Plasticity in the Period of the Circadian Pacemaker Induced by Phase Dispersion of Its Constituent Cellular Clocks

Optogenetic stimulation of VIPergic SCN neurons induces photoperiodic-like changes in the mammalian circadian clock

Circadian clocks play key roles in how organisms respond to and even anticipate seasonal change in day length, or photoperiod. In mammals, photoperiod is encoded by the central circadian pacemaker in the brain, the suprachiasmatic nucleus (SCN). The subpopulation of SCN neurons that secrete the neuropeptide VIP mediates the transmission of light information within the SCN neural network, suggesting a role for these neurons in circadian plasticity in response to light information that has yet to be directly tested. Here, we used in vivo optogenetic stimulation of VIPergic SCN neurons followed by ex vivo PERIOD 2::LUCIFERASE (PER2::LUC) bioluminescent imaging to test whether activation of this SCN neuron subpopulation can induce SCN network changes that are hallmarks of photoperiodic encoding. We found that optogenetic stimulation designed to mimic a long photoperiod indeed altered subsequent SCN entrained phase, increased the phase dispersal of PER2 rhythms within the SCN network, and shortened SCN free-running period-similar to the effects of a true extension of photoperiod. Optogenetic stimulation also induced analogous changes on related aspects of locomotor behaviour in vivo. Thus, selective activation of VIPergic SCN neurons induces photoperiodic network plasticity in the SCN that underpins photoperiodic entrainment of behaviour.

Distinct Components of Photoperiodic Light Are Differentially Encoded by the Mammalian Circadian Clock

Seasonal light cycles influence multiple physiological functions and are mediated through photoperiodic encoding by the circadian system. Despite our knowledge of the strong connection between seasonal light input and downstream circadian changes, less is known about the specific components of seasonal light cycles that are encoded and induce persistent changes in the circadian system. Using combinations of 3 T cycles (23, 24, 26 h) and 2 photoperiods per T cycle (long and short, with duty cycles scaled to each T cycle), we investigate the after-effects of entrainment to these 6 light cycles. We measure locomotor behavior duration (α), period (τ), and entrained phase angle (ψ) in vivo and SCN phase distribution (σ_φ), τ, and ψ ex vivo to refine our understanding of critical light components for influencing particular circadian properties. We find that both photoperiod and T-cycle length drive determination of in vivo ψ but differentially influence after-effects in α and τ, with photoperiod driving changes in α and photoperiod length and T-cycle length combining to influence τ. Using skeleton photoperiods, we demonstrate that in vivo ψ is determined by both parametric and nonparametric components, while changes in α are driven nonparametrically. Within the ex vivo SCN, we find that ψ and σ_φ of the PER2∷LUCIFERASE rhythm follow closely with their likely behavioral counterparts (ψ and α of the locomotor activity rhythm) while also confirming previous reports of τ after-effects of gene expression rhythms showing negative correlations with behavioral τ after-effects in response to T cycles. We demonstrate that within-SCN σ_φ changes, thought to underlie α changes in vivo, are induced primarily nonparametrically. Taken together, our results demonstrate that distinct components of seasonal light input differentially influence ψ, α, and τ and suggest the possibility of separate mechanisms driving the persistent changes in circadian behaviors mediated by seasonal light.

Modeling the Influence of Synaptic Plasticity on After-effects

While circadian rhythms in physiology and behavior demonstrate remarkable day-to-day precision, they are also able to exhibit plasticity in a variety of parameters and under a variety of conditions. After-effects are one type of plasticity in which exposure to non-24-h light-dark cycles (T-cycles) will alter the animal's free-running rhythm in subsequent constant conditions. We use a mathematical model to explore whether the concept of synaptic plasticity can explain the observation of after-effects. In this model, the SCN is composed of a set of individual oscillators randomly selected from a normally distributed population. Each cell receives input from a defined set of oscillators, and the overall period of a cell is a weighted average of its own period and that of its inputs. The influence that an input has on its target's period is determined by the proximity of the input cell's period to the imposed T-cycle period, such that cells with periods near T will have greater influence. Such an arrangement is able to duplicate the phenomenon of after-effects, with relatively few inputs per cell (~4-5) being required. When the variability of periods between oscillators is low, the system is quite robust and results in minimal after-effects, while systems with greater between-cell variability exhibit greater magnitude after-effects. T-cycles that produce maximal after-effects have periods within ~2.5 to 3 h of the population period. Overall, this model demonstrates that synaptic plasticity in the SCN network could contribute to plasticity of the circadian period.

Mathematical modeling of circadian rhythms

Circadian rhythms are endogenous ~24-hr oscillations usually entrained to daily environmental cycles of light/dark. Many biological processes and physiological functions including mammalian body temperature, the cell cycle, sleep/wake cycles, neurobehavioral performance, and a wide range of diseases including metabolic, cardiovascular, and psychiatric disorders are impacted by these rhythms. Circadian clocks are present within individual cells and at tissue and organismal levels as emergent properties from the interaction of cellular oscillators. Mathematical models of circadian rhythms have been proposed to provide a better understanding of and to predict aspects of this complex physiological system. These models can be used to: (a) manipulate the system in silico with specificity that cannot be easily achieved using in vivo and in vitro experimental methods and at lower cost, (b) resolve apparently contradictory empirical results, (c) generate hypotheses, (d) design new experiments, and (e) to design interventions for altering circadian rhythms. Mathematical models differ in structure, the underlying assumptions, the number of parameters and variables, and constraints on variables. Models representing circadian rhythms at different physiologic scales and in different species are reviewed to promote understanding of these models and facilitate their use. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Quantitative analysis of circadian single cell oscillations in response to temperature

Body temperature rhythms synchronize circadian oscillations in different tissues, depending on the degree of cellular coupling: the responsiveness to temperature is higher when single circadian oscillators are uncoupled. So far, the role of coupling in temperature responsiveness has only been studied in organotypic tissue slices of the central circadian pacemaker, because it has been assumed that peripheral target organs behave like uncoupled multicellular oscillators. Since recent studies indicate that some peripheral tissues may exhibit cellular coupling as well, we asked whether peripheral network dynamics also influence temperature responsiveness. Using a novel technique for long-term, high-resolution bioluminescence imaging of primary cultured cells, exposed to repeated temperature cycles, we were able to quantitatively measure period, phase, and amplitude of central (suprachiasmatic nuclei neuron dispersals) and peripheral (mouse ear fibroblasts) single cell oscillations in response to temperature. Employing temperature cycles of different lengths, and different cell densities, we found that some circadian characteristics appear cell-autonomous, e.g. period responses, while others seem to depend on the quality/degree of cellular communication, e.g. phase relationships, robustness of the oscillation, and amplitude. Overall, our findings indicate a strong dependence on the cell's ability for intercellular communication, which is not only true for neuronal pacemakers, but, importantly, also for cells in peripheral tissues. Hence, they stress the importance of comparative studies that evaluate the degree of coupling in a given tissue, before it may be used effectively as a target for meaningful circadian manipulation.

Measuring Relative Coupling Strength in Circadian Systems

Modern imaging techniques allow the monitoring of circadian rhythms of single cells. Coupling between these single cellular circadian oscillators can generate coherent periodic signals on the tissue level that subsequently orchestrate physiological outputs. The strength of coupling in such systems of oscillators is often unclear. In particular, effects on coupling strength by varying cell densities, by knockouts, and by inhibitor applications are debated. In this study, we suggest to quantify the relative coupling strength via analyzing period, phase, and amplitude distributions in ensembles of individual circadian oscillators. Simulations of different oscillator networks show that period and phase distributions become narrower with increasing coupling strength. Moreover, amplitudes can increase due to resonance effects. Variances of periods and phases decay monotonically with coupling strength, and can serve therefore as measures of relative coupling strength. Our theoretical predictions are confirmed by studying recently published experimental data from PERIOD2 expression in slices of the suprachiasmatic nucleus during and after the application of tetrodotoxin (TTX). On analyzing the corresponding period, phase, and amplitude distributions, we can show that treatment with TTX can be associated with a reduced coupling strength in the system of coupled oscillators. Analysis of an oscillator network derived directly from the data confirms our conclusions. We suggest that our approach is also applicable to quantify coupling in fibroblast cultures and hepatocyte networks, and for social synchronization of circadian rhythmicity in rodents, flies, and bees.