitude changes correspond to altered size of the limit cycle attractor, we expect that mutants that change both period and amplitude will change the entrained behavior of the clock in a way which requires a casebycase investigation of each mutant. For this reason, we probed this issue in the reverse way, by changing the frequency of the driving cycle for WT cells. These data are in Figure 6C along with a simultaneous fit showing selfconsistency of the model (unchanged from the original submission).
Moreover, it would have been informative to identify mutants with entrainment defects. Or perhaps some entrainment mutants are already reported, and could have been analyzed?
This is a good suggestion, though most known entrainment mutants (e.g. cikA, glgC, pr1 allele of kaiC) seem to act in part by altering metabolism in light vs. dark, and will again need to be studied in a casebycase way to understand how these effects can be translated into the dynamical systems language of attractor geometry. We think it’s a good direction for future work.
Also, from the dynamical systems points of view, 1d maps like the one proposed exhibit complex behavior (period doubling, chaos, etc.). It would have been very interesting to try and probe whether other regimes besides 1:1 can occur.
We have now generated a bifurcation diagram as a function of driving period for the model based on the L and D maps derived from our biochemical measurements. This map is in Figure 3—figure supplement 1. As expected, we find a band of frequencies around the natural frequency where entrainment occurs (an Arnold tongue). Outside of this band, apparently chaoslike dynamics emerge, though entrainment reappears at multiples of the natural frequency (e.g. near 12 hour driving period).
Reviewer #2:
The work appears to be of high quality, has very robust data, and should be interesting to the eLife audience. I do have the following concerns however.
1) Concepts developed in the paper. In the paper it is assumed that clocks in individual cells can only track one phase, and it is argued that tracking midday should be the optimal strategy, as it allows proportional expression of dawn and dusk genes. I think both these ideas require further thought and justification. It is well known in the plant clock field that the clock network can track dawn and dusk. For example (Edwards et al., 2010). There has also been theoretical studies on clocks (without assuming any multicellularity or coupling between cells) to examine what it takes to track more than one phase (Rand et al., J. Royal Society Interface, 2004). As the cyanobacterial clock is limited to one feedback loop this presumably means it is limited to tracking one phase, but this point should be discussed. If you are going to track one phase, it is not clear that tracking midday is an optimal decision. It could be that evening genes become more important under shorter days for example. It is not clear to me that equipartitioning of the resources into two classes is necessarily clearly the optimal strategy.
We agree with the reviewer that it is not obvious that an organism with a single feedback loop clock should track the middle of the day. We have removed our speculative hypothesis about metabolic balance from the beginning of the paper and instead raise the idea briefly in the Discussion section. We have now included citations to this previous work in the following paragraph:
“The ability of circadian systems to keep track of the phase of the lightdark cycle has been long recognized in plants, insects, rodents and higher mammals (de Montaigu et al., 2015; Daan et al., 2001; Edwards et al., 2010; Hut, Oort and Daan, 1999; Hut et al., 2013; Wehr 2001). […] Consistent with this picture, we find that the core circadian oscillator in cyanobacteria, which relies on a single posttranslational feedback loop, keeps track of a single phase—the midpoint of the day portion of the cycle….”.
2) Figure 7F: The cycle in phosphorylation state for the 'Day cycle' in the invitro system is quite elliptical in shape. It is not clear how one would estimate X and R from this ellipse, and whether or not one would gain a linear phase shift relationship in $L(\theta )$ from the elliptical orbits. It would be useful if the authors could comment on this, and perhaps show how the experimental orbits would map to the calculated $D(\theta )$ and $L(\theta )$ from the geometrical model.
We agree that the model in Figure 7 with the assumption of circular, strongly attracting limit cycles is unrealistically simple. We have added new material in Figure 7—figure supplement 2 that shows the results of relaxing these assumptions. We consider a finite relaxation time when the system is displaced from the limit cycle following a dusk or dawn transition, ellipticity, and nonuniform angular velocity. We find that the entrainment behavior is very similar until the relaxation time becomes comparable to the length of the day itself. Similarly, ellipticity or nonuniform rotational velocity have minor effects, but do not change the basic conclusion that limit cycle separation in day vs. night must be comparable to the radius of the larger orbit to obtain entrainment similar to our observations in S. elongatus.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
Please see comments below – the reviewers would like you to emphasize aspects related to the reproducibility, as was raised in the previous review. Specifically, please show all that data in the main figures (in Figure 2C, there's plenty of space to plot the two additional in vitro measurements). Also, please adapt your interpretation to take into the observed variability and discuss what the potential sources are.
We have moved all of our in vitro measurements of entrainment and stepresponse functions into the main panels in Figures 2 and 3. We’ve added the following text to the Discussion interpreting our results in light of the variability we observe:
“The stepresponse curves underlying entrainment in our model are nonlinear functions of clock phase, but they can be successfully approximated by linear functions over the interval of clock phases used during entrainment. […] In this study, we achieve this by measuring freerunning oscillations in an automated way using the fluorescence polarization probe.”
Additionally, we have modified the text in the Results section to discuss variability in our in vitro measurements and its possible sources:
“As mentioned above, we also measured $L(\theta )$ and $D(\theta )$ stepresponse functions with a separate preparation of Kai proteins using a gelbased assay to read out the phase of the KaiC phosphorylation rhythm (Figure 3EF). […] The discrepancy in magnitude between these measurements may point to differences in sensitivity to input cues between different preparations of Kai proteins, or to a slight perturbative effect of fluorescently labeled KaiB.”
Taking into account our observed variability, we have also modified the text to deemphasize the specific value of the scaling coefficient, m; that m takes intermediate values both in vivo and in vitro shows that the reconstitution captures the essential physics of the system, but we have no reason to expect the in vivo and in vitro phase responses to be precisely the same:
“To more accurately measure the scaling of entrained phase with day length in KaiABC oscillator reactions, we turned to a fluorescence polarization probe that enables automated measurement of oscillator state with high temporal resolution (Figure 2— figure supplement 1) (Chang et al., 2012; Heisler et al., in press). […] Despite the variability in our estimates of m, these measurements argue that the in vitro oscillator successfully captures the essential feature of seasonal entrainment we observed in vivo: linear scaling of entrained phase with an intermediate slope.”
We have added text explaining why we have higher confidence in our fluorescence polarization measurements than in the traditional SDSPAGE analysis of KaiC phosphorylation:
“We found that simulated entrainment of the phase oscillator model was particularly sensitive to the regions surrounding subjective dusk (on $D(\theta )$) and subjective dawn (on $L(\theta )$) (Figure 3EF, colored arrows), which were only sampled with small numbers of points. Because the fluorescence polarization approach allows us to measure many conditions in an automated way over many days, and thus to disentangle phase shifts from period differences (Figure 3CD), these higher time resolution measurements better constrain the portions of the response functions critical for entrainment.”
Reviewer #1:
I believe the revised version addresses the general editorial comments adequately.
In addition, though the authors have partially addressed my comments, I regret that they did not consider my first major point on integrating the purF data in the main text, and also not analyze their KaiC data in the same way. I liked the purF analysis with the multiple peaks, which seemed more thorough than that presented for KaiC in the main. In particular this modification seemed not to represent a lot of extra work, so it is not intuitive why it was not done.
We agree with the reviewer that locally fitting parabolas to mark peak positions is a good way to estimate peak times of our oscillations. We have done this analysis for the strain carrying the kaiBC reporter and have replaced data in Figure 1C with peak times obtained using this approach.
In particular, we found good agreement in the value of the scaling coefficient m calculated using the sinusoidal fitting (m=0.55 ± 0.02) and parabolic fitting approaches (m=0.53 ± 0.01). Our estimates of the slope m for the kaiBC expression reporter are listed in Table 1.
We feel that adding the purF data to Figure 1C would distract from Figure 1’s focus on the expression of the kaiBC gene reporter and, in turn, the main narrative. Interested readers can find these data in Figure 1—figure supplement 3.
Reviewer #2:
In general I am happy with the revision, but I have the following concerns.
The new in vitro experiments slopes using a different method based on a fluorescent probe are less near to m = 0.5 than one would have expected (at 0.38). It isn't clear why the nonrepeated in vitro study based on% KAICP is used in Figure 2 (with a m value of 0.51), whilst the new method that is repeated (showing an answer that is less close to the in vivo values) is in the supplement. This is especially true as Figure 3 now uses the new data from the fluorescent probe. In Figure 2C the comparison is only made between the nonrepeated in vitro method based on% KAICP, but the slopes based on the new method using an in situ fluorescent probe are not included. The authors should include the values from the new experiments in Figure 2C.
We agree with the reviewer’s suggestion and have added all of the data into main figure panels (see above).
In Figure 3A it appears that phase shifts much larger than 4 hours are possible, even though phase shifts of <4 hours are claimed in the text and displayed in Figure 3C. Could the authors please explain the differences?
The reviewer raises an excellent point that there is a visual discrepancy between the phase shift magnitudes in Figure 3A and 3C. The reason for this difference is that there is a ≈2 hour difference in oscillator periods in daymimicking versus nightmimicking conditions. Thus, differences in peak times in Figure 3A, which shows oscillations over a 72 hour time period after the step perturbation, have two contributions: (1) the instantaneous phase shift caused by the step and (2) the two rhythms drifting out of phase over time as a result of the different periods of the control and stepdown reactions. Thus the peaktopeak time differences in Figure 3A are larger than in Figure 3C, which displays our estimates of instantaneous phase shifts at the time of the step and excludes any contributions from period differences accumulated in freerunning conditions after the step transition.
In addressing the reviewer’s comment, we refined our procedure for computing phase shifts, as described in the third paragraph of the subsection “Seasonal adaptation of the circadian oscillator can be decomposed into step responses to individual metabolic cues”. While this shifts certain numbers slightly, it does not have any significant impact on the results or their interpretation. To avoid confusion, we have clarified how these measurements are described in the main text and have added a new supplementary figure (Figure 3—figure supplement 1) illustrating the data analysis procedure:
“We proceeded to calculate phase shifts from these measurements, taking into consideration that a switch to nighttime buffer conditions could affect the oscillator in two ways: the phase of the oscillation can shift, and the oscillator period may change, both of which can change the relative timing of peaks. [...] We detail our procedure for analyzing these data to extract phase shifts in Figure 3—figure supplement 1.”
https://doi.org/10.7554/eLife.23539.044Article and author information
Author details
Funding
National Institutes of Health (5T32EB94125)
 Eugene Leypunskiy
National Institutes of Health (T32GM07197)
 UnJin Lee
National Science Foundation (PHY1305542)
 Aaron R Dinner
Pew Charitable Trusts
 Michael J Rust
National Institute of General Medical Sciences (R01GM10736901)
 Michael J Rust
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
We thank Arvind Murugan and members of the Rust and Dinner labs, particularly H Gudjonson and G Pattanayak, for feedback and useful discussions. Joel Heisler and Andy LiWang assisted us with fluorescence polarization measurements. This work was supported by NIH Training Grant 5T32EB009412 (EL), NIH Training Grant T32GM07197 (UL), NIH R01 GM10736901 (to MJR), NSF PHY1305542 (to ARD), a Pew Biomedical Scholarship (to MJR). EL, JL, HY, and UL carried out experiments. EL analyzed data. EL, ARD and MJR designed the study, performed mathematical modeling, and wrote the paper.
Reviewing Editor
 Naama Barkai, Weizmann Institute of Science, Israel
Publication history
 Received: November 22, 2016
 Accepted: July 6, 2017
 Accepted Manuscript published: July 7, 2017 (version 1)
 Version of Record published: September 19, 2017 (version 2)
Copyright
© 2017, Leypunskiy et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics

 2,640
 Page views

 519
 Downloads

 17
 Citations
Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.
Download links
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
Further reading

 Computational and Systems Biology
 Microbiology and Infectious Disease
It is known that research into human genes is heavily skewed towards genes that have been widely studied for decades, including many genes that were being studied before the productive phase of the Human Genome Project. This means that the genes most frequently investigated by the research community tend to be only marginally more important to human physiology and disease than a random selection of genes. Based on an analysis of 10,395 research publications about SARSCoV2 that mention at least one human gene, we report here that the COVID19 literature up to midOctober 2020 follows a similar pattern. This means that a large number of host genes that have been implicated in SARSCoV2 infection by four genomewide studies remain unstudied. While quantifying the consequences of this neglect is not possible, they could be significant.

 Computational and Systems Biology
 Immunology and Inflammation
From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on costimulators. We discover new functional synergies between TNF and IFNβ controlling dendritic cellT cell crosstalk. Analysis of laboratory or public combination treatment studies with this userfriendly webbased resource will help resolve the complex role of interaction effects on immune processes.