Fluctuating estrogen and progesterone receptor expression in brainstem norepinephrine neurons through the rat estrous cycle.

Abstract

Norepinephrine (NE) neurons within the nucleus tractus solitarii (NTS; A2 neurons) and ventrolateral medulla (A1 neurons) represent gonadal steroid-dependent components of several neural networks regulating reproduction. Previous studies have shown that both A1 and A2 neurons express estrogen receptors (ERs). Using double labeling immunocytochemistry we report here that substantial numbers of NE neurons located within the NTS express progesterone receptor (PR) immunoreactivity, whereas few PRs are found in ventrolateral medulla. The evaluation of ERa and PR immunoreactivity in NE neurons through the estrous cycle revealed a fluctuating pattern of expression for both receptors within the NTS. The percentage of A2 neurons expressing PR immunoreactivity was low on metestrus and diestrus (3-7%), but increased significantly to approximately 24% on proestrous morning and remained at intermediate levels until estrus. The pattern of ERalpha immunoreactivity in A2 neurons was more variable, but a similar increment from 11% to 40% of NE neurons expressing ERa was found from diestrus to proestrus. Experiments in ovariectomized, estrogen-treated and estrogen-plus progesterone-treated rats revealed that PR immunoreactivity in A2 neurons was induced strongly by estrogen treatment, whereas progesterone had no significant effect. The numbers of ERalpha-positive NE neurons were not influenced by steroid treatment. These observations provide direct evidence for PRs in NE neurons of the brainstem and show that cyclical patterns of gonadal steroid receptor expression exist in A2, but not A1, neurons through the rat estrous cycle. The expression of PR in A2 neurons appears to be driven principally by circulating estrogen concentrations. The fluctuating levels of ERalpha and PR expression in these brainstem NE neurons may help generate cyclical patterns of biosynthetic and electrical activity within reproductive neural networks.