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Melanin-concentrating hormone peptidergic system: Comparative morphology between muroid species.

Giovanne B DinizDaniella S BattagelloPedro M CherubiniJulio D Reyes-MendozaCesar Luna-IlladesMarianne O KleinLívia C Motta-TeixeiraLuciane V SitaManuel Miranda-AnayaTeresa MoralesJackson Cioni Bittencourt
Published in: The Journal of comparative neurology (2019)
Melanin-concentrating hormone (MCH) is a conserved neuropeptide, predominantly located in the diencephalon of vertebrates, and associated with a wide range of functions. While functional studies have focused on the use of the traditional mouse laboratory model, critical gaps exist in our understanding of the morphology of the MCH system in this species. Even less is known about the nontraditional animal model Neotomodon alstoni (Mexican volcano mouse). A comparative morphological study among these rodents may, therefore, contribute to a better understanding of the evolution of the MCH peptidergic system. To this end, we employed diverse immunohistochemical protocols to identify key aspects of the MCH system, including its spatial relationship to another neurochemical population of the tuberal hypothalamus, the orexins. Three-dimensional (3D) reconstructions were also employed to convey a better sense of spatial distribution to these neurons. Our results show that the distribution of MCH neurons in all rodents studied follows a basic plan, but individual characteristics are found for each species, such as the preeminence of a periventricular group only in the rat, the lack of posterior groups in the mouse, and the extensive presence of MCH neurons in the anterior hypothalamic area of Neotomodon. Taken together, these data suggest a strong anatomical substrate for previously described functions of the MCH system, and that particular neurochemical and morphological features may have been determinant to species-specific phenotypes in rodent evolution.
Keyphrases
  • spinal cord
  • genetic diversity
  • oxidative stress
  • big data
  • machine learning
  • deep learning
  • data analysis