Integrated procedures for accelerating, deepening, and leading genetic inquiry: A first application on human muscle secretome.

PURPOSE: Beyond classical procedures, bioinformatic-assisted approaches and computational biology offer unprecedented opportunities for scholars. However, these amazing possibilities still need epistemological criticism, as well as standardized procedures. Especially those topics with a huge body of data may benefit from data science (DS)-assisted methods. Therefore, the current study dealt with the combined expert-assisted and DS-assisted approaches to address the broad field of muscle secretome. We aimed to apply DS tools to fix the literature research, suggest investigation targets with a data-driven approach, predict possible scenarios, and define a workflow. METHODS: Recognized scholars with expertise on myokines were invited to provide a list of the most important myokines. GeneRecommender, GeneMANIA, HumanNet, and STRING were selected as DS tools. Networks were built on STRING and GeneMANIA. The outcomes of DS tools included the top 5 recommendations. Each expert-led discussion has been then integrated with an DS-led approach to provide further perspectives. RESULTS: Among the results, 11 molecules had already been described as bona-fide myokines in literature, and 11 molecules were putative myokines. Most of the myokines and the putative myokines recommended by the DS tools were described as present in the cargo of extracellular vesicles. CONCLUSIONS: Including both supervised and unsupervised learning methods, as well as encompassing algorithms focused on both protein interaction and gene represent a comprehensive approach to tackle complex biomedical topics. DS-assisted methods for reviewing existent evidence, recommending targets of interest, and predicting original scenarios are worth exploring as in silico recommendations to be integrated with experts' ideas for optimizing molecular studies.

Juvenile Huntington's Disease and Other PolyQ Diseases, Update on Neurodevelopmental Character and Comparative Bioinformatic Review of Transcriptomic and Proteomic Data.

Polyglutamine (PolyQ) diseases are neurodegenerative disorders caused by the CAG repeat expansion mutation in affected genes resulting in toxic proteins containing a long chain of glutamines. There are nine PolyQ diseases: Huntington's disease (HD), spinocerebellar ataxias (types 1, 2, 3, 6, 7, and 17), dentatorubral-pallidoluysian atrophy (DRPLA), and spinal bulbar muscular atrophy (SBMA). In general, longer CAG expansions and longer glutamine tracts lead to earlier disease presentations in PolyQ patients. Rarely, cases of extremely long expansions are identified for PolyQ diseases, and they consistently lead to juvenile or sometimes very severe infantile-onset polyQ syndromes. In apparent contrast to the very long CAG tracts, shorter CAGs and PolyQs in proteins seems to be the evolutionary factor enhancing human cognition. Therefore, polyQ tracts in proteins can be modifiers of brain development and disease drivers, which contribute neurodevelopmental phenotypes in juvenile- and adult-onset PolyQ diseases. Therefore we performed a bioinformatics review of published RNAseq polyQ expression data resulting from the presence of polyQ genes in search of neurodevelopmental expression patterns and comparison between diseases. The expression data were collected from cell types reflecting stages of development such as iPSC, neuronal stem cell, neurons, but also the adult patients and models for PolyQ disease. In addition, we extended our bioinformatic transcriptomic analysis by proteomics data. We identified a group of 13 commonly downregulated genes and proteins in HD mouse models. Our comparative bioinformatic review highlighted several (neuro)developmental pathways and genes identified within PolyQ diseases and mouse models responsible for neural growth, synaptogenesis, and synaptic plasticity.