RESUMO
The human gut microbiome is the ecosystem of microorganisms that live in the human digestive system. Several studies have related gut microbiome variants to metabolic, immune and nervous system disorders. Fragile X syndrome (FXS) is a neurodevelopmental disorder considered the most common cause of inherited intellectual disability and the leading monogenetic cause of autism. The role of the gut microbiome in FXS remains largely unexplored. Here, we report the results of a gut microbiome analysis using a FXS mouse model and 16S ribosomal RNA gene sequencing. We identified alterations in the fmr1 KO2 gut microbiome associated with different bacterial species, including those in the genera Akkermansia, Sutterella, Allobaculum, Bifidobacterium, Odoribacter, Turicibacter, Flexispira, Bacteroides, and Oscillospira. Several gut bacterial metabolic pathways were significantly altered in fmr1 KO2 mice, including menaquinone degradation, catechol degradation, vitamin B6 biosynthesis, fatty acid biosynthesis, and nucleotide metabolism. Several of these metabolic pathways, including catechol degradation, nucleotide metabolism and fatty acid biosynthesis, were previously reported to be altered in children and adults with autism. The present study reports a potential association of the gut microbiome with FXS, thereby opening new possibilities for exploring reliable treatments and non-invasive biomarkers.
RESUMO
Fragile X syndrome (FXS), an X-chromosome linked intellectual disability, is the leading monogenetic cause of autism spectrum disorder (ASD), a neurodevelopmental condition that currently has no specific drug treatment. Building upon the demonstrated therapeutic effects on spatial memory of bryostatin-1, a relatively specific activator of protein kinase C (PKC)ε, (also of PKCα) on impaired synaptic plasticity/maturation and spatial learning and memory in FXS mice, we investigated whether bryostatin-1 might affect the autistic phenotypes and other behaviors, including open field activity, activities of daily living (nesting and marble burying), at the effective therapeutic dose for spatial memory deficits. Further evaluation included other non-spatial learning and memory tasks. Interestingly, a short period of treatment (5 weeks) only produced very limited or no therapeutic effects on the autistic and cognitive phenotypes in the Fmr1 KO2 mice, while a longer treatment (13 weeks) with the same dose of bryostatin-1 effectively rescued the autistic and non-spatial learning deficit cognitive phenotypes. It is possible that longer-term treatment would result in further improvement in these fragile X phenotypes. This effect is clearly different from other treatment strategies tested to date, in that the drug shows little acute effect, but strong long-term effects. It also shows no evidence of tolerance, which has been a problem with other drug classes (mGluR5 antagonists, GABA-A and -B agonists). The results strongly suggest that, at appropriate dosing and therapeutic period, chronic bryostatin-1 may have great therapeutic value for both ASD and FXS.
Assuntos
Transtorno do Espectro Autista/genética , Transtorno do Espectro Autista/terapia , Briostatinas/administração & dosagem , Briostatinas/fisiologia , Transtornos Cognitivos/genética , Transtornos Cognitivos/terapia , Síndrome do Cromossomo X Frágil/genética , Síndrome do Cromossomo X Frágil/terapia , Animais , Comportamento Animal , Briostatinas/farmacologia , Aprendizagem , Camundongos Endogâmicos C57BL , Camundongos Knockout , Fenótipo , Proteína Quinase C/metabolismo , Memória EspacialRESUMO
The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.