Started working on "Contribution to the field" section; Hard references for Supplementary Material

Frontiers_manuscript/Koch_frontiers.tex

@@ -175,7 +175,7 @@ To examine the role of cell-type specific ionic current environments on the impa
 (1) firing responses were characterized with rheobase and \(\Delta\)AUC, (2) a set of neuronal models was used and properties of channels common across models were altered systematically one at a time, and (3) the effects of a set of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing was then examined across different neuronal models with different ionic current environments.
 
 \subsection{Variety of model neurons}
-Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of single-compartment, conductance-based neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (Figure \ref{fig:diversity_in_firing}). The models chosen for this study all fire tonically and do not exhibit bursting (see methods for details and naming of the models). Models are qualitatively sorted based on their firing curves and labeled model A through L accordingly. Some models, such as models A and B, display type I firing, whereas others such as models J and L exhibit type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency; \citealt{ermentrout_type_1996, Rinzel_1998}). The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current thresholds. However, the models  I, J, and K have large hysteresis \textcolor{red}{ (Figure \ref{fig:diversity_in_firing, ramp_firing})}. Different types of underlying current dynamics are known to generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}.
+Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of single-compartment, conductance-based neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (Figure \ref{fig:diversity_in_firing}). The models chosen for this study all fire tonically and do not exhibit bursting (see methods for details and naming of the models). Models are qualitatively sorted based on their firing curves and labeled model A through L accordingly. Some models, such as models A and B, display type I firing, whereas others such as models J and L exhibit type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency; \citealt{ermentrout_type_1996, Rinzel_1998}). The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current thresholds. However, the models  I, J, and K have large hysteresis (Figure \ref{fig:diversity_in_firing} and Supplementary Figure S1). Different types of underlying current dynamics are known to generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}. %  \textcolor{red}{ (Figure \ref{fig:diversity_in_firing} and Supplementary Figure \ref{ramp_firing})}
 
 \subsection{Characterization of Neuronal Firing Properties}
 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 (Figure \ref{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.
@@ -295,8 +295,29 @@ For additional requirements for specific article types and further information p
 
 \subsection*{\textcolor{red}{Contribution to the field}}
 \notenk{Contribution to the field. Ahead of submission, you should prepare a statement summarizing in 200 words your manuscript’s contribution to, and position in, the existing literature of your field. This should be written avoiding any technical language or non-standard acronyms. The aim should be to convey the meaning and importance of this research to a non-expert. (Note that you will NOT be able to provide a traditional cover letter.)}
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+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 in the context of neurological disorders such as epilepsy and ataxia, determination of their role in altering neuronal firing is more difficult.
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+ However, cell-type dependency of ion channel mutations on firing has been observed experimentally. Here we demonstrate 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 nor used to directly inform therapeutic approaches. The knowledge of whether a mutation increases or decreases neuronal excitability is vital in the context of state-of-the-art personalized medicine approaches in this area where treatments are mainly based on biophysical properties only. As such taking cell-type dependence of the firing effects of ion channel mutations provides a promising direction to increase efficacy in personalized medicine approaches.
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+Taken together, our findings highlight that neuronal type and the respective ionic current composition is of vital importance and must be considered when assessing the effects of mutations on neuronal firing and for determining therapeutic strategies for ion channal mutations.
+
+
 \textit{Significance statement from eNeuro}\\
 \textit{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.}
+
+%\textit{Cover letter from eNeuro}\\
+%\textit{Dear Editors,
+%the effects of ion channel mutations on biophysical properties are routinely assessed in expression systems. However, how these altered biophysical properties translate into changes in neuronal firing and network activity is not well understood yet. As a first step, our manuscript "Loss or Gain of Function? Neuronal Firing Effects of Ion Channel Mutations Depend on Cell Type" emphasizes how much the effects of ion channel mutations on neuronal firing may depend on the specific neuron type. The knowledge of whether a mutation increases or decreases neuronal excitability is vital in the context of state-of-the-art personalized medicine approaches in this area where treatments are mainly based on biophysical properties only.
+%
+%We extensively simulated 12 conductance-based models to explore the dependence of the effects of ion channel mutations on neuron type. We systematically varied properties (maximal conductance, position and slope of activation and inactivation curves) of voltage-gated ionic currents over ranges typically measured experimentally and assess the effect of changes on both threshold and sensitivity of firing. Secondly, we simulated the effects of reported episodic ataxia type 1 associated KCNA1 mutations in the models and assessed the dependence of firing changes on neuron type in a similar manner. Overall, we find that the effects of ion channel mutations, both generally and in the case of reported KCNA1 mutations, depends on neuron type. Our findings highlight that neuronal type and the respective ionic current composition is of vital importance and must be considered when assessing the effects of mutations on neuronal firing.
+%
+%Our manuscript addresses clinicians investigating channelopathies, systemic neuroscientists as well as computational neuroscientists, all readers of eNeuro.
+%
+%We hope that you find our manuscript convincing and exciting, and look forward to hearing from you.}
+
 \section*{Conflict of Interest Statement}
 %All financial, commercial or other relationships that might be perceived by the academic community as representing a potential conflict of interest must be disclosed. If no such relationship exists, authors will be asked to confirm the following statement: 
 
@@ -353,7 +374,7 @@ The datasets generated for this study as well as the code/software described in
         \centering
         \includegraphics[width=0.83\linewidth]{Figures/diversity_in_firing.pdf}
         \linespread{1.}\selectfont
-        \caption[]{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate  \textbf{(A)}, RS inhibitory  \textbf{(B)}, FS \textbf{(C)}, RS pyramidal  \textbf{(D)}, RS inhibitory +\Kv  \textbf{(E)}, Cb stellate +\Kv  \textbf{(F)}, FS +\Kv  \textbf{(G)}, RS pyramidal +\Kv  \textbf{(H)}, STN +\Kv  \textbf{(I)}, Cb stellate \(\Delta\)\Kv  \textbf{(J)}, STN \(\Delta\)\Kv  \textbf{(K)}, and STN  \textbf{(L)} neuron models. Models are sorted qualitatively based on their fI curves. Black markers on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp  \textcolor{red}{(see Figure \ref{ramp_firing}).}}
+        \caption[]{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate  \textbf{(A)}, RS inhibitory  \textbf{(B)}, FS \textbf{(C)}, RS pyramidal  \textbf{(D)}, RS inhibitory +\Kv  \textbf{(E)}, Cb stellate +\Kv  \textbf{(F)}, FS +\Kv  \textbf{(G)}, RS pyramidal +\Kv  \textbf{(H)}, STN +\Kv  \textbf{(I)}, Cb stellate \(\Delta\)\Kv  \textbf{(J)}, STN \(\Delta\)\Kv  \textbf{(K)}, and STN  \textbf{(L)} neuron models. Models are sorted qualitatively based on their fI curves. Black markers on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp (see Supplementary Figure S1).} % \textcolor{red}{(see Supplementary Figure \ref{ramp_firing}).}}
         \label{fig:diversity_in_firing}
 \end{figure}