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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8464664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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