BioMed Research International
Volume 2019 (2019), Article ID 3945475, 10 pages
https://doi.org/10.1155/2019/3945475

Research Article

Expression Profiles of Long Noncoding RNA and mRNA in Epicardial Adipose Tissue in Patients with Heart Failure

Meili Zheng, Lei Zhao, and Xinchun Yang

Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China

Correspondence should be addressed to Xinchun Yang; haiyang_beauty@163.com

Received 29 April 2019; Accepted 27 June 2019; Published 4 July 2019

Academic Editor: Gelin Xu

Copyright © 2019 Meili Zheng 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.

The expression profile of long noncoding RNA (lncRNA) in human epicardial adipose tissue (EAT) has not been widely studied. In the present study, we performed RNA sequencing to analyze the expression profiles of lncRNA and mRNA in EAT in coronary artery disease (CAD) patients with and without heart failure (HF). Our results showed RNA sequencing disclosed 35673 mRNA and 11087 lncRNA corresponding to 15554 genes in EAT in total, while 30 differentially expressed lncRNAs (17 upregulated and 13 downregulated) and 278 differentially expressed mRNAs (129 upregulated and 149 downregulated) were discriminated between CAD patients with and without HF (<0.05; fold change>2); lncRNA ENST00000610659 drew specific attention for it was the top upregulated lncRNA with highest fold change and corresponded to UNC93B1 gene, which was proved to be related to HF and encoded UNC93B1 protein regulating toll-like receptor signaling, and both of them significantly increased in HF patients in qRT-PCR validation; the top significant upregulated enriched GO terms and KEGG pathway analysis were regulation of lymphocyte activation (GO:0051249) and T cell receptor signaling pathway (hsa04660), respectively. The current findings support the fact that EAT lncRNAs are involved in the inflammatory response leading to the development of HF.

1. Introduction

Recent studies have reported on long noncoding RNA (lncRNA) expression profiling in various human tissues [1]; however, expression profile of lncRNA in human epicardial adipose tissue (EAT) has yet to be described in detail. It is known that a large proportion of the mammalian genome is transcribed as lncRNA, which resides within or between coding genes. In addition, many lncRNAs have been shown to be functional and involved in specific physiological and pathological processes, through transcriptional or posttranscriptional regulatory mechanisms [2, 3]. To date, however, lncRNAs have never been included in analyses of the human EAT transcriptome. EAT is a key cardiometabolic factor, where, by releasing various inflammatory factors [4], EAT can modulate cardiac function and correlate with heart failure (HF) [5, 6], independently of metabolic status or the presence of coronary artery disease (CAD).

In the present study, we sought to supplement EAT lncRNA and mRNA expression profiles to provide a more complete picture of the myocardial transcriptional landscape in heart failure and also provide possible biomarkers for HF.

2. Materials and Methods

2.1. Study Participants

EAT samples were taken from 10 CAD patients who underwent coronary artery bypass graft surgery, in the Department of Heart Center, Beijing Chao-yang Hospital of Capital Medical University. Subjects were divided into two groups: HF group (n=5) and non-HF group (n=5). HF group included patients with Brain Natriuretic Peptide (BNP)>500ng/L and abnormal echocardiography finding (left ventricular end diastolic diameter [LVEDD]>50mm in female and >55mm in male and left ventricular ejection fraction [LVEF]<50%); non-HF group included patients with BNP<100 ng/L and normal views in echocardiography. The protocol was approved by the Ethics Committee of Beijing Chao-yang Hospital affiliated with Capital Medical University and written informed consent was obtained from participants before the study.

2.2. RNA Sequencing Procedure

Total RNA was extracted from the EAT and quantified using a NanoDrop ND-1000 instrument. 1-2μg total RNA was used to prepare the sequencing library in the following steps: (1) Total RNA is enriched by oligo (dT) magnetic beads (rRNA removed). (2) Using KAPA Stranded RNA-Seq Library Prep Kit (Illumina), RNA-seq library preparation incorporates dUTP into the second cDNA strand and makes the RNA-seq library strand-specific. (3) After completing, libraries were qualified with Agilent 2100 Bioanalyzer and quantified by absolute quantification qPCR method. (4) Sequence the libraries on the Illumina HiSeq 4000 instrument (we followed the methods of Wang et al. 2019 [7]).

2.3. Quantitative RT-PCR

qRT-PCR was used to measure selected lncRNA ENST00000610659 and UNC93B1 mRNA. Total RNA samples were extracted from the EAT samples using TRIzol (Invitrogen, Carlsbad, CA). The relative expression levels of mRNA and lncRNA were quantified using ViiA 7 Real-Time PCR System (Applied Biosystems, Foster City, USA) according to standard methods. lncRNA ENST00000610659: the forward primer was 5′ CGGCTTCAACAAGACGGTTC 3′, the reverse primer was 5′ AAGGCTCCACTCCGCACAAA 3′; UNC93B1 mRNA: the forward primer was 5′ GCTCACCTACGGCGTCTACC 3′, the reverse primer was 5′ CGGTAGGTCTCGT CGTAGTGC 3′.

2.4. Statistical Analysis

R package was used to calculate the FPKM value and differential expression for gene and transcript level and perform hierarchical clustering, GO enrichment, pathway analysis, scatter plots, and volcano plots with the differentially expressed genes. Descriptive statistics for each variable were determined. Continuous variables were expressed as the mean ± SD and compared using unpaired Student’s t-test, and categorical variables were expressed as percentages and numbers and were compared using the chi-squared test. Significant GO enrichment and pathways were selected by Fisher’s exact test, and p<0.05.

3. Results

3.1. Characteristics of Participants

The present study comprised 10 CAD patients (5 with HF and 5 without). The main clinical characteristics of the two groups are summarized in Table 1. There were no significant differences in subject characteristics between the two groups; they were well balanced with regard to main clinical and laboratory characteristics. The CAD patients with HF had higher BNP level and LVEDD and lower LVEF.

Table 1: Participants Characteristics.

CAD with HF
CAD without HF
P value

(N=5)
(N=5)

Male, n(%)
2(40)
3(60)
0.999

Age, years
67.8±5.0
60.8±8.1
0.139

BMI, kg/m²
23.8±4.6
25.0±5.5
0.718

Diabetes, n(%)
2(40)
2(40)
1.000

Hypertension, n(%)
4(80)
5(100)
1.000

HbA1c, %
6.85±0.87
7.00±1.81
0.871

Total cholesterol, mmol/L
3.67±3.07
3.60±1.52
0.965

LDL-C, mmol/L
2.34±2.52
2.24±1.46
0.941

HDL-C, mmol/L
0.84±0.53
0.70±0.12
0.580

Triglyceride, mmol/L
0.89±0.38
1.86±0.89
0.055

Creatinine, μmol/L
88.2±25.2
76.8±16.8
0.424

Uric acid, μmol/L
363.2±82.4
357.0±69.3
0.901

BNP, pg/ml
1795.4±1053.3
76.4±22.6
0.006

LVEDD, mm
60.0±5.9
47.5±5.2
0.007

LVEF, %
44.2±10.2
62.0±4.8
0.008

Notes: BMI, body mass index; HbA1c, glycosylated hemoglobin; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BNP, brain natriuretic peptide; LVEDD, left ventricular end diastolic diameter; LVEF, left ventricular ejection fraction.

3.2. RNA Sequencing Data

Using RNA sequencing, we detected 46760 transcripts (including 35673 protein-coding and 11087 non-protein-coding with linear structure and length>200bp) corresponding to 15554 genes in EAT in total. The top 30 highly expressed protein-coding and non-protein-coding transcripts are summarized in Table 2.

Table 2: The 30 highly expressed protein-coding and non protein-coding transcripts in EAT in CAD patients.

Track ID
Gene Name
Transcript Type
Length(bp)
Protein

ENST00000165086.8
NPIPB4
Processed transcript
464
No

ENST00000173785.4
KLF6
Processed transcript
925
No

ENST00000214893.9
ERMP1
Processed transcript
4974
No

ENST00000216463.8
TIMM9
Processed transcript
1075
No

ENST00000216520.6
ERH
Processed transcript
668
No

ENST00000217890.10
ARSD
Processed transcript
2160
No

ENST00000230914.4
MRPS30
Processed transcript
4331
No

ENST00000233699.8
POLE4
Processed transcript
602
No

ENST00000237177.10
CASP8AP2
Processed transcript
6719
No

ENST00000244070.7
PPP4R1L
Processed transcript
1474
No

ENST00000253320.8
TXLNGY
Retained intron
7299
No

ENST00000254109.9
CLUHP3
Processed transcript
1812
No

ENST00000254299.8
GCH1
Processed transcript
2901
No

ENST00000256692.5
PLEKHA8P1
Processed transcript
1839
No

ENST00000263511.8
CROCCP3
Processed transcript
5368
No

ENST00000264785.11
WDR1
Processed transcript
549
No

ENST00000265450.5
TSPAN14
Processed transcript
2588
No

ENST00000265870.7
SLC25A16
Processed transcript
2295
No

ENST00000267869.8
GTF2A2
Processed transcript
518
No

ENST00000267918.9
ANP32A
Processed transcript
1084
No

ENST00000273411.2
RPL39P5
Processed transcript
449
No

ENST00000274820.7
RPL13P5
Processed transcript
349
No

ENST00000276096.10
EBP
Processed transcript
904
No

ENST00000282943.9
ADGRA3
Processed transcript
3534
No

ENST00000286777.6
RWDD2B
Processed transcript
1625
No

ENST00000294661.8
C1orf52
Processed transcript
3391
No

ENST00000295549.8
LINC01116
lincRNA
1407
No

ENST00000295748.7
AZI2
Processed transcript
3127
No

ENST00000296031.4
CXCL2
Processed transcript
577
No

ENST00000296325.9
LRPAP1
Processed transcript
1078
No

ENST00000361624.2
MT-CO1
Protein coding
1542
513aa

ENST00000362079.2
MT-CO3
Protein coding
784
261aa

ENST00000361390.2
MT-ND1
Protein coding
956
318aa

ENST00000361381.2
MT-ND4
Protein coding
1378
459aa

ENST00000361453.3
MT-ND2
Protein coding
1042
347aa

ENST00000361789.2
MT-CYB
Protein coding
1141
380aa

ENST00000361739.1
MT-CO2
Protein coding
684
227aa

ENST00000361851.1
MT-ATP8
Protein coding
207
68aa

ENST00000331825.11
FTL
Protein coding
871
175aa

ENST00000361335.1
MT-ND4L
Protein coding
297
98aa

ENST00000361567.2
MT-ND5
Protein coding
1812
603aa

ENST00000361227.2
MT-ND3
Protein coding
346
115aa

ENST00000361899.2
MT-ATP6
Protein coding
681
226aa

ENST00000320868.9
HBA1
Protein coding
577
142aa

ENST00000331523.6
EEF1A1
Protein coding
1923
462aa

ENST00000327726.10
CFD
Protein coding
1201
253aa

ENST00000302754.5
JUNB
Protein coding
1830
347aa

ENST00000239938.4
EGR1
Protein coding
3137
543aa

ENST00000356524.9
SAA1
Protein coding
518
122aa

ENST00000633942.1
PLIN4
Protein coding
6484
1372aa

ENST00000367029.5
G0S2
Protein coding
876
103aa

ENST00000309311.7
EEF2
Protein coding
3158
858aa

ENST00000251595.11
HBA2
Protein coding
576
142aa

ENST00000335295.4
HBB
Protein coding
628
147aa

ENST00000256104.4
FABP4
Protein coding
941
132aa

ENST00000451311.7
TMSB4X
Protein coding
622
44aa

ENST00000233143.6
TMSB10
Protein coding
461
44aa

ENST00000330871.3
SOCS3
Protein coding
2734
225aa

ENST00000336615.9
PNPLA2
Protein coding
2416
504aa

ENST00000300055.10
PLIN1
Protein coding
2916
522aa

Note: EAT, epicardial adipose tissue; CAD, coronary artery disease; Track ID, The transcript name in Ensembl database; Gene Name, The corresponding gene name of transcript; Transcript Type, the biotype of transcript; Protein, the residue number of protein.

Scatter plot (Figure 1) was performed to group lncRNA and mRNA and display the levels of lncRNA and mRNA in CAD patients with and without HF according to their expression levels among samples, and the results indicated that the lncRNA and mRNA expression profiles in CAD patients with HF were distinctly different from those in CAD patients without HF. 85 lncRNA and 866 mRNA whose levels changed significantly (p<0.05) were identified, including 45 upregulated and 40 downregulated lncRNA, as well as 404 upregulated and 462 downregulated mRNA.

(a)

(b)

Figure 1: The scatter plot of the differential expressed (p<0.05) lncRNA (a) and mRNA (b) in patients with heart failure (HF) and without heart failure (non-HF) (red or green represented upregulated or downregulated genes, respectively); 85 lncRNA and 866 mRNA were identified, including 45 upregulated and 40 downregulated lncRNA, as well as 404 upregulated and 462 downregulated mRNA.

Using a 2-fold expression difference as a cutoff, a total of 30 differentially expressed lncRNAs (17 upregulated and 13 downregulated) (Figure 2, Table 3) and 278 differentially expressed mRNAs (129 upregulated and 149 downregulated) were discriminated between CAD patients with and without HF (Figure 2). Among them, lncRNA ENST00000610659 was the top upregulated lncRNA with highest fold change and corresponded to UNC93B1 gene, which was proved to be related to HF and encoded UNC93B1 protein regulating toll-like receptor signaling. lncRNA ENST00000610659 and UNC93B1 mRNA were both significantly increased in HF patients in qRT-PCR validation (p=0.040 for lncRNA ENST00000610659 and p=0.019 for UNC93B1 mRNA) (Figure 3). lncRNA ENST00000610659 might be a potential biomarker for HF.

Table 3: Differentially expressed lncRNA in EAT in CAD patients with HF compared with CAD patients without HF.

lncRNA
Type
Regulation
Gene Name
Fold Change
P Value

ENST00000610659
exon sense-overlapping
Up
UNC93B1
4.778
0.0118

ENST00000379935
natural antisense
Up
RBL2
3.711
0.0347

ENST00000603195
exon sense-overlapping
Up
ZSWIM8
3.329
0.0039

ENST00000439904
exon sense-overlapping
Up
SLC25A16
2.955
0.0179

ENST00000622120
intergenic
Up
LINC00963
2.952
0.0000

ENST00000514805
exon sense-overlapping
Up
TRIM52
2.901
0.0294

ENST00000492356
exon sense-overlapping
Up
RPS21
2.869
0.0461

ENST00000394225
exon sense-overlapping
Up
NDUFC1
2.840
0.0331

ENST00000548989
exon sense-overlapping
Up
CRIP2
2.247
0.0248

ENST00000465589
exon sense-overlapping
Up
OBSL1
2.229
0.0067

ENST00000398078
exon sense-overlapping
Up
PDXK
2.111
0.0157

ENST00000476113
exon sense-overlapping
Up
TCEA2
2.111
0.0223

ENST00000421064
natural antisense
Up
AP000347.2
2.099
0.0220

ENST00000470322
exon sense-overlapping
Up
ACTR1A
2.083
0.0304

ENST00000587762
intergenic
Up
AC020916.1
2.054
0.0443

ENST00000512955
exon sense-overlapping
Up
AMOTL2
2.052
0.0164

ENST00000508948
exon sense-overlapping
Up
ARRDC3
2.012
0.0289

ENST00000543826
exon sense-overlapping
Down
ADGRD1
0.161
0.0021

ENST00000467318
exon sense-overlapping
Down
DDX56
0.243
0.0126

ENST00000556961
exon sense-overlapping
Down
FBLN5
0.251
0.0033

ENST00000505923
exon sense-overlapping
Down
WDFY3
0.283
0.0221

ENST00000578571
exon sense-overlapping
Down
PTPRM
0.305
0.0360

ENST00000427261
intergenic
Down
RP11-640M9.2
0.331
0.0051

ENST00000319685
exon sense-overlapping
Down
TMTC1
0.370
0.0256

ENST00000493951
exon sense-overlapping
Down
TACC2
0.391
0.0341

ENST00000480603
exon sense-overlapping
Down
PPIA
0.414
0.0048

ENST00000345896
exon sense-overlapping
Down
CERS2
0.453
0.0391

ENST00000498053
exon sense-overlapping
Down
LRRFIP1
0.475
0.0350

ENST00000467178
exon sense-overlapping
Down
CIZ1
0.486
0.0448

ENST00000468975
exon sense-overlapping
Down
ARFGAP1
0.495
0.0128

Note: EAT, epicardial adipose tissue; CAD, coronary artery disease; HF, heart failure; lncRNA, The lncRNA name in Ensembl database; Type, the type of lncRNA; Regulation, the regulation expression of lncRNA; Gene Name, The corresponding gene name of lncRNA.

Figure 2: The hierarchical clustering and volcano plot of the substantially differential expressed (P<0.05; fold change>2) lncRNA ((a1) and (a2)) and mRNA ((b1) and (b2)) in patients with and without heart failure (red or green represented upregulated or downregulated genes, respectively); 30 lncRNA and 278 mRNA were identified, including 17 upregulated and 13 downregulated lncRNA, as well as 129 upregulated and 149 downregulated mRNA.

Figure 3: qRT-PCR analysis of expression of lncRNA ENST00000610659 (left) and UNC93B1 mRNA (right) in patients with heart failure (HF) and without heart failure (non-HF) (n=5 in each group), p<0.05.

3.3. GO and KEGG Pathway Analysis of Differentially Expressed mRNAs

The Gene Ontology (GO) project (Figure 4) provided a controlled vocabulary to describe gene and gene product attributes in any organism. The ontology covered three domains: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). For upregulated genes, the top enriched GO terms in three domains were regulation of lymphocyte activation (GO:0051249) in BP, T cell receptor complex (GO:0042101) in CC, and phosphotyrosine residue binding (GO:0001784) in MF; for downregulated genes, those were oxidation reduction process (GO:0055114) in BP, extracellular space (GO:0005615) in CC, and oxidoreductase activity (GO:0016491) in MF, respectively.

Figure 4: Enriched GO terms analysis for differentially expressed mRNAs. Top 10 significantly upregulated GO terms-Biological Process (a) and Molecular Function (c); all significantly upregulated GO terms-Cellular Component (b); top 10 significantly downregulated GO terms-Biological Process (d) and Molecular Function (f); all significantly downregulated GO terms-Cellular Component (e).

Pathway analysis (Figure 5) showed that, when comparing to controls, 17 pathways were significantly upregulated while 4 pathways were significantly downregulated. The top 3 significantly upregulated pathways were T cell receptor signaling pathway (hsa04660), primary immunodeficiency (hsa05340), and endometrial cancer (hsa05213). Meanwhile, the significantly downregulated pathways were drug metabolism cytochrome P450 (hsa00982), tyrosine metabolism (hsa00350), complement and coagulation cascades (hsa04610), and Jak-STAT signaling pathway (hsa04630).

(a)

(b)

Figure 5: KEGG pathway analysis of differentially expressed mRNAs. Top 10 significantly upregulated pathways (a) and all significantly downregulated pathways (b).

4. Discussion

In the present study, we assessed the expression profiles of EAT lncRNA and mRNA in CAD patients with and without HF. The results showed a total of 35673 mRNA and 11087 lncRNA corresponding to 15554 genes in EAT were detected, and using a 2-fold expression difference as a cutoff, a total of 30 differentially expressed lncRNAs (17 upregulated and 13 downregulated) and 278 differentially expressed mRNAs (129 upregulated and 149 downregulated) were discriminated between CAD patients with and without HF.

The differentially expressed lncRNAs corresponded to genes associated with inflammatory response or other factors which are involved in HF. UNC93B1, the top upregulated gene lncRNA corresponded to, encodes UNC93B1 protein that is involved in innate and adaptive immune response by regulating toll-like receptor signaling [8, 9] and is proved to be related to left ventricular diastolic function, heart failure morbidity, and mortality [10]. RBL2 is related to TGF-beta signaling [11]. LINC00963 encodes lncRNA963 playing an important role in chronic renal failure, which is closely associated with chronic diseases such as congestive heart failure [12]. TRIM52 encodes TRIM52 protein that positively regulates the nuclear factor-kappa B signaling pathway [13]. RPS21 (also known as HLDF) encodes HLDF protein that is involved in the mechanisms of blood pressure regulation [14]. AMOTL2 is required for migration and proliferation of endothelial cells during angiogenesis [15]. FBLN5 protein expression significantly decreases in human aneurysmatic aortas and may mediate cell-extracellular matrix interactions and elastic fibre assembly by inflammation [16]. TMTC1 is associated with the risk of incident HF [17]. LRRFIP1 is associated with adiposity and inflammation [18], and LRRFIP1 protein may regulate platelet function [19].

EAT refers to the fat depot that exists on the surface of the myocardium and is contained entirely beneath the pericardium, which generates various inflammatory factors [20, 21]. Factors released from EAT have vasocrine and paracrine effects on the myocardium contributing to modulating properties on cardiac function [4, 22]. As our study showed, lncRNA can also be released from EAT and may be involved in HF; top upregulated lncRNA in HF corresponded to genes associated with inflammatory response and top upregulated enriched GO terms and KEGG pathway of mRNA were also about inflammatory cells activity.

The present study showed the expression profiles of EAT lncRNA and mRNA in CAD patients and also characterized specific EAT lncRNA expression in HF. The EAT lncRNA may be important effector molecules for cardiovascular disease. Through the paracrine and vasocrine transmission, the EAT lncRNA may diffuse across the interstitial fluid or blood into the myocardium to be involved in the development of HF. Our data supplement lncRNA expression profiles in the EAT for lncRNA identifying in heart tissues and also provide possible biomarkers for HF, and further studies are needed to prove it.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Meili Zheng and Lei Zhao analyzed the results and wrote the paper; Xinchun Yang designed the study. Meili Zheng and Lei Zhao contributed equally to this work.

Acknowledgments

This research was supported by Natural Science Foundation of China (NO. 81800304).

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