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

Abstract

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.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

“…Other hub-high traffic genes in this module, such as BTAF1 ( 216 ), EZH2 ( 346 ), NPM1 ( 347 ), TPT1 ( 151 , 269 , 348 ), LDHA ( 217 , 268 ), PTPRC ( 189 ), SOS1 ( 349 , 350 ), DDX5 ( 351 , 352 ), BCL10 ( 162 , 353 ), EEF1B2 ( 354 ), CALM1 ( 344 ), EIF4A2 , EIF4B ( 336 , 355 , 356 ), EEF1A1 ( 269 , 354 ), HNRNPA1 ( 228 , 347 ), and IFNG ( 8 , 357 , 358 ), have important roles in development/inhibition of COVID-19. Overexpression of SARS-CoV-2 ORF6 caused HNRNPA1 nuclear accumulation, subverting the host mRNA export system and reducing nucleus size ( 359 ).…”

An L-theanine derivative targets against SARS-CoV-2 and its Delta and Omicron variants

“…Therefore, cancer patients appearing vulnerable to SARS-CoV-2 infection and having poor outcomes may be partly due to host genetic factors and dysregulation of SARS-CoV-2-required genes (Huang et al, 2021). There is a report based on the data analysis results showing that more transcript variants (i.e., alternative splicing [AS] events are found in SARS-CoV-2 infected lung cancer A549 cells than in mock-treated cells; the transcript variants are enriched in important biological pathways that were not detected in the studies, suggesting the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis (Sun et al, 2021). It may exist the complications associated with COVID variants and progression of lung cancer.…”