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. 2022 Jun;8(6):mgen000848.
doi: 10.1099/mgen.0.000848.

Identification and characterization of virus-encoded circular RNAs in host cells

Shuting Chen  1 , Jie Zheng  1 , Bingyue Zhang  1 , Xinyue Tang  1 , Yewei Cun  1 , Tao Wu  1 , Yue Xu  1 , Ting Ma  1 , Jingxin Cheng  2 , Zuoren Yu  3 , Haiyun Wang  1
Affiliations

Identification and characterization of virus-encoded circular RNAs in host cells

Shuting Chen et al. Microb Genom. 2022 Jun.

Abstract

Emerging evidence has identified viral circular RNAs (circRNAs) in human cells infected by viruses, interfering with the immune system and inducing diseases including human cancer. However, the biogenesis and regulatory mechanisms of virus-encoded circRNAs in host cells remain unknown. In this study, we used the circRNA detection tool CIRI2 to systematically determine the virus-encoded circRNAs in virus-infected cancer cell lines and cancer patients, by analysing RNA-Seq datasets derived from RNase R-treated samples. Based on the thousands of viral circRNAs we identified, the biological characteristics and potential roles of viral circRNAs in regulating host cell function were determined. In addition, we developed a Viral-circRNA Database (http://www.hywanglab.cn/vcRNAdb/), which is open to all users to search, browse and download information on circRNAs encoded by viruses upon infection.

Keywords: RNA-Seq; RNase R; Viral-circRNA Database; host cells; viral circRNA.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
An analysis pipeline to identify viral circRNAs from RNase R-treated RNA-Seq data of virus-infected tumour samples. The schematic depiction shows the three steps of the workflow, which include data collection and processing, identification of viral circRNAs, and data analysis and construction of the Viral-circRNA Database.
Fig. 2.
Fig. 2.
Statistical data of viral circRNAs. (a) Pie chart showing the distribution of cancers included in this analysis. The subgraph of the pie chart shows the subtypes of lymphoma. (b) The number of viral circRNAs identified from five viruses, MERS-CoV, KSHV, EBV, rLCV and HPV18. (c) The distribution of the number of junction reads for detecting viral circRNAs from each sample. (d) The distribution of circRNA expression measured by the natural logarithm of CPM. (e) The distribution of viral circRNA length. (f) The number of circRNAs identified across the samples. Graphical representation of EBV (g), KSHV (h), MERS-CoV (i) and rLCV (j) circRNAs using the program circlize. The outer circle of the viral genome is arranged in clockwise order, with each arch connecting the starting position and ending position of a single circRNA in the inner circle. The gradient colour of the arches corresponds to the expression level of circRNAs, characterized by the natural logarithm of the average CPM for each circRNA. Both positive- (red) and negative- (blue) strand circRNAs are shown. The purple curve and blue curve in the middle circle indicate the number of viral circRNAs for which each nucleotide is covered on the plus strand and minus strand, respectively.
Fig. 3.
Fig. 3.
Viral genes originating from EBV (a), KSHV (b), MERS-CoV (c) and rLCV (d). The number of circRNAs that overlap with each viral gene is shown in the bar plot, which reflects the number of circRNA isoforms transcribed from each gene. (e) Graphical representation of EBV circRNAs with abundant isoforms made by SpliceV. Orange arrows demonstrate the genome structure of each virus. Genome regions outlined with black solid lines depict different circRNA isoforms generated from those regions.
Fig. 4.
Fig. 4.
Functional analysis and negative strand bias of viral circRNAs. KEGG pathway and GO enrichment analyses of viral circRNA interacting genes. Only the top 10 enriched terms of the KEGG pathway (a), biological process (BP) (b), molecular function (MF) (c) and cellular component (CC) (d) are shown. The coverage curve of circRNAs on the reference genomes of MERS-CoV (e) and SARS-CoV-2 (f). Genes with known mapped positions in the genome are shown in orange. The pink curve and blue curve show the number of viral circRNAs for which each nucleotide is covered on the plus strand and minus strand, respectively.
Fig. 5.
Fig. 5.
Long-range cyclization of the viral genome and subgenome. The scatter plot shows the cyclization positions of circRNAs in EBV (a) and MERS-CoV (b). The circRNA, formed by long-range cyclization, is highlighted in red when its length is ≥90 % of the reference genome and in orange when its length is ≥50 % but <90 %. Violin plot of ln(CPM) for EBV (Wilcoxon rank-sum test P=0.018) (c) and MERS-CoV (Wilcoxon rank-sum test P<2×10−16) (d) circRNAs grouped into long (length ≥50 % of the reference genome) and non-long (length <50 % of the reference genome) groups. (e) Scatter plot of the cyclization positions of SARS-CoV-2 circRNAs. The circRNA, formed by long-range cyclization, is highlighted in red when its length is ≥90 % of the reference genome and in orange when its length is ≥50 % but <90 %. (f) Violin plot of ln(CPM) for SARS-CoV-2 circRNAs grouped into long (length ≥50 % of the reference genome) and non-long (length <50 % of the reference genome) groups (Wilcoxon rank-sum test P=0.049).
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