KISS1 and KISS1R expression in the human and rat carotid body and superior cervical ganglion
AbstractKISS1 and its receptor, KISS1R, have both been found to be expressed in central nervous system, but few data are present in the literature about their distribution in peripheral nervous structures. Thus, the aim of the present study was to investigate, through immunohistochemistry, the expression and distribution of KISS1 and KISS1R in the rat and human carotid bodies and superior cervical ganglia, also with particular reference to the different cellular populations. Materials consisted of carotid bodies and superior cervical ganglia were obtained at autopsy from 10 adult subjects and sampled from 10 adult Sprague-Dawley rats. Immunohistochemistry revealed diffuse expression of KISS1 and KISS1R in type I cells of both human and rat carotid bodies, whereas type II cells were negative. In both human and rat superior cervical ganglia positive anti-KISS1 and -KISS1R immunostainings were also selectively found in ganglion cells, satellite cells being negative. Endothelial cells also showed moderate immunostaining for both KISS1 and KISS1R. The expression of both kisspeptins and kisspeptin receptors in glomic type I cells and sympathetic ganglion cells supports a modulatory role of KISS1 on peripheral chemoreception and sympathetic function. Moreover, local changes in blood flow have been considered to be involved in carotid body chemoreceptor discharge and kisspeptins and kisspeptin receptors have also been found in the endothelial cells. As a consequence, a possible role of kisspeptins in the regulation of carotid body blood flow and, indirectly, in chemoreceptor discharge may also be hypothesized.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2011 A. Porzionato, G. Fenu, M. Rucinski, V. Macchi, A. Montella, L. K. Malendowicz, R. De Caro
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.