DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)

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Accession:256632
We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
Reference:
1 . Zheng Y, Liu P, Bai L, Trimmer JS, Bean BP, Ginty DD (2019) Deep Sequencing of Somatosensory Neurons Reveals Molecular Determinants of Intrinsic Physiological Properties Neuron
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Mouse;
Cell Type(s): Dorsal Root Ganglion (DRG) cell;
Channel(s): I Sodium; I Potassium; I K; I A;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Nav1.6 SCN8A; Nav1.7 SCN9A; Nav1.8 SCN10A; Kv1.1 KCNA1; Kv1.2 KCNA2; Kv2.1 KCNB1; Kv3.1 KCNC1; Kv3.3 KCNC3; Kv3.4 KCNC4; Kv4.3 KCND3;
Transmitter(s):
Simulation Environment: NEURON; R;
Model Concept(s): Action Potential Initiation; Action Potentials; Activity Patterns; Delay; Ion Channel Kinetics; Membrane Properties;
Implementer(s): Zheng, Yang [zylittlep at gmail.com]; Bean, Bruce [bruce_bean at hms.harvard.edu];
Search NeuronDB for information about:  I A; I K; I Sodium; I Potassium;
Kv1_20190516_1n<-read.table('kv1_20190516_1n.dat',header=T)
dat<-Kv1_20190516_1n
dat$Idensity<-dat$reci*1000/30
dat$gbarkv1<-dat$recscale*1000
dat$reclat<-dat$reclat-100
fac_exp<-as.factor(dat$gbarkv1)
levels(fac_exp)

df<-as.data.frame(matrix(,ncol=nrow(dat)/length(levels(fac_exp)),nrow=length(levels(fac_exp))))
colnames(df)<-dat$Idensity[dat$gbarkv1==levels(fac_exp)[1]]
row.names(df)<-levels(fac_exp)

for (i in 1:length(levels(fac_exp))) {
  df[row.names(df)==levels(fac_exp)[i],]<-dat$recn[dat$gbarkv1==levels(fac_exp)[i]]
}

kv1_exp<-dat[dat$gbarkv1%in%c(0,6),]
kv1_exp$gbarkv1<-as.factor(kv1_exp$gbarkv1)

p<-ggplot(kv1_exp,aes(Idensity,recn,group=gbarkv1))+geom_line(aes(color = gbarkv1),size=0.5)+
  labs(x="Current Density (pA/pF)",y="Number of APs",color=bquote(bar('g')*'Kv1 (mS/'*cm^2*')'))+
  theme_bw()+
  scale_color_manual(values=c("red","black"))+
  theme(axis.title=element_text(color='black',size=14),
        axis.text=element_text(color='black',size=14),
        legend.title=element_text(size=14),
        legend.text = element_text(size=14),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"))
p

cairo_pdf(filename = 'Kv1_AbLTMR_line_20190516.pdf',
          width=8.4,height=4.2,family = 'Arial Unicode MS')
p
dev.off()

df[df==0]<-NA
colnames(df) = c('0',rep('',9),'10',rep('',9),'20',rep('',9),'30',rep('',9),'40',rep('',9),'50')
ht = Heatmap(df, 
             name = "Number of\nAPs",
             #name = "Onset Latency(ms)",
             row_names_side = "left",
             column_title = "Current Density (pA/pF)",
             column_title_side = "bottom",
             row_title = expression(paste(bar(g),'Kv1 (mS/',cm^2,')',sep='')),
             col = blue2green2red(60),
             na_col= "black",
             cluster_rows = FALSE,
             cluster_columns = FALSE, 
             show_heatmap_legend = T,
             heatmap_legend_param =list(at=c(1,10,20,30,40,50,60),legend_height=unit(10, "cm"),
                                        labels_gp = gpar(fontsize = 15, fontface='bold'),
                                        title_gp = gpar(fontsize = 15, fontface='bold')),
             row_names_gp = gpar(fontsize = 15, fontface='bold'),
             column_names_gp = gpar(fontsize = 15, fontface='bold'),
             row_title_gp = gpar(fontsize = 15, fontface='bold'),
             column_title_gp = gpar(fontsize = 15, fontface='bold')
)

ht

cairo_pdf(filename = 'kv1_20190516.pdf',
          width=8.5,height=6,family = 'Arial Unicode MS')
draw(ht)
dev.off()

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