We collected the heart tissues from three AF patients and three healthy controls. The clinical information of these six samples were given in Table 1. The circRNA expression profiles of these samples were measured with Arraystar Human circRNA Array V2 (8 × 15K, Arraystar). The arrays were scanned by the Agilent Scanner G2505C and analyzed with Agilent Feature Extraction software (version 11.0.1.1). The circRNAs presented in at least 3 out of 6 samples were retained. Finally, the expression levels of 12,515 circRNA probes were log2 transformed and quantile normalized. The circRNA expression profiles was given in Supplementary Table S1 and uploaded onto GEO (Gene Expression Omnibus) under accession number of GSE129409.

Written informed consent was obtained from patients before collection of the abandoned left atrial appendages. All experimental procedures were conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Shanghai East Hospital (approval no. 040-2017).

The statistical significance of differential expression between two groups was estimated with t-test using the R software limma package and further filtered with fold change. CircRNAs with t-test p-value smaller than 0.05 and fold change greater than 2 were considered as significant differentially expressed circRNAs.

The interactions between circRNAs and microRNAs play important roles for disease regulation (Ghosal et al., 2013). Some circRNAs contain microRNA sites and act as an endogenous microRNA “sponge” to adsorb and quench the normal biological functions of the microRNA (Lukiw, 2013). To discover such circRNA-microRNA interactions, we applied the TargetScan (Enright et al., 2003) and miRanda (Pasquinelli, 2012) to predict the microRNA targets within circRNAs. What’s more, we predicted the microRNA targets in mRNAs. At last, we constructed the genome wide integrative regulatory network of circRNAs, microRNAs and mRNAs.

Since the functions of circRNAs are still poorly annotated, we investigated the functions of microRNAs interacted with differentially expressed circRNAs. These microRNAs may reflect the functions of differentially expressed circRNAs. We extracted 92 AF related microRNAs from HMDD (the Human microRNA Disease Database) v3.0 (Huang et al., 2018). These 92 AF related microRNAs were listed in Supplementary Table S2. If a circRNA interact with these microRNAs, it may be also related to AF. A complete interaction module of circRNAs, microRNAs and mRNAs with strong literature support from each angle will be a promising regulatory model for AF.

If the t-test p-value was smaller than 0.05 and the fold change was greater than 2, a circRNA was considered as differentially expressed between AF patients and healthy controls. With these criteria, there were 537 up-regulated circRNAs and 199 down-regulated circRNAs in AF patients. These differentially expressed circRNAs between AF patients and healthy controls were listed in Supplementary Table S3. Since the sample size of the AF patients and healthy controls was too small, we did not use the FDR (False Discovery Rate) cutoff to identify the differentially expressed genes. But we still calculated the FDRs and the FDR was 0.556. We also calculated the mean and standard deviation (SD) of AF patients and healthy controls. For 537 up-regulated circRNAs, Mean_AF - SD_AF was always greater than Mean_Control + SD_Control; for 199 down-regulated circRNAs, Mean_AF + SD_AF was always smaller than Mean_Control – SD_Control. Such mean and SD results confirmed that there was difference between AF patients and healthy controls and the difference was greater than their variance.

We calculated the frequencies of microRNAs that targeted these up and down-regulated circRNAs, respectively. The top three most frequent microRNAs for up-regulated circRNAs were hsa-miR-597-3p that interacted with 18 up-regulated circRNAs, hsa-miR-136-5p that interacted with 16 up-regulated circRNAs and hsa-miR-103a-2-5p that interacted with 15 up-regulated circRNAs while the top three most frequent microRNAs for down-regulated circRNAs were hsa-miR-103a-2-5p that interacted with 11 down-regulated circRNAs, hsa-miR-4739 that interacted with 8 down-regulated circRNAs and hsa-miR-627-3p that interacted with 8 down-regulated circRNAs. These microRNAs may be the associated with the differentially expression pattern of circRNAs in AF.

We extracted 92 AF related microRNAs from HMDD (the Human microRNA Disease Database) v3.0 (Huang et al., 2018). If the differentially expressed circRNAs we identified interact with these reported AF related microRNAs, they were more likely to be AF associated circRNAs. Therefore, we highlighted the differentially expressed circRNAs that interact with AF related microRNAs.

There were eight up-regulated and two down-regulated circRNAs interact with AF related microRNAs. Figures 1, 2 plotted the expression pattern of these eight up-regulated circRNAs and these two down-regulated circRNAs, respectively.

Within the eight up-regulated circRNAs, five of them interacted with hsa-miR-892a, three of them interacted with hsa-miR-3149, two of them interacted with hsa-miR-3171. Within the two down-regulated circRNAs, one of them interacted with hsa-miR-892a while another interacted with hsa-miR-133b.

hsa-miR-892a interacted with both up-regulated and down-regulated circRNAs. A large number of differentially expressed circRNAs interact with hsa-miR-892a. Xu et al. (2016) reported that the expression level of has-miR-892a increased significantly from the early stage to the end stage of AF and it can be used as early diagnosis biomarker of AF.

has-miR-3149 had similar expression pattern with hsa-miR-892a and its expression level also increased in AF (Xu et al., 2016).

But hsa-miR-3171 had opposite expression pattern, its expression level decreased in AF (Xu et al., 2016).

The associations of hsa-miR-133b and AF has been reported by several studies but different expression patterns were observed. da Silva et al. found that hsa-miR-133b was up-regulated in acute new-onset AF patients with a 1.4-fold increased expression compared with well-controlled AF patients and control patients (da Silva et al., 2018). Li et al. (2012) reported that miR-133 was down-regulated in chronic AF canines. It was not clear whether such difference was cased by species or miR-133b functions differently at different stages of AF.

circRNA is a member of ncRNA family which could capture other RNA molecules and have recently shown as regulators of other proteins or RNAs including miRNAs. Recent studies have focused more attention on the potential of circRNAs to contribute toward disease etiology. And the expression pattern of circRNAs vary widely on different organism and cell types. Several recent studies have suggested that circRNAs may play essential roles in the initiation and development of cardiovascular diseases (Li et al., 2017). And miRNAs could regulate cardiac function through regulating the proliferation, migration, apoptosis, differentiation of cells during the progression of disease. A large number of literatures has reported association between miRNAs and AF related to remodel processes, and miRNAs might have important roles in signaling during the pathogenesis of AF (Flemming, 2014; Danielson et al., 2018). It has been shown that circRNAs may act as endogenous sponge RNAs to interact with miRNAs and influence the expression of miRNA target genes.

In our research, we found that circRNA_100612 which located on chromosome 10 could lead to AF by interacting with miR-133b. One of the target gene of miR-133b, KCNIP1, is a member of the family of cytosolic voltage-gated potassium (Kv) channel-interacting proteins and related to cardiac conduction pathway. In zebrafish, overexpression of KCNIP1 could lead to inducible AF. Genome-wide approach show a common 4,470 bp CNV in most AF patients indicated that KCNIP1 could be a genetic predictor of AF risk (Tsai et al., 2016).

Another important downstream target gene of miR-133b is JPH2 which have an important role in sarcoplasmic reticulum Ca²⁺ handling and modulation of ryanodine receptor Ca²⁺ channels. Knockdown JPH2 in mice was related to loss of junctional membrane complexes numbers, reduced Ca2+-induced Ca2+ release, and acute heart failure (van Oort et al., 2011). Mutation E169K in JPH2 could result in AF because of defective RyR2-mediated SR Ca2+ release events that representing a potential novel therapeutic target for AF (Beavers et al., 2013).

miR-133b could affect ADRB1 which is a member of the superfamily of cell surface receptors and has a great effect on the myocardium (Cresci, 2012; Pasquier et al., 2016). ADRB1 is also an effective target for pharmacotherapy in cardiovascular diseases, and β-blocking medications are acknowledged as first line agents for ventricular rate control in patients with AF (Chen et al., 2003; McMurray and van Veldhuisen, 2014).

has-miR-892b interact with down-regulated has_circRNA_405917 and up-regulated hsa_circRNA_008132, hsa_circRNA_104052, hsa_circRNA_101021, hsa_circRNA_101020, hsa_circRNA_102341.

One important target gene of miR-892b is GJA1 which encodes the gap junction protein connexin 43 on chromosome 6q22.31 (Van Norstrand et al., 2012). A recent study using large-scale genotyping reported novel AF risk loci at or near GJA1. They found that SNPs associated with AF could influence the transcription of GJA1 in both left atrial tissue and whole heart (Thibodeau et al., 2010; Sinner et al., 2014).