Biophysically based computational models of astrocyte ~ neuron coupling and their functional significance

Wade, John; McDaid, Liam; Harkin, Jim; Crunelli, Vincenzo; Kelso, J. A. Scott

doi:10.3389/fncom.2013.00044

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Based on these findings, one could argue that memantine-induced reduction of glutamate uptake by astrocytes is a direct adaptive response to the reduced release of glutamate by neurons. This assumption reinforces a theoretical framework in which neurons and astrocytes are capable of sensing each other while regulating tripartite glutamatergic synapses ( Wade et al, 2013 ; Karus et al, 2015 ). However, further studies using additional methodologies, such as immunostaining and electron microscopy, are necessary to better understand neuron-astrocyte coupling in the context of anxiety-like phenotypes.…”

Section: Discussionsupporting

confidence: 73%

Long-term NMDAR antagonism correlates reduced astrocytic glutamate uptake with anxiety-like phenotype

Zimmer

Torrez

Kalinine

et al. 2015

Front. Cell. Neurosci.

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The role of glutamate N-methyl-D-aspartate receptor (NMDAR) hypofunction has been extensively studied in schizophrenia; however, less is known about its role in anxiety disorders. Recently, it was demonstrated that astrocytic GLT-1 blockade leads to an anxiety-like phenotype. Although astrocytes are capable of modulating NMDAR activity through glutamate uptake transporters, the relationship between astrocytic glutamate uptake and the development of an anxiety phenotype remains poorly explored. Here, we aimed to investigative whether long-term antagonism of NMDAR impacts anxiety-related behaviors and astrocytic glutamate uptake. Memantine, an NMDAR antagonist, was administered daily for 24 days to healthy adult CF-1 mice by oral gavage at doses of 5, 10, or 20 mg/kg. The mice were submitted to a sequential battery of behavioral tests (open field, light–dark box and elevated plus-maze tests). We then evaluated glutamate uptake activity and the immunocontents of glutamate transporters in the frontoparietal cortex and hippocampus. Our results demonstrated that long-term administration of memantine induces anxiety-like behavior in mice in the light–dark box and elevated plus-maze paradigms. Additionally, the administration of memantine decreased glutamate uptake activity in both the frontoparietal cortex and hippocampus without altering the immunocontent of either GLT-1 or GLAST. Remarkably, the memantine-induced reduction in glutamate uptake was correlated with enhancement of an anxiety-like phenotype. In conclusion, long-term NMDAR antagonism with memantine induces anxiety-like behavior that is associated with reduced glutamate uptake activity but that is not dependent on GLT-1 or GLAST protein expression. Our study suggests that NMDAR and glutamate uptake hypofunction may contribute to the development of conditions that fall within the category of anxiety disorders.

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Section: Discussionsupporting

confidence: 73%

Long-term NMDAR antagonism correlates reduced astrocytic glutamate uptake with anxiety-like phenotype

Zimmer

Torrez

Kalinine

et al. 2015

Front. Cell. Neurosci.

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“…This increase in the number of models does not, however, solely reflect the vast amount of experimental data that is currently presented for astrocytes and for their roles in brain functions and the regulation of the neuronal system. Several focused reviews of computational astrocyte and neuron-astrocyte models have appeared during the last few years (see e.g., Jolivet et al, 2010 ; Mangia et al, 2011 ; De Pittà et al, 2012 ; Fellin et al, 2012 ; Min et al, 2012 ; Volman et al, 2012 ; Wade et al, 2013 ; Linne and Jalonen, 2014 ; Tewari and Parpura, 2014 ; De Pittà et al, 2016 ; Manninen et al, 2018 ); of which our study (Manninen et al, 2018 ) is the most comprehensive evaluation of more than 60 models published by the end of 2014. In the present study, we characterize in more detail the biophysical and biochemical components of astrocytes that were taken into account in the astrocyte and neuron-astrocyte interaction models published by the end of 2017.…”

Section: Methodsmentioning

confidence: 99%

“…Several reviews of theoretical and computational models have already been presented (for a review, see e.g., Jolivet et al, 2010 ; Mangia et al, 2011 ; De Pittà et al, 2012 ; Fellin et al, 2012 ; Min et al, 2012 ; Volman et al, 2012 ; Wade et al, 2013 ; Linne and Jalonen, 2014 ; Tewari and Parpura, 2014 ; De Pittà et al, 2016 ; Manninen et al, 2018 ). We found out in our previous study (Manninen et al, 2018 ) that most astrocyte models are based on the models by De Young and Keizer ( 1992 ), Li and Rinzel ( 1994 ), and Höfer et al ( 2002 ), of which the model by Höfer et al ( 2002 ) is the only one built specifically to describe astrocytic functions and data obtained from astrocytes.…”

Section: Introductionmentioning

confidence: 99%

Computational Models for Calcium-Mediated Astrocyte Functions

Manninen

Havela

Linne

2018

Front. Comput. Neurosci.

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The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes.

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“…Similarly to neuronal signal transduction models, hundreds of single astrocyte, astrocytic network, and neuron-astrocyte interaction models have been developed to study different phenomena. Several reviews of computational astrocyte and neuron-astrocyte models have appeared during the last few years, some focusing only to a few models and some giving a general overview of the field (see e.g., Jolivet et al, 2010 ; Mangia et al, 2011 ; De Pittà et al, 2012 , 2016 ; Fellin et al, 2012 ; Min et al, 2012 ; Volman et al, 2012 ; Wade et al, 2013 ; Linne and Jalonen, 2014 ; Tewari and Parpura, 2014 ; Manninen et al, 2018a , b ). In Table 3 , we list the models that we evaluated in this study.…”

Section: Methodsmentioning

confidence: 99%

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Manninen

Acimovic

Havela

et al. 2018

Front. Neuroinform.

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The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

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Biophysically based computational models of astrocyte ~ neuron coupling and their functional significance

Cited by 14 publications

References 9 publications

Long-term NMDAR antagonism correlates reduced astrocytic glutamate uptake with anxiety-like phenotype

Long-term NMDAR antagonism correlates reduced astrocytic glutamate uptake with anxiety-like phenotype

Computational Models for Calcium-Mediated Astrocyte Functions

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

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