A population of in silico models identifies the interplay between Nav 1.8 conductance and potassium currents as key in regulating human dorsal root ganglion neuron excitability

Figures

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  Figure 1.. Experimentally-calibrated population of 328 human DRG neuron AP models. A) Overlaid voltage traces from every model in the population stimulated with a step stimulus at each model’s rheobase. Each model was calibrated against hDRG neuron AP biomarker ranges calculated from data in Davidson et al. [2014]. B) Model schematic. The model approximates the hDRG neuron soma as a single compartment Hodgkin-Huxley model with 8 ionic currents. Arrows indicate the usual direction of current flow for each current. Each model in the population has the same 8 ionic currents but different channel densities of each to simulate inter-neuronal variability in ionic conductances.

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  Figure 2.. Scatter plots and histograms of the distribution of conductances in the population. Histograms on the diagonal panels show the distributions of individual conductances. Scatter plots show distributions for all pairs of conductances. Conductances are given in terms of a dimensionless scaling factor applied to the baseline conductance values (which are provided in the supplementary material).

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  Figure 3.. Conductances and ionic currentscapes for six different models from the population of models. A: Conductance scaling factors for six models selected to show a wide variety of different conductances and ionic current profiles. B-G: Currentscapes [ Alonso and Marder, 2019] showing the fractions of each ionic current active during a simulated step stimulus. Each plot shows, from top to bottom, the voltage trace, the total outward current, the fraction each outward current contributes to the total outward current during the simulation, the fraction each inward current contributes to the total inward current during the simulation, and the total inward current.

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  Figure 4.. Effects of co-varying Nav 1.8 conductance and stimulus amplitude on firing rate and AP half width. A) Simulation study design showing the grid search design used over stimulus amplitude and GNav1.8. Each dot represents a simulation on all 328 models in the population with the given Nav 1.8 conductance scaling factor applied to all models, and an 800 ms step stimulus current applied with the given amplitude. B) Mean firing rate of the population for each of the different simulation conditions. Each square represents a simulation of all models in the population with a different pair of values for GNav 1.8 scaling factor and stimulus amplitude. C) Mean AP half width of the population for each of the different simulation conditions.

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  Figure 5.. Relationship between AP half width and firing rate for individual models in the population of models under different GNav 1.8 scaling factors. Blue dots show results from individual models in different simulations from the simulation design shown in Figure 4A. Red lines show mean values of plotted results. Descending panels show results from simulations with progressively larger GNav 1.8 scaling factors.

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  Figure 6.. GKdr and GKM are key determinants for whether a model fires rapidly under many different combinations of GNav 1.8 and stimulus amplitude. A) Conductance distributions of two subpopulations in the population of models. Blue histograms show conductance distribution for the top 25% of models ranked by the number of simulations in Figure 4 in which they fired at a very rapid rate (> 20 AP/s). Red histograms show the other 75% of models. Histograms of the two subpopulations are normalised as the number of models in each subpopulation were not equal. B) Results of varying GNav 1.8, stimulus amplitude and one of GKdr (top row) or GKM (bottom row). Each square in a plot shows the mean firing rate across the population of models for one simulation.

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  Figure 7.. Average firing rate of the population of models when non-Nav 1.8 conductances are varied. Each row of plots shows the results of changing one non-Nav 1.8 conductance to 0%, 50%, 100%, 150% and 200% of each model in the population’s original value and rerunning the simulation study design shown in Figure 4A. Only variation in GKdr and GKM show substantial effects on firing rate. Within each plot each square represents the mean number of AP/s fired during a step stimulus averaged across all models in the population.

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  Figure 8.. Average firing rate of the population of models when GKM, GKdr, GNav 1.8 and stimulus amplitude are co-varied. Each panel shows the results of simulations with a specific pair of GKM and GKdr scaling factors applied. Within each panel each square represents the mean number of AP/s fired during a step stimulus averaged across all models in the population with GKM, GKdr and GNav1.8 scaled by the relevant values. GKM increases in each panel from left to right, while GKdr increases from bottom to top.

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  Figure 9.. Mean firing rate of the population of models averaged over all scaling factors of GNav 1.8 simulated in Figure 8. Left) Each line shows a different GKM scaling factor, with GKdr scaling factor varying on the x-axis. Right) Each line shows a different GKdr scaling factor, with GKM scaling factor varying on the x-axis.