Specific Circular RNA Signature of Endothelial Cells: Potential Implications in Vascular Pathophysiology

Abstract

Circular RNAs (circRNAs) are a recently characterized family of gene transcripts forming a covalently closed loop of single-stranded RNA. The extent of their potential for fine-tuning gene expression is still being discovered. Several studies have implicated certain circular RNAs in pathophysiological processes within vascular endothelial cells and cancer cells independently. However, to date, no comparative study of circular RNA expression in different types of endothelial cells has been performed and analysed through the lens of their central role in vascular physiology and pathology. In this work, we analysed publicly available and original RNA sequencing datasets from arterial, veinous, and lymphatic endothelial cells to identify common and distinct circRNA expression profiles. We identified 4713 distinct circRNAs in the compared endothelial cell types, 95% of which originated from exons. Interestingly, the results show that the expression profile of circular RNAs is much more specific to each cell type than linear RNAs, and therefore appears to be more suitable for distinguishing between them. As a result, we have discovered a specific circRNA signature for each given endothelial cell type. Furthermore, we identified a specific endothelial cell circRNA signature that is composed four circRNAs: circCARD6, circPLXNA2, circCASC15 and circEPHB4. These circular RNAs are produced by genes that are related to endothelial cell migration pathways and cancer progression. More detailed studies of their functions could lead to a better understanding of the mechanisms involved in physiological and pathological (lymph)angiogenesis and might open new ways to tackle tumour spread through the vascular system.

Keywords: (lymph)angiogenesis; circRNA; endothelial cell; gene signature.

Figure 1

Extended Landscapes of circRNAs in Endothelial Cells (ECs). (A) RNA sequencing data overview. Only circRNAs with 10 or more reads are accounted. (B) Number of distinct circRNAs sorted by genomic origin for each cell type (left) and the global proportion of exonic, intronic and intergenic circRNAs for all cell types combined (right). (C) Number of distinct circRNAs expressed per chromosome for each cell type. (D) Number of distinct circRNAs expressed per gene for each cell type.

Figure 2

The nature and abundance of circRNAs varies between ECs. (A) Different transcript read counts normalized by million (M) of total reads for each cell type. (B) CircRNAs, lncRNAs and mRNAs independent expression variance coefficient across all cell types or across ECs, and matched circRNA/linear RNA expression variance coefficient. Ordinary one-way ANOVA comparison p values are plotted on the graphs: * <0.05; **** <0.0001. (C) Density plot of circRNAs with expression variance over 0.75 and matched linear RNA variance from the same gene (red dotted rectangle). Density plot of linear RNAs with expression variance over 0.75 and matched circRNA variance from the same gene (blue dotted rectangle). (D) Circular on linear transcript read counts for circRNA-expressing genes (mean in white digits). All differences are significant by two-way ANOVA comparison.

Figure 3

circRNAs are better at distinguishing between ECs than mRNAs. (A) Heatmap representing expression levels for circRNA and matched linear RNA from the same gene, all noncoding RNAs and mRNAs. Relevant circRNA positions are indicated by black arrows. (B) Corresponding Pearson correlation matrixes. p values are presented in Supplementary Figure S4.

Figure 4

There are cell-type-specific circRNAs. (A) Venn diagram representing the comparison of the lists of circRNAs expressed in all ECs (left) and the crossing between non-EC circRNAs and the 28 circRNAs common to ECs. (B) Description of the four EC-specific circRNAs (top) and their linear counterpart (bottom), with the read count in each cell type and gene description. (C) Enrichr gene ontology analysis for EC-specific circRNAs.

Figure 5

Validation of specific circRNAs expression by RT-qPCR. (A) Heatmap representing RNAseq expression levels for selected circRNAs and corresponding qPCR quantifications. (B) Heatmap representing RNAseq expression levels for selected circRNA linear counterparts and corresponding qPCR quantifications. (C) Expression coefficient of variance for selected circRNAs and linear counterparts across cell types from RNAseq data and qPCR quantifications. T test comparisons p values are plotted: ** <0.01. ns: not significant.

Figure 6

RNase R treatment validation of EC-specific circRNAs. (A) Quantification of circRNAs that are common to all cell types and their linear counterparts via RT-qPCR following RNase R treatment. The results are normalized using the untreated control for each transcript. (B) Quantification of cell-type-specific circRNAs and their linear counterparts via RT-qPCR following RNase R treatment. The results are normalized using the untreated control for each transcript. The experiment was repeated twice independently.