Integrative analysis of the circRNA-miRNA regulatory network in atrial fibrillation.

We aimed to investigate the circRNA-miRNA regulatory network in atrial fibrillation (AF) by using Cytoscape and HMDD v3.0. Finally, 120 differentially expressed circRNAs in peripheral blood monocytes of 4 AF patients were preliminarily screened by circRNA microarray. circRNA_4648, circRNA_4631, and circRNA_2875 were the first four circRNAs with the most binding nodes in the circRNA-miRNA network. The top three most frequent miRNAs for up-regulated circRNAs were hsa-miR-328 that interacted with 5 up-regulated circRNAs, hsa-miR-4685-5p with 4 up-regulated circRNAs, hsa-miR-3150a-3p, hsa-miR-4649-5p, hsa-miR-4783-3p, and hsa-miR-8073 with 3 up-regulated circRNAs,, while the top three most frequent miRNAs for down-regulated circRNAs were hsa-miR-328 that interacted with 14 down-regulated circRNAs, hsa-miR-4685-5p with 11 down-regulated circRNAs and hsa-miR-661 with 9 down-regulated circRNAs. According to HMDD v3.0, five up-regulated and eleven down-regulated circRNAs were found to interact with AF related miRNAs. These results indicated the possible regulatory network between circRNAs and miRNAs in the pathogenesis of AF.

Introduction

Atrial fibrillation (AF), one of the most common arrhythmias in clinical practice, with a prevalence about 1–2% in the general population, is characterized with high relative risk of heart failure and embolic stroke. AF is also considered as a potential factor for high mortality and morbidity, especially in elderly individuals[1,2]. Recent growing reports indicate that structural remodeling and electrical remodeling are important pathophysiological contributors to onset and maintenance of AF [3,4]. However, exact mechanism of how AF occurs is still unknown.

To our knowledge, non-coding RNAs (ncRNAs), include a class of RNAs, such as long non-coding RNAs (lncRNAs), micro-RNAs (miRNAs) and circular RNAs (circRNAs), play crucial roles in regulating gene expression under pathological and physiological conditions[5-7]. circRNAs, a novel type of endogenous ncRNAs , have be reported as a key ncRNAs in gene regulation and the pathophysiology of cardiovascular diseases[8,9]. It has been well-known that dysregulated miRNAs can contribute to the prevalence of AF by deregulating transcription factor, regulating atrial excitability and increasing atrial arrhythmogenicity[10,11]. Accumulating studies indicate that circRNAs may interact with miRNAs by a sequence-driven sponging effect and the circRNA–miRNA-network has emerging roles in physiological and pathological processes of cardiovascular diseases[12,13]. However, to our knowledge, there are few studies pointing to the expression of circRNAs in AF, and circRNA–miRNA network in AF remains unclear.

In the present study, we analyzed and predicted the differentially expressed circRNAs in human monocytes from patients with AF and healthy controls using microarray, the potential regulatory network between circRNAs and miRNAs were explored by using Cytoscape and HMDD v3.0. We hypothesized that there were differentially expressed circRNAs in human monocytes and highly possible interaction between circRNAs and miRNAs, which would provide an important landmark for investigating the mechanism of AF.

Materials and methods

Study population and specimen collection

10 patients with AF (AF group) and 10 matched healthy subjects (Control group) who excluded AF were enrolled (Table 1). 10 ml of peripheral blood was collected, monocytes were purified from peripheral blood and frozen for analysis. The diagnosis of AF was consistent with the criteria listed in the 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS[14]. The Ethics Committee of Taizhou People’s Hospital approved the study, which was conducted according to the principles of the Declaration of Helsinki and the International Conference on Harmonisation Good Clinical Practice guidelines. All the enrolled subjects provided written informed consent before entering this experiment.

Table 1

Baseline characteristics of the subjects.

Variable	AF group	Control group	P value
Age	52.10 ± 8.19	49.60 ± 10.92	> 0.05
Gender (%)
Female	6	5	> 0.05
Male	4	5	> 0.05
Complicated diseases
Rheumatic heart disease	0	0	> 0.05
Hypertension	1	0	> 0.05
Hyperlipidemia	1	0	> 0.05
Diabetes mellitus	0	0	> 0.05
Coronary heart disease	0	0	> 0.05
Infectious disease	0	0	> 0.05
Connective tissue disease	0	0	> 0.05
Other autoimmune diseases	0	0	> 0.05
Other cardiovascular diseases	0	0	> 0.05
Left atrial diameter (mm)	43.20 ± 4.02	31.3 ± 3.59	< 0.05
Ejection fraction	48.10 ± 8.26	53.20 ± 8.43	> 0.05

The differentially expressed circRNAs of AF detected by microarray analysis

The total RNA in monocytes was extracted using Trizol reagent (Ambion, USA) and purified by QIAGEN RNeasy Mini Kit (QIGEN, Germany). Sample labeling and microarray hybridization were conducted by Outdo Bio‐tech (Shanghai, P.R. China) with the same method as previously described [15]. Simply, the circRNAs were measured with the Agilent One-Color Microarray Based Gene Expression Analysis Low. The arrays were scanned by Axon microarray 4000B microarray scanner and extracted using Agilent Feature Extraction software (version 11.0.1.1). Quantile normalization and data processing were conducted by the Gene Spring GXv11.5.1 software package (Agilent, USA). The fold-change between AF patients and healthy controls was calculated. The statistical significance was calculated by t test and further filtered with fold change. circRNAs with foldchange > 2 and p < 0.05 were regarded as significant differential expression.

qRT-PCR validation of differentially expressed circRNAs

In order to confirm the results of microarray analysis, four upregulated circRNAs (circRNA_0031, circRNA_1837,circRNA_5901 and circRNA_7571) and four downregulated circRNAs (circRNA_2773, circRNA_5801, circRNA_7386 and circRNA_7577) were selected randomly for validation by qRT-PCR in all study population. Simply, 1 μl of cDNAs was added to 12.5 μl of SYBR‐Green Gene Expression Master Mix (Applied Biosystems, Inc.), 10.5 μl of DEPC‐treated water, and 0.5 μl of reverse and forward primers. The gene expression level of target circRNAs was normalized to the housekeeping gene GAPDH (Sangon Biotech, Shanghai, China) and calculated using the (2−ΔΔCt) method. The primer sequences for RT-PCR were shown in Table 2.

Table 2

Primer sequences for reverse transcription polymerase chain reaction.

Gene name	circbase_id	Primer sequences	Fragment (bp)
GAPDH	–	F:5′-TCTCTGCTCCTCCCTGTTCTA-3′	177
		R:5′-ATGAAGGGGTCGTTGATGGC-3′
circRNA_0031	hsa_circ_0008737	F:5′-ACUGCCCUAAGUGCUCCUUCUGG-3′	179
		R:5′-AGAGAAGGGGCCTGAGGGCAGA-3′
circRNA_1837	–	F:5′-GCUGGGAUUACAGGCAUGAGCC-3′	192
		R:5′-GGCTCACGCCTGTAATCCCAGG-3′
circRNA_5901	hsa_circ_0001240	F:5′-CAGUGGCCAGAGCCCUGACGUG-3′	159
		R:5′-TGCTGCCGGGAGCATCGGCCACTG-3′
circRNA_7571	–	F:5′-GGUCCAGAGGGCCGTCGT-3′	165
		R:5′-ATCCCTGTCCATCTCTGGACC-3′
circRNA_2773	–	F:5′-GGGGUUCCUGGGGAUGGGAUUU-3′	163
		R:5′-TCAAAAAGAACCCTAGGAACCCc-3′
circRNA_5801	hsa_circ_0062426	F:5′-UGGGUAGAGAAGGAGCUCAGAGGA-3′	181
		R:5′-CTCTCTGCAGCCCTTTGTCTACCCA-3′
circRNA_7386	–	F:5′-UGAGGCCCUUGGGGCACAGUGG-3′	166
		R:5′-ACACTTAGTGCTTACAAGGGCCTCA-3′
circRNA_7577	hsa_circ_0006109	F:5′-UGCCCCACCUGCUGACCACCCUC-3′	166
		R:5′-CCCGGTGG-CGGCTTGTGGGGCT-3′

Construction of circRNA–miRNA regulatory networks

Acting as competing miRNA sponge, the sponging activity of differentially expressed circRNAs over corresponding miRNAs was calculated by the prediction of miRNA target binding sites using the miRanda software. Enrichment results of total differentially expressed circRNAs were sorted by p value, and the potential connections between circRNAs and miRNAs were further explored by using Cytoscape 3.4.0 (http://cytoscape.org/). Finally, the regulatory networks of circRNA–microRNA in AF patients were constructed.

Analyze the AF related circRNAs according to HMDD v3.0

In order to further explore the AF related circRNAs, we used the website of HMDD v3.0. HMDD v3.0, a database for experimentally supported human microRNA–disease associations, integrated many past publications about miRNA–disease associations, and offered evidence-stratified miRNA–disease data based on six categories of 20 evidence codes[16]. We used the keywords ‘atrial fibrillation’ to obtain AF related miRNAs from HMDD v3.0. If the differentially expressed circRNAs identified by microarray interacted with these reported AF related miRNAs, they were considered to be AF associated circRNAs.

Results

The differentially expressed circRNAs between AF patients and healthy controls

A total of 120 circRNAs was calculated as differentially expressed between AF patients and healthy controls (fold change > 2, and p < 0.05) (Fig. 1). In which, 65 circRNAs were upregulated (Table 3) and 55 circRNAs were downregulated (Table 4).

Figure 1

Differentially expressed circRNAs between AF group and control group. (A) Volcano plots are displayed for visualizing the differential expression of circRNAs. The red and green points in the plot represent the differentially expressed circRNAs with statistical significance. (B) Hierarchical cluster analysis of all the deregulated circRNAs.

Table 3

Upregulation circular RNA.

circRNA_id	circbase_id	circRNA_Chr	Type	Gene	Fold change	P value
circRNA_0031	hsa_circ_0008737	Chr1	Sense-overlapping	CAMTA1	3.34	0.031
circRNA_0095	–	Chr1	Intronic	CAPZB	8.01	0.011
circRNA_0161	–	Chr1	Antisense	THEMIS2	4.14	0.001
circRNA_0312	hsa_circ_0004877	Chr1	Sense-overlapping	EPS15	4.06	0.011
circRNA_0544	–	Chr1	Intergenic		10.15	0.017
circRNA_0685	hsa_circ_0000160	Chr1	Sense-overlapping	SUCO	2.49	0.014
circRNA_1166	–	Chr10	Intronic	JMJD1C	8.73	0.042
circRNA_1402	–	Chr11	Sense-overlapping	IFITM2	5.78	0.049
circRNA_1415	hsa_circ_0000274	Chr11	Sense-overlapping	NUP98	5.24	0.047
circRNA_1417	–	Chr11	Intronic	NUP98	3.84	0.015
circRNA_1513	hsa_circ_0000302	Chr11	Sense-overlapping	SPI1	3.06	0.040
circRNA_1741	hsa_circ_0005589	Chr11	Sense-overlapping	ARCN1	4.21	0.012
circRNA_1837	–	Chr12	Sense-overlapping	KLRC2	9.3	0.025
circRNA_2116	hsa_circ_0004901	Chr12	Sense-overlapping	APAF1	3.88	0.037
circRNA_2294	hsa_circ_0007547	Chr13	Sense-overlapping	SKA3	4.18	0.011
circRNA_2371	–	Chr13	Sense-overlapping	ELF1	10.23	0.029
circRNA_2482	–	Chr13	Sense-overlapping	SLAIN1	3.86	0.020
circRNA_2551	–	Chr14	Intergenic		3.8	0.029
circRNA_2616	hsa_circ_0008002	Chr14	Sense-overlapping	POLE2	3.24	0.030
circRNA_2681	hsa_circ_0032109	Chr14	Sense-overlapping	PPM1A	3.54	0.020
circRNA_3140	hsa_circ_0003916	Chr15	Sense-overlapping	PIAS1	5.52	0.002
circRNA_3337	hsa_circ_0000672	Chr16	Sense-overlapping	CLEC16A	3.08	0.040
circRNA_3359	hsa_circ_0002771	Chr16	Sense-overlapping	PARN	3.64	0.024
circRNA_3421	hsa_circ_0008223	Chr16	Sense-overlapping	XPO6	2.91	0.048
circRNA_3448	hsa_circ_0039161	Chr16	Sense-overlapping	ITGAX	8.18	0.000
circRNA_4003	hsa_circ_0005347	Chr17	Sense-overlapping	BPTF	5.73	0.034
circRNA_4284	hsa_circ_0008699	Chr18	Exonic	ZNF516	5.63	0.008
circRNA_4314	hsa_circ_0004891	Chr19	Sense-overlapping	CNN2	4.06	0.040
circRNA_4656	hsa_circ_0008847	Chr2	Sense-overlapping	MBOAT2	3.76	0.015
circRNA_4657	hsa_circ_0000972	Chr2	Sense-overlapping	MBOAT2	2.45	0.010
circRNA_4661	–	Chr2	Sense-overlapping	MBOAT2	5.89	0.022
circRNA_4864	hsa_circ_0001006	Chr2	Sense-overlapping	RTN4	3.43	0.029
circRNA_4959	–	Chr2	Sense-overlapping	DYSF	3.69	0.026
circRNA_5325	–	Chr2	Antisense	NOP58	3.21	0.045
circRNA_5335	hsa_circ_0003493	Chr2	Sense-overlapping	CARF	3.55	0.026
circRNA_5399	hsa_circ_0058514	Chr2	Sense-overlapping	AGFG1	3.89	0.014
circRNA_5664	–	Chr20	Intronic	CTSZ	6.47	0.024
circRNA_5691	hsa_circ_0061286	Chr21	Sense-overlapping	USP25	3.08	0.045
circRNA_5774	hsa_circ_0008021	Chr21	Sense-overlapping	PDXK	13.23	0.004
circRNA_5897	hsa_circ_0008806	Chr22	Sense-overlapping	CCDC134	5.19	0.022
circRNA_5901	hsa_circ_0001240	Chr22	Exonic	NFAM1	6.34	0.033
circRNA_5988	hsa_circ_0001274	Chr3	Sense-overlapping	PLCL2	8.66	0.046
circRNA_6087	hsa_circ_0001289	Chr3	Sense-overlapping	SETD2	3.18	0.032
circRNA_6264	hsa_circ_0066959	Chr3	Sense-overlapping	HCLS1	3.62	0.028
circRNA_6360	–	Chr3	Sense-overlapping	PLOD2	3.69	0.015
circRNA_6574	hsa_circ_0001394	Chr4	Exonic	TBC1D14	4.04	0.004
circRNA_6624	–	Chr4	Exonic	TLR6	3.43	0.033
circRNA_6644	–	Chr4	Sense-overlapping	RBM47	3.13	0.050
circRNA_6903	hsa_circ_0071174	Chr4	Sense-overlapping	LRBA	3.18	0.032
circRNA_6955	hsa_circ_0001460	Chr4	Sense-overlapping	NEIL3	3.25	0.044
circRNA_6991	–	Chr5	Intergenic		5.86	0.002
circRNA_7097	hsa_circ_0072697	Chr5	Sense-overlapping	PPWD1	6.69	0.008
circRNA_7571	–	Chr6	Sense-overlapping	HLA-A	28.22	0.005
circRNA_7672	hsa_circ_0003700	Chr6	Sense-overlapping	FBXO9	6.12	0.030
circRNA_7952	hsa_circ_0004662	Chr6	Sense-overlapping	SOD2	5.68	0.011
circRNA_7964	hsa_circ_0078665	Chr6	Sense-overlapping	RNASET2	3.43	0.033
circRNA_8132	hsa_circ_0001707	Chr7	Intronic	ABCA13	15.44	0.010
circRNA_8233	–	Chr7	Sense-overlapping	ANKIB1	3.43	0.037
circRNA_8255	hsa_circ_0007940	Chr7	Sense-overlapping	ARPC1B	3.62	0.028
circRNA_8317	hsa_circ_0082096	Chr7	Sense-overlapping	ZNF800	4.88	0.031
circRNA_8548	hsa_circ_0006376	Chr8	Sense-overlapping	HOOK3	3.31	0.043
circRNA_8895	hsa_circ_0003945	Chr9	Sense-overlapping	UBAP2	3.37	0.015
circRNA_9098	hsa_circ_0008192	Chr9	Sense-overlapping	PTBP3	4.22	0.014
circRNA_9396	hsa_circ_0001947	ChrX	Exonic	AFF2	7.79	0.001
circRNA_9422	hsa_circ_0008297	ChrY	Sense-overlapping	DDX3Y	5.27	0.037

Table 4

Downregulation circRNA.

circRNA_id	circbase_id	circRNA_Chr	Type	Gene	FoldChange	pValue
circRNA_0259	hsa_circ_0009142	Chr1	Sense-overlapping	CAP1	3.41	0.029
circRNA_0323	hsa_circ_0012553	Chr1	Sense-overlapping	ZCCHC11	2.88	0.014
circRNA_0831	–	Chr1	Sense-overlapping	LYPLAL1	4.38	0.024
circRNA_0835	hsa_circ_0004417	Chr1	Sense-overlapping	LYPLAL1	9.69	0.023
circRNA_0947	hsa_circ_0002802	Chr1	Sense-overlapping	ZNF124	6.37	0.042
circRNA_0995	hsa_circ_0000211	Chr10	Sense-overlapping	SFMBT2	4.55	0.024
circRNA_1111	–	Chr10	Sense-overlapping	CCDC7	2.94	0.028
circRNA_1292	–	Chr10	Sense-overlapping	EXOSC1	3.23	0.015
circRNA_1335	hsa_circ_0000260	Chr10	Sense-overlapping	SMC3	4.44	0.037
circRNA_1450	–	Chr11	Sense-overlapping	SERGEF	3.47	0.010
circRNA_1496	–	Chr11	Sense-overlapping	PRR5L	3.79	0.011
circRNA_1693	hsa_circ_0006208	Chr11	Sense-overlapping	NPAT	7.11	0.003
circRNA_1786	hsa_circ_0002881	Chr12	Sense-overlapping	KDM5A	3.08	0.019
circRNA_1787	hsa_circ_0024946	Chr12	Sense-overlapping	KDM5A	3.82	0.009
circRNA_1800	–	Chr12	Antisense	CACNA1C	5.31	0.005
circRNA_1834	–	Chr12	Sense-overlapping	KLRC4-KLRK1	2.95	0.000
circRNA_2370	–	Chr13	Exonic	ELF1	3.09	0.021
circRNA_2527	hsa_circ_0004096	Chr13	Sense-overlapping	RASA3	4.44	0.001
circRNA_2683	hsa_circ_0032116	Chr14	Sense-overlapping	MNAT1	3.67	0.007
circRNA_2773	–	Chr14	Intergenic		12.02	0.043
circRNA_2875	–	Chr14	Intergenic		3.06	0.030
circRNA_3138	–	Chr15	Intronic	PIAS1	4.33	0.036
circRNA_3307	hsa_circ_0007788	Chr16	Sense-overlapping	NMRAL1	10.03	0.023
circRNA_3807	–	Chr17	Sense-overlapping	CCL3L3	7.42	0.016
circRNA_3830	–	Chr17	Sense-overlapping	ERBB2	3.01	0.004
circRNA_4184	–	Chr18	Sense-overlapping	RNF138	6.13	0.000
circRNA_4402	–	Chr19	Sense-overlapping	ZNF564	3.51	0.014
circRNA_4581	hsa_circ_0003912	Chr19	Exonic	DBP	4.63	0.005
circRNA_4624	–	Chr19	Sense-overlapping	LILRA1	7.92	0.002
circRNA_4631	–	Chr19	Sense-overlapping	KIR2DL1	8.77	0.009
circRNA_4648	–	Chr2	Intergenic		4.41	0.007
circRNA_4737	–	Chr2	Exonic	GTF3C2	4.23	0.011
circRNA_5440	hsa_circ_0001112	Chr2	Sense-overlapping	DGKD	2.13	0.050
circRNA_5625	hsa_circ_0003998	Chr20	Sense-overlapping	ARFGEF2	6.95	0.037
circRNA_5801	hsa_circ_0062426	Chr22	Sense-overlapping	PPIL2	4.82	0.043
circRNA_5996	–	Chr3	Intergenic		4.12	0.021
circRNA_6086	–	Chr3	Sense-overlapping	SETD2	4.63	0.005
circRNA_6610	hsa_circ_0069397	Chr4	Sense-overlapping	ARAP2	7.28	0.043
circRNA_6775	hsa_circ_0002782	Chr4	Sense-overlapping	SLC39A8	5.38	0.019
circRNA_6810	hsa_circ_0007477	Chr4	Sense-overlapping	PPA2	5.64	0.030
circRNA_7032	hsa_circ_0072380	Chr5	Exonic	ZNF131	4.18	0.009
circRNA_7335	hsa_circ_0006716	Chr5	Sense-overlapping	UBE2D2	3.66	0.032
circRNA_7386	–	Chr5	Sense-overlapping	SGCD	4.37	0.007
circRNA_7577	hsa_circ_0006109	Chr6	Sense-overlapping	C6orf136	2.29	0.028
circRNA_7599	–	Chr6	Sense-overlapping	HLA-DRB1	3.16	0.042
circRNA_7797	hsa_circ_0001638	Chr6	Sense-overlapping	MFSD4B	3.21	0.031
circRNA_8031	hsa_circ_0005519	Chr7	Sense-overlapping	SNX13	8.57	0.045
circRNA_8108	–	Chr7	Sense-overlapping	TARP	6.28	0.001
circRNA_8280	hsa_circ_0007395	Chr7	Sense-overlapping	KMT2E	12.57	0.033
circRNA_8455	–	Chr8	Intronic	ERI1	9.61	0.023
circRNA_8731	hsa_circ_0085438	Chr8	Sense-overlapping	TBC1D31	5.03	0.002
circRNA_8841	–	Chr9	Sense-overlapping	KIAA2026	3.34	0.025
circRNA_8857	hsa_circ_0008732	Chr9	Sense-overlapping	BNC2	3.62	0.022
circRNA_9064	–	Chr9	Sense-overlapping	NIPSNAP3A	7.75	0.000
circRNA_9326	hsa_circ_0091175	ChrX	Sense-overlapping	BRWD3	3.69	0.020

Four upregulated circRNAs (circRNA_0031, circRNA_1837, circRNA_5901 and circRNA_7571) and four downregulated circRNAs (circRNA_2773, circRNA_5801, circRNA_7386 and circRNA_7577) were selected randomly by Random Number Generator Pro V1.79 software for for qRT-PCR validation to confirm the microarray results. As a results, all of 4 upregulated circRNAs (p < 0.05 or p < 0.01 for circRNA_0031, circRNA_1837, circRNA_5901 and circRNA_7571, respectively) and 3 out of 4 downregulated circRNAs (p < 0.05 or p < 0.01 for circRNA_5801, circRNA_7386 and circRNA_7577, respectively) showed a significantly different expression (Fig. 2), which was consistent with microarray results.

Figure 2

Quantitative reverse transcription polymerase chain reaction analysis for validation of differentially expressed circRNAs. Compared with control group, *P < 0.05 and **P < 0.01.

Construction of circRNA–miRNA networks

We calculated the terms of miRNAs that targeted these dysregulated circRNAs by using Cytoscape 3.4.0 (http://cytoscape.org/) and conducted the circRNA–miRNA networks (shown in Fig. 3). Results showed that circRNA_7571, circRNA_4648, circRNA_4631, and circRNA_2875 were the first four circRNAs with the most binding nodes in the co-expression network, interacted with 34 miRNAs, 26 miRNAs, 24 miRNAs and 24 miRNAs, respectively (Fig. 4). The top three most frequent miRNAs for up-regulated circRNAs were hsa-miR-328 that interacted with 5 up-regulated circRNAs, hsa-miR-4685-5p with 4 up-regulated circRNAs, hsa-miR-3150a-3p, hsa-miR-4649-5p, hsa-miR-4783-3p, and hsa-miR-8073 that interacted with 3 up-regulated circRNAs, while the top three most frequent miRNAs for down-regulated circRNAs were hsa-miR-328 that interacted with 14 down-regulated circRNAs, hsa-miR-4685-5p that interacted with 11 down-regulated circRNAs and hsa-miR-661 that interacted with 9 down-regulated circRNAs. We predicted that these miRNAs may be more relevant with the differentially expressed circRNAs in AF.

Figure 3

circRNA–miRNA coexpression network explored by using Cytoscape. The size of each node represents functional connectivity of each circRNA. The network consists of 37 circRNAs and 90 miRNAs. The red node represents circRNA and the green node represents miRNA. circRNA_7571, circRNA_4648, circRNA_4631 and circRNA_2875 were the four largest nodes in the network. hsa-miR-328 was the highest positive correlated miRNA in the networks.

Figure 4

Sponging capabilities of circRNA_7571, circRNA_4648, circRNA_4631, circRNA_2875and circRNA_7599 quantified by particularmiRNA. Diameters of circles are proportional to the number of miRNA targets in each circRNAs.

We confirmed 100 AF related miRNAs from HMDD v3.0 by using the keywords ‘atrial fibrillation’. If the differentially expressed circRNAs identified by microarray interacted with these reported AF related miRNAs, they were considered to be AF associated circRNAs. Finally, five up-regulated (hsa_circRNA_7571, hsa_circRNA_3448, hsa_circRNA_1402, hsa_circRNA_4284 and hsa_circRNA_1415) and eleven down-regulated circRNAs (hsa_circRNA_2527, hsa_circRNA_4648, hsa_circRNA_4624, hsa_circRNA_1496, hsa_circRNA_3138, hsa_circRNA_3138, hsa_circRNA_6086, hsa_circRNA_2875, hsa_circRNA_3807, hsa_circRNA_4402, hsa_circRNA_4631 and hsa_circRNA_2773) were found to interact with AF related miRNAs. Figures 5 and 6 showed the expression pattern of these dysregulated circRNAs, respectively.

Figure 5

The expression pattern of the five up-regulated circRNAs that interact with AF related miRNAs. (A) The expression pattern of hsa_circRNA_7571 that interact with has-miR-133a. (B) The expression pattern of hsa_circRNA_3448 that interact with has-miR-328. (C) The expression pattern of hsa_circRNA_1402 that interact with has-miR-486. (D) The expression pattern of hsa_circRNA_4284 that interact with has-miR-328. (E) The expression pattern of hsa_circRNA_1415 that interact with has-miR-328.

Figure 6

The expression pattern of the eleven down-regulated circRNAs that interact with atrial fibrillation related miRNAs. (A) The expression pattern of hsa_circRNA_2527 that interact with has-miR-328. (B) The expression pattern of hsa_circRNA_4648 that interact with has-miR-328. (C) The expression pattern of hsa_circRNA_4624 that interact with has-miR-328. (D) The expression pattern of hsa_circRNA_1496 that interact with has-miR-328. (E) The expression pattern of hsa_circRNA_3138 that interact with has-miR-574. (E) The expression pattern of hsa_circRNA_3138 that interact with has-miR-574. (F) The expression pattern of hsa_circRNA_6086 that interact with has-miR-574. (G) The expression pattern of hsa_circRNA_2875 that interact with has-miR-92a. (H) The expression pattern of hsa_circRNA_3807 that interact with has-miR-26b. (I) The expression pattern of hsa_circRNA_4402 that interact with has-miR-328. (J) The expression pattern of hsa_circRNA_4631 that interact with has-miR-199a. (K) The expression pattern of hsa_circRNA_2773 that interact with has-miR-574.

Within the five up-regulated circRNAs, three of them (circRNA_7571, circRNA_3448, circRNA_1415) interacted with hsa-miR-328, one of them (circRNA_1402, circRNA_4284, respectively) interacted with hsa-miR-486 and hsa-miR-133a, respectively. Within the eleven down-regulated circRNAs, five of them (circRNA_4648, circRNA_4624, circRNA_4402, circRNA_2527 and circRNA_1496, respectively) interacted with hsa-miR-328, three of them (circRNA_6086, circRNA_3138 and circRNA_2773, respectively) interacted with hsa-miR-574, while another three (circRNA_2875, circRNA_3807 and circRNA_4631, respectively) interacted with hsa-miR-92a, hsa-miR-26b and hsa-miR-199a, respectively.

Ethical approval

No treatment was tested in patients by the authors for this article. Informed consent was obtained from all individual participants included in the study.

Discussion

In the present study, we provide two experimental findings on circRNAs involved in AF. On the one hand, there was significantly different expression profiles of circRNAs between AF patients and normal controls. On the other hand, five up-regulated (hsa_circRNA_7571, hsa_circRNA_3448, hsa_circRNA_1402, hsa_circRNA_4284 and hsa_circRNA_1415) and eleven down-regulated circRNAs (hsa_circRNA_2527, hsa_circRNA_4648, hsa_circRNA_4624, hsa_circRNA_1496, hsa_circRNA_3138, hsa_circRNA_3138, hsa_circRNA_6086, hsa_circRNA_2875, hsa_circRNA_3807, hsa_circRNA_4402, hsa_circRNA_4631 and hsa_circRNA_2773) were found to interact with AF related miRNAs and considered as the AF associated circRNAs by the construction of circRNA–miRNA network and the analysis using HMDD v3.0.

Atrial electric remodeling associated with profound reduction of L-type Ca2+ current and shortening of the action potential duration was the characteristic of both clinical and experimental AF. It was reported that miR-328, diminished L-type calcium current, shorted the atrial action potential duration, and increased AF vulnerability, would contribute to the atrial electric remodeling in AF and can be used as a diagnosis biomarker of AF[17,18]. Our findings indicated that hsa-miR-328 interacted with both up-regulated and downregulated circRNAs, which was consistent with the reports and indicated that circRNA_7571, circRNA_3448, circRNA_1415, circRNA_4648, circRNA_4624, circRNA_4402, circRNA_2527 and circRNA_1496 colud be regarded as the diagnosis biomarkers of circRNAs for AF.

miR-486 was related to the accumulation of superoxide anion, induction of DNA damage, reduction of cell proliferation and senescent phenotype in human fibroblasts[19]. Slagsvold et al. reported that hsa-miR-486 was upregulated in AF within left atria[20]. Another report from Wang et al. showed that hsa-miR-486 was found to be up-regulated in left atrial appendage in patients with AF[21]. Thus, hsa-miR-486 was considered as a AF related miRNA. At the same time, circRNA_1402, interacted with hsa-miR-486 in our findings could be considered as one of the AF related circRNAs.

A large number of studies have reported the relationships between the miRNAs (hsa-miR-133a, hsa-miR-574, hsa-miR-92a, hsa-miR-26b and hsa-miR-199a) and AF. For example, miR-133 has a cardioprotective role dependent on AKT serine/threonine kinase (AKT) signaling in control situation, inducing apoptosis in AF patients due to its down-regulation[22]. hsa-miR-26b increases IK1 current and membrane resting potential, the downregulation of hsa-miR-26b may reduce AF vulnerability[23]. hsa-miR-574 may promote electrical remodeling via Cav1.2 and contribute to cardiac arrhythmia pathogenesis of AF[24].

hsa-miR-92a can attenuate cardiomyocyte apoptosis in AF patients induced by hypoxia/reoxygenation via the up-regulation of SMAD7 and down-regulation of nuclear NF-κB p65[25]. MiR-26b directly targeted KCNJ2. Both in vivo and in vitro inhibition of miR-26b increased IK1 and AF vulnerability, whereas overexpression of dampened AF vulnerability[26]. miR-199a down-regulation induces Sirtuin 1, a cardio-protective protein, as a compensatory mechanism to inhibit the process of oxidative stress which contributes to the pathogenesis of AF[27]. These miRNAs were considered as the potential biomarkers and therapeutic targets related to AF. Therefore, the differentially expressed circRNAs of circRNA_4284, circRNA_6086, circRNA_3138, circRNA_2773, circRNA_2875, circRNA_3807 and circRNA_4631 in the current study were more likely to be AF associated circRNAs.

Study limitations

First, the small sample size does not provide sufficient power for such an analysis. Second, we just preliminarily investigated the circRNA–miRNA regulatory network in AF, the target gene or pathway analysis and functional assays of circRNA–miRNA regulatory network in the AF process should be further explored.

Conclusions

Our study showed that there were differentially expressed circRNAs in AF patients, five up-regulated and eleven down-regulated circRNAs were considered as the AF related circRNAs. The differentially expressed circRNAs had a possible regulatory network with miRNAs, which indicated the possible regulatory network between circRNAs and miRNAs in the pathogenesis of AF.