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Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever wi...

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Autores principales: Noor, Fatima, Ashfaq, Usman Ali, Bakar, Abu, ul Haq, Waqar, Allemailem, Khaled S., Alharbi, Basmah F., Al-Megrin, Wafa Abdullah I., Tahir ul Qamar, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208410/
https://www.ncbi.nlm.nih.gov/pubmed/37234545
http://dx.doi.org/10.3389/fmicb.2023.1175844
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author Noor, Fatima
Ashfaq, Usman Ali
Bakar, Abu
ul Haq, Waqar
Allemailem, Khaled S.
Alharbi, Basmah F.
Al-Megrin, Wafa Abdullah I.
Tahir ul Qamar, Muhammad
author_facet Noor, Fatima
Ashfaq, Usman Ali
Bakar, Abu
ul Haq, Waqar
Allemailem, Khaled S.
Alharbi, Basmah F.
Al-Megrin, Wafa Abdullah I.
Tahir ul Qamar, Muhammad
author_sort Noor, Fatima
collection PubMed
description Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein–protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.
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spelling pubmed-102084102023-05-25 Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning Noor, Fatima Ashfaq, Usman Ali Bakar, Abu ul Haq, Waqar Allemailem, Khaled S. Alharbi, Basmah F. Al-Megrin, Wafa Abdullah I. Tahir ul Qamar, Muhammad Front Microbiol Microbiology Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein–protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS. Frontiers Media S.A. 2023-05-05 /pmc/articles/PMC10208410/ /pubmed/37234545 http://dx.doi.org/10.3389/fmicb.2023.1175844 Text en Copyright © 2023 Noor, Ashfaq, Bakar, ul Haq, Allemailem, Alharbi, Al-Megrin and Tahir ul Qamar. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Noor, Fatima
Ashfaq, Usman Ali
Bakar, Abu
ul Haq, Waqar
Allemailem, Khaled S.
Alharbi, Basmah F.
Al-Megrin, Wafa Abdullah I.
Tahir ul Qamar, Muhammad
Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title_full Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title_fullStr Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title_full_unstemmed Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title_short Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning
title_sort discovering common pathogenic processes between covid-19 and hfrs by integrating rna-seq differential expression analysis with machine learning
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208410/
https://www.ncbi.nlm.nih.gov/pubmed/37234545
http://dx.doi.org/10.3389/fmicb.2023.1175844
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