Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease.

Gonçalves, Ricardo Lemes; de Souza, Gabriel Augusto Pires; de Souza Terceti, Mateus; de Castro, Renato Fróes Goulart; de Mello Silva, Breno; Novaes, Romulo Dias; Malaquias, Luiz Cosme Cotta; Coelho, Luiz Felipe Leomil

Gonçalves, Ricardo Lemes; de Souza, Gabriel Augusto Pires; de Souza Terceti, Mateus; de Castro, Renato Fróes Goulart; de Mello Silva, Breno; Novaes, Romulo Dias; Malaquias, Luiz Cosme Cotta; Coelho, Luiz Felipe Leomil.

Afiliação

Gonçalves RL; Núcleo de Pesquisas em Ciências Biológicas, NUPEB, Universidade Federal de Ouro Preto, Ouro Preto, 35400-000, Brazil.
de Souza GAP; Laboratório de Vacinas, Departamento de Microbiologia e Imunologia, Instituto de Ciências Biomédicas, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700, 37130-001, Alfenas, Brazil.
de Souza Terceti M; Laboratório de Vírus, Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, 31270-901, Brazil.
de Castro RFG; Laboratório de Vacinas, Departamento de Microbiologia e Imunologia, Instituto de Ciências Biomédicas, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700, 37130-001, Alfenas, Brazil.
de Mello Silva B; Laboratório de Vacinas, Departamento de Microbiologia e Imunologia, Instituto de Ciências Biomédicas, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700, 37130-001, Alfenas, Brazil.
Novaes RD; Núcleo de Pesquisas em Ciências Biológicas, NUPEB, Universidade Federal de Ouro Preto, Ouro Preto, 35400-000, Brazil.
Malaquias LCC; Instituto de Ciências Biomédicas, Departamento de Biologia Estrutural, Universidade Federal de Alfenas, Alfenas, Minas Gerais, Brazil.
Coelho LFL; Laboratório de Vacinas, Departamento de Microbiologia e Imunologia, Instituto de Ciências Biomédicas, Universidade Federal de Alfenas, Rua Gabriel Monteiro da Silva, 700, 37130-001, Alfenas, Brazil.

Braz J Microbiol ; 54(1): 53-68, 2023 Mar.

Article em En | MEDLINE | ID: mdl-36435956

RESUMO

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.

Assuntos

COVID-19; Aprendizado Profundo; Humanos; SARS-CoV-2; Simulação de Acoplamento Molecular; Perfilação da Expressão Gênica

Palavras-chave

COVID-19; Deep learning; Emerging virus disease; Integrative bioinformatic; Integrative transcriptome analysis; SARS-CoV-2

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Limite: Humans Idioma: En Revista: Braz J Microbiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Brasil

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