Comprehensive Analysis of Differentially Expressed Proles of circRNAs in Diabetic Cardiomyopathy

Objective: To assess the circular RNAs (circRNA) expression prole and explore their potential functions in diabetic cardiomyopathy (DCM). Methods: Using an STZ induced DCM model, microarray analysis was adopted to assess the circRNAs proles. Then 6 differentially expressed circRNAs were conrmed by quantitative Real-Time PCR. Furthermore, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and TargetScan were used to reveal the underlying function of the differentially expressed circRNAs. Cytoscape was used to visualize the interaction networks as well. Results: We revealed a total of 171dyrgulated circRNAs, including 89 upregulated, and 82 downregulated circRNAs in DCM. And conrmed 5 circRNAs distinct expressions (rno_circRNA_000466, rno_circRNA_000964, rno_circRNA_003395, rno_circRNA_000173, rno_circRNA_013989) in DCM by qRT-PCR. GO, and KEGG pathway enrichment revealed the differentially expressed circRNAs might participate in the insulin signaling pathway, autophagy, HIF-1 signaling pathway, inammatory mediator regulation of TRP channels. rno_circRNA_000466 function through competing endogenous RNA mechanism, and may involve in the TGF-β signaling pathway, regulation of glucose transmembrane transport, endocrine process and response to lipoprotein particle. Conclusions: This study opens new avenues for a better understanding of the involvement of circRNA leading to DCM. And unveiled the specic role of rno_circRNA_000466 in the pathogenesis of DCM. These results may provide important information and direction for the future development of novel targets for the treatment of DCM. expressed circRNAs proles between the diseased group and the normal control group. It has the characteristics of high sensitivity, high throughput, and parallel detection. The present work was a systematic study to nd out the differentially expressed circRNA prole of DCM. And we have studied in depth its regulatory role in the development of DCM and pave the way for further verication and research.


Background
Diabetes Mellitus is a global disease with high morbidity and mortality, which is one of the most important diseases severely harmful to the health of mankind [1,2]. It can cause myocardial microvascular disease and metabolic disorders [3,4]. On this basis, pathological changes such as myocardial microvascular injury and myocardial interstitial brosis can occur, leading to cardiac dysfunction, eventually leading to congestive heart failure, cardiogenic shock or arrhythmia, and sudden death in severe cases [5]. Although the advancement in pharmaceutical treatment and interventional and surgical techniques, it has not yet to nd an effective therapeutic method to halt the deterioration of cardiac function due to high levels of blood glucose [6]. There is an urgent need to deepen the understanding of the pathogenesis and molecular mechanism of DCM to assist the development of novel therapeutic approaches 7 .
Circular RNA (circRNA) is a kind of non-coding RNA molecules with a closed circular structure, without 5' cap-structure and 3' poly(A)-tail [7]. CircRNA mainly exists in cytoplasm or exosomes and has the characteristics of tissue speci city, disease speci city, timing speci city and high stability [8]. In recent years, a large number of studies have shown that circRNA is closely related to biological growth and development, stress response, disease occurrence and development, etc. CircRNA can act as a sponge of microRNA, involving in the regulation of disease-related genes in the transcriptional level and posttranscriptional level through a competing endogenous RNA mechanism [9]. Although increasing evidence has been found to reveal the function of circRNA in biological processes, especially in various kinds of diseased conditions, there's still a long way to go to bridge the knowledge gap. Recently, a series of studies reveal the association between circRNA and cardiovascular disease. For example, Garikipat reported that circular RNA CircFndc3b actively participated in cardiac repair after myocardial infarction through regulation of FUS/VEGF-A Axis [10]. What's more, circRNAs are of prime importance in heart failure. Hsa_circ_0097435 is overexpressed in heart failure patients and can sponge multiple microRNAs to accelerate the progression of heart failure. To develop a new therapeutic method, hsa_circ_0097435 can be a promising target [11]. Additionally, circRNAs play a role in atherosclerosis, coronary artery disease, myocardial infarction, as well as cardiomyopathy [12].
Although mRNA [13], lncRNA [14], and circRNA [15] were studied in DCM, the mechanism of circRNAs in the pathogenesis of DCM has not been systematically studied. Therefore, a comprehensive study of the functions of circRNAs in the heart is an advantageous strategy to understand this metabolic disease.
In this study, we performed circular RNA array analysis; screen differentially expressed circRNAs; and reveal their biological functions and molecular mechanisms, combined with bioinformatics analysis. The circRNAs' expression and enrichment analysis of pathways would greatly facilitate the study of DCM pathogenesis and provide potential novel targets for DCM therapeutics.

Methods
Animals and specimens 20 male Sprague Dawley rats, weighted 210-240 gram, were intraperitoneally injected streptozocin (60mg/Kg, Sigma) to induce type 1 diabetes mellitus (T1DM). After 3 days, if fasting blood glucose was greater than 11.1mmol/L, it is considered to be a successful induction of T1DM model. 8 weeks after modeling, the rats were anesthetized and its left ventricular free wall tissues were cut off for isolation of total RNA. The study was approved by the medical ethics committee of Chinese PLA General Hospital.
Total RNA isolation and quality control Total tissue RNA was extracted from normal and diabetic Sprague Dawley rats's left ventricular free wall tissues using TRIzol reagent (Invitrogen, NY, USA), according to the manufacturer's instructions. The quality and concentration of RNA samples (Supplementary Table 1) were determined using the NanoDrop ND-1000 (Thermo Fisher Scienti c, Wilmington, DE, USA). The RNA Integrity and genomic DNA (gDNA) contamination test were achieved by electrophoresis on a denaturing agarose gel ( Supplementary Fig 1). The samples were preserved at -80 °C for further experiments. circRNA microarray analysis Sample labeling and array hybridization were performed according to the manufacturer's protocol. Briefly, total RNA was digested with RNase R (Epicentre, Inc.) to remove linear RNAs and enrich the circular RNAs. These circular RNAs were ampli ed and transcribed into fluorescent cRNA utilizing a random priming method (Arraystar Super RNA Labeling Kit; Arraystar). The labeled cRNAs were hybridized onto the Arraystar Rat circRNA Array V2.0 (8×15 K), with a total of 14,145 circRNA probes on the microarray. After washing the slides, the arrays were scanned using an Agilent G2505C Scanner. Agilent Feature Extraction software (version 11.0.1.1, USA) was used to analyze the acquired array images.

Validation of array results by qPCR
To validate the reliability of microarray analysis and explore the expression trend of circRNAs in DCM, ten circRNAs were randomly selected to verify their differential expression levels by qRT-PCR. Total RNAs isolated from DCM and control samples were reverse transcribed into cDNA for qRT-PCR analysis. Quantitative RT-PCR was conducted in ViiA 7 Real-time PCR System (Applied Biosystems) following the manufacturer's instructions. The expression of circRNAs was de ned based on the threshold cycle (Ct), and relative expression levels were calculated via the 2 -ΔΔCt method. Outward-facing primers were designed to amplify the fragment across the junction from cDNA using software Primer 5.0 and are listed in Table 1. Both DCM and control groups had three independent samples and all reactions were processed in triplicate.

GO enrichment and KEGG analysis of host genes
Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the biological function of the host gene of differential expressed circRNA using the clusterPro ler (version 3.8.0) http://bioconductor.org/packages/release/bioc/html/clusterPro ler.html. We described the enrichment results from three aspects, including biological process (BP), cellular component (CC), and molecular function (MF). GO terms and pathway analyses with P value<0.05 adjusted by Benjamin and Hochberg method were considered to be signi cantly enriched functional annotations.

Construction of circRNA-miRNA interaction
Since the physiological and pathological roles of circRNAs potentially act through a competing endogenous RNA mechanism, therefore, it is of prime importance to identify the association between miRNAs and circRNA. What's more the discovery of the molecules involved in the crosstalk among miRNAs and circRNAs now represents an important challenge for scientists in elucidating the complexity of biological processes. Using the Arraystar's home-made miRNA target prediction software based on TargetScan & miRanda, the interaction between circRNA and microRNA was predicted and the differentially expressed circRNAs were annotated in detail with the circRNA/miRNA interaction information. Then, the crosstalk networks were represented in Cytoscape (www.cytoscape.org/) and the topological properties were analyzed using the Cytoscape 'Network Analysis' plugin. CytoHubba, a plugin of Cytoscape, was used to predict the important nodes and subnetworks in the network using the Degree method.

Results
Successful establishment of the diabetic cardiomyopathy rat model After the STZ induction, blood sugar level and body weight of rats were measured and documented in a certain time interval. A distinct difference between DCM and normal control rats was observed. In the DCM group, the rat had signi cantly higher blood sugar levels and lower body weight, which were inconsistent with the typical manifestations and symptoms of diabetes mellitus (Figure1A, B).. This experiment re ects left ventricular systolic function in rats through left ventricular ejection fraction (LVEF) and short-axis shortening rate (FS). LVEF values were signi cantly decreased in the DCM group (56.33 ± 1.662%) compared with the normal control group (74.06 ± 2.631%). (Fig1C).. In diabetic cardiomyopathy, the increased deposition of collagen and interstitial brosis in the myocardium is one of the characteristic structural changes within it. In this study, LV collagen deposition and brosis ratio were markedly increased in DCM rats compared to normal controls (5.74 ± 0.3472 vs 0.94 ± 0.07924), using Masson staining method (Figure1D)..

Identi cation of differential expressed circRNAs
To investigate the circRNA expression pro le of diabetic cardiomyopathy, we have completed the Arraystar Rat circRNA Array analysis of specimen from 3 DCM samples and 3 normal controls. A total of 14139 circRNAs were detected by Arraystar Rat circRNA Microarray. When comparing two groups of pro le differences, the "fold change" between the groups for each circRNA is computed. The statistical signi cance of the difference is estimated by t-test. CircRNAs having |fold changes| 2.0 and p-values 0.05 are selected as the signi cantly differentially expressed. Eventually, 171 differentials expressed circRNAs, including 89 upregulated and 82 downregulated circRNAs were identi ed. A Scatter plot (Fig 2A) and a volcano plot (Fig 2B) showed the distributions of circRNAs more directly. The top 20 dysregulated circRNAs based on fold changes were summarized in Table 1. The result of hierarchical clustering showed a distinguishable circRNA expression pro ling among samples. The data suggested that the circRNA in DCM was different in normal control samples (Fig 2C)..

Real-time qPCR validation of 6 differentially expressed circRNAs
After the comparison and the analysis of the differentially expressed multiple circRNAs in the microarray experiments, we randomly veri ed 6 circRNAs (rno_circRNA_000466, rno_circRNA_000964, rno_circRNA_003395, rno_circRNA_000173, rno_circRNA_013989, and rno_circRNA_003643), using GAPDH as the internal control. There were signi cant differences in all of the selected circRNAs (P<0.05) except for rno_circRNA_003643 (P = 0.06361). Among the 6 selected circRNAs, rno_circRNA_000466 was founded to be the highest increase of nearly 28-fold change. rno_circRNA_000964 was down-regulated with a 0.51-fold decrease. The results are shown in Fig 3. There was a great consistency between the realtime qPCR results and microarray analysis data, which demonstrated the high reliability of the microarray expression data.
Functional pro les of the parental genes of differentially expressed circRNAs in DCM circRNA cis-regulates the expression of parental genes. On the one hand, circRNA can combine with RNA binding proteins to affect the expression of the parental gene mRNA. On the other hand, competitive complementary pairing between introns during the formation of circular RNA can achieve a balance between linear RNA and affect mRNA expression, even protein translation. Parental genes from 171 differentially expressed circRNAs were subjected to GO and KEGG pathway enrichment analyses. The top 10 classes of GO enrichment terms and the top 10 classes of KEGG pathway enrichment terms are presented (Figure4).Compared to the normal heart sample, the data demonstrated that the gene expression pro le of linear counterparts of differentially expressed circRNAs in DCM involves in the insulin signaling pathway, autophagy, HIF-1 signaling pathway, in ammatory mediator regulation of TRP channels, insulin secretion, regulation of lipolysis in adipocytes and so on. As for the insulin signaling pathway, CALM3, CBLB, INSR, PIK3R3, PRKACA, SORBS1 were dysregulated in DCM heart tissue. And ATP1B1, CALM3, CAMK2A, GRIA2, PIK3R3, PRKACA in the cAMP signaling pathway were down in DCM heart tissue.In the autophagy pathway, differentially expressed IGF1R, MTMR3, PIK3R3 PRKACA, SH3GLB1 were found to be altered. To be noted, many genes participated in cell membrane biological activity, including regulation of transmembrane transport, regulation of potassium ion transmembrane transporter activity, etc.

circRNA-microRNA interaction network construction
CircRNA is generally produced by RNA alternative splicing and circRNA has been found to play the role of miRNA sponge. As a competitive endogenous RNA (ceRNA) binding to intracellular miRNA, blocking the inhibition of miRNA on its target gene is its main regulatory mode. The circRNA/microRNA interaction was predicted with Arraystar's home-made miRNA target prediction software based on TargetScan and miRanda, and the differentially expressed circRNAs within all the comparisons were annotated in detail with the circRNA/miRNA interaction information. All the differentially expressed circRNAs prediction was based on the principle of complementary sequence pairing, and targeted miRNAs were ranked according to their mirSVR scores, and ve miRNAs with the highest mirSVR score were identi ed as MREs for each circRNAs. In the topological network (Fig 5A), the orange oval nodes represent upregulated circRNAs and the yellow rectangle nodes represent down-regulated circRNAs. It can be founded in the network that one circRNA interacts with a couple of miRNAs and some miRNA can also interact with multiple circRNAs. Furthermore, we using CytoHubba plugin to screen the top 20 nodes in network ranked by the Degree method, to assess the key modules that signi cantly regulated the pathogenesis of DCM. The coexpression network reveals that rno-miR-22, rno-miR-27, rno-miR-28, rno-miR-466, rno-miR-320 may play important roles in the development of DCM. And the magni ed network was visualized using Cytoscape (v.3.8.0) as well (Fig 5B).
The construction of rno_circRNA_000466-associated ceRNA networks In our previous array and qRT-PCR test, rno_circRNA_000466 was highly expressed in DCM heart tissue with a 28-fold change. And based on the hypothesis that circRNAs may function through the ceRNA mechanism, we used Arraystar's home-made miRNA target prediction software to predict the interaction miRNA of rno_circRNA_000466. Five miRNAs, including rno-miR-1306-3p, rno-miR-665, rno-miR-3068-5p, rno-miR-223-3p, rno-miR-3593-5p, were identi ed most likely to be interacted with rno_circRNA_000466. And then we use DIANA microT-CDS online tools to predict the target of the ve miRNAs. At last, 139 targets were found with the threshold set to 0.9. The topical relationship was visualized through Cytoscape software. The functional annotation was performed using the CluGO and CluPedia plugins. The results show that some of the target genes were enriched in the TGF-β signaling pathway, regulation of glucose transmembrane transport, endocrine process and response to lipoprotein particle, which are related to metabolic remodeling and cardiac brogenesis in DCM. These results were presented in Fig 6. Discussion Circular RNA Microarray is a common method to reveal the differentially expressed circRNAs pro les between the diseased group and the normal control group. It has the characteristics of high sensitivity, high throughput, and parallel detection. The present work was a systematic study to nd out the differentially expressed circRNA pro le of DCM. And we have studied in depth its regulatory role in the development of DCM and pave the way for further veri cation and research.
CircRNAs may play a role in the pathogenesis of diseases by regulating the expression of their parental gene. We performed Go analysis and KEGG analysis of the parental genes of circRNAs that were differentially expressed in DCM. Gene ontology analysis is an informatics method for linking differentially expressed genes to the GO classi cation for functional analysis, including classifying and analyzing differentially expressed genes in terms of participation in molecular functions, biological processes, and cellular components. And the KEGG analysis can identify the altered pathways in the pathogenesis process. The inference from the parental gene analysis of the differentially up-regulated circRNAs in DCM is that the abnormally expressed parental genes of circRNAs participate in various biological processes, speci cally, insulin receptor signaling pathway, regulation of potassium ion transmembrane transporter activity, regulation of glycoprotein metabolic process. These are all consistent with the characteristics of DCM myocardial metabolism abnormality and impaired myocardial cell function. Impaired glucose metabolism, manifesting insulin resistance and metabolic syndrome, promotes the pathogenesis of diabetes and related complications [16]. Abnormal insulin receptor inactivation is often considered an important mechanism for insulin resistance, both in vivo and in cell models [17]. CircRNAs are a widespread RNA species in various species and have important biological functions. They are involved in the regulation of islet cell function and insulin metabolism [18], also mediate vascular damage caused by high glucose as well [19,20].
MAP3K5, host gene of the up-regulated rno_circRNA_000466, has been reported to be elevated in DCM [21,22]. We speculate that rno_circRNA_000466 may also participate in the regulation of cell apoptosis, oxidative stress injury and cardiac brogenesis. In terms of rno_circRNA_003643 and its parental gene Insr, the phosphorylation of IRSs proteins leads to the activation of the PI3K-AKT/PKB pathway and the Ras-MAPK pathway. Thus, we predicted that rno_circRNA_003643 may play an important role in the metabolic actions of insulin and the control of cell growth and differentiation. Because of cardiac insulin resistance contributes to diabetic cardiomyopathy to a large extent, further studies of rno_circRNA_003643 will advance our understanding of insulin resistance in the pathogenesis of DCM. Hopefully, it can be considered as a novel therapeutic target and used as a biomarker in monitoring disease outcomes.
In our six veri ed circRNAs, rno_circRNA_000466 was identi ed to be signi cantly differentially expressed with a 28-folder in DCM heart tissue. Its ceRNA regulatory network shows that the target genes involved in the TGF-β signaling pathway, which participates in cardiac remodeling and brosis in the high glucose damaged myocardium [23,24]. It is noteworthy that myocardial brosis is of prime importance in the structural remodeling of diabetic hearts and ultimately affect cardiac function. Thus, nding a therapeutic target of cardiac brosis for DCM will be helpful to improve the prognosis of DCM. Focusing on the role of rno_circRNA_000466 in the regulation of cardiac brosis will provide us a novel approach to prevent the development of brosis and halt the deterioration of cardiac function. Moreover, rno_circRNA_000466 may also involve in the regulation of glucose transmembrane transport. Disorders of glucose metabolism will dramatically affect the functional phenotype of cardiomyocytes and the surrounding stromal cells. Consequentially, alterations in cardiac structure and function will manifest in DCM.
Although this study will contribute to our knowledge of circRNAs in DCM, there is still some limitation. First, the sample size is relatively small, so there will be a possible selection bias in the present study. Larger sample sizes are required to con rm and validate the conclusions. Second, there are differences in type 2 and type 1 diabetes mellitus. In this study, we STZ-induced T1DM model was adopted and thus the difference between these two disease conditions was unable to be clari ed. Third, more functional experiments are needed to verify these results for most of our ndings were based on bioinformatic analysis.

Conclusion
In conclusion, we have demonstrated, for the rst time, a comprehensive circRNAs expression pro les of DCM. Bioinformatics analysis predicted that differentially expressed circRNAs were involved in the regulation of glucose metabolism, insulin resistance and cardiac remolding. This study opens new avenues for a better understanding of the pivotal role of circRNA in DCM.    Con rmation of the differential expression of circRNAs by RT-qPCR.  The construction of rno_circRNA_000466-associated ceRNA networks.(A) ceRNA network of rno_circRNA_000466, (B) Go enrichment and KEGG pathway analysis of target genes of rno_circRNA_000466.