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@ -158,42 +158,19 @@ Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Ulrike
\textsuperscript{2}Bernstein Center for Computational Neuroscience Tuebingen, 72076 Tuebingen, Germany\\ \textsuperscript{2}Bernstein Center for Computational Neuroscience Tuebingen, 72076 Tuebingen, Germany\\
\textsuperscript{3} Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany \textsuperscript{3} Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
\section*{Abstract (250 Words Maximum - Currently 252)} \section*{Abstract (250 Words Maximum - Currently 205)}
%\textit{It should provide a concise summary of the objectives, methodology (including the species and sex studied), key results, and major conclusions of the study.} %\textit{It should provide a concise summary of the objectives, methodology (including the species and sex studied), key results, and major conclusions of the study.}
Clinically relevant mutations to voltage-gated ion channels, called channelopathies, alter ion channel function, properties of ionic current and neuronal firing. The effects of ion channel mutations are routinely assessed and characterized as loss of function (LOF) or gain of function (GOF) at the level of ionic currents. Emerging personalized medicine approaches based on LOF/GOF characterization have limited therapeutic success. Potential reasons are that the translation from this binary characterization to neuronal firing especially when considering different neuronal cell types is currently not well understood. Here we investigate the impact of neuronal cell type on the firing outcome of ion channel mutations with simulations of a diverse collection of neuron models. We systematically analyzed the effects of changes in ion current properties on firing in different neuronal types. Additionally, we simulated the effects of mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). These simulations revealed that the outcome of a given change in ion channel properties on neuronal excitability is cell-type dependent. As a result, cell-type specific effects are vital to a full understanding of the effects of channelopathies on neuronal excitability and present an opportunity to further the efficacy and precision of personalized medicine approaches. Clinically relevant mutations to voltage-gated ion channels, called channelopathies, alter ion channel function, properties of ionic current and neuronal firing. The effects of ion channel mutations are routinely assessed and characterized as loss of function (LOF) or gain of function (GOF) at the level of ionic currents. Emerging personalized medicine approaches based on LOF/GOF characterization have limited therapeutic success. Potential reasons are that the translation from this binary characterization to neuronal firing especially when considering different neuronal cell types is currently not well understood. Here we investigate the impact of neuronal cell type on the firing outcome of ion channel mutations with simulations of a diverse collection of neuron models. We systematically analyzed the effects of changes in ion current properties on firing in different neuronal types. Additionally, we simulated the effects of mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). These simulations revealed that the outcome of a given change in ion channel properties on neuronal excitability is cell-type dependent. As a result, cell-type specific effects are vital to a full understanding of the effects of channelopathies on neuronal excitability and present an opportunity to further the efficacy and precision of personalized medicine approaches.
% Are these heuristic translation approaches sufficient given that cell-type specific effects of ion channel mutations have been reported? % Are these heuristic translation approaches sufficient given that cell-type specific effects of ion channel mutations have been reported?
%Clinically relevant genetic alterations to ion channels, or channelopathies, are often categorized as gain or loss of function at the ionic current level. This characterization is often instinctively extended to the level of neuronal firing to aid in personalized medicine approaches. However, the direct translation from gain or loss of function at the current level to firing level effects is not directly possible without consideration of the context in which the mutated ion channel operates. %Clinically relevant genetic alterations to ion channels, or channelopathies, are often categorized as gain or loss of function at the ionic current level. This characterization is often instinctively extended to the level of neuronal firing to aid in personalized medicine approaches. However, the direct translation from gain or loss of function at the current level to firing level effects is not directly possible without consideration of the context in which the mutated ion channel operates.
%By using a collection of established neuronal models, cell-type dependent diversity in the effects of altered ion current properties on firing are demonstrated. The importance of cell-type dependent in the outcome of channelopathies on firing is illustrated by simulation of the effect of episodic ataxia type 1 associated \textit{KCNA1} mutations on firing across the diverse collection of neuronal models. In the investigation of channelopathies and translation into personalized medicine approaches, cell-type dependent effects of channelopathies must be considered to understand the effects of altered channel function at the level of neuronal firing. %By using a collection of established neuronal models, cell-type dependent diversity in the effects of altered ion current properties on firing are demonstrated. The importance of cell-type dependent in the outcome of channelopathies on firing is illustrated by simulation of the effect of episodic ataxia type 1 associated \textit{KCNA1} mutations on firing across the diverse collection of neuronal models. In the investigation of channelopathies and translation into personalized medicine approaches, cell-type dependent effects of channelopathies must be considered to understand the effects of altered channel function at the level of neuronal firing.
% intro: channelopathies and personalized med
%intro trans: difficult to go for LOF/GOF to firing
%use simulation to investigate the role of cell-type on firing outcomes of changes in ion currents generally and in KCNA1 EA1 assoc mutation
%conclusions: Cell-type dependent effects of channelopathies must be considered and will likely improve personalized medicine approaches
%Neuronal excitability is shaped by kinetics of ion channels and disruption in ion channel properties caused by mutations can result in neurological disorders called channelopathies. Often, mutations within one gene are associated with a specific channelopathy. The effects of these mutations on channel function, i.e. the ionic current conducted by the affected ion channels, are generally characterized using heterologous expression systems. Nevertheless, the impact of such mutations on neuronal firing is essential not only for determining brain function, but also for selecting personalized treatment options for the affected patient. The effect of ion channel mutations on firing in different cell types has been mostly neglect and it is unclear whether the effect of a given mutation on firing can simply be inferred from the effects identified at the current level. Here we use a diverse collection of computational neuronal models to determine that ion channel mutation effects at the current level cannot be indiscriminantly used to infer firing effects without consideration of cell-type. In particular, systematic simulation and evaluation of the effects of changes in ion current properties on firing properties in different neuronal types as well as for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1) was performed. The effects of changes in ion current properties generally and due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Thus, while characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current, this characterization should not be extended to the level of neuronal excitability as the effects of ion channel mutations on the firing of a cell is dependent on the cell type and the composition of different ion channels and subunits therein. For increased efficiency and efficacy of personalized medicine approaches in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur. %Neuronal excitability is shaped by kinetics of ion channels and disruption in ion channel properties caused by mutations can result in neurological disorders called channelopathies. Often, mutations within one gene are associated with a specific channelopathy. The effects of these mutations on channel function, i.e. the ionic current conducted by the affected ion channels, are generally characterized using heterologous expression systems. Nevertheless, the impact of such mutations on neuronal firing is essential not only for determining brain function, but also for selecting personalized treatment options for the affected patient. The effect of ion channel mutations on firing in different cell types has been mostly neglect and it is unclear whether the effect of a given mutation on firing can simply be inferred from the effects identified at the current level. Here we use a diverse collection of computational neuronal models to determine that ion channel mutation effects at the current level cannot be indiscriminantly used to infer firing effects without consideration of cell-type. In particular, systematic simulation and evaluation of the effects of changes in ion current properties on firing properties in different neuronal types as well as for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1) was performed. The effects of changes in ion current properties generally and due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Thus, while characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current, this characterization should not be extended to the level of neuronal excitability as the effects of ion channel mutations on the firing of a cell is dependent on the cell type and the composition of different ion channels and subunits therein. For increased efficiency and efficacy of personalized medicine approaches in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur.
%%Using a diverse collection of computational neuronal models, the effects of changes in ion current properties on firing properties of different neuronal types were simulated systematically and for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). The effects of changes in ion current properties or changes due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current. However, the effects of mutations causing channelopathies on the firing of a cell is dependent on the cell type and thus on the composition of different ion channels and subunits. To further the efficacy of personalized medicine in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur. %%Using a diverse collection of computational neuronal models, the effects of changes in ion current properties on firing properties of different neuronal types were simulated systematically and for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). The effects of changes in ion current properties or changes due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current. However, the effects of mutations causing channelopathies on the firing of a cell is dependent on the cell type and thus on the composition of different ion channels and subunits. To further the efficacy of personalized medicine in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur.
% ion channel importance
% channelopathies
% firing effects
% a collection of neuronal models with diverse currents and dynamics used
% cell-type specificity of ion channel mutations occurs
% as a result,
% channelopathies must be examined in the context of a cell type
% LOF and GOF are not generally useful for firing
% accounting for cell-type specific effects may improve clinical outcomes and increase effiicacy of presonalized med approaches
\par\null \par\null
\section*{Significance Statement (120 Words Maximum - Currently 119 )} \section*{Significance Statement (120 Words Maximum - Currently 119 )}
@ -201,47 +178,32 @@ Clinically relevant mutations to voltage-gated ion channels, called channelopat
Although the genetic nature of ion channel mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of their role in altering neuronal firing is more difficult. In particular, cell-type dependency of ion channel mutations on firing has been observed experimentally, and should be accounted for. In this context, computational modelling bridges this gap and demonstrates that the cell type in which a mutation occurs is an important determinant in the effects of neuronal firing. As a result, classification of ion channel mutations as loss or gain of function is useful to describe the ionic current but should not be blindly extend to classification at the level of neuronal firing. Although the genetic nature of ion channel mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of their role in altering neuronal firing is more difficult. In particular, cell-type dependency of ion channel mutations on firing has been observed experimentally, and should be accounted for. In this context, computational modelling bridges this gap and demonstrates that the cell type in which a mutation occurs is an important determinant in the effects of neuronal firing. As a result, classification of ion channel mutations as loss or gain of function is useful to describe the ionic current but should not be blindly extend to classification at the level of neuronal firing.
% old 136 word Significance Statement
%Ion channels determine neuronal excitability and mutations that alter ion channel properties result in neurological disorders called channelopathies. Although the genetic nature of such mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of the role in altering neuronal firing is more difficult. In particular, cell-type dependency of ion channel mutations on firing has been observed experimentally, and should be accounted for. In this context, computational modelling bridges this gap and demonstrates that the cell type in which a mutation occurs is an important determinant in the effects of neuronal firing. As a result, classification of ion channel mutations as loss or gain of function is useful to describe the ionic current but should not be blindly extend to classification at the level of neuronal firing.
\par\null \par\null
\section*{Introduction (750 Words Maximum - Currently 882)} \section*{Introduction (750 Words Maximum - Currently 882 ** with additions in \textcolor{red}{red} and deletions in \textit{italics}: 821)}
%\textit{The Introduction should briefly indicate the objectives of the study and provide enough background information to clarify why the study was undertaken and what hypotheses were tested.} %\textit{The Introduction should briefly indicate the objectives of the study and provide enough background information to clarify why the study was undertaken and what hypotheses were tested.}
%Voltage-gated ion channels are vital in determining neuronal excitability, action potential generation and firing patterns \citep{bernard_channelopathies_2008, carbone_ion_2020}. In particular, the properties and combinations of ion channels and subunits and their resulting currents determine the firing properties of a neuron \citep{rutecki_neuronal_1992, pospischil_minimal_2008}. However, ion channel function can be disturbed, for instance through genetic alterations, resulting in altered ionic current properties and altered neuronal firing behavior \citep{carbone_ion_2020}. The properties and combinations of voltage-gated ion channels are vital in determining \textit{action potential generation, neuronal firing properties and excitability} \textcolor{red}{neuronal excitability.} \citep{bernard_channelopathies_2008, carbone_ion_2020, rutecki_neuronal_1992, pospischil_minimal_2008}. \textit{However, ion channel function can be disturbed, for instance through genetic alterations, resulting in altered neuronal firing behavior \citep{carbone_ion_2020}}. In recent years, next generation sequencing has led to an increasing number of clinically relevant \textcolor{red}{ ion channels mutations} \textit{genetic mutations} and has provided the basis for pathophysiological studies of genetic epilepsies, pain disorders, dyskinesias, intellectual disabilities, myotonias, and periodic paralyses \citep{bernard_channelopathies_2008, carbone_ion_2020}. Ongoing efforts of many research groups have contributed to the current understanding of underlying disease mechanism in channelopathies, however a complex pathophysiological landscape has emerged for many channelopathies and is likely a reason for limited therapeutic success with standard care.
\textcolor{red}{The properties and combinations of voltage-gated ion channels are vital in determining action potential generation, neuronal firing properties and excitability \citep{bernard_channelopathies_2008, carbone_ion_2020, rutecki_neuronal_1992, pospischil_minimal_2008}. However, ion channel function can be disturbed, for instance through genetic alterations, resulting in altered neuronal firing behavior \citep{carbone_ion_2020}.} In recent years, next generation sequencing has led to an increasing number of clinically relevant genetic mutations and has provided the basis for pathophysiological studies of genetic epilepsies, pain disorders, dyskinesias, intellectual disabilities, myotonias, and periodic paralyses \citep{bernard_channelopathies_2008, carbone_ion_2020}. Ongoing efforts of many research groups have contributed to the current understanding of underlying disease mechanism in channelopathies, however a complex pathophysiological landscape has emerged for many channelopathies and is likely a reason for limited therapeutic success with standard care.
% Ion channel mutations are the most common cause of such channelopathies and are often associated with hereditary clinical disorders including ataxias, epilepsies, pain disorders, dyskinesias, intellectual disabilities, myotonias, and periodic paralyses \citep{bernard_channelopathies_2008, carbone_ion_2020}.
\textcolor{orange}{The effects of mutations in ion channel genes on ionic current kinetics are frequently assessed using heterologous expression systems which do not express endogenous currents \citep{Balestrini1044, Noebels2017, Dunlop2008}. Ion channel variants are frequently classified as either a loss of function (LOF) or a gain of function (GOF) with respect to changes in gating of the altered ion channels \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}.} This classification of the effects on ionic currents is often directly used to predict the effects on neuronal firing \citep{Niday2018, Wei2017, Wolff2017,Masnada2017}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. Genotype-phenotype relationships are complex and the understanding of the relationships between these is still evolving \citep{Wolff2017, johannesen_genotype-phenotype_2021}. Experimentally, the effects of channelopathies on neuronal firing can be assessed using primary neuronal cultures \citep{Scalmani2006, Smith2018, Liu2019} or \textit{in vitro} recordings from slices of transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015, Hedrich14874}. The effects of mutations in ion channel genes on ionic current kinetics are frequently assessed using heterologous expression systems \textcolor{red}{not expressing} \textit{which do not express} endogenous currents \citep{Balestrini1044, Noebels2017, Dunlop2008}. Ion channel variants are frequently classified as either a loss of function (LOF) or a gain of function (GOF) with respect to changes in \textcolor{red}{ionic current} \textit{gating of the altered ion channels} \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}. This classification of the effects on ionic currents is often directly used to predict the effects on neuronal firing \citep{Niday2018, Wei2017, Wolff2017,Masnada2017}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. \textcolor{red}{However, the understanding of these complex genotype-phenotype relationships is still evolving \citep{Wolff2017, johannesen_genotype-phenotype_2021}.} \textit{Genotype-phenotype relationships are complex and the understanding of the relationships between these is still evolving \citep{Wolff2017, johannesen_genotype-phenotype_2021}.} Experimentally, the effects of channelopathies on neuronal firing can be assessed using primary neuronal cultures \citep{Scalmani2006, Smith2018, Liu2019} or \textit{in vitro} recordings from slices of transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015, Hedrich14874}. However, the effect of a given channelopathy on the firing behavior of different neuronal types across the brain is often unclear and not feasible to obtain experimentally. Different neuron types differ in their composition of ionic currents \citep{yao2021taxonomy, Cadwell2016, BICCN2021, Scala2021} and therefore likely respond differently to changes in the properties of a single ionic current. For example, the expression level of an affected gene can correlate with firing behavior \textit{in the simplest case} \citep{Layer2021} and altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012}. Cell-type specific effects on firing have been experimentally observed. For example, the firing effects of the R1648H \textit{SCN1A} and R1627H \textit{SCN8A} mutations are different in interneurons and pyramidal neurons \citep{Hedrich14874, makinson_scn1a_2016}.
However, the effect of a given channelopathy on the firing behavior of different neuronal types across the brain is often unclear and not feasible to obtain experimentally. Different neuron types differ in their composition of ionic currents \citep{yao2021taxonomy, Cadwell2016, BICCN2021, Scala2021} and therefore likely respond differently to changes in the properties of a single ionic current. For example, the expression level of an affected gene can correlate with firing behavior in the simplest case \citep{Layer2021} and altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012}. Cell-type specific effects on firing have been experimentally observed. For example, the firing effects of the R1648H \textit{SCN1A} and R1627H \textit{SCN8A} mutations are different in interneurons and pyramidal neurons \citep{Hedrich14874, makinson_scn1a_2016}.
%However, if gating kinetics are affected this can have complex consequences on the firing behavior of a specific cell type and the network activity within the brain. %However, if gating kinetics are affected this can have complex consequences on the firing behavior of a specific cell type and the network activity within the brain.
%Altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012}, however other properties of ionic currents impact neuronal firing as well. %Altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012}, however other properties of ionic currents impact neuronal firing as well.
%In addition, cell-type specific effects on firing are possible. For instance, the R1648H mutation in \textit{SCN1A} increases firing in inhibitory interneurons but not pyramidal neurons \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the homologous mutation R1627H in \textit{SCN8A} is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}. %In addition, cell-type specific effects on firing are possible. For instance, the R1648H mutation in \textit{SCN1A} increases firing in inhibitory interneurons but not pyramidal neurons \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the homologous mutation R1627H in \textit{SCN8A} is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}.
%For instance, the R1648H mutation in \textit{SCN1A} increases firing in inhibitory interneurons but not pyramidal neurons \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the homologous mutation R1627H in \textit{SCN8A} is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}. %For instance, the R1648H mutation in \textit{SCN1A} increases firing in inhibitory interneurons but not pyramidal neurons \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the homologous mutation R1627H in \textit{SCN8A} is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}.
Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited success in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations \citep{Yu2006}, alternative approaches based on this understanding began to show promise \citep{Colasante2020}. Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited success in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. \textcolor{red}{until it became evident that inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations \citep{Yu2006, Colasante2020}}. \textit{Once it became evident that inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations \citep{Yu2006}, alternative approaches based on this understanding began to show promise \citep{Colasante2020}.}
%
%Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations \citep{Yu2006} alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020}. %Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations \citep{Yu2006} alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020}.
Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, computational modelling approaches are used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of a specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, computational modelling approaches are used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of a specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
%Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. %Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
In this study, we therefore investigated how the outcome of ionic current kinetic changes on firing depend on neuronal cell type by (1) characterizing firing responses with 2 measures, (2) simulating the response of a repertoire of different neuronal models to changes in single current parameters as well as (3) to more complex changes as they were observed for specific mutations. For this task we chose mutations in the \textit{KCNA1} gene, encoding the potassium channel subunit \Kv, that are associated with episodic ataxia type~1 \citep{Browne1994, Browne1995, lauxmann_therapeutic_2021}. In this study, we therefore investigated how the outcome of ionic current kinetic changes on firing depend on neuronal cell type by (1) characterizing firing responses with 2 measures, (2) simulating the response of a repertoire of different neuronal models to changes in single current parameters as well as (3) to more complex changes as they were observed for \textit{specific mutations. For this task we chose mutations in the \textit{KCNA1} gene, encoding the potassium channel subunit \Kv, that are associated with episodic ataxia type~1 \citep{Browne1994, Browne1995, lauxmann_therapeutic_2021}.} \textcolor{red}{ specific \textit{KCNA1} mutations that are associated with episodic ataxia type~1 \citep{Browne1994, Browne1995, lauxmann_therapeutic_2021}}
\par\null \par\null
\section*{Materials and Methods} \section*{Materials and Methods}
% \textit{The materials and methods section should be brief but sufficient to allow other investigators to repeat the research (see also Policy Concerning Availability of Materials). Reference should be made to published procedures wherever possible; this applies to the original description and pertinent published modifications. } % \textit{The materials and methods section should be brief but sufficient to allow other investigators to repeat the research (see also Policy Concerning Availability of Materials). Reference should be made to published procedures wherever possible; this applies to the original description and pertinent published modifications. }
\par\null \par\null
All modelling and simulation was done in parallel with custom written Python 3.8 software, run on a Cent-OS 7 server with an Intel(R) Xeon (R) E5-2630 v2 CPU. All modelling and simulation was done in parallel with custom written Python 3.8 software, run on a Cent-OS 7 server with an Intel(R) Xeon (R) E5-2630 v2 CPU.
% @ 2.60 GHz Linux 3.10.0-123.e17.x86_64. % @ 2.60 GHz Linux 3.10.0-123.e17.x86_64.
@ -262,7 +224,7 @@ The membrane responses to 200 equidistant two second long current steps were sim
To ensure accurate firing frequencies at low firing rates and reduced spike sampling bias, steady-state firing was defined as the mean firing frequency in a 500\,ms window in the last second of the current steps starting at the inital action potential in this last second. To ensure accurate firing frequencies at low firing rates and reduced spike sampling bias, steady-state firing was defined as the mean firing frequency in a 500\,ms window in the last second of the current steps starting at the inital action potential in this last second.
Firing characterization was performed in the last second of current steps to ensure steady-state firing is captured and adaptation processes are neglected in our analysis. Alteration in current magnitudes can have different effects on rheobase and the initial slope of the fI curve \citep{Kispersky2012}. Firing characterization was performed in the last second of current steps to ensure steady-state firing is captured and adaptation processes are neglected in our analysis. Alteration in current magnitudes can have different effects on rheobase and the initial slope of the fI curve \citep{Kispersky2012}.
For this reason, we quantified neuronal firing using the rheobase as well as the area under the curve (AUC) of the initial portion of the fI curve as a measure of the initial slope of the fI curve \Cref{fig:firing_characterization}A. For this reason, we quantified neuronal firing using the rheobase as well as the area under the curve (AUC) of the initial portion of the fI curve as a measure of the initial slope of the fI curve \Cref{fig:firing_characterization}~A.
The smallest current at which steady state firing occured was identified and the current step interval preceding the occurrence of steady state firing was simulated at higher resolution (100 current steps) to determine the current at which steady state firing began. Firing was simulated with 100 current steps from this current upwards for 1/5 of the overall current range. Over this range a fI curve was constructed and the integral, or area under the curve (AUC), of the fI curve over this interval was computed with the composite trapezoidal rule and used as a measure of firing rate independent from rheobase. The smallest current at which steady state firing occured was identified and the current step interval preceding the occurrence of steady state firing was simulated at higher resolution (100 current steps) to determine the current at which steady state firing began. Firing was simulated with 100 current steps from this current upwards for 1/5 of the overall current range. Over this range a fI curve was constructed and the integral, or area under the curve (AUC), of the fI curve over this interval was computed with the composite trapezoidal rule and used as a measure of firing rate independent from rheobase.
@ -275,8 +237,6 @@ All models exhibited tonic steady-state firing with default parameters. In limit
Properties of ionic currents common to all models (\(\textrm{I}_{\textrm{Na}}\), \(\textrm{I}_{\textrm{K}}\), \(\textrm{I}_{\textrm{A}}\)/\IKv, and \(\textrm{I}_{\textrm{Leak}}\)) were systematically altered in a one-factor-at-a-time sensitivity analysis for all models. The gating curves for each current were shifted (\(\Delta V_{1/2}\)) from -10 to 10\,mV in increments of 1\,mV. The voltage dependence of the time constant associated with the shifted gating curve was correspondingly shifted. The slope (\(k\)) of the gating curves were altered from half to twice the initial slope. Similarly, the maximal current conductance (\(g\)) was also scaled from half to twice the initial value. For both slope and conductance alterations, alterations consisted of 21 steps spaced equally on a \(\textrm{log}_2\) scale. We neglected the variation of time constants for the practical reason that estimation and assessment of time constants and changes to them is not straightforward \citep{Clerx2019, Whittaker2020}. Properties of ionic currents common to all models (\(\textrm{I}_{\textrm{Na}}\), \(\textrm{I}_{\textrm{K}}\), \(\textrm{I}_{\textrm{A}}\)/\IKv, and \(\textrm{I}_{\textrm{Leak}}\)) were systematically altered in a one-factor-at-a-time sensitivity analysis for all models. The gating curves for each current were shifted (\(\Delta V_{1/2}\)) from -10 to 10\,mV in increments of 1\,mV. The voltage dependence of the time constant associated with the shifted gating curve was correspondingly shifted. The slope (\(k\)) of the gating curves were altered from half to twice the initial slope. Similarly, the maximal current conductance (\(g\)) was also scaled from half to twice the initial value. For both slope and conductance alterations, alterations consisted of 21 steps spaced equally on a \(\textrm{log}_2\) scale. We neglected the variation of time constants for the practical reason that estimation and assessment of time constants and changes to them is not straightforward \citep{Clerx2019, Whittaker2020}.
\subsection*{Model Comparison} \subsection*{Model Comparison}
Changes in rheobase (\drheo) were calculated in relation to the original model rheobase. The contrast of each AUC value (\(AUC_i\)) was computed in comparison to the AUC of the unaltered wild type model (\(AUC_{wt}\)) Changes in rheobase (\drheo) were calculated in relation to the original model rheobase. The contrast of each AUC value (\(AUC_i\)) was computed in comparison to the AUC of the unaltered wild type model (\(AUC_{wt}\))
\begin{equation}\label{eqn:AUC_contrast} \begin{equation}\label{eqn:AUC_contrast}
@ -284,7 +244,6 @@ Changes in rheobase (\drheo) were calculated in relation to the original model r
\end{equation} \end{equation}
To assess whether the effects of a given alteration on \ndAUC or \drheo were robust across models, the correlation between \ndAUC or \drheo and the magnitude of the alteration of a current property was computed for each alteration in each model and compared across alteration types. To assess whether the effects of a given alteration on \ndAUC or \drheo were robust across models, the correlation between \ndAUC or \drheo and the magnitude of the alteration of a current property was computed for each alteration in each model and compared across alteration types.
The Kendall's \(\tau\) coefficient, a non-parametric rank correlation, is used to describe the relationship between the magnitude of the alteration and AUC or rheobase values. A Kendall \(\tau\) value of -1 or 1 is indicative of monotonically decreasing and increasing relationships respectively. The Kendall's \(\tau\) coefficient, a non-parametric rank correlation, is used to describe the relationship between the magnitude of the alteration and AUC or rheobase values. A Kendall \(\tau\) value of -1 or 1 is indicative of monotonically decreasing and increasing relationships respectively.
\subsection*{\textit{KCNA1} Mutations}\label{subsec:mut} \subsection*{\textit{KCNA1} Mutations}\label{subsec:mut}
@ -328,9 +287,9 @@ Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occup
\label{fig:firing_characterization} \label{fig:firing_characterization}
\end{figure} \end{figure}
Neuronal firing is a complex phenomenon, and a quantification of firing properties is required for comparisons across cell types and between different conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}A). The characterization of the firing properties of a neuron by using rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC) by two independent measures. Note that AUC is essentially quantifying the slope of a neuron's fI curve. Neuronal firing is a complex phenomenon, and a quantification of firing properties is required for comparisons across cell types and between different conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}~A). The characterization of the firing properties of a neuron by using rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC) by two independent measures. Note that AUC is essentially quantifying the slope of a neuron's fI curve.
Using these two measures we quantified the effects a changed property of an ionic current has on neural firing by the differences in both rheobase, \drheo, and in AUC, \(\Delta\)AUC, relative to the wild type neuron. \(\Delta\)AUC is in addition normalized to the AUC of the wild type neuron, see Eq.~\eqref{eqn:AUC_contrast}. Each fI curve resulting from an altered ionic current is a point in a two-dimensional coordinate system spanned by \drheo and \ndAUC (\Cref{fig:firing_characterization}B). An fI curve similar to the one of the wild type neuron is marked by a point close to the origin. In the upper left quadrant, fI curves become steeper (positive difference of AUC values: \(+\Delta\)AUC) and are shifted to lower rheobases (negative difference of rheobases: \(-\)\drheo), unambigously indicating an increased firing that clearly might be classified as a gain of function (GOF) of neuronal firing. The opposite happens in the bottom right quadrant where the slope of fI curves decreases (\(-\Delta\)AUC) and the rheobase is shifted to higher currents (\(+\)\drheo), indicating a decreased, loss of function (LOF) firing. In the lower left (\(-\Delta\)AUC and \(-\)\drheo) and upper right (\(+\Delta\)AUC and \(+\)\drheo) quadrants, the effects on firing are less clear-cut, because the changes in rheobase and AUC have opposite effects on neuronal firing. Changes in a neuron's fI curves in these two quadrants cannot uniquely be described as a gain or loss of excitability. Using these two measures we quantified the effects a changed property of an ionic current has on neural firing by the differences in both rheobase, \drheo, and in AUC, \(\Delta\)AUC, relative to the wild type neuron. \(\Delta\)AUC is in addition normalized to the AUC of the wild type neuron, see Eq.~\eqref{eqn:AUC_contrast}. Each fI curve resulting from an altered ionic current is a point in a two-dimensional coordinate system spanned by \drheo and \ndAUC (\Cref{fig:firing_characterization}~B). An fI curve similar to the one of the wild type neuron is marked by a point close to the origin. In the upper left quadrant, fI curves become steeper (positive difference of AUC values: \(+\Delta\)AUC) and are shifted to lower rheobases (negative difference of rheobases: \(-\)\drheo), unambigously indicating an increased firing that clearly might be classified as a gain of function (GOF) of neuronal firing. The opposite happens in the bottom right quadrant where the slope of fI curves decreases (\(-\Delta\)AUC) and the rheobase is shifted to higher currents (\(+\)\drheo), indicating a decreased, loss of function (LOF) firing. In the lower left (\(-\Delta\)AUC and \(-\)\drheo) and upper right (\(+\Delta\)AUC and \(+\)\drheo) quadrants, the effects on firing are less clear-cut, because the changes in rheobase and AUC have opposite effects on neuronal firing. Changes in a neuron's fI curves in these two quadrants cannot uniquely be described as a gain or loss of excitability.
\subsection*{Sensitivity Analysis} \subsection*{Sensitivity Analysis}
@ -395,7 +354,7 @@ To compare the effects of ion channel mutations on neuronal firing of different
\subsection*{Firing Frequency Analysis} \subsection*{Firing Frequency Analysis}
Although, firing differences can be characterized by an area under the curve of the fI curve for fixed current steps this approach characterizes firing as a mixture of key features: rheobase and the initial slope of the fI curve. By probing rheobase directly and using an AUC relative to rheobase, we disambiguate these features and enable insights into the effects on rheobase and initial fI curve steepness. This increases the specificity of our understanding of how ion channel mutations alter firing across cells types and enable classification as described in \Cref{fig:firing_characterization}. Importantly, in cases when ion channel mutations alter rheobase and initial fI curve steepness in ways that opposing effects on firing (upper left and bottom right quadrants of \Cref{fig:firing_characterization}B) this disamgibuation is important for understanding the outcome of the mutation. In these cases, the regime the neuron is operating in is vital in determining the cells firing outcome. If it is in its excitable regime and only occasionally generates an action potential, then the effect on the rheobase is more important. If it is firing periodically with high rates, then the change in AUC might be more relevant. Although, firing differences can be characterized by an area under the curve of the fI curve for fixed current steps this approach characterizes firing as a mixture of key features: rheobase and the initial slope of the fI curve. By probing rheobase directly and using an AUC relative to rheobase, we disambiguate these features and enable insights into the effects on rheobase and initial fI curve steepness. This increases the specificity of our understanding of how ion channel mutations alter firing across cells types and enable classification as described in \Cref{fig:firing_characterization}. Importantly, in cases when ion channel mutations alter rheobase and initial fI curve steepness in ways that opposing effects on firing (upper left and bottom right quadrants of \Cref{fig:firing_characterization}~B) this disamgibuation is important for understanding the outcome of the mutation. In these cases, the regime the neuron is operating in is vital in determining the cells firing outcome. If it is in its excitable regime and only occasionally generates an action potential, then the effect on the rheobase is more important. If it is firing periodically with high rates, then the change in AUC might be more relevant.
\subsection*{Modelling Limitations} \subsection*{Modelling Limitations}
The models used here are simple and while they all capture key aspects of the firing dynamics for their respective cell, they fall short of capturing the complex physiology and biophysics of real cells. However, for the purpose of understanding how different cell-types, or current environments, contribute to diversity in firing outcomes of ion channel mutations, the fidelity of the models to the physiological cells they represent is of a minor concern. For exploring possible cell-type specific effects, variety in currents and dynamics across models is of utmost importance. With this context in mind, the collection of models used here are labelled as models A-L to highlight that the physiological cells they represent is not of chief concern, but rather that the collection of models with different attributes respond heterogeneously to the same perturbation. Additionally, the development of more realistic models is a high priority and will enable cell-type specific predictions that may aid in precision medicine approaches. Thus, weight should not be put on any single predicted firing outcome here in a specific model, but rather on the differences in outcomes that occur across the cell-type spectrum the models used here represent. The models used here are simple and while they all capture key aspects of the firing dynamics for their respective cell, they fall short of capturing the complex physiology and biophysics of real cells. However, for the purpose of understanding how different cell-types, or current environments, contribute to diversity in firing outcomes of ion channel mutations, the fidelity of the models to the physiological cells they represent is of a minor concern. For exploring possible cell-type specific effects, variety in currents and dynamics across models is of utmost importance. With this context in mind, the collection of models used here are labelled as models A-L to highlight that the physiological cells they represent is not of chief concern, but rather that the collection of models with different attributes respond heterogeneously to the same perturbation. Additionally, the development of more realistic models is a high priority and will enable cell-type specific predictions that may aid in precision medicine approaches. Thus, weight should not be put on any single predicted firing outcome here in a specific model, but rather on the differences in outcomes that occur across the cell-type spectrum the models used here represent.