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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

Lopez-Cortes, Andres and Guevara-Ramírez, Patricia and Kyriakidis, Nikolaos C. and Barba-Ostria, Carlos and Leon Caceres, Angela and Guerrero, Santiago and Ortiz-Prado, Esteban and Munteanu, Cristian R. and Tejera, Eduardo and Cevallos-Robalino, Domenica and Gomez-Jaramillo, Ana Maria and Simbana-Rivera, Katherine and Granizo-Martinez, Adriana and Perez-M, Gabriela and Moreno, Silvana and García-Cárdenas, Jennyfer M. and Zambrano, Ana Karina and Perez-Castillo, Yunierkis and Cabrera-Andrade, Alejandro and Puig San Andrés, Lourdes and Proano-Castro, Carolina and Bautista, Jhommara and Quevedo, Andreina and Varela, Nelson and Quinones, Luis Abel and Paz-y-Miño, César (2021). 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. Frontiers in Pharmacology. 12 , 598925
[Research article]

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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.

Authors/Creators:Lopez-Cortes, Andres and Guevara-Ramírez, Patricia and Kyriakidis, Nikolaos C. and Barba-Ostria, Carlos and Leon Caceres, Angela and Guerrero, Santiago and Ortiz-Prado, Esteban and Munteanu, Cristian R. and Tejera, Eduardo and Cevallos-Robalino, Domenica and Gomez-Jaramillo, Ana Maria and Simbana-Rivera, Katherine and Granizo-Martinez, Adriana and Perez-M, Gabriela and Moreno, Silvana and García-Cárdenas, Jennyfer M. and Zambrano, Ana Karina and Perez-Castillo, Yunierkis and Cabrera-Andrade, Alejandro and Puig San Andrés, Lourdes and Proano-Castro, Carolina and Bautista, Jhommara and Quevedo, Andreina and Varela, Nelson and Quinones, Luis Abel and Paz-y-Miño, César
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
Series Name/Journal:Frontiers in Pharmacology
Year of publishing :2021
Volume:12
Article number:598925
Number of Pages:24
Publisher:FRONTIERS MEDIA SA
ISSN:1663-9812
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 3 Medical and Health Sciences > 301 Basic Medicine > Pharmaceutical Sciences
Keywords:COVID-19, immune system, single-cell RNA sequencing, artificial neural networks, drug repurposing
URN:NBN:urn:nbn:se:slu:epsilon-p-111548
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-111548
Additional ID:
Type of IDID
DOI10.3389/fphar.2021.598925
Web of Science (WoS)000627583900001
ID Code:23321
Deposited By: SLUpub Connector
Deposited On:22 Apr 2021 10:24
Metadata Last Modified:22 Apr 2021 10:31

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