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- Title
In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19.
- Authors
López-Cortés, Andrés; Guevara-Ramírez, Patricia; Kyriakidis, Nikolaos C.; Barba-Ostria, Carlos; León Cáceres, Ángela; Guerrero, Santiago; Ortiz-Prado, Esteban; Munteanu, Cristian R.; Tejera, Eduardo; Cevallos-Robalino, Doménica; Gómez-Jaramillo, Ana María; Simbaña-Rivera, Katherine; Granizo-Martínez, Adriana; Pérez-M, Gabriela; Moreno, Silvana; García-Cárdenas, Jennyfer M.; Zambrano, Ana Karina; Pérez-Castillo, Yunierkis; Cabrera-Andrade, Alejandro; Puig San Andrés, Lourdes
- Abstract
Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.
- Subjects
ARTIFICIAL neural networks; COVID-19; RNA sequencing; IMMUNE system
- Publication
Frontiers in Pharmacology, 2021, Vol 11, pN.PAG
- ISSN
1663-9812
- Publication type
Article
- DOI
10.3389/fphar.2021.598925