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Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional reg...

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Autores principales: Sun, Jiao, Fahmi, Naima Ahmed, Nassereddeen, Heba, Cheng, Sze, Martinez, Irene, Fan, Deliang, Yong, Jeongsik, Zhang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464664/
https://www.ncbi.nlm.nih.gov/pubmed/34575842
http://dx.doi.org/10.3390/ijms22189684
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author Sun, Jiao
Fahmi, Naima Ahmed
Nassereddeen, Heba
Cheng, Sze
Martinez, Irene
Fan, Deliang
Yong, Jeongsik
Zhang, Wei
author_facet Sun, Jiao
Fahmi, Naima Ahmed
Nassereddeen, Heba
Cheng, Sze
Martinez, Irene
Fan, Deliang
Yong, Jeongsik
Zhang, Wei
author_sort Sun, Jiao
collection PubMed
description Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.
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spelling pubmed-84646642021-09-27 Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells Sun, Jiao Fahmi, Naima Ahmed Nassereddeen, Heba Cheng, Sze Martinez, Irene Fan, Deliang Yong, Jeongsik Zhang, Wei Int J Mol Sci Article Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis. MDPI 2021-09-07 /pmc/articles/PMC8464664/ /pubmed/34575842 http://dx.doi.org/10.3390/ijms22189684 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Jiao
Fahmi, Naima Ahmed
Nassereddeen, Heba
Cheng, Sze
Martinez, Irene
Fan, Deliang
Yong, Jeongsik
Zhang, Wei
Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title_full Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title_fullStr Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title_full_unstemmed Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title_short Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells
title_sort computational methods to study human transcript variants in covid-19 infected lung cancer cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464664/
https://www.ncbi.nlm.nih.gov/pubmed/34575842
http://dx.doi.org/10.3390/ijms22189684
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