Behavioral alterations in autism model induced by valproic acid and translational analysis of circulating microRNA

May 1, 2018·
Mauro Mozael Hirsch
,
Iohanna Deckmann
,
Mellanie Fontes-Dutra
Guilherme Bauer-Negrini
Guilherme Bauer-Negrini
,
Gustavo Della-Flora Nunes
,
Walquiria Nunes
,
Bruna Rabelo
,
Rudimar Riesgo
,
Rogerio Margis
,
Victorio Bambini-Junior
,
Carmem Gottfried
· 0 min read
DOI
Abstract
Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication and language, and restricted repertoire of activities and interests. The etiology of ASD remains unknown and no clinical markers for diagnosis were identified. Environmental factors, including prenatal exposure to valproic acid (VPA), may contribute to increased risk of developing ASD. MicroRNA (miRNA) are small noncoding RNA that regulate gene expression and are frequently linked to biological processes affected in neurodevelopmental disorders. In this work, we analyzed the effects of resveratrol (an antioxidant and anti-inflammatory molecule) on behavioral alterations of the VPA model of autism, as well as the levels of circulating miRNA. We also evaluated the same set of miRNA in autistic patients. Rats of the VPA model of autism showed reduced total reciprocal social interaction, prevented by prenatal treatment with resveratrol (RSV). The levels of miR134–5p and miR138–5p increased in autistic patients. Interestingly, miR134–5p is also upregulated in animals of the VPA model, which is prevented by RSV. In conclusion, our findings revealed important preventive actions of RSV in the VPA model, ranging from behavior to molecular alterations. Further evaluation of preventive mechanisms of RSV can shed light in important biomarkers and etiological triggers of ASD.
Type
Publication
Food and Chemical Toxicology
Status
Peer-reviewed
publications
Guilherme Bauer-Negrini
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.