Integrando transcriptomas como ferramenta de entendimento de patogênese e seleção de alvos terapêuticos em infecções virais emergentes e desenvolvimento de um material didático para aulas práticas de virologia

Abstract

The increase of the incidence of viral emerging disease events has been observed since the 1940s. Recently we have observed several viruses being introduced in new regions of the globe, for example, the spread of the Zika virus in South America in 2015. Even more recently, we witnessed the emergence of a new coronavirus in China that quickly reached pandemic levels. In addition to raising concerns about public health and the economy, this type of event requires a quick response from the scientific community, either to understand the mechanisms that lead to the disease or in the search for treatments against the disease in question. Proteomics, transcriptomics, and genomics techniques are great allies after the emergence of new viral diseases, as they are widely disseminated, and it is common for them to be used to understand the virus replication cycle and viral pathogenesis and to define therapeutic targets for new viruses. Transcriptomics studies generate large amounts of data that have potential applications far beyond the original objectives of an experiment since they are stored in databases, usually publicly accessible. Through the data available in the database, we sought to develop a protocol for the integrative analysis of transcriptomes, using it as a basis for validating this protocol using data from neural cells infected with ZIKV. Shared differentially expressed genes were identified. Some of these genes, pathways and biological functions have been linked to neurogenesis and apoptosis and integrated into silico analysis capable of predicting and identifying putative biomarkers from different transcriptome data that would be useful for understanding viral disease pathogenesis and for application in identifying antiviral candidates. With the emergence of SARS-CoV-2, this protocol was revised and updated, with a deep learning strategy to identify targets in infected cells that can be inhibited by already approved molecules available in libraries, suggesting controlling mechanisms for SARS-CoV-2 infection. This type of analysis is only possible due to the large volume of general information generated from transcriptomics and virology studies. In the classroom, transferring all this knowledge to students is often challenging. One of the factors identified is the difficulty of preparing for practical virology classes. Therefore, we sought to develop didactic material that allowed the visualization of Amoeba's giant virus particles and cytopathic effects of viruses in animal cells, to move virology away from the abstract field of learning, also including 10 these giant viruses as models for understanding coevolution by reviewing their invasion mechanisms in host cells.