Effects of optogenetic astroglia activation in modulation of synaptic transmission and rhythmogenesis of the hippocampus
- Authors: Lebedeva A.V.1, Maltseva K.E.1, Sokolov R.A.1, Barabash N.V.1, Levanova T.A.1, Rozov A.V.1
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Affiliations:
- National Research Lobachevsky State University of Nizhny Novgorod
- Issue: Vol 18, No 4 (2023)
- Pages: 504-507
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623466
- DOI: https://doi.org/10.17816/gc623466
- ID: 623466
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Abstract
In the field of neuronal signal transduction research, astroglia have become increasingly important. Astrocytes, or astroglial cells, are recognized as the third element in synaptic transmission regulation. This complex is conventionally known as a tripartite synapse. A multitude of mathematical models are being developed to investigate the function of astroglia in the tripartite synapse. Neurobiological experiments were carried out to confirm the dynamics observed in the previously proposed mean field activity mathematical model [1]. The experiments focused on evaluating the effect of optogenetic activation of astrocytes on synaptic transmission in C57BL/6 inbred mouse hippocampal slices. One month prior to the experiments, the AAV GFAP ChR2 eYFP virus was injected into the lateral ventricles of the brains of the experimental mice. This procedure was crucial for the expression of astrocyte-specific photosensitive channels, also known as channel rhodopsin. The patch-clamp method was used to conduct experiments on both the experimental mice and a control group of mice that did not receive virus injections. Spontaneous neuronal activity (local field potentials) and synaptic currents (GABA currents) were simultaneously recorded in the experiments. After activating astrocytes that expressed the photosensitive channel, an increase in GABAergic currents became evident during the transmission of synaptic signals between neurons. This suggests that astrocytes likely modulate synaptic transmission by releasing a gliotransmitter into the synaptic cleft. Thus, the study confirmed the presence of mean field activity patterns in a phenomenological model that describes the dynamics of a population of neurons. This model was developed based on the Tsodyks–Markram model and considers the primary attributes of neuron-glial interaction through a tripartite synapse. The model includes the short-term synaptic plasticity of the Tsodyks–Markram model and the astrocyte potential of synaptic transmission. The activation of astrocytes results in a diverse range of dynamic modes that describe various patterns of network activity under the mean field approach framework.
Currently, multiple experimental hypotheses exist regarding astrocytes’ release of a gliotransfer into the synaptic cleft and its specific type. Alternatively, a complex cascade of sequential activation of glutamateergic and GABAergic receptors may occur. Additional experimental work is necessary to assess the pharmacological contribution of channels and transporters involved in modulating synaptic transmission during astrocyte optogenetic activation.
The obtained results will refine the mathematical model of mean field neuronal activity to increase its biological plausibility. The methodology used involves identifying sparse nonlinear dynamical systems from data by solving for the system’s equations of motion [2]. These equations are reconstructed from noisy measurement data, allowing for a more accurate representation of the dynamic system. The only assumption regarding the arrangement of a dynamical system is that there exist only a handful of significant factors regulating the dynamics, leading to equations that are sparse within the realm of potential functions. Sparse regression is used to ascertain the minimum number of terms required in dynamic equations for precise data representation. This strategy enables the construction of mathematical models that are both as accurate as feasible, and maximally uncomplicated, eliminating the need for retraining. Notably, this method is suitable for parameterized systems and systems subjected to external influences or changes over time. The two approaches were used to forecast the dynamics of the mean field of a neural population. The accuracy of the forecast was subsequently evaluated.
Keywords
Full Text
In the field of neuronal signal transduction research, astroglia have become increasingly important. Astrocytes, or astroglial cells, are recognized as the third element in synaptic transmission regulation. This complex is conventionally known as a tripartite synapse. A multitude of mathematical models are being developed to investigate the function of astroglia in the tripartite synapse. Neurobiological experiments were carried out to confirm the dynamics observed in the previously proposed mean field activity mathematical model [1]. The experiments focused on evaluating the effect of optogenetic activation of astrocytes on synaptic transmission in C57BL/6 inbred mouse hippocampal slices. One month prior to the experiments, the AAV GFAP ChR2 eYFP virus was injected into the lateral ventricles of the brains of the experimental mice. This procedure was crucial for the expression of astrocyte-specific photosensitive channels, also known as channel rhodopsin. The patch-clamp method was used to conduct experiments on both the experimental mice and a control group of mice that did not receive virus injections. Spontaneous neuronal activity (local field potentials) and synaptic currents (GABA currents) were simultaneously recorded in the experiments. After activating astrocytes that expressed the photosensitive channel, an increase in GABAergic currents became evident during the transmission of synaptic signals between neurons. This suggests that astrocytes likely modulate synaptic transmission by releasing a gliotransmitter into the synaptic cleft. Thus, the study confirmed the presence of mean field activity patterns in a phenomenological model that describes the dynamics of a population of neurons. This model was developed based on the Tsodyks–Markram model and considers the primary attributes of neuron-glial interaction through a tripartite synapse. The model includes the short-term synaptic plasticity of the Tsodyks–Markram model and the astrocyte potential of synaptic transmission. The activation of astrocytes results in a diverse range of dynamic modes that describe various patterns of network activity under the mean field approach framework.
Currently, multiple experimental hypotheses exist regarding astrocytes’ release of a gliotransfer into the synaptic cleft and its specific type. Alternatively, a complex cascade of sequential activation of glutamateergic and GABAergic receptors may occur. Additional experimental work is necessary to assess the pharmacological contribution of channels and transporters involved in modulating synaptic transmission during astrocyte optogenetic activation.
The obtained results will refine the mathematical model of mean field neuronal activity to increase its biological plausibility. The methodology used involves identifying sparse nonlinear dynamical systems from data by solving for the system’s equations of motion [2]. These equations are reconstructed from noisy measurement data, allowing for a more accurate representation of the dynamic system. The only assumption regarding the arrangement of a dynamical system is that there exist only a handful of significant factors regulating the dynamics, leading to equations that are sparse within the realm of potential functions. Sparse regression is used to ascertain the minimum number of terms required in dynamic equations for precise data representation. This strategy enables the construction of mathematical models that are both as accurate as feasible, and maximally uncomplicated, eliminating the need for retraining. Notably, this method is suitable for parameterized systems and systems subjected to external influences or changes over time. The two approaches were used to forecast the dynamics of the mean field of a neural population. The accuracy of the forecast was subsequently evaluated.
ADDITIONAL INFORMATION
Funding sources. The study was supported by RSF grant No. 19-72-10128.
Authors' contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, and final approval of the version to be published and agree to be accountable for all aspects of the work.
Competing interests. The authors declare that they have no competing interests.
About the authors
A. V. Lebedeva
National Research Lobachevsky State University of Nizhny Novgorod
Author for correspondence.
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
K. E. Maltseva
National Research Lobachevsky State University of Nizhny Novgorod
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
R. A. Sokolov
National Research Lobachevsky State University of Nizhny Novgorod
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
N. V. Barabash
National Research Lobachevsky State University of Nizhny Novgorod
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
T. A. Levanova
National Research Lobachevsky State University of Nizhny Novgorod
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
A. V. Rozov
National Research Lobachevsky State University of Nizhny Novgorod
Email: lebedeva@neuro.nnov.ru
Russian Federation, Nizhny Novgorod
References
- Barabash N, Levanova T, Stasenko S. Rhythmogenesis in the mean field model of the neuron-glial network. The European Physical Journal Special Topics. 2023;232:529–534. doi: 10.1140/epjs/s11734-023-00778-9
- Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences. 2016;113(15):3932–3937. doi: 10.1073/pnas.1517384113
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