| Autor: |
Plaza-Díaz J; Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071 Granada, Spain;. jrplaza@ugr.es.; Institute of Nutrition and Food Technology 'José Mataix', Center of Biomedical Research,University of Granada, 18016 Armilla, Granada, Spain. jrplaza@ugr.es.; Instituto de Investigación Biosanitaria IBS.GRANADA, Complejo Hospitalario Universitario de Granada,18014 Granada, Spain. jrplaza@ugr.es., Gómez-Fernández A; Pediatric Research and Metabolism Unit, Reina Sofia University Hospital, Maimónides Institute forBiomedical Research of Córdoba (IMIBIC), University of Córdoba, 14010 Córdoba, Spain. antoniogofedez@hotmail.com., Chueca N; Instituto de Investigación Biosanitaria IBS.GRANADA, Complejo Hospitalario Universitario de Granada,18014 Granada, Spain. naisses@yahoo.es., Torre-Aguilar MJ; Pediatric Research and Metabolism Unit, Reina Sofia University Hospital, Maimónides Institute forBiomedical Research of Córdoba (IMIBIC), University of Córdoba, 14010 Córdoba, Spain. delatorremj4@gmail.com., Gil Á; Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071 Granada, Spain;. agil@ugr.es.; Institute of Nutrition and Food Technology 'José Mataix', Center of Biomedical Research,University of Granada, 18016 Armilla, Granada, Spain. agil@ugr.es.; Instituto de Investigación Biosanitaria IBS.GRANADA, Complejo Hospitalario Universitario de Granada,18014 Granada, Spain. agil@ugr.es.; CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III,28029 Madrid, Spain. agil@ugr.es., Perez-Navero JL; Pediatric Research and Metabolism Unit, Reina Sofia University Hospital, Maimónides Institute forBiomedical Research of Córdoba (IMIBIC), University of Córdoba, 14010 Córdoba, Spain. jrplaza@correo.ugr.es., Flores-Rojas K; Pediatric Research and Metabolism Unit, Reina Sofia University Hospital, Maimónides Institute forBiomedical Research of Córdoba (IMIBIC), University of Córdoba, 14010 Córdoba, Spain. katherine1.flores@gmail.com.; CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III,28029 Madrid, Spain. katherine1.flores@gmail.com., Martín-Borreguero P; Department of Child and Adolescent Clinical Psychiatry and Psychology, Reina Sofia University Hospital,Maimónides Institute for Biomedical Research of Córdoba (IMIBIC), 14010 Cordoba, Spain. pmartin.psicologa@gmail.com., Solis-Urra P; PROFITH 'PROmoting FITness and Health through physical activity' research group, Department ofPhysical Education and Sport, Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain. patricio.solis.u@gmail.com.; IRyS Research Group, School of Physical Education, Pontificia Universidad Católica de Valparaíso,Valparaiso 2374631, Chile. patricio.solis.u@gmail.com., Ruiz-Ojeda FJ; RG Adipocytes and metabolism, Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Center Munich, 85748 Garching, Munich, Germany. francisco.ruiz@helmholtz-muenchen.de., Garcia F; Instituto de Investigación Biosanitaria IBS.GRANADA, Complejo Hospitalario Universitario de Granada,18014 Granada, Spain. fegarcia@ugr.es., Gil-Campos M; Pediatric Research and Metabolism Unit, Reina Sofia University Hospital, Maimónides Institute forBiomedical Research of Córdoba (IMIBIC), University of Córdoba, 14010 Córdoba, Spain. mercedes_gil_campos@yahoo.es.; CIBEROBN (CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III,28029 Madrid, Spain. mercedes_gil_campos@yahoo.es. |
| Abstrakt: |
New microbiome sequencing technologies provide novel information about the potential interactions among intestinal microorganisms and the host in some neuropathologies as autism spectrum disorders (ASD). The microbiota⁻gut⁻brain axis is an emerging aspect in the generation of autistic behaviors; evidence from animal models suggests that intestinal microbial shifts may produce changes fitting the clinical picture of autism. The aim of the present study was to evaluate the fecal metagenomic profiles in children with ASD and compare them with healthy participants. This comparison allows us to ascertain how mental regression (an important variable in ASD) could influence the intestinal microbiota profile. For this reason, a subclassification in children with ASD by mental regression (AMR) and no mental regression (ANMR) phenotype was performed. The present report was a descriptive observational study. Forty-eight children aged 2⁻6 years with ASD were included: 30 with ANMR and 18 with AMR. In addition, a control group of 57 normally developing children was selected and matched to the ASD group by sex and age. Fecal samples were analyzed with a metagenomic approach using a next-generation sequencing platform. Several differences between children with ASD, compared with the healthy group, were detected. Namely, Actinobacteria and Proteobacteria at phylum level, as well as, Actinobacteria, Bacilli , Erysipelotrichi , and Gammaproteobacteria at class level were found at higher proportions in children with ASD. Additionally, Proteobacteria levels showed to be augmented exclusively in AMR children. Preliminary results, using a principal component analysis, showed differential patterns in children with ASD, ANMR and AMR, compared to healthy group, both for intestinal microbiota and food patterns. In this study, we report, higher levels of Actinobacteria , Proteobacteria and Bacilli , aside from Erysipelotrichi , and Gammaproteobacteria in children with ASD compared to healthy group. Furthermore, AMR children exhibited higher levels of Proteobacteria . Further analysis using these preliminary results and mixing metagenomic and other "omic" technologies are needed in larger cohorts of children with ASD to confirm these intestinal microbiota changes. |
