BioMed Research International
Volume 2017 (2017), Article ID 4820275, 7 pages
https://doi.org/10.1155/2017/4820275

Research Article

MicroRNA Expression Signature in Human Calcific Aortic Valve Disease

Hui Wang,¹ Jing Shi,¹ Beibei Li,¹ Qiulian Zhou,² Xiangqing Kong,¹ and Yihua Bei²

¹Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
²Cardiac Regeneration and Ageing Lab, School of Life Science, Shanghai University, Shanghai 200444, China

Correspondence should be addressed to Xiangqing Kong; xiangqingkong_nj@163.com and Yihua Bei; yihuabei_sh@163.com

Received 12 September 2016; Accepted 13 February 2017; Published 11 April 2017

Academic Editor: Giovanni Mariscalco

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.

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 [4–6]. 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 [10–15]. 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).

Figure 1: PCA of the miRNA profiles in CAVD tissue samples and control subjects. Red dots represent samples from control group, while blue dots represent samples from CAVD group.

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).

Table 1: Fifty-three underexpressed and 39 overexpressed miRNAs in aortic tissue from CAVD patients compared to the control group.

Systematic name
value
Fold change
Regulation

hsa-miR-3656
0.011202
2.0143232
Up

hsa-miR-765
0.033639
2.0401733
Up

hsa-miR-2861
0.008741
2.0782373
Up

hsa-miR-663a
0.00633
2.0819619
Up

hsa-miR-1246
0.027045
2.1549542
Up

hsa-miR-3141
0.002969
2.2772849
Up

hsa-miR-125a-3p
0.012002
2.3428166
Up

hsa-miR-4327
0.004807
2.367507
Up

hsa-miR-638
0.002785
2.3686504
Up

hsa-miR-4270
0.030574
2.4421182
Up

hsa-miR-642b-3p
0.034575
2.5763547
Up

hsa-miR-513a-5p
0.007538
2.6546612
Up

hsa-miR-483-5p
0.008099
2.655595
Up

hsa-miR-3679-5p
0.004016
2.7413363
Up

hsa-miR-3648
0.017053
2.8097122
Up

hsa-miR-30c-1-3p
7.64E-04
2.893416
Up

hsa-miR-1275
0.005457
2.9556682
Up

hsa-miR-513b
0.020604
3.4257321
Up

hsa-miR-1972
0.011389
3.689852
Up

hsa-miR-3138
0.004076
3.974949
Up

hsa-miR-3663-3p
0.005874
4.314916
Up

hsa-miR-21-5p
0.003826
4.317176
Up

hsa-miR-718
0.031718
4.957687
Up

hsa-miR-630
0.001304
7.3841376
Up

hsa-miR-575
0.001586
10.079804
Up

hsa-miR-3934
0.048827
13.458452
Up

hsa-miR-143-5p
0.033632
16.2909
Up

hsa-miR-3131
0.047501
22.065104
Up

hsa-miR-125b-1-3p
0.00519
23.412596
Up

hsa-miR-625-3p
0.005867
23.648891
Up

hsa-miR-1471
0.035145
25.31259
Up

hsa-miR-4314
0.032125
27.92129
Up

hsa-miR-636
0.00923
28.36478
Up

hsa-miR-3945
0.018336
38.864952
Up

hsa-miR-3610
0.014352
49.196358
Up

hsa-miR-1182
1.46E-05
73.30232
Up

hsa-miR-3713
2.44E-05
117.90961
Up

hsa-miR-21-3p
5.95E-05
149.08258
Up

hsa-miR-516a-5p
2.88E-05
155.79349
Up

hsa-miR-654-3p
0.002559
2.0230756
Down

hsa-miR-93-5p
0.020279
2.0238087
Down

hsa-miR-320d
0.038247
2.052202
Down

hsa-miR-381
0.035566
2.0584567
Down

hsa-miR-214-3p
0.011756
2.0957778
Down

hsa-miR-125b-5p
0.036771
2.1063256
Down

hsa-miR-361-3p
0.036059
2.1256602
Down

hsa-miR-29c-3p
0.009743
2.1358023
Down

hsa-miR-495
0.001289
2.1425073
Down

hsa-miR-374a-5p
0.01904
2.1439137
Down

hsa-miR-20b-5p
0.027368
2.1694279
Down

hsa-miR-382-5p
0.043289
2.1756916
Down

hsa-miR-4324
0.039752
2.178068
Down

hsa-miR-25-3p
0.004579
2.1911306
Down

hsa-miR-100-5p
0.035509
2.1913323
Down

hsa-miR-193b-3p
0.022676
2.191656
Down

hsa-miR-107
0.007782
2.2016506
Down

hsa-miR-660-5p
8.68E-04
2.2094207
Down

hsa-miR-103a-3p
0.020178
2.2370007
Down

hsa-miR-195-5p
0.049708
2.3292358
Down

hsa-miR-299-5p
0.002066
2.3574395
Down

hsa-miR-487b
7.71E-04
2.422462
Down

hsa-miR-128
0.022531
2.4691415
Down

hsa-miR-181d
0.019566
2.5412066
Down

hsa-miR-374b-5p
0.038373
2.5474217
Down

hsa-let-7b-5p
0.042981
2.6197023
Down

hsa-miR-140-5p
0.005972
2.639281
Down

hsa-let-7g-5p
0.02818
2.7145965
Down

hsa-miR-151a-5p
0.010444
2.7681546
Down

hsa-miR-532-5p
0.020008
2.7994845
Down

hsa-miR-26b-5p
0.025414
2.8088717
Down

hsa-miR-30e-3p
0.020305
2.8739653
Down

hsa-miR-140-3p
0.040144
2.8943768
Down

hsa-miR-29c-5p
0.004665
2.9975233
Down

hsa-miR-181c-5p
0.015322
3.0353231
Down

hsa-miR-204-5p
0.044941
3.0471704
Down

hsa-let-7d-5p
0.025658
3.0987353
Down

hsa-miR-98
0.027495
3.2012997
Down

hsa-miR-10a-5p
0.019225
3.3482268
Down

hsa-let-7f-5p
0.037282
3.4332418
Down

hsa-let-7e-5p
0.018337
3.6489105
Down

hsa-let-7a-5p
0.029403
3.6912477
Down

hsa-miR-99a-5p
0.035923
3.8172672
Down

hsa-let-7c
0.032672
3.9863558
Down

hsa-miR-126-3p
0.026073
4.528461
Down

hsa-miR-29b-1-5p
0.006566
15.589992
Down

hsa-miR-181c-3p
0.008219
16.225191
Down

hsa-miR-194-5p
0.011511
20.940771
Down

hsa-miR-335-5p
0.006273
36.750603
Down

hsa-miR-126-5p
0.015298
38.692142
Down

hsa-miR-505-5p
1.18E-05
42.592148
Down

hsa-miR-625-5p
0.008835
43.434204
Down

hsa-miR-200b-3p
1.60E-06
70.24472
Down

Figure 2: Unsupervised hierarchical clustering identified two distinct groups (CAVD versus control) based on their miRNA expression profile. Sample names are listed at top. The names of the significantly altered ( value < 0.05) miRNAs are shown at right. Ninety-two miRNAs were expressed differently between the two groups.

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).

Figure 3: The red, green, and blue sets stand for target genes predicted by database microRNAorg, TargetScan, and PITA, respectively.

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).

Table 2: The pathways incorporated by downregulated microRNAs in CAVD.

KEGG pathway
value
Genes
miRNAs

Cell cycle
<1 − 16
48
19

PI3K-Akt signaling pathway

95
17

ECM-receptor interaction

9
4

HIF-1 signaling pathway

30
15

p53 signaling pathway

28
14

ErbB signaling pathway

23
12

Neurotrophin signaling pathway
0.00249
19
10

Focal adhesion
0.0029
19
6

DNA replication
0.013417
19
2

Table 3: The pathways affected by upregulation of specific microRNAs in CAVD.

KEGG pathway
value
Genes
miRNAs

p53 signaling pathway

11
2

HIF-1 signaling pathway
0.00023
10
2

Valine, leucine, and isoleucine biosynthesis
0.00227
1
1

ErbB signaling pathway
0.003959
5
1

Cell cycle
0.003959
10
2

mTOR signaling pathway
0.004909
6
1

MAPK signaling pathway
0.005193
15
2

PI3K-Akt signaling pathway
0.005193
17
2

Wnt signaling pathway
0.008586
10
1

Synthesis and degradation of ketone bodies
0.009366
2
1

TGF-beta signaling pathway
0.021216
7
1

Basal transcription factors
0.029417
4
1

Glycerophospholipid metabolism
0.030271
8
1

Hypertrophic cardiomyopathy (HCM)
0.030271
6
1

Focal adhesion
0.030271
11
2

Circadian rhythm
0.030848
3
1

Mismatch repair
0.034518
2
1

Lysine degradation
0.038471
4
1

Butanoate metabolism
0.038615
3
1

Specific types of cancers and infections were not included.

4. Discussion

miRNAs have been shown to be critical regulators in cardiovascular diseases [18–25]. 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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