Activity-dependent long-term potentiation of electrical synapses in the mammalian thalamus

Fricker, Brandon A.; Heckman, Emily L.; Cunningham, Patrick C.; Wang, Huaixing; Haas, Julie S.

doi:10.1152/jn.00471.2020

Cited by 11 publications

(29 citation statements)

References 110 publications

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“…Distances of dendritically located electrical synapses between cerebellar Golgi cells do not correlate with coupling strength measured between somas (Szoboszlay et al, 2016), indicating a possible compensation for distance by strength upregulation for those cells. Further, our previous work demonstrating activity-dependent plasticity showed that asymmetry changes systematically with unidirectional activity or ion flow across the gap junction (Fricker et al, 2021;Haas et al, 2011).…”

Section: Discussionmentioning

confidence: 85%

“…Demonstrations of electrical synapse asymmetry are numerous throughout the mammalian brain, including retina (Veruki & Hartveit, 2002), cortex (Galarreta & Hestrin, 2002), inferior olive (Devor & Yarom, 2002), dorsal cochlear nucleus (Apostolides & Trussell, 2013), mesencephalic trigeminal nucleus (Curti et al, 2012), cerebellar Golgi cells (Dugué et al, 2009;Szoboszlay et al, 2016), and the thalamic reticular nucleus (TRN) (Haas et al, 2011;Sevetson & Haas, 2015;Zolnik & Connors, 2016). Recent results show that asymmetry can be modified during the activity that results in electrical synapse plasticity (Fricker et al, 2021;Haas et al, 2011), indicating that it is a dynamic property that is under activity-dependent regulation.…”

Section: Introductionmentioning

confidence: 99%

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Electrical synapse asymmetry results from, and masks, neuronal heterogeneity

2021

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Electrical synapses couple inhibitory neurons across the brain, underlying a variety of functions that are modifiable by activity. Despite recent advances, many basic functions and contributions of electrical synapses within neural circuitry remain underappreciated. Among these is the source and impact of electrical synapse asymmetry. Using multi-compartmental models of neurons coupled through dendritic electrical synapses, we investigated intrinsic factors that contribute to synaptic asymmetry and that result in modulation of spike time between coupled cells. We show that electrical synapse location along a dendrite, input resistance, internal dendritic resistance, or directional conduction of the electrical synapse itself each alter asymmetry as measured by coupling between cell somas. Conversely, true synapse asymmetry can be masked by each of these properties. Furthermore, we show that asymmetry alters the spiking timing and latency of coupled cells by up to tens of milliseconds, depending on direction of conduction or dendritic location of the electrical synapse. These simulations illustrate that causes of asymmetry are multifactorial, may not be apparent in somatic measurements of electrical coupling, influence dendritic processing, and produce a variety of outcomes on spike timing of coupled cells. Our findings highlight aspects of electrical synapses that should be considered in experimental demonstrations of coupling, and when assembling networks containing electrical synapses.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Electrical synapse asymmetry results from, and masks, neuronal heterogeneity

2021

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“…Recent study of this system has suggested the direction of compensation, i.e. potentiation or depression, depends on the degree of calcium influx elicited (Fricker et al, 2020). Activity dependent modulation of gap junctions has also been observed in the Retizius cells of Hirudo medicinalis (Welzel and Schuster, 2018).…”

Section: Figure 22 Electrical Synapse Potentiation (Eltp) Requires Desynchronized Activity and Camentioning

confidence: 98%

“…The past decade has yielded evidence suggestive of a voltage-dependent modulation of electrical synapses. The thalamic reticular nucleus (TRN) undergoes eLTD or eLTP following evoked bursting (Haas et al, 2011c) or spiking (Fricker et al, 2020). Evoked spiking has been shown to cause eLTP in Hirudo medicinalis as well (Welzel and Schuster, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Evoked spiking has been shown to cause eLTP in Hirudo medicinalis as well (Welzel and Schuster, 2018). The TRN results suggest a depolarization-dependent sign change, and it has been proposed that this is regulated via intracellular calcium (Sevetson et al, 2017a;Fricker et al, 2020). However, these experiments have not been conducted with mGluR antagonists and the complexity of the circuits involved make it challenging to disentangle potential voltage-dependent effects from metabotropic ones.…”

Section: Introductionmentioning

confidence: 99%

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Coupling, membrane conductance, and ion channel mRNA profiles in the establishment and maintenance of network activity in the crustacean cardiac ganglion

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Neural networks produce critical rhythmic behaviors throughout an animal's lifespan, despite growth, differing environments, and changes in physiological state. This requires networks which balance stability in their properties with the plasticity necessary to respond to altered demands or perturbations. Studying the mechanisms which confer these properties requires a well characterized system with a known network topology and identifiable neurons that are amenable to both electrophysiological and molecular characterization and manipulation. Here, we use two networks from Cancer borealis to explore activity dependent regulation of cell connectivity, changes in cell properties with prolonged perturbation, and reliability of gene expression as a means for cell identification. For the first two topics we use the cardiac ganglion alone. The cardiac ganglion consists of a kernel of four interneurons that drive five motor neurons (termed large cells, LCs) which innervate the heart musculature. LCs burst synchronously due to simultaneous stimulation and electrical coupling through gap junctions. Depolarizing pharmacological perturbations have been shown to result in hyperexcitability (Ransdell et al., 2012a) and disrupt synchrony between LCs (Lane et al., 2016) eliciting rapid plasticity in ionic currents and electrical coupling which restores synchrony and excitability (Ransdell et al., 2012a; Lane et al., 2016). The salient electrophysiological signal which elicits coupling plasticity has not been identified. Using voltage clamp we directly control LC depolarizations to vary amplitude and timing of activity between LCs. We find that timing between cells, rather than depolarization elicits plasticity with the direction, i.e., potentiation or depression, being determined by the degree of desynchronization. With dynamic clamp we artificially couple networks from two animals and show that strong coupling with sufficient desynchronization can compromise a cell's output. These results suggest that coupling strength is tuned promoting synchrony or baseline cellular activity in a degree dependent manner. While rapid compensatory plasticity to hyperexcitability has been shown, it is unknown whether the changes are solely post-transcriptional and whether the short-term changes persist over longer time scales. We perturb networks for one or twenty-four hours and compare LCs' excitability, membrane properties, and abundances of ion channel and gap junction transcripts. We find evidence of rapid transcriptional changes at one hour, which may be maintained or regress at twenty-four hours. Additionally, we find that membrane properties and excitability are not maintained from one to twenty-four hours, suggesting a failure to maintain homeostasis or that additional compensatory changes are occurring at the network level. To address our third topic, we use LCs in addition to neurons collected form the stomatogastric ganglion which coordinates mastication and filtering in the digestive track. Both systems allow for unambiguous identification of cells based on anatomy or neuronal projections. We use this to evaluate the efficacy of cluster estimation procedures, clustering methods, and classification algorithms to determine the number of cell types present, group like cells together, and identify cells based on gene expression alone. We use single cell RNA-seq and single cell qRT-PCR to measure all contigs or a select set of ion channel, receptor, and gap junction mRNAs. We find these methods do not reproduce the known number of cell types present. Furthermore, although clustering and classification both outperform chance, we are unable to recapitulate cell type with complete accuracy from these data. These results indicate that, while promising, determining cell type by molecular profiling should not be relied on as the sole metric of cell type determination.

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