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Caramelo, F.

Publications and source records attributed to Caramelo, F..

2 recordsLinked to original sources

Convergent Decay of Skin-specific Gene Modules in Pangolins

The Mammalia skin exhibits a rich spectrum of evolutionary adaptations. The pilosebaceous unit, composed of the hair shaft, follicle, and the sebaceous gland, is the most striking synapomorphy. The evolutionary diversification of mammals across different ecological niches was paralleled by the appearance of an ample variety of skin modifications. Pangolins, order Pholidota, exhibit keratin-derived scales, one of the most iconic skin appendages. This formidable armor is intended to serve as a deterrent against predators. Surprisingly, while pangolins have hair on their abdomens, the occurrence of sebaceous and sweat glands is contentious. Here, we explore various molecular modules of skin physiology in four pangolin genomes, including that of sebum production. We show that genes driving wax monoester formation - Awat1/2, show patterns of inactivation in the stem pangolin branch; while the triacylglycerol synthesis gene Dgat2l6 seems independently eroded in the African and Asian clades. In contrast, Elovl3 implicated in the formation of specific neutral lipids required for skin barrier function, is intact and expressed in the skin. An extended comparative analysis shows that genes involved in skin pathogen defense and structural integrity of keratinocyte layers also show inactivating mutations: associated with both ancestral and independent pseudogenization events. Finally, we deduce that the suggested absence of sweat glands is not paralleled by the inactivation of the ATP-binding cassette transporter Abcc11, as previously described in Cetacea. Our findings reveal the sophisticated, convergent and complex history of gene retention and loss as key mechanisms in the evolution of the highly modified mammalian skin phenotypes.

evolutionary biology↗

Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

The neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study. Lay SummaryThere are important issues in the differential diagnosis of Autism Spectrum Disorders. Gene dosage effects may be important in this context. In this work, we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%.

genetics↗