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Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach

BACKGROUND: Severe coronavirus disease 2019 (COVID-19) has caused a great threat to human health. Metabolic associated fatty liver disease (MAFLD) is a liver disease with a high prevalence rate. Previous studies indicated that MAFLD led to increased mortality and severe case rates of COVID-19 patien...

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Autores principales: Huang, Tengda, Zheng, Dawei, Song, Yujia, Pan, Hongyuan, Qiu, Guoteng, Xiang, Yuchu, Wang, Zichen, Wang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476796/
https://www.ncbi.nlm.nih.gov/pubmed/37657050
http://dx.doi.org/10.1097/MD.0000000000034570
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author Huang, Tengda
Zheng, Dawei
Song, Yujia
Pan, Hongyuan
Qiu, Guoteng
Xiang, Yuchu
Wang, Zichen
Wang, Fang
author_facet Huang, Tengda
Zheng, Dawei
Song, Yujia
Pan, Hongyuan
Qiu, Guoteng
Xiang, Yuchu
Wang, Zichen
Wang, Fang
author_sort Huang, Tengda
collection PubMed
description BACKGROUND: Severe coronavirus disease 2019 (COVID-19) has caused a great threat to human health. Metabolic associated fatty liver disease (MAFLD) is a liver disease with a high prevalence rate. Previous studies indicated that MAFLD led to increased mortality and severe case rates of COVID-19 patients, but its mechanism remains unclear. METHODS: This study analyzed the transcriptional profiles of COVID-19 and MAFLD patients and their respective healthy controls from the perspectives of bioinformatics and systems biology to explore the underlying molecular mechanisms between the 2 diseases. Specifically, gene expression profiles of COVID-19 and MAFLD patients were acquired from the gene expression omnibus datasets and screened shared differentially expressed genes (DEGs). Gene ontology and pathway function enrichment analysis were performed for common DEGs to reveal the regulatory relationship between the 2 diseases. Besides, the hub genes were extracted by constructing a protein-protein interaction network of shared DEGs. Based on these hub genes, we conducted regulatory network analysis of microRNA/transcription factors–genes and gene - disease relationship and predicted potential drugs for the treatment of COVID-19 and MAFLD. RESULTS: A total of 3734 and 589 DEGs were screened from the transcriptome data of MAFLD (GSE183229) and COVID-19 (GSE196822), respectively, and 80 common DEGs were identified between COVID-19 and MAFLD. Functional enrichment analysis revealed that the shared DEGs were involved in inflammatory reaction, immune response and metabolic regulation. In addition, 10 hub genes including SERPINE1, IL1RN, THBS1, TNFAIP6, GADD45B, TNFRSF12A, PLA2G7, PTGES, PTX3 and GADD45G were identified. From the interaction network analysis, 41 transcription factors and 151 micro-RNAs were found to be the regulatory signals. Some mental, Inflammatory, liver diseases were found to be most related with the hub genes. Importantly, parthenolide, luteolin, apigenin and MS-275 have shown possibility as therapeutic agents against COVID-19 and MAFLD. CONCLUSION: This study reveals the potential common pathogenesis between MAFLD and COVID-19, providing novel clues for future research and treatment of MAFLD and severe acute respiratory syndrome coronavirus 2 infection.
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spelling pubmed-104767962023-09-05 Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach Huang, Tengda Zheng, Dawei Song, Yujia Pan, Hongyuan Qiu, Guoteng Xiang, Yuchu Wang, Zichen Wang, Fang Medicine (Baltimore) 4500 BACKGROUND: Severe coronavirus disease 2019 (COVID-19) has caused a great threat to human health. Metabolic associated fatty liver disease (MAFLD) is a liver disease with a high prevalence rate. Previous studies indicated that MAFLD led to increased mortality and severe case rates of COVID-19 patients, but its mechanism remains unclear. METHODS: This study analyzed the transcriptional profiles of COVID-19 and MAFLD patients and their respective healthy controls from the perspectives of bioinformatics and systems biology to explore the underlying molecular mechanisms between the 2 diseases. Specifically, gene expression profiles of COVID-19 and MAFLD patients were acquired from the gene expression omnibus datasets and screened shared differentially expressed genes (DEGs). Gene ontology and pathway function enrichment analysis were performed for common DEGs to reveal the regulatory relationship between the 2 diseases. Besides, the hub genes were extracted by constructing a protein-protein interaction network of shared DEGs. Based on these hub genes, we conducted regulatory network analysis of microRNA/transcription factors–genes and gene - disease relationship and predicted potential drugs for the treatment of COVID-19 and MAFLD. RESULTS: A total of 3734 and 589 DEGs were screened from the transcriptome data of MAFLD (GSE183229) and COVID-19 (GSE196822), respectively, and 80 common DEGs were identified between COVID-19 and MAFLD. Functional enrichment analysis revealed that the shared DEGs were involved in inflammatory reaction, immune response and metabolic regulation. In addition, 10 hub genes including SERPINE1, IL1RN, THBS1, TNFAIP6, GADD45B, TNFRSF12A, PLA2G7, PTGES, PTX3 and GADD45G were identified. From the interaction network analysis, 41 transcription factors and 151 micro-RNAs were found to be the regulatory signals. Some mental, Inflammatory, liver diseases were found to be most related with the hub genes. Importantly, parthenolide, luteolin, apigenin and MS-275 have shown possibility as therapeutic agents against COVID-19 and MAFLD. CONCLUSION: This study reveals the potential common pathogenesis between MAFLD and COVID-19, providing novel clues for future research and treatment of MAFLD and severe acute respiratory syndrome coronavirus 2 infection. Lippincott Williams & Wilkins 2023-09-01 /pmc/articles/PMC10476796/ /pubmed/37657050 http://dx.doi.org/10.1097/MD.0000000000034570 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.
spellingShingle 4500
Huang, Tengda
Zheng, Dawei
Song, Yujia
Pan, Hongyuan
Qiu, Guoteng
Xiang, Yuchu
Wang, Zichen
Wang, Fang
Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title_full Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title_fullStr Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title_full_unstemmed Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title_short Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
title_sort demonstration of the impact of covid-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach
topic 4500
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476796/
https://www.ncbi.nlm.nih.gov/pubmed/37657050
http://dx.doi.org/10.1097/MD.0000000000034570
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