BioMed Research International

BioMed Research International / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 3945475 | 10 pages | https://doi.org/10.1155/2019/3945475

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

Academic Editor: Gelin Xu
Received29 Apr 2019
Accepted27 Jun 2019
Published04 Jul 2019

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.


CAD with HFCAD without HFP value
(N=5)(N=5)

Male, n(%)2(40)3(60)0.999
Age, years67.8±5.060.8±8.10.139
BMI, kg/m223.8±4.625.0±5.50.718
Diabetes, n(%)2(40)2(40)1.000
Hypertension, n(%)4(80)5(100)1.000
HbA1c, %6.85±0.877.00±1.810.871
Total cholesterol, mmol/L3.67±3.073.60±1.520.965
LDL-C, mmol/L2.34±2.522.24±1.460.941
HDL-C, mmol/L0.84±0.530.70±0.120.580
Triglyceride, mmol/L0.89±0.381.86±0.890.055
Creatinine, μmol/L88.2±25.276.8±16.80.424
Uric acid, μmol/L363.2±82.4357.0±69.30.901
BNP, pg/ml1795.4±1053.376.4±22.60.006
LVEDD, mm60.0±5.947.5±5.20.007
LVEF, %44.2±10.262.0±4.80.008

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.


Track IDGene NameTranscript TypeLength(bp)Protein

ENST00000165086.8NPIPB4Processed transcript464No
ENST00000173785.4KLF6Processed transcript925No
ENST00000214893.9ERMP1Processed transcript4974No
ENST00000216463.8TIMM9Processed transcript1075No
ENST00000216520.6ERHProcessed transcript668No
ENST00000217890.10ARSDProcessed transcript2160No
ENST00000230914.4MRPS30Processed transcript4331No
ENST00000233699.8POLE4Processed transcript602No
ENST00000237177.10CASP8AP2Processed transcript6719No
ENST00000244070.7PPP4R1LProcessed transcript1474No
ENST00000253320.8TXLNGYRetained intron7299No
ENST00000254109.9CLUHP3Processed transcript1812No
ENST00000254299.8GCH1Processed transcript2901No
ENST00000256692.5PLEKHA8P1Processed transcript1839No
ENST00000263511.8CROCCP3Processed transcript5368No
ENST00000264785.11WDR1Processed transcript549No
ENST00000265450.5TSPAN14Processed transcript2588No
ENST00000265870.7SLC25A16Processed transcript2295No
ENST00000267869.8GTF2A2Processed transcript518No
ENST00000267918.9ANP32AProcessed transcript1084No
ENST00000273411.2RPL39P5Processed transcript449No
ENST00000274820.7RPL13P5Processed transcript349No
ENST00000276096.10EBPProcessed transcript904No
ENST00000282943.9ADGRA3Processed transcript3534No
ENST00000286777.6RWDD2BProcessed transcript1625No
ENST00000294661.8C1orf52Processed transcript3391No
ENST00000295549.8LINC01116lincRNA1407No
ENST00000295748.7AZI2Processed transcript3127No
ENST00000296031.4CXCL2Processed transcript577No
ENST00000296325.9LRPAP1Processed transcript1078No
ENST00000361624.2MT-CO1Protein coding1542513aa
ENST00000362079.2MT-CO3Protein coding784261aa
ENST00000361390.2MT-ND1Protein coding956318aa
ENST00000361381.2MT-ND4Protein coding1378459aa
ENST00000361453.3MT-ND2Protein coding1042347aa
ENST00000361789.2MT-CYBProtein coding1141380aa
ENST00000361739.1MT-CO2Protein coding684227aa
ENST00000361851.1MT-ATP8Protein coding20768aa
ENST00000331825.11FTLProtein coding871175aa
ENST00000361335.1MT-ND4LProtein coding29798aa
ENST00000361567.2MT-ND5Protein coding1812603aa
ENST00000361227.2MT-ND3Protein coding346115aa
ENST00000361899.2MT-ATP6Protein coding681226aa
ENST00000320868.9HBA1Protein coding577142aa
ENST00000331523.6EEF1A1Protein coding1923462aa
ENST00000327726.10CFDProtein coding1201253aa
ENST00000302754.5JUNBProtein coding1830347aa
ENST00000239938.4EGR1Protein coding3137543aa
ENST00000356524.9SAA1Protein coding518122aa
ENST00000633942.1PLIN4Protein coding64841372aa
ENST00000367029.5G0S2Protein coding876103aa
ENST00000309311.7EEF2Protein coding3158858aa
ENST00000251595.11HBA2Protein coding576142aa
ENST00000335295.4HBBProtein coding628147aa
ENST00000256104.4FABP4Protein coding941132aa
ENST00000451311.7TMSB4XProtein coding62244aa
ENST00000233143.6TMSB10Protein coding46144aa
ENST00000330871.3SOCS3Protein coding2734225aa
ENST00000336615.9PNPLA2Protein coding2416504aa
ENST00000300055.10PLIN1Protein coding2916522aa

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.

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.


lncRNATypeRegulationGene NameFold ChangeP Value

ENST00000610659exon sense-overlappingUpUNC93B14.7780.0118
ENST00000379935natural antisenseUpRBL23.7110.0347
ENST00000603195exon sense-overlappingUpZSWIM83.3290.0039
ENST00000439904exon sense-overlappingUpSLC25A162.9550.0179
ENST00000622120intergenicUpLINC009632.9520.0000
ENST00000514805exon sense-overlappingUpTRIM522.9010.0294
ENST00000492356exon sense-overlappingUpRPS212.8690.0461
ENST00000394225exon sense-overlappingUpNDUFC12.8400.0331
ENST00000548989exon sense-overlappingUpCRIP22.2470.0248
ENST00000465589exon sense-overlappingUpOBSL12.2290.0067
ENST00000398078exon sense-overlappingUpPDXK2.1110.0157
ENST00000476113exon sense-overlappingUpTCEA22.1110.0223
ENST00000421064natural antisenseUpAP000347.22.0990.0220
ENST00000470322exon sense-overlappingUpACTR1A2.0830.0304
ENST00000587762intergenicUpAC020916.12.0540.0443
ENST00000512955exon sense-overlappingUpAMOTL22.0520.0164
ENST00000508948exon sense-overlappingUpARRDC32.0120.0289
ENST00000543826exon sense-overlappingDownADGRD10.1610.0021
ENST00000467318exon sense-overlappingDownDDX560.2430.0126
ENST00000556961exon sense-overlappingDownFBLN50.2510.0033
ENST00000505923exon sense-overlappingDownWDFY30.2830.0221
ENST00000578571exon sense-overlappingDownPTPRM0.3050.0360
ENST00000427261intergenicDownRP11-640M9.20.3310.0051
ENST00000319685exon sense-overlappingDownTMTC10.3700.0256
ENST00000493951exon sense-overlappingDownTACC20.3910.0341
ENST00000480603exon sense-overlappingDownPPIA0.4140.0048
ENST00000345896exon sense-overlappingDownCERS20.4530.0391
ENST00000498053exon sense-overlappingDownLRRFIP10.4750.0350
ENST00000467178exon sense-overlappingDownCIZ10.4860.0448
ENST00000468975exon sense-overlappingDownARFGAP10.4950.0128

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.
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.

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).

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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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.


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