Hyperpolarization-activated cyclic nucleotide-gated channel mRNA and protein expression in large versus small diameter dorsal root ganglion neurons: correlation with hyperpolarization-activated current gating

E V Kouranova, B W Strassle, R H Ring, M R Bowlby, D V Vasilyev
Neuroscience 2008 June 2, 153 (4): 1008-19
Hyperpolarization-activated cyclic nucleotide-gated channels (HCN) are responsible for the functional hyperpolarization-activated current (I(h)) in dorsal root ganglion (DRG) neurons. We studied HCN1-4 channel mRNA and protein expression and correlated these findings with I(h) functional properties in rat DRG neurons of different size. Quantitative RT-PCR (TaqMan) analysis demonstrated that HCN2 and HCN1 mRNAs were more abundantly expressed in large diameter (55-80 microm) neurons, while HCN3 mRNA was preferentially expressed in small diameter (20-30 microm) neurons. HCN4 mRNA expression was very low in neurons of all sizes. At the protein level, subunit-selective polyclonal antibodies and immunofluorescence indicated that HCN1 and HCN3 are present in large diameter neurons and small diameter neurons. Staining in small diameter neurons was in IB4-positive (non-peptidergic) and IB4-negative (peptidergic) cells. HCN2 immunofluorescent staining was heterogeneous and predominantly in large diameter neurons and in small diameter IB4-negative neurons. HCN4 was poorly expressed in all neurons. Functionally, I(h) amplitude and density were significantly larger, and activation kinetics faster, in large diameter neurons when compared with small neurons. I(h) activation rates in small and large diameter DRG neurons were consistent with the relative abundance of HCN subunits in the respective cell type, considering the reported HCN channel activation rates in heterologous systems (HCN1>HCN2 approximately HCN3>HCN4), suggesting exclusivity of roles of different HCN subunits contributing to the excitability of DRG neurons of different size. Additionally, a functional role of I(h) in small DRG neuron excitability was evaluated using a computational model.

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