RESUMO
The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.
RESUMO
BACKGROUND: Friedreich ataxia (FRDA) is an autosomal recessive neurodegenerative disease caused by GAA repeat expansion in the first intron of the FXN gene, which encodes frataxin, an essential mitochondrial protein. To further characterise the molecular abnormalities associated with FRDA pathogenesis and to hasten drug screening, the development and use of animal and cellular models is considered essential. Studies of lower organisms have already contributed to understanding FRDA disease pathology, but mammalian cells are more related to FRDA patient cells in physiological terms. METHODOLOGY/PRINCIPAL FINDINGS: We have generated fibroblast cells and neural stem cells (NSCs) from control Y47R mice (9 GAA repeats) and GAA repeat expansion YG8R mice (190+120 GAA repeats). We then differentiated the NSCs in to neurons, oligodendrocytes and astrocytes as confirmed by immunocytochemical analysis of cell specific markers. The three YG8R mouse cell types (fibroblasts, NSCs and differentiated NSCs) exhibit GAA repeat stability, together with reduced expression of frataxin and reduced aconitase activity compared to control Y47R cells. Furthermore, YG8R cells also show increased sensitivity to oxidative stress and downregulation of Pgc-1α and antioxidant gene expression levels, especially Sod2. We also analysed various DNA mismatch repair (MMR) gene expression levels and found that YG8R cells displayed significant reduction in expression of several MMR genes, which may contribute to the GAA repeat stability. CONCLUSIONS/SIGNIFICANCE: We describe the first fibroblast and NSC models from YG8R FRDA mice and we confirm that the NSCs can be differentiated into neurons and glia. These novel FRDA mouse cell models, which exhibit a FRDA-like cellular and molecular phenotype, will be valuable resources to further study FRDA molecular pathogenesis. They will also provide very useful tools for preclinical testing of frataxin-increasing compounds for FRDA drug therapy, for gene therapy, and as a source of cells for cell therapy testing in FRDA mice.