conference notes with \notels

manuscript.tex

@@ -360,6 +360,8 @@ Mutations in \Kv are associated with episodic ataxia type~1 (EA1) and have been
 \section*{Discussion (3000 Words Maximum - Currently 2145)}
 % \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.}\\
 
+\notels{change AUC to "fI-AUC" or "AUC of the fI-curve"}
+
 To compare the effects of changes to properties of ionic currents on neuronal firing of different neuron types, a diverse set of conductance-based models were simulated. Changes to single ionic current properties, as well as known episodic ataxia type~1 associated \Kv mutations showed consistent effects on the rheobase across cell types, whereas the effects on AUC of the steady-state fI-curve depend on cell type. Our results demonstrate that LOF and GOF on the biophysical level cannot be uniquely transfered to the level of neuronal firing. The effects depend on the properties of the other currents expressed in a cell and are therefore depending on cell type.
 
 
@@ -375,7 +377,7 @@ Diversity across neurons is not limited to gene expression and can also be seen
  
 Taken together, the nervous system consists of a vastly diverse and heterogenous collection of neurons with variable properties and characteristics including diverse combinations and expression levels of ion channels which are vital for neuronal firing dynamics.
 
-
+\notels{with our models we tried to get this diversity and it's relevant}
 
 
 
@@ -390,17 +392,18 @@ Taken together, the nervous system consists of a vastly diverse and heterogenous
 \notejb{The following three paragraphs are rather technical and if possible should be shorter.}\\ \notenk{shortened single vs multicompartment model paragraphs. We could remove the \Kv paragraph I've shortened - see below}\\
 Our findings are based on simulations of a range of single-compartment conductance-based models. Single-compartment models do 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. More realistic models are more computationally expensive, and require knowledge of the distribution of conductances across the cell. However, each of the single-compartment models used here 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. 
 %Many aspects of these models can be questioned.
+\notels{in a few sentences in the methods}
 
-\notenk{We could remove this paragraph about \Kv}
-The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and simplifies the complex reality of these channels \textit{in vivo} including their function as heteromers, and dynamic modulation and regulation  \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994, shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001, jonas_regulation_1996, stuhmer_molecular_1989, glasscock_kv11_2019, xu_kv2_1997, ranjan_kinetic_2019}. 
+%\notenk{We could remove this paragraph about \Kv}
+%The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and simplifies the complex reality of these channels \textit{in vivo} including their function as heteromers, and dynamic modulation and regulation  \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994, shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001, jonas_regulation_1996, stuhmer_molecular_1989, glasscock_kv11_2019, xu_kv2_1997, ranjan_kinetic_2019}. 
 
     
 
 
-\notejb{If this could be enriched with some citations than fine. Otherwise move this as a half sentence into methods/results} \notenk{moved steady-state firing characterization to methods}
+%\notejb{If this could be enriched with some citations than fine. Otherwise move this as a half sentence into methods/results} \notenk{moved steady-state firing characterization to methods}
 %The firing characterization was performed on steady-state firing and as such adaptation processes 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.
 
-Despite all these shortcomings of the models we used in our simulations, they do not touch our main conclusion that the quantitative as well as qualitative effects of a given ionic current variant in general depend on the specific properties of all the other ionic currents expressed in a given cell.
+%Despite all these shortcomings of the models we used in our simulations, they do not touch our main conclusion that the quantitative as well as qualitative effects of a given ionic current variant in general depend on the specific properties of all the other ionic currents expressed in a given cell.
     
 \subsection*{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
 \notenk{We could add a brief discussion somewhere in this section about time constants and why we neglect them despite likely being important in determining the outcome of a mutation.} \notejb{If we have citations for the time constant issue then yes, do it.}\\
@@ -411,14 +414,16 @@ For these practical reasons, we neglect the effect of mutation altered time cons
 
 \notejb{Too technical, shorter! These aspects do not questions our result.} \notenk{Made a little shorter}
 %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 current parameter space around the wild type neuron is explored here with a one-factor-at-a-time (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.
+The local current parameter space around the wild type neuron is explored here with a one-factor-at-a-time (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. \notels{methods: time constants are difficult to obtain (clerx et al 2019), therefore we ignore them}
+
+ \notels{we measured OFAT, and it would only get more complicated, if we would look at interactions}
 
 \notejb{Too technical, shorter! These aspects do not questions our result.} \notenk{Tried to shorten, not sure about it...}
 %Characterization of the effects of a parameter on firing with non-parametric Kendall \(\tau\) correlations takes into account the sign and monotonicity of the correlation. In other words Kendall \(\tau\) coefficients provide information as to whether changing a parameter is positively or negatively correlated with AUC or rheobase as well as the extent to which this correlation is positive or negative across the parameter range examined. 
-Kendall \(\tau\) coefficients provide general information as to whether different models exhibit positive or negative correlation of AUC or rheobase to a change in a given current property, however more nuanced difference between the sensitivities of models to current property changes, such which models show faster/slower increases/decreases in firing properties in response to a given current property change are not included in our analysis. 
+Kendall \(\tau\) coefficients provide general information as to whether different models exhibit positive or negative correlation of AUC or rheobase to a change in a given current property, however more nuanced difference between the sensitivities of models to current property changes, such which models show faster/slower increases/decreases in firing properties in response to a given current property change are not included in our analysis. \notels{formulate more understandable}
 % The inter-model differences seen with the OFAT sensitivity analysis highlight the need for cell specific models. The observed dependence of neuronal firing on voltage-gated sodium channels and delayed-rectifier potassium channels is known \citep{verma_computational_2020, arhem_channel_2006} and substantiated by OFAT analysis across models. It is suggested that variability in these currents may underlie within cell population variability in neuronal firing behaviour \citep{verma_computational_2020}. Although increases in low-voltage activated inward currents are generally accepted to increase firing rates and outward currents to decrease firing rates \citep{nowacki_sensitivity_2011}, this was not always observed in AUC. The heterogeneity in outcomes of model OFAT analysis, especialy with AUC, suggest that the effects of changes in current properties are neuronal dependent and the current environment encompassing the relative conductances, gating \(V_{1/2}\) positions, and gating slopes of other currents plays an important role in modulating firing behaviour and in determining the outcome of a current property change such as a mutation.
 
-\notejb{Super important paragraph!}
+\notejb{Super important paragraph!} \notels{move to top of paragraph}
 Although, to our knowledge, no comprehensive evaluation of how ionic current environment and cell type affect the outcome of ion channel mutations, comparisons between the effects of such mutations in certain cells have been reported. For instance, mutations in the SCN1A gene encoding \(\textrm{Na}_{\textrm{V}}\textrm{1.1}\) result in epileptic phenotypes by selective hypoexcitability of inhibitory but not excitatory neurons in the cortex resulting in circuit hyperexcitability \citep{Hedrich14874}. In CA3 of the hippocampus, mutation of \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) similarly results in increased excitability of pyramidal neurons and decreased excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\textrm{Na}_\textrm{V}\textrm{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\textrm{Na}_\textrm{V}\textrm{1.7}\) on firing is dependent on the presence or absence of another sodium channel \(\textrm{Na}_\textrm{V}\textrm{1.8}\) \citep{Waxman2007, Rush2006}. In a modelling study, it was found that altering the sodium conductance in 2 stomatogastric ganglion neuron models from a population models decreases rheobase in both models, however the initial slope of the fI curves (proportional to AUC) is increased in one model and decreased in the other suggesting that the magnitude of other currents in these models (such as \(\textrm{K}_\textrm{d}\)) determines the effect of a change in sodium current \citep{Kispersky2012}. These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing.
 
 \notejb{Also important, also see my comment at the beginning of the Discussion}
@@ -428,25 +433,29 @@ Variability in ion channel expression often correlates with the expression of ot
 The variability of ion currents and degeneracy of neurons may account, at least in part, for the observation that the effect of toxins within a neuronal type is frequently not constant \citep{khaliq_relative_2006, puopolo_roles_2007, ransdell_neurons_2013}.
 
 \subsection*{Effects of KCNA1 Mutations}
-Moderate changes in delayed rectifier potassium currents change the bifurcation structure
-of Hodgkin Huxley model, with changes analogous to those seen with KV1.1 mutations resulting
-in increased excitability due to reduced thresholds for repetitive firing (Hafez and
-Gottschalk, 2020). Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases
-in excitability seen are in line with both score-based and simulation-based predictions
-of the outcomes of KCNA1 mutations.
-
-Moderate changes in delayed rectifier potassium currents change the bifurcation structure of Hodgkin Huxley model, analogous to those seen in \Kv mutations, result in reduced thresholds for repetitive firing and thus contribute to increased excitability \citep{hafez_altered_2020} \notejb{I do not get this first sentence. Where are the bifurcations (citation?) and why is the increased excitatbility a bifurcation?} \notenk{I have tried to fix this section to make it more understandable. The bifurcations change by changing the delayed rectifier in the HH model and as a result of that there is a lower threshold for tonic firing. This lower threshold is what they (Hafez and Gottschalk) use to say that excitability has changed.}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen by \citet{hafez_altered_2020}\notejb{seen where? Here in this manuscript or in which citation?} are in line with our simulation-based predictions of the outcomes of \Kv mutations \notejb{our simulations?}\notenk{Yes}.  
+%Moderate changes in delayed rectifier potassium currents change the bifurcation structure \notels{firing dynamics} of Hodgkin Huxley model, with changes analogous to those seen with KV1.1 mutations resulting
+%in increased excitability due to reduced thresholds for repetitive firing \citep{hafez_altered_2020}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases
+%in excitability seen are in line with both score-based and simulation-based predictions
+%of the outcomes of KCNA1 mutations.
+
+Moderate changes in delayed rectifier potassium currents change the bifurcation structure \notels{firing dynamics} of Hodgkin Huxley model, analogous to those seen in \Kv mutations, result in reduced thresholds for repetitive firing and thus contribute to increased excitability \citep{hafez_altered_2020} \notejb{I do not get this first sentence. Where are the bifurcations (citation?) and why is the increased excitatbility a bifurcation?} \notenk{I have tried to fix this section to make it more understandable. The bifurcations change by changing the delayed rectifier in the HH model and as a result of that there is a lower threshold for tonic firing. This lower threshold is what they (Hafez and Gottschalk) use to say that excitability has changed.}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen by \citet{hafez_altered_2020}\notejb{seen where? Here in this manuscript or in which citation?} are in line with our simulation-based predictions of the outcomes of \Kv mutations \notejb{our simulations?}\notenk{Yes}.  
 LOF KCNA1 mutations generally increase neuronal excitability, however the varying susceptibility on rheobase and different effects on AUC of KCNA1 mutations across models are indicative that a certain cell type specific complexity exists.
 %LOF KCNA1 mutations generally increase neuronal excitability, however the different effects of KCNA1 mutations across models on AUC are indicative that a certain cell type specific complexity exists. 
 Increased excitability is seen experimentally with \Kv null mice \citep{smart_deletion_1998, zhou_temperature-sensitive_1998}, with pharmacological \Kv block \citep{chi_manipulation_2007, morales-villagran_protection_1996} and by \citet{hafez_altered_2020} with simulation-based predictions of KCNA1 mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of KCNA1 mutations broaden action potentials and interfere negatively with high frequency action potential firing, however they varied stimulus duration between different models and therefore comparability of firing rates is lacking in this study.     
 
-Different current properties, such as the difference in \(\textrm{I}_\textrm{A}\) and \IKv in the Cb stellate and STN model families alter the impact of KCNA1 mutations on firing highlighting that knowledge of the biophysical properties of a current and its neuronal expression is vital for holistic understanding of the effects of a given ion channel mutation both at a single cell and network level.
+Different current properties, such as the difference in \(\textrm{I}_\textrm{A}\) and \IKv in the Cb stellate and STN model families alter the impact of KCNA1 mutations on firing highlighting that knowledge of the biophysical properties of a current and its neuronal expression is vital for holistic understanding of the effects of a given ion channel mutation both at a single cell and network level. \notels{our data show} 
 
 \subsection*{Loss or Gain of Function Characterizations Do Not Fully Capture Ion Channel Mutation Effects on Firing}
 The effects of changes in current properties depend in part on the neuronal model in which they occur and can be seen in the variance of correlations (especially in AUC) across models for a given current property change. Therefore, relative conductances and gating properties of currents in the ionic current environment in which an alteration in current properties occurs plays an important role in determining the outcome on firing. The use of loss of function (LOF) and gain of function (GOF) is useful at the level of ion channels and whether a mutation results in more or less ionic current, however the extension of this thinking onto whether mutations induce LOF or GOF at the level of neuronal firing based on the ionic current LOF/GOF is problematic due to the dependency of neuronal firing changes on the ionic current environment. Thus the direct leap from current level LOF/GOF characterizations to effects on firing without experimental or modelling-based evidence, although tempting, should be refrained from and viewed with caution when reported. This is especially relevant in the recent development of personalized medicine for channelopathies, where a patients specific channelopathy is identified and used to tailor treatments \citep{Weber2017, Ackerman2013, Helbig2020, Gnecchi2021, Musto2020, Brunklaus2022}. However, the effects of specific ion channel mutations are often characterized in expression systems and classified as LOF or GOF to aid in treatment decisions \citep{johannesen_genotype-phenotype_2021, Brunklaus2022, Musto2020}.  Interestingly, both LOF and GOF \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) mutations can benefit from treatment with sodium channel blockers \citep{johannesen_genotype-phenotype_2021}, suggesting that the relationship between effects at the level of ion channels and effects at the level of firing and therapeutics is not linear or evident without further contextual information. Therefore, this approach must be used with caution and the cell type which expressed the mutant ion channel must be taken into account. Experimental assessment of the effects of a patient's specific ion channel mutation \textit{in vivo} is not feasible at a large scale due to time and cost constraints, modelling of the effects of patient specific channelopathies is a desirable approach.
 Accordingly, for accurate modelling and predictions of the effects of mutations on neuronal firing, information as to the type of neurons containing the affected channel, and the properties of the affected and all currents in the affected neuronal type is needed. When modelling approaches are sought out to overcome the limitations of experimental approaches, care must be taken to account for model dependency and the generation of relevant cell-type or cell specific populations of models should be standard in assessing the effects of mutations in specific neurons.
+
+\notels{move small sentences down here}
+\notels{Conclusion, ionic current composition defines how changes in ionic current properties affect neurons, personalized medicin could benefit from simulations of simulating cell types}
+
 \par\null
 
+
+
 \selectlanguage{english}
 \FloatBarrier
 \section*{References}\sloppy

ref.bib

@@ -1738,7 +1738,7 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   number    = {7879},
   pages     = {214--219},
   volume    = {598},
-  abstract  = {The cerebellar cortex is a well-studied brain structure with diverse roles in motor learning, coordination, cognition and autonomic regulation. However,  a complete inventory of cerebellar cell types is currently lacking. Here, using recent advances in high-throughput transcriptional profiling1–3, we molecularly define cell types across individual lobules of the adult mouse cerebellum. Purkinje neurons showed considerable regional specialization, with the greatest diversity occurring in the posterior lobules. For several types of cerebellar interneuron, the molecular variation within each type was more continuous, rather than discrete. In particular, for the unipolar brush cells—an interneuron population previously subdivided into discrete populations—the continuous variation in gene expression was associated with a graded continuum of electrophysiological properties. Notably, we found that molecular layer interneurons were composed of two molecularly and functionally distinct types. Both types show a continuum of morphological variation through the thickness of the molecular layer, but electrophysiological recordings revealed marked differences between the two types in spontaneous firing, excitability and electrical coupling. Together, these findings provide a comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological and physiological ontologies for defining brain cell types.},
+  abstract  = {The cerebellar cortex is a well-studied brain structure with diverse roles in motor learning, coordination, cognition and autonomic regulation. However,  a complete inventory of cerebellar cell types is currently lacking. Here, using recent advances in high-throughput transcriptional profiling1–3, we molecularly define cell types across individual lobules of the adult mouse cerebellum. Purkinje neurons showed considerable regional specialization, with the greatest diversity occurring in the posterior lobules. For several types of cerebellar interneuron, the molecular variation within each type was more continuous, rather than discrete. In particular, for the unipolar brush cells—an interneuron population previously subdivided into discrete populations—the continuous variation in gene expression was associated with a graded continuum of electrophysiological properties. Notably, we found that molecular layer interneurons were composed of two molecularly and functionally distinct types. Both types show a continuum of morphological variation through the thickness of the molecular layer, but electrophysiological recordings revealed marked differences between the two types in spontaneous firing, excitability and electrical coupling. Together, these findings provide a comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological and physiological ontologies for defining brain cell types.},
   copyright = {2021 The Author(s)},
   doi       = {10.1038/s41586-021-03220-z},
   file      = {Full Text PDF:https\://www.nature.com/articles/s41586-021-03220-z.pdf:application/pdf},
@@ -1925,7 +1925,7 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   number    = {1},
   pages     = {710},
   volume    = {9},
-  abstract  = {The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.},
+  abstract  = {The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.},
   copyright = {2018 The Author(s)},
   doi       = {10.1038/s41467-017-02718-3},
   file      = {Full Text PDF:https\://www.nature.com/articles/s41467-017-02718-3.pdf:application/pdf},