Multicellular Model for Intercellular Synchronization in Circadian Neural Networks

Vasalou, Christina; Herzog, Erik D.; Henson, Michael A.

doi:10.1016/j.bpj.2011.04.051

Cited by 24 publications

(45 citation statements)

References 51 publications

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“…Although individual neurons within the SCN act as autonomous circadian pacemakers, they display stochastic variation in period length and must communicate to maintain stable period lengths and phase relationships for system-wide control of daily cycles (8)(9)(10). SCN network dynamics are contingent on properties of the cell-autonomous oscillator (11,12), communication via neurotransmitters (10,(13)(14)(15)(16), and the underlying connectivity of the network.…”

mentioning

confidence: 99%

Functional network inference of the suprachiasmatic nucleus

Abel

Meeker

Granados‐Fuentes

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

100

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In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.

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mentioning

confidence: 99%

Functional network inference of the suprachiasmatic nucleus

Abel

Meeker

Granados‐Fuentes

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

100

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“…Recently, the role of another important coupling signal GABA [64,144] has been investigated with both classes of models [64,65,[145][146][147].…”

Section: The Synchronized Periods Of Coupled Oscillatorsmentioning

confidence: 99%

Protein sequestration versus Hill‐type repression in circadian clock models

Kim¹

2016

IET syst. biol.

107

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Circadian (~24hr) clocks are self-sustained endogenous oscillators with which organisms keep track of daily and seasonal time. Circadian clocks frequently rely on interlocked transcriptionaltranslational feedback loops to generate rhythms that are robust against intrinsic and extrinsic perturbations. To investigate the dynamics and mechanisms of the intracellular feedback loops in circadian clocks, a number of mathematical models have been developed. The majority of the models use Hill functions to describe transcriptional repression in a way that is similar to the Goodwin model. Recently, a new class of models with protein sequestration-based repression has been introduced. Here, we discuss how this new class of models differs dramatically from those based on Hill-type repression in several fundamental aspects: conditions for rhythm generation, robust network designs and the periods of coupled oscillators. Consistently, these fundamental properties of circadian clocks also differ among Neurospora, Drosophila, and mammals depending on their key transcriptional repression mechanisms (Hill-type repression or protein sequestration). Based on both theoretical and experimental studies, this review highlights the importance of careful modeling of transcriptional repression mechanisms in molecular circadian clocks.

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“…For example, Webb and colleagues (2012) predicted that SCN cells would likely synchronize to each other faster if the most highly connected cells are intrinsically weaker (e.g., damped) circadian oscillators. Additional studies have also predicted that increasing connectivity from the SCN core to the shell during winter, for example, could explain the seasonally tighter distribution of phases among SCN cells, whereas the loss of connections from the core to the shell could explain the fragmentation of daily rhythms with aging (Vasalou et al 2011;Bodenstein et al 2012). Others have predicted that the topology of connections within the SCN opposes large shifts and thus underlies the robust nature of SCN rhythmicity and its modern consequence, jet lag (Hafner et al 2012).…”

Section: Intrinsic Mechanisms Of Synchronizationmentioning

confidence: 99%

“…This mechanistic question has inspired many computational modelers because of its profound implications for understanding how emergent properties arise from a network of coupled oscillators (Gonze et al 2005;Bernard et al 2007;Locke et al 2008;Vasalou et al 2011;Hafner et al 2012;DeWoskin et al 2015). For example, Webb and colleagues (2012) predicted that SCN cells would likely synchronize to each other faster if the most highly connected cells are intrinsically weaker (e.g., damped) circadian oscillators.…”

Section: Intrinsic Mechanisms Of Synchronizationmentioning

confidence: 99%

Regulating the Suprachiasmatic Nucleus (SCN) Circadian Clockwork: Interplay between Cell-Autonomous and Circuit-Level Mechanisms

Herzog

Hermanstyne

Smyllie

et al. 2017

Cold Spring Harb Perspect Biol

205

171

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The suprachiasmatic nucleus (SCN) is the principal circadian clock of the brain, directing daily cycles of behavior and physiology. SCN neurons contain a cell-autonomous transcription-based clockwork but, in turn, circuit-level interactions synchronize the 20,000 or so SCN neurons into a robust and coherent daily timer. Synchronization requires neuropeptide signaling, regulated by a reciprocal interdependence between the molecular clockwork and rhythmic electrical activity, which in turn depends on a daytime Na þ drive and nighttime K þ drag. Recent studies exploiting intersectional genetics have started to identify the pacemaking roles of particular neuronal groups in the SCN. They support the idea that timekeeping involves nonlinear and hierarchical computations that create and incorporate timing information through the interactions between key groups of neurons within the SCN circuit. The field is now poised to elucidate these computations, their underlying cellular mechanisms, and how the SCN clock interacts with subordinate circadian clocks across the brain to determine the timing and efficiency of the sleep -wake cycle, and how perturbations of this coherence contribute to neurological and psychiatric illness.W e wake and sleep each day. Hormones reach peak plasma levels at specified times, for example cortisol peaks in the early morning. These, and many other physiological and behavioral, daily rhythms depend on an internal circadian clock, the suprachiasmatic nucleus (SCN) of the hypothalamus. Prior reviews have provided excellent summaries of research progress in the location and function of this body clock (Weaver 1998). This work focuses on recent advances in our understanding of the genetic basis for cell-autonomous generation of circadian time, and how cells within the SCN synchronize their daily rhythms across the circuit to produce a coherent oscillation in neuronal activity. It is these circuit-level emergent properties of the SCN that ultimately direct daily behaviors such as wake and sleep.

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Multicellular Model for Intercellular Synchronization in Circadian Neural Networks

Cited by 24 publications

References 51 publications

Functional network inference of the suprachiasmatic nucleus

Functional network inference of the suprachiasmatic nucleus

Protein sequestration versus Hill‐type repression in circadian clock models

Regulating the Suprachiasmatic Nucleus (SCN) Circadian Clockwork: Interplay between Cell-Autonomous and Circuit-Level Mechanisms

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