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.

Keywords: 3′-UTR; COVID-19; RNA-seq; alternative polyadenylation; alternative splicing; transcript variants.

Figure 1

Alternative splicing (AS) and alternative polyadenylation (APA). (A) Schematic representation of the five major types of alternative splicing in eukaryotes: Skipped Exon (SE), Retained Intron (RI), Alternative 3$${}^{\prime }$$ Splice Site (A3SS), Alternative 5$${}^{\prime }$$ Splice Site (A5SS), and Mutually Exclusive Exon (MXE). Light blue boxes represent the constitutive exons, whereas light yellow and pink exons represent the spliced ones. (B) Two types of alternative polyadenylation, i.e., coding region alternative polyadenylation (CR-APA) and 3$${}^{\prime }$$-UTR alternative polyadenylation (UTR-APA), and their impact on the functional proteome. PAS: polyadenylation signal; TSS: transcription start site; RBP: RNA-binding protein.

Figure 2

(A) KEGG pathways enriched by alternative splicing events. The blue and red bar charts show the pathways enriched by the splicing events in mock-treated cells and SARS-CoV-2 infected cells, respectively. (B) A plot for the SDCCAG3 AS event. The spliced exon is highlighted in orange. The first two subplots show the read coverage of the gene in both groups, whereas the bottom subplot denotes the gene annotation with exon information. The x-axis and y-axis of the plot represent the position of the specific gene and read coverage of that sample, respectively. To show the differential splicing event, the altered exon is highlighted in orange to provide a clear insight into the phenomenon.

Figure 3

One example of a gene (HNRNPH3) shows an alternative polyadenylation event in the 3$${}^{\prime }$$-UTR region. The black vertical line indicates the potential cleavage site. The x-axis and y-axis represent the position of the gene in its chromosome and the read coverage, respectively. The top two subplots show the changes in read coverage surrounding the event, and the cleavage site is indicated by a black vertical line. The bottom part of the plot shows both the full length and truncated 3$${}^{\prime }$$-UTRs.

Figure 4

RNA-seq data of mock-treated and SARS-CoV-2 infected A549 cells were analyzed for the CR-APA. The x-axis and y-axis represent the CR-truncation ratios ((short mRNA)/(total mRNA)) of a gene. Each dot represents a gene, where the blue dots and red dots show the significant ones in mock and SARS-CoV-2 infected cells, respectively. Upon SARS-CoV-2 infection, 656 genes showed up-regulated CR-APA while 1206 genes showed down-regulated CR-APA. A total of 8832 genes remained unchanged in their CR-APA.

Figure 5

(A) KEGG pathways enriched by CR-APA events. The blue and red bar charts show the pathways enriched by the APA events in mock-treated cells and SARS-CoV-2 infected cells, respectively. (B) One example of a gene (LARP6) shows an alternative polyadenylation event in the coding region. The truncated coding exon is highlighted in orange.

Figure 6

COVID-19 A549 cell lines are clustered by the top 100 transcript markers detected by differential transcript expression analysis.

Figure 7

Differentially expressed transcripts enriched KEGG pathways. The blue and red bar charts show the pathways enriched by the up-regulated and down-regulated transcripts in mock-treated samples over SARS-CoV-2 infected samples, respectively.

Figure 8

Scatter plot of APA and differential gene expression. Red dots represent the individual gene in the analysis. Horizontal blue-dashed lines represent the cutoff values for two-fold changes in differential gene expression. Vertical green-dashed lines represent the cutoff values for the $$lo{g}_{10}$$(p-value) of UTR-APA (A) determined by the Chi-squared test, and the $$lo{g}_{10}$$(p-value) of CR-APA (B) determined by the Student’s t-test.

Figure 9

Four-set Venn diagram shows the overlapped genes in three different types of post-transcriptional regulations and differentially expressed genes (DEG).