BioMed Research International

BioMed Research International / 2017 / Article
Special Issue

Genetic and Epigenetic Regulation Networks: Governing from Cardiovascular Development to Remodeling

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Research Article | Open Access

Volume 2017 |Article ID 4820275 | 7 pages | https://doi.org/10.1155/2017/4820275

MicroRNA Expression Signature in Human Calcific Aortic Valve Disease

Academic Editor: Giovanni Mariscalco
Received12 Sep 2016
Accepted13 Feb 2017
Published11 Apr 2017

Abstract

Altered microRNA (miRNA, miR) expression has been related to many disease processes; however, the miRNA expression signature in calcific aortic valve disease (CAVD) is unclear. In this study, microarrays were used to determine the miRNA expression signature of tissue samples from healthy individuals () and patients with CAVD (). TargetScan, PITA, and microRNAorg 3-way databases were used to predict the potential target genes. DIANA-miRPath was used to incorporate the aberrant miRNAs into gene pathways. miRNA microarrays identified 92 differentially expressed miRNAs in CAVD tissues. The principal component analysis (PCA) of these samples and the unsupervised hierarchical clustering analysis based on the 92 aberrantly expressed miRNAs noted that miRNA expression could be categorized into two well-defined clusters that corresponded to healthy control and CAVD. Bioinformatic analysis showed the miRNA targets and potential molecular pathways. Collectively, our study reported the miRNA expression signature in CAVD and may provide potential therapeutic targets for CAVD.

1. Background

Valve diseases continue to occur in many patients with significant morbidity and mortality. The age-adjusted prevalence of moderate or severe valve diseases was estimated at 2.5% [1]. Calcific aortic valve disease (CAVD) is the most common valve heart disease in the elderly and a leading cause of aortic stenosis [2]. In developing countries, CAVD represents a major cause for surgical valve replacement [3]. As a result of rising life expectancy and ageing populations, the burden of CAVD will significantly increase in the near future.

While CAVD was originally thought to be a degenerative process with passive deposition of calcium phosphate in the valve occurring with age, it now appears to be a complex and actively regulated progress mediated by inflammation, cell apoptosis, lipid deposition, renin-angiotensin system activation, extracellular matrix remodeling, and bone formation [46]. To better monitor progression of CAVD and identify the most appropriate instances for surgical intervention, biomarkers can be serially monitored. Such biomarkers would represent objective laboratory measurements, as older patients with CAVD might have atypical symptoms associated with comorbidities such as pulmonary disease or orthopaedic disabilities [7, 8].

MicroRNAs (miRNAs, miRs) are endogenous, small, single-stranded, 21–25 nucleotide noncoding RNAs, regulating target gene expressions by hybridizing to messenger RNAs (mRNAs). An individual miRNA is able to target tens to hundreds of genes while a single gene can also be targeted by lots of miRNAs [9]. Since miRNAs play critical roles in many physiological processes, increasing reports indicate that a distinct pattern of altered miRNA expressions may be linked to specific disease processes [1015]. We previously reported the miRNA expression signature in degenerative aortic stenosis [14]. In this study, we explored the miRNA expression signature in CAVD.

2. Materials and Methods

2.1. Tissue Sample Collection and RNA Isolation

This study was officially approved by the Ethics Committees of the First Affiliated Hospital of Nanjing Medical University and conformed to the principles outlined in the Declaration of Helsinki. All written informed consent was obtained from patients, and parents where applicable. Tissue samples from four healthy subjects were collected from prospective multiorgan donors in cases because of technical reasons that prevented transplantation, while stenotic aortic valve samples were obtained from four patients who underwent surgical valve replacement for aortic stenosis. All samples were examined by gross examination, and microscopic examination of hematoxylin and eosin-stained cryosections was conducted to confirm the presence/absence of CAVD. Tissue samples harvested from subject donors were snap-frozen in liquid nitrogen, and RNA was then isolated using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions.

2.2. miRNA Microarray Analysis

Total RNAs were isolated from heart tissues using mirVana™ RNA Isolation Kit, quantified by NanoDrop ND-2100 (Thermo Scientific), and controlled for RNA integrity using Agilent Bioanalyzer 2100 (Agilent Technologies) according to the manufacturer’s instructions. miRNA profiling was performed with OE Biotech’s (Shanghai, China) miRNA microarray service. The arrays from the control group are the same as we previously used [14].

2.3. Bioinformatic Analysis

TargetScan, PITA, and microRNAorg 3-way databases were used to identify potential human miRNA target genes and a Venn diagram was made to provide relations among the 3 databases. DIANA tool miRPath v2.0, a web-based analysis tool, was used for pathway enrichment analysis for the miRNA set identified [16]. DIANA tool miRPath assigns Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway with the significance level determined by the number of target genes affected by the identified microRNAs.

2.4. Statistical Analysis

Independent Student’s -test was used to determine whether there were any significant differences between the miRNA expression profiles between two groups. values less than 0.05 () were considered to be statistically significant. Significant data were further analyzed by clustering, and the expression profiles were visualized with GeneSpring 10.0 (Agilent Technology).

3. Results

3.1. Principal Component Analysis of miRNA Expression Profiles

Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set [17]. Dots in two colors separated in two axes based on the differences of the data, suggesting that samples in this study were prepared appropriately and could be grouped as CAVD or healthy control (Figure 1).

3.2. Unsupervised Hierarchical Cluster Analysis of miRNA Microarray Data

miRNA arrays identified 92 miRNAs with a statistically significant differential expression of 2.0-fold or greater in CAVD samples relative to normal controls. Fifty-three miRNAs were underexpressed and 39 were overexpressed in aortic tissue from CAVD patients (Table 1). Unsupervised hierarchic clustering of the two groups was performed on the 92 differently expressed miRNAs and displayed as heatmap (Figure 2).


Systematic name valueFold changeRegulation

hsa-miR-36560.0112022.0143232Up
hsa-miR-7650.0336392.0401733Up
hsa-miR-28610.0087412.0782373Up
hsa-miR-663a0.006332.0819619Up
hsa-miR-12460.0270452.1549542Up
hsa-miR-31410.0029692.2772849Up
hsa-miR-125a-3p0.0120022.3428166Up
hsa-miR-43270.0048072.367507Up
hsa-miR-6380.0027852.3686504Up
hsa-miR-42700.0305742.4421182Up
hsa-miR-642b-3p0.0345752.5763547Up
hsa-miR-513a-5p0.0075382.6546612Up
hsa-miR-483-5p0.0080992.655595Up
hsa-miR-3679-5p0.0040162.7413363Up
hsa-miR-36480.0170532.8097122Up
hsa-miR-30c-1-3p7.64E-042.893416Up
hsa-miR-12750.0054572.9556682Up
hsa-miR-513b0.0206043.4257321Up
hsa-miR-19720.0113893.689852Up
hsa-miR-31380.0040763.974949Up
hsa-miR-3663-3p0.0058744.314916Up
hsa-miR-21-5p0.0038264.317176Up
hsa-miR-7180.0317184.957687Up
hsa-miR-6300.0013047.3841376Up
hsa-miR-5750.00158610.079804Up
hsa-miR-39340.04882713.458452Up
hsa-miR-143-5p0.03363216.2909Up
hsa-miR-31310.04750122.065104Up
hsa-miR-125b-1-3p0.0051923.412596Up
hsa-miR-625-3p0.00586723.648891Up
hsa-miR-14710.03514525.31259Up
hsa-miR-43140.03212527.92129Up
hsa-miR-6360.0092328.36478Up
hsa-miR-39450.01833638.864952Up
hsa-miR-36100.01435249.196358Up
hsa-miR-11821.46E-0573.30232Up
hsa-miR-37132.44E-05117.90961Up
hsa-miR-21-3p5.95E-05149.08258Up
hsa-miR-516a-5p2.88E-05155.79349Up
hsa-miR-654-3p0.0025592.0230756Down
hsa-miR-93-5p0.0202792.0238087Down
hsa-miR-320d0.0382472.052202Down
hsa-miR-3810.0355662.0584567Down
hsa-miR-214-3p0.0117562.0957778Down
hsa-miR-125b-5p0.0367712.1063256Down
hsa-miR-361-3p0.0360592.1256602Down
hsa-miR-29c-3p0.0097432.1358023Down
hsa-miR-4950.0012892.1425073Down
hsa-miR-374a-5p0.019042.1439137Down
hsa-miR-20b-5p0.0273682.1694279Down
hsa-miR-382-5p0.0432892.1756916Down
hsa-miR-43240.0397522.178068Down
hsa-miR-25-3p0.0045792.1911306Down
hsa-miR-100-5p0.0355092.1913323Down
hsa-miR-193b-3p0.0226762.191656Down
hsa-miR-1070.0077822.2016506Down
hsa-miR-660-5p8.68E-042.2094207Down
hsa-miR-103a-3p0.0201782.2370007Down
hsa-miR-195-5p0.0497082.3292358Down
hsa-miR-299-5p0.0020662.3574395Down
hsa-miR-487b7.71E-042.422462Down
hsa-miR-1280.0225312.4691415Down
hsa-miR-181d0.0195662.5412066Down
hsa-miR-374b-5p0.0383732.5474217Down
hsa-let-7b-5p0.0429812.6197023Down
hsa-miR-140-5p0.0059722.639281Down
hsa-let-7g-5p0.028182.7145965Down
hsa-miR-151a-5p0.0104442.7681546Down
hsa-miR-532-5p0.0200082.7994845Down
hsa-miR-26b-5p0.0254142.8088717Down
hsa-miR-30e-3p0.0203052.8739653Down
hsa-miR-140-3p0.0401442.8943768Down
hsa-miR-29c-5p0.0046652.9975233Down
hsa-miR-181c-5p0.0153223.0353231Down
hsa-miR-204-5p0.0449413.0471704Down
hsa-let-7d-5p0.0256583.0987353Down
hsa-miR-980.0274953.2012997Down
hsa-miR-10a-5p0.0192253.3482268Down
hsa-let-7f-5p0.0372823.4332418Down
hsa-let-7e-5p0.0183373.6489105Down
hsa-let-7a-5p0.0294033.6912477Down
hsa-miR-99a-5p0.0359233.8172672Down
hsa-let-7c0.0326723.9863558Down
hsa-miR-126-3p0.0260734.528461Down
hsa-miR-29b-1-5p0.00656615.589992Down
hsa-miR-181c-3p0.00821916.225191Down
hsa-miR-194-5p0.01151120.940771Down
hsa-miR-335-5p0.00627336.750603Down
hsa-miR-126-5p0.01529838.692142Down
hsa-miR-505-5p1.18E-0542.592148Down
hsa-miR-625-5p0.00883543.434204Down
hsa-miR-200b-3p1.60E-0670.24472Down

3.3. Target Genes Analysis

MicroRNAorg, TargetScan, and PITA were used to predict the targets of differentially expressed miRNAs in CAVD samples. A Venn diagram was made to highlight the relations among the three databases. There are 8717 genes overlapping by all three sets, which are most likely to be targets of miRNAs in patients with CAVD (Figure 3).

3.4. DIANA miRNA Pathway Analysis

To better understand the putative mechanisms underlying CAVD, we used DIANA-miRPath (v2.0), a web-based server developed to identify the potential cellular pathways regulated by microRNAs. We first evaluated downregulated miRNAs in CAVD samples compared to control samples. The potential affected pathways included the following: cell cycle, PI3K-Akt signaling pathway, ECM-receptor interaction, HIF-1 signaling pathway, p53 signaling pathway, ErbB signaling pathway, Neurotrophin signaling pathway, focal adhesion, and DNA replication (Table 2). Upregulated miRNAs were also used to generate the potential affected pathways by DIANA-miRPath and identified p53 signaling pathway, HIF-1 signaling pathway, valine, leucine, and isoleucine biosynthesis, ErbB signaling pathway, cell cycle, mTOR signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, Wnt signaling pathway, synthesis and degradation of ketone bodies, TGF-beta signaling pathway, basal transcription factors, glycerophospholipid metabolism, hypertrophic cardiomyopathy (HCM), focal adhesion, circadian rhythm, mismatch repair, lysine degradation, and butanoate metabolism (Table 3).


KEGG pathway valueGenesmiRNAs

Cell cycle<1 − 164819
PI3K-Akt signaling pathway9517
ECM-receptor interaction94
HIF-1 signaling pathway3015
p53 signaling pathway2814
ErbB signaling pathway2312
Neurotrophin signaling pathway0.002491910
Focal adhesion0.0029196
DNA replication0.013417192


KEGG pathway valueGenesmiRNAs

p53 signaling pathway112
HIF-1 signaling pathway0.00023102
Valine, leucine, and isoleucine biosynthesis0.0022711
ErbB signaling pathway0.00395951
Cell cycle0.003959102
mTOR signaling pathway0.00490961
MAPK signaling pathway0.005193152
PI3K-Akt signaling pathway0.005193172
Wnt signaling pathway0.008586101
Synthesis and degradation of ketone bodies0.00936621
TGF-beta signaling pathway0.02121671
Basal transcription factors0.02941741
Glycerophospholipid metabolism0.03027181
Hypertrophic cardiomyopathy (HCM)0.03027161
Focal adhesion0.030271112
Circadian rhythm0.03084831
Mismatch repair0.03451821
Lysine degradation0.03847141
Butanoate metabolism0.03861531

Specific types of cancers and infections were not included.

4. Discussion

miRNAs have been shown to be critical regulators in cardiovascular diseases [1825]. However, there are no reports revealing distinct miRNA expression signatures in the CAVD patients and healthy controls. In this study, we identified global changes in the miRNA expression profile in CAVD and healthy control. Calcific aortic valve stenosis is characterized by lipid accumulation, inflammation, formation of plaque neovessels, hemorrhages, neointimal formation, vascular fibrosis, and ectopic calcification [4, 26]. Previous studies have shown that miRNAs play crucial roles in those processes such as angiogenesis, fibrogenesis, proliferation, and apoptosis [9].

miR-126 is one of the most abundantly expressed microRNAs in endothelial cells (ECs) [27]. Upregulation of miR-126 increases EC survival, decreases EC apoptosis, and prevents reactive oxygen species (ROS) mediated endothelial damage [28]. Our findings of decreased miR-126 in CAVD may suggest a detrimental effect in human calcific aortic valve.

The differentially expressed miRNAs identified in the current study also included many profibrotic miRNAs such as miR-21 and miR-125b that might contribute to CAVD by promoting fibrosis. Several expression profiling studies identify that increased level of miR-21-5p in cardiac fibroblasts promotes cardiac fibrosis via its target genes: phosphatase and tensin homolog (PTEN) [29] and Sprouty-1 (Spry1) [30]. Additionally, miR-125b is a novel regulator of cardiac fibrogenesis, proliferation, and fibroblast-to-myofibroblast transition. Nagpal et al. demonstrated the upregulation of miR-125b in fibrotic human heart and murine models of cardiac fibrosis [31].

Interestingly, our miRNA array data revealed that several members of the let-7 (let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, and let-7g) were downregulated in calcific aortic valve. Let-7g targets the genes related to vascular smooth muscle cell (VSMC) functions, including ROS, autophagy-related proteins (expression of beclin-1, LC3-II, and Atg5), and apoptosis-related proteins (expression of caspase-3, Bax, Bcl-2, and Bcl-xL) [32]. Let-7 family members might directly influence aortic valve sclerosis by regulating the proliferation, migration, autophagy, and apoptosis of VSMC, which have been implicated in the progression of CAVD [4, 26].

The abnormal expression of miR-21-5p was found in many cardiovascular diseases [33]. Programmed cell death 4 (PDCD4) is identified as a direct target gene of miR-21-5p. It has been reported that miR-21-5p prevented cardiomyocyte apoptosis in ischaemia/reperfusion heart model through PDCD4 repression [34]. Furthermore, miR-21/PDCD4 pathway was proved to be involved in cardiac valvulogenesis by regulating endothelial cell migration [35]. In our work, miR-21-5p was upregulated in calcific aortic valve which indicates that miR-21-5p might take part in CAVD. However, the effects and mechanisms of miR-21-5p on calcific aortic valve are still to be investigated in further studies.

A comprehensive knowledge of miRNA expression is essential to improve our understanding of this disease. This study provides the first evidence that there exists a distinct miRNA expression signature in individuals with CAVD, as compared to healthy controls. There are 92 differently expressed miRNAs in the CAVD patients compared with healthy controls by miRNA arrays. PCA and unsupervised hierarchical clustering with these miRNAs demonstrates that this profile could accurately classify the samples according to their disease status. Moreover, bioinformatic tools indicate that the differential expression of miRNAs could be linked to several targets and pathways.

As a limitation of our study, the exact pathways by which dysregulated miRNAs cause CAVD in human remain elusive. Further studies are required to fully characterize the function of candidate miRNAs.

5. Conclusions

Taken together, the current study provides insight into the importance of microRNA expression signature in CAVD. A deeper understanding of the molecular alternations in CAVD may provide potential targets for future clinical applications.

Conflicts of Interest

The authors have declared that no conflicts of interest exist.

Acknowledgments

This work was supported by the grant from National Natural Science Foundation of China (81400647 to Y. Bei). Dr. X. Kong is a Fellow at the Collaborative Innovation Center For Cardiovascular Disease Translational Medicine and this work was supported by the grants from the center.

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Copyright © 2017 Hui Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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