Cargando…

Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screen...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Wenbo, Jin, Yan, Shi, Hongshuo, Zhang, Xuecheng, Chen, Jinshu, Jia, Hongling, Zhang, Yongchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256337/
https://www.ncbi.nlm.nih.gov/pubmed/37335656
http://dx.doi.org/10.1097/MD.0000000000033912
_version_ 1785057082203963392
author Dong, Wenbo
Jin, Yan
Shi, Hongshuo
Zhang, Xuecheng
Chen, Jinshu
Jia, Hongling
Zhang, Yongchen
author_facet Dong, Wenbo
Jin, Yan
Shi, Hongshuo
Zhang, Xuecheng
Chen, Jinshu
Jia, Hongling
Zhang, Yongchen
author_sort Dong, Wenbo
collection PubMed
description Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screened using the Gene Expression Omnibus. Common differentially expressed genes were then identified using a Venn diagram. Gene ontology analysis and KEGG pathway enrichment were performed on the differentially expressed genes. A protein–protein interaction network was also constructed using the STRING platform, and key genes were identified using the Cytoscape plugin. GES63067 was selected for validation of the results. Analysis of ferroptosis gene expression during the development of the 2 diseases and prediction of their upstream miRNAs and lncRNAs. In addition, transcription factors (TFs) and miRNAs related to key genes were identified. Effective drugs that act on target genes were found in the DSigDB. The GSE147507 and GSE126848 datasets were crossed to obtain 28 co-regulated genes, 22 gene ontology terms, 3 KEGG pathways, and 10 key genes. NAFLD may affect COVID-19 progression through immune function and inflammatory signaling pathways. CYBB was predicted to be a differential ferroptosis gene associated with 2 diseases, and the CYBB-hsa-miR-196a/b-5p-TUG1 regulatory axis was identified. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Eckol, sulfinpyrazone, and phenylbutazone) were considered as target drugs for Patients with COVID-19 and NAFLD. This study identified key gene and defined molecular mechanisms associated with the progression of COVID-19 and NAFLD. COVID-19 and NAFLD progression may regulate ferroptosis through the CYBB-hsa-miR-196a/b-5p-TUG1 axis. This study provides additional drug options for the treatment of COVID-19 combined with NAFLD disease.
format Online
Article
Text
id pubmed-10256337
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-102563372023-06-10 Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease Dong, Wenbo Jin, Yan Shi, Hongshuo Zhang, Xuecheng Chen, Jinshu Jia, Hongling Zhang, Yongchen Medicine (Baltimore) 4400 Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screened using the Gene Expression Omnibus. Common differentially expressed genes were then identified using a Venn diagram. Gene ontology analysis and KEGG pathway enrichment were performed on the differentially expressed genes. A protein–protein interaction network was also constructed using the STRING platform, and key genes were identified using the Cytoscape plugin. GES63067 was selected for validation of the results. Analysis of ferroptosis gene expression during the development of the 2 diseases and prediction of their upstream miRNAs and lncRNAs. In addition, transcription factors (TFs) and miRNAs related to key genes were identified. Effective drugs that act on target genes were found in the DSigDB. The GSE147507 and GSE126848 datasets were crossed to obtain 28 co-regulated genes, 22 gene ontology terms, 3 KEGG pathways, and 10 key genes. NAFLD may affect COVID-19 progression through immune function and inflammatory signaling pathways. CYBB was predicted to be a differential ferroptosis gene associated with 2 diseases, and the CYBB-hsa-miR-196a/b-5p-TUG1 regulatory axis was identified. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Eckol, sulfinpyrazone, and phenylbutazone) were considered as target drugs for Patients with COVID-19 and NAFLD. This study identified key gene and defined molecular mechanisms associated with the progression of COVID-19 and NAFLD. COVID-19 and NAFLD progression may regulate ferroptosis through the CYBB-hsa-miR-196a/b-5p-TUG1 axis. This study provides additional drug options for the treatment of COVID-19 combined with NAFLD disease. Lippincott Williams & Wilkins 2023-06-09 /pmc/articles/PMC10256337/ /pubmed/37335656 http://dx.doi.org/10.1097/MD.0000000000033912 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
spellingShingle 4400
Dong, Wenbo
Jin, Yan
Shi, Hongshuo
Zhang, Xuecheng
Chen, Jinshu
Jia, Hongling
Zhang, Yongchen
Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title_full Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title_fullStr Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title_full_unstemmed Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title_short Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
title_sort using bioinformatics and systems biology methods to identify the mechanism of interaction between covid-19 and nonalcoholic fatty liver disease
topic 4400
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256337/
https://www.ncbi.nlm.nih.gov/pubmed/37335656
http://dx.doi.org/10.1097/MD.0000000000033912
work_keys_str_mv AT dongwenbo usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT jinyan usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT shihongshuo usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT zhangxuecheng usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT chenjinshu usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT jiahongling usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease
AT zhangyongchen usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease