Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents

Model Status

The authors highlight that the original code they wrote for this model can be downloaded here. This particular CellML version of the model has the A-type potassium current switched on (gA=25). For the alternative model which has the BK-type current switched on and the A-type potassium current switched off please see the gBK version of the model. This CellML model runs in PCEnv and COR to replicate the published results (figure 5c). Please note that the model needs to be run for at least 3000ms to allow the model to reach stability.

Model Structure

ABSTRACT: Dopamine (DA) released from the hypothalamus tonically inhibits pituitary lactotrophs. DA (at micromolar concentration) opens potassium channels, hyperpolarizing the lactotrophs and thus preventing the calcium influx that triggers prolactin hormone release. Surprisingly, at concentrations ~1000 lower, DA can stimulate prolactin secretion. Here, we investigated whether an increase in a K+ current could mediate this stimulatory effect. We considered the fast K+ currents flowing through large-conductance BK channels and through A-type channels. We developed a minimal lactotroph model to investigate the effects of these two currents. Both I BK and I A could transform the electrical pattern of activity from spiking to bursting, but through distinct mechanisms. I BK always increased the intracellular Ca2+ concentration, while I A could either increase or decrease it. Thus, the stimulatory effects of DA could be mediated by a fast K+ conductance which converts tonically spiking cells to bursters. In addition, the study illustrates that a heterogeneous distribution of fast K+ conductances could cause heterogeneous lactotroph firing patterns.

Schematic diagram of the model.

The original paper reference is cited below:

Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents, Joel Tabak, Natalia Toporikova, Marc E. Freeman, and Richard Bertram, 2007, Journal of Computational Neuroscience, 22, 211-222. PubMed ID: 17058022