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added more discussion to adaption

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sonnenberg 2022-05-05 11:28:26 +02:00
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@ -279,10 +279,12 @@ Mutations in \Kv are associated with episodic ataxia type 1 (EA1) have been char
% \textit{The discussion section should include a brief statement of the principal findings, a discussion of the validity of the observations, a discussion of the findings in light of other published work dealing with the same or closely related subjects, and a statement of the possible significance of the work. Extensive discussion of the literature is discouraged.}\\ % \textit{The discussion section should include a brief statement of the principal findings, a discussion of the validity of the observations, a discussion of the findings in light of other published work dealing with the same or closely related subjects, and a statement of the possible significance of the work. Extensive discussion of the literature is discouraged.}\\
Using a set of diverse conductance-based neuronal models, the effects of changes to current properties and conductances on firing were determined to be heterogenous for the AUC of the steady state fI curve but more homogenous for rheobase. For a known channelopathy, episodic ataxia type 1 associated \Kv mutations, the effects on rheobase is consistent across cell types, whereas the effect on AUC is cell type dependent. Using a set of diverse conductance-based neuronal models, the effects of changes to current properties and conductances on firing were determined to be heterogenous for the AUC of the steady state fI curve but more homogenous for rheobase. For a known channelopathy, episodic ataxia type 1 associated \Kv mutations, the effects on rheobase is consistent across cell types, whereas the effect on AUC is cell type dependent.
\subsection*{Validity of Neuronal Models} \subsection*{Validity of Neuronal Models \textcolor{red}{ - put this section in a less prominent place?}}
The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and represents the biophysical properties of \Kv homotetramers and not heteromers. Thus the \Kv model used here neglects the complex reality of these channels \textit{in vivo} including their expression as heteromers and the altered biophyiscal properties of these heteromers \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994}. Furthermore, dynamic modulation of \Kv channels, although physiologically relevant, is neglected here. For example, \(\textrm{K}_{\textrm{V}}\upbeta\)2 plays a role in \(\textrm{K}_{\textrm{V}}\textrm{1}\) channel trafficking and cell membrane expression \citep{shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001} and \Kv phosphorylation increases cell membrane \Kv \citep{jonas_regulation_1996}. It should be noted that the discrete classification of potassium currents into delayed rectifier and A-type is likely not biological, but rather highlights the characteristics of a spectrum of potassium channel inactivation that arises in part due to additional factors such as heteromer composition \citep{stuhmer_molecular_1989, glasscock_kv11_2019}, non-pore forming subunits (e.g. \(\textrm{K}_{\textrm{V}}\upbeta\) subunits) \citep{rettig_inactivation_1994, xu_kv2_1997}, and temperature \citep{ranjan_kinetic_2019} modulating channel properties. The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and represents the biophysical properties of \Kv homotetramers and not heteromers. Thus the \Kv model used here neglects the complex reality of these channels \textit{in vivo} including their expression as heteromers and the altered biophyiscal properties of these heteromers \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994}. Furthermore, dynamic modulation of \Kv channels, although physiologically relevant, is neglected here. For example, \(\textrm{K}_{\textrm{V}}\upbeta\)2 plays a role in \(\textrm{K}_{\textrm{V}}\textrm{1}\) channel trafficking and cell membrane expression \citep{shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001} and \Kv phosphorylation increases cell membrane \Kv \citep{jonas_regulation_1996}. It should be noted that the discrete classification of potassium currents into delayed rectifier and A-type is likely not biological, but rather highlights the characteristics of a spectrum of potassium channel inactivation that arises in part due to additional factors such as heteromer composition \citep{stuhmer_molecular_1989, glasscock_kv11_2019}, non-pore forming subunits (e.g. \(\textrm{K}_{\textrm{V}}\upbeta\) subunits) \citep{rettig_inactivation_1994, xu_kv2_1997}, and temperature \citep{ranjan_kinetic_2019} modulating channel properties.
Additionally, the single-compartment model does not take into consideration differential effects on neuronal compartments (i.e. axon, soma, dendrites), possible different spatial cellular distribution of channel expression across and within these neuronal compartments or across CNS regions nor does it consider different channel types (e.g \(\textrm{Na}_{\textrm{V}}\text{1.1}\) vs \(\textrm{Na}_{\textrm{V}}\text{1.8}\)). More realistic models would consist of multiple compartments, take more currents into account and take the spatial distribution of channels into account, however these models are more computationally expensive, require current specific models and knowledge of the distribution of conductances across the cell. Despite these limitations, each of the models can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types. The firing characterization was performed on adapted firing and as such currents that cause adaptation are neglected in our analysis. Additionally, the single-compartment model does not take into consideration differential effects on neuronal compartments (i.e. axon, soma, dendrites), possible different spatial cellular distribution of channel expression across and within these neuronal compartments or across CNS regions nor does it consider different channel types (e.g \(\textrm{Na}_{\textrm{V}}\text{1.1}\) vs \(\textrm{Na}_{\textrm{V}}\text{1.8}\)). More realistic models would consist of multiple compartments, take more currents into account and take the spatial distribution of channels into account, however these models are more computationally expensive, require current specific models and knowledge of the distribution of conductances across the cell. Despite these limitations, each of the models can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types.
The firing characterization was performed on steady-state firing and as such processes that cause adaption are neglected in our analysis. These could be seen as further dimensions to analyze the influence of mutations on neuronal firing and can only increase the uncertainty of these estimations.
\subsection*{Current Environments Determine the Effect of Ion Channel Mutations} \subsection*{Current Environments Determine the Effect of Ion Channel Mutations}
One-factor-at-a-time (OFAT) sensitivity analyses such as the one performed here are predicated on assumptions of model linearity, and cannot account for interactions between factors \citep{czitrom_one-factor-at--time_1999, saltelli_how_2010}. OFAT approaches are local and not global (i.e. always in reference to a baseline point in the parameter space) and therefore cannot be generalized to the global parameter space unless linearity is met \citep{saltelli_how_2010}. The local space around the wild type neuron is explored with an OFAT sensitivity analysis without taking interactions between parameters into account. Comparisons between the effects of changes in similar parameters across different models can be made at the wild type locale indicative of experimentally observed neuronal behaviour. In this case, the role of deviations in the ionic current properties from their wild type in multiple neuronal models presented here provides a starting point for understanding the general role of these current properties in neurons. However, a more global approach would provide a more holistic understanding of the parameter space and provide insight into interactions between properties. One-factor-at-a-time (OFAT) sensitivity analyses such as the one performed here are predicated on assumptions of model linearity, and cannot account for interactions between factors \citep{czitrom_one-factor-at--time_1999, saltelli_how_2010}. OFAT approaches are local and not global (i.e. always in reference to a baseline point in the parameter space) and therefore cannot be generalized to the global parameter space unless linearity is met \citep{saltelli_how_2010}. The local space around the wild type neuron is explored with an OFAT sensitivity analysis without taking interactions between parameters into account. Comparisons between the effects of changes in similar parameters across different models can be made at the wild type locale indicative of experimentally observed neuronal behaviour. In this case, the role of deviations in the ionic current properties from their wild type in multiple neuronal models presented here provides a starting point for understanding the general role of these current properties in neurons. However, a more global approach would provide a more holistic understanding of the parameter space and provide insight into interactions between properties.