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

Area of Science:

• Genomics
• Virology
• Molecular Biology

Background:

• Viruses and microbes can reprogram host cell transcriptomes during infection.
• The COVID-19 pandemic necessitates a deeper understanding of disease mechanisms at the transcriptome level.
• Research on transcriptome reprogramming via post-transcriptional regulation, including alternative splicing and polyadenylation, is limited.

Purpose of the Study:

• To investigate transcriptome reprogramming in SARS-CoV-2 infected cells using computational analysis of RNA-seq data.
• To detect and analyze alternative splicing (AS) and alternative polyadenylation (APA) events in response to SARS-CoV-2 infection.
• To identify novel molecular signatures and biological pathways associated with COVID-19 pathogenesis.

Main Methods:

• Utilized computational methods to analyze publicly available RNA-seq data from mock-treated and SARS-CoV-2 infected A549 lung alveolar cells.
• Detected transcript variants, specifically alternative splicing (AS) and alternative polyadenylation (APA) events.
• Combined differential gene expression analysis with transcript variant analysis.

Main Results:

• SARS-CoV-2 infected cells exhibited an increase in AS events and a decrease in APA events compared to mock-treated cells.
• Most genes with transcript variants were not differentially expressed, indicating a weak correlation between differential gene expression and AS/APA events.
• Transcript variants were enriched in important biological pathways not identified by differential gene expression analysis alone.

Conclusions:

• Transcript variants, particularly AS and APA events, serve as valuable molecular signatures for COVID-19 research.
• These transcript variants reveal biological pathways critical for understanding SARS-CoV-2 pathogenesis, offering new insights beyond traditional gene expression studies.