Effects of single-dose antipurinergic therapy on behavioral and molecular alterations in the valproic acid-induced animal model of autism
May 1, 2020·,,,,,,,
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Mauro Mozael Hirsch
Iohanna Deckmann
Júlio Santos-Terra
Gabriela Zanotto Staevie
Mellanie Fontes-Dutra
Giovanna Carello-Collar
Marília Körbes-Rockenbach
Gustavo Brum Schwingel
Guilherme Bauer-Negrini
Bruna Rabelo
Maria Carolina Bittencourt Gonçalves
Juliana Corrêa-Velloso
Yahaira Naaldijk
Ana Regina Geciauskas Castillo
Tomasz Schneider
Victorio Bambini-Junior
Henning Ulrich
Carmem Gottfried
Abstract
Autism spectrum disorder (ASD) is characterized by deficits in communication and social interaction, restricted interests, and stereotyped behavior. Environmental factors, such as prenatal exposure to valproic acid (VPA), may contribute to the increased risk of ASD. Since disturbed functioning of the purinergic signaling system has been associated with the onset of ASD and used as a potential therapeutic target for ASD in both clinical and preclinical studies, we analyzed the effects of suramin, a non-selective purinergic antagonist, on behavioral, molecular and immunological in an animal model of autism induced by prenatal exposure to VPA. Treatment with suramin (20 mg/kg, intraperitoneal) restored sociability in the three-chamber apparatus and decreased anxiety measured by elevated plus maze apparatus, but had no impact on decreased reciprocal social interactions or higher nociceptive threshold in VPA rats. Suramin treatment did not affect VPA-induced upregulation of P2X4 and P2Y2 receptor expression in the hippocampus, and P2X4 receptor expression in the medial prefrontal cortex, but normalized an increased level of interleukin 6 (IL-6). Our results suggest an important role of purinergic signaling modulation in behavioral, molecular, and immunological aberrations described in VPA model, and indicate that the purinergic signaling system might be a potential target for pharmacotherapy in preclinical studies of ASD.
Type
Publication
Neuropharmacology
Status
Peer-reviewed

Authors
Biomedical Data Scientist
Computational neuroscientist working at the intersection of machine learning, biomedical
imaging, and human genetics in neurodegenerative disease. My work applies deep learning to
high-dimensional medical images, harmonizes imaging measurements across sites and acquisition
protocols, and integrates imaging with genomic, proteomic, and longitudinal clinical data to
characterise Alzheimer’s disease and related dementias, with particular focus on fluid and
imaging biomarkers of neurodegeneration.