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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/22/18/9684 |
| _version_ | 1797518981448335360 |
|---|---|
| author | Jiao Sun Naima Ahmed Fahmi Heba Nassereddeen Sze Cheng Irene Martinez Deliang Fan Jeongsik Yong Wei Zhang |
| author_facet | Jiao Sun Naima Ahmed Fahmi Heba Nassereddeen Sze Cheng Irene Martinez Deliang Fan Jeongsik Yong Wei Zhang |
| author_sort | Jiao Sun |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T07:36:56Z |
| format | Article |
| id | doaj.art-9fc1b55e0cdd44e4ad4deb6f174d5fd0 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T07:36:56Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-9fc1b55e0cdd44e4ad4deb6f174d5fd02023-11-22T13:25:46ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-09-012218968410.3390/ijms22189684Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer CellsJiao Sun0Naima Ahmed Fahmi1Heba Nassereddeen2Sze Cheng3Irene Martinez4Deliang Fan5Jeongsik Yong6Wei Zhang7Department of Computer Science, University of Central Florida, Orlando, FL 32816, USADepartment of Computer Science, University of Central Florida, Orlando, FL 32816, USAGenomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USADepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USADepartment of Molecular Biotechnology, Universität Heidelberg, 69120 Heidelberg, GermanySchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USADepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USADepartment of Computer Science, University of Central Florida, Orlando, FL 32816, USAMicrobes 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.https://www.mdpi.com/1422-0067/22/18/9684COVID-19transcript variantsalternative splicingalternative polyadenylationRNA-seq3′-UTR |
| spellingShingle | Jiao Sun Naima Ahmed Fahmi Heba Nassereddeen Sze Cheng Irene Martinez Deliang Fan Jeongsik Yong Wei Zhang Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells International Journal of Molecular Sciences COVID-19 transcript variants alternative splicing alternative polyadenylation RNA-seq 3′-UTR |
| 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 | COVID-19 transcript variants alternative splicing alternative polyadenylation RNA-seq 3′-UTR |
| url | https://www.mdpi.com/1422-0067/22/18/9684 |
| work_keys_str_mv | AT jiaosun computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT naimaahmedfahmi computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT hebanassereddeen computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT szecheng computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT irenemartinez computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT deliangfan computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT jeongsikyong computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells AT weizhang computationalmethodstostudyhumantranscriptvariantsincovid19infectedlungcancercells |