The long noncoding RNA expression profiles of paroxysmal atrial fibrillation identified by microarray analysis

Accepted Manuscript The long noncoding RNA expression profiles of paroxysmal atrial fibrillation identified by microarray analysis

Ying Su, Long Li, Sheng Zhao, Yunan Yue, Shuixiang Yang PII: DOI: Reference:

S0378-1119(17)30989-7 doi:10.1016/j.gene.2017.11.025 GENE 42333

To appear in:

Gene

Received date: Revised date: Accepted date:

26 July 2017 28 October 2017 8 November 2017

Please cite this article as: Ying Su, Long Li, Sheng Zhao, Yunan Yue, Shuixiang Yang , The long noncoding RNA expression profiles of paroxysmal atrial fibrillation identified by microarray analysis. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2017.11.025

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ACCEPTED MANUSCRIPT The Long Noncoding RNA Expression Profiles of Paroxysmal Atrial Fibrillation Identified by Microarray Analysis

Ying Su1, MD; Long Li1, PhD; Sheng Zhao1, MD; Yunan Yue 1, PhD; Shuixiang

Department of Cardiology, Beijing Shijitan Hospital Affiliated Capital Medical

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Yang1,2*, PhD

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University, Beijing, China.

Department of Cardiology, Hebei Medical University Affiliated Yiling Hospital,

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Hebei, China

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* Corresponding author: Shuixiang Yang

Postal address: No.10, Tieyi Road, Beijing, China.

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Postcode: 100038

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E-mail: [email protected] Tel & Fax: (86) 010-69326376

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Article type: original research

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Key words: long noncoding RNA; microarray analysis; paroxysmal atrial fibrillation Funding:

This research was supported by the Beijing Natural Science Foundation [grant number 7083108] and by the Scientific Developing Foundation of Shijitan Hospital [grant number 2012XXGNK]. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Disclosures: The Authors declare that there is no conflict of interest.

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ACCEPTED MANUSCRIPT Abstract Background: Long noncoding RNAs (lncRNAs) represent a novel class of noncoding RNAs that are involved in a variety of biological processes and human diseases. Recent evidence suggested that lncRNAs were associated with cardiac

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disorders. However, the roles of lncRNAs in paroxysmal atrial fibrillation (PAF)

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remain elusive. The purpose of the present study was to identify differentially

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expressed lncRNAs in PAF and predict their potential functions.

Methods: Between May 2014 and December 2015, a total of 67 patients,

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including 34 patients with PAF and 33 patients without PAF were recruited in this

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study. Of these participants, 3 PAF patients and 3 controls were used for the microarray analysis and a separate cohort (31 PAF patients and 30 controls) were used

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for further validation. LncRNA profiles in the leukocytes were detected by

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microarray.

Results: A total of 2095 and 1584 differentially expressed lncRNAs and mRNAs,

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respectively, were identified between the PAF patients and controls. Four lncRNAs

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(uc002nvy.3, ENST00000561094, uc004aef.3, ENST00000559960) were randomly selected for quantitative real-time PCR (qRT-PCR) in a separate cohort, validating that ENST00000559960 was upregulated and uc004aef.3 was downregulated in the PAF patients. uc002nvy.3 and ENST00000561094 showed no significant difference between

PAF

and

the

controls.

Multiple

logistic

analyses

showed

that

ENST00000559960 (OR 1.47; 95% CI 1.09 to 2.00; P = 0.01) and uc004aef.3 (OR 0.63; 95% CI 0.41 to 0.96; P = 0.03) were independently associated with PAF.

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ACCEPTED MANUSCRIPT Receiver

operating

characteristic

(ROC)

curves

analyses

revealed

that

ENST00000559960 and uc004aef.3 were modest predictors of PAF. The area under the curve (AUC) was 0.67 ± 0.07 (95% CI 0.54–0.81; P = 0.02) for uc004aef.3 and 0.70 ± 0.07 (95% CI 0.56-0.83; P < 0.01) for ENST00000559960. Bioinformatic

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analyses (lncRNAs classification and subgroup, gene ontology analysis, pathway

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analysis and gene co-expression network construction) were performed for predicting

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the role of lncRNAs.Conclusions: Our results demonstrated that lncRNA profiles were differentially expressed in the PAF leukocytes, and two lncRNAs

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(ENST00000559960 and uc004aef.3) may help in prediction of PAF. This motivates

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further investigation of the role of lncRNAs for PAF.

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ACCEPTED MANUSCRIPT 1. Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. AF can cause reduced left ventricular function, thromboembolism and stroke and is associated with increasing morbidity and mortality (Ferrari et al., 2015). According to

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the clinical presentation, AF is classified as paroxysmal AF (PAF), which is defined

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by self-termination within 7 days, persistent and permanent forms that fail to

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self-terminate (Iwasaki et al., 2011). Nearly 50% of PAF patients progressed to persistent forms or were dead within 10 years (Padfield et al., 2017). Current

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pharmacological approaches have major limitations, including limited efficacy

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and potentially serious side effects, such as malignant ventricular arrhythmia and extra-cardiac toxicity (Lau et al., 2015). A better understanding of the

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therapeutic strategies.

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mechanisms underlying AF are expected for the development of novel

Non-coding RNAs (ncRNAs) constitute nearly 98% of human transcripts and

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provide novel insight in the pathogenesis of disease (Djebali et al., 2012). Among the

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noncoding RNAs, short noncoding RNAs, such as microRNAs (miRNAs), have been extensively investigated in cardiac disorders. Several miRNAs have been involved in the regulation of the electrical and structural remodeling of atrial tissues in AF(detailed reviewed in (Santulli et al., 2014)). We previously that reported miR-892a, miR-3171 and miR-3149 are potential markers of AF (Xu et al., 2016). A novel class of long noncoding RNAs (lncRNAs), defined as transcripts longer than 200 nucleotides, has recently emerged. Similar with miRNAs, recent evidence has

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ACCEPTED MANUSCRIPT also revealed the vital regulatory role of lncRNAs in cardiovascular systems (Thum and Condorelli, 2015; Uchida and Dimmeler, 2015). Several lncRNAs are regulated in cardiac hypertrophy (e.g., CHRF), myocardial infarction (e.g., ANRIL), cardiac apoptosis (e.g., CARL) and heart development (e.g., Bvht) (Klattenhoff et al., 2013;

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Vausort et al., 2014; Wang et al., 2014; Wang et al., 2014). Though many lncRNAs

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have been confirmed to be associated with multiple cardiac disorders including atrial

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fibrillation (Ruan et al., 2015; Xu et al., 2016), the role of lncRNAs in PAF is still in its infancy.

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In the present study, we aimed to detect lncRNA profiles in the blood leukocytes

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of PAF patients and evaluate the selected lncRNAs by quantitative real-time PCR (qRT-PCR) in a separate cohort. Our results demonstrated that differentially expressed

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lncRNAs may serve as possible biomarkers for PAF in the future.

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2. Materials and Methods 2.1. Ethics Statement

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The protocol was approved by the Ethics Committee of Beijing Shijitan Hospital

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affiliated with Capital Medical University and written informed consent was obtained from participants before the study. 2.2. Study participants Between May 2014 and December 2015, a total of 67 patients, including 34 patients with PAF and 33 patients without PAF were recruited in this study. Of these participants, 3 PAF patients and 3 controls were used for microarray analysis and a separate cohort (31 PAF patients and 30 controls) was used for qRT-PCR validation.

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ACCEPTED MANUSCRIPT PAF was defined as the occurrence of 1 or more episodes lasting 7 days in the previous 6 months, with all episodes terminating spontaneously. Every patient was monitored using 7-day surface electrocardiography (ECG). The medical history of every patient was recorded, along with demographics, medication history and

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echocardiogram. Any patient meeting the following criteria was excluded: age <18

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years, valvular disorder, structural heart diseases, left ventricular dysfunction with an

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ejection fraction <40%, acute coronary syndrome within 6 months, hyperthyroidism, malignancy, acute or chronic inflammatory diseases, liver or renal dysfunction, or

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nervous system disorders.

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2.3. RNA Isolation

All blood samples were collected in Shijitan Hospital. Leukocytes from blood

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samples were separated as described previously (Vausort et al., 2014). Total RNA was

according

to

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extracted from leukocytes using the Trizol reagent (Invitrogen, Carlsbad, CA, USA) the

manufacturer’s

instructions.

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NanoDrop

ND-1000

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spectrophotometer was used for to evaluate the RNA quantity and quality. RNA

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integrity was assessed by standard denaturing agarose gel electrophoresis. 2.4. Microarray Analysis and Computational Analysis LncRNA expression profiles of blood leukocytes were examined between the PAF patients and controls. A Human Arraystar LncRNA Microarray V3.0 (8 x 60K, Arraystar) was used for the global profiling of human lncRNAs and protein-coding transcripts. Approximately 30,586 lncRNAs and 26,109 coding transcripts could be detected by the lncRNA microarray, derived from public transcriptome databases

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ACCEPTED MANUSCRIPT (Refseq, UCSC known genes, Gencode, etc.) and landmark publications. The RNA preparation and array hybridization were performed according to the Agilent One-Color

Microarray-Based

Gene

Expression

Analysis

protocol

(Agilent

Technology) with minor modifications. Briefly, total RNAs were purified after

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removal of the rRNAs. Then, RNAs were amplified and transcribed into fluorescent

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cRNAs along the entire length of the transcripts without the 3’ bias by a random

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priming method. The labeled cRNAs were hybridized onto the Human LncRNA Array v3.0. After having washed the slides, the arrays were scanned using the Agilent DNA

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Microarray Scanner (part number G2505C). Acquired array images were analyzed by

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Agilent Feature Extraction software (version 11.0.1.1). Quantile normalization and subsequent data processing were conducted using the GeneSpring GX v11.5.1

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software package (Agilent Technologies). After quantile normalization of the raw

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data, we chose lncRNAs and mRNAs that have at least 3 out of 9 samples with flags in Present or Marginal for further data analysis. Differentially expressed

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lncRNAs/mRNAs with statistical significance were identified through Volcano Plot

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filtering (Fold Change ≥ 2.0 and P ≤ 0.05) and Fold Change filtering(Fold Change ≥ 2.0). Hierarchical clustering was carried out using Agilent GeneSpring GX software (version 11.5.1). The microarray data have been deposited in NCBI's Gene Expression Omnibus

under

accession

number

(http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75092). 2.5. Gene Functional Analysis

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GSE75092

ACCEPTED MANUSCRIPT Functional analysis, including gene ontology (GO) analysis and pathway analysis, were carried out by the standard enrichment computation method. GO analysis is a functional analysis associating differentially expressed mRNAs with GO categories, which were derived from Gene Ontology (http://www.geneontology.org). Gene

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Ontology comprises three structured networks of defined terms that describe gene

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product attributes. The P-value indicates the significance of the GO Term enrichment

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(the cut-off is 0.05). Pathway analysis for differentially expressed mRNAs was also performed based on the latest KEGG (Kyoto Encyclopedia of Genes and Genomes,

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http://www.genome.jp/kegg) database. This analysis helped us to determine the

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biological pathway in which there was significant enrichment of the differentially expressed mRNAs. The P-value denotes the significance of the pathway (the cut-off is

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0.05).

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2.6. Construction of the Co-expression Network Several differentially expressed lncRNAs and mRNAs were selected for

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coding-noncoding (CNC) gene co-expression analysis by Cytoscape software (v2.8.1)

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as previously described (Dong et al., 2014). Pearson’s correlation coefficient was calculated between the differentially expressed lncRNA and mRNA by R statistical analysis. The cut-off of Pearson’s correlation coefficient was ≥ 0.945. 2.7. Quantitative Real-time PCR Validation Reverse transcription and quantitative real-time PCR (qRT-PCR) were performed according to a modified protocol as previously described (Xu et al., 2014). Four lncRNAs were randomly selected for validation. Total RNAs extracted from all blood

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ACCEPTED MANUSCRIPT samples were reverse transcribed into cDNA using an RT Reagent Kit (Thermo Scientific). Then, qRT-PCR reactions were carried out in triplicate in 384-well plates using SYBR green master mix (Takara). After normalization to GAPDH, relative RNA expression levels were calculated by the 2-ΔΔCt method. All primers used for

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lncRNA detection are listed in Online Table S1.

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2.8. Statistical Analysis

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Student’s t test, Chi-square test, Fischer’s exact test and Mann–Whitney U tests were used as appropriate for assessing the differences among the variables studied.

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Values are the mean ± SD or median (interquartile range) for continuous variables and

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percentages for categorical variables. The relationships between the lncRNA levels and the presence of PAF were assessed by binary logistic regression analyses. On

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logistic regression, variables with P values <0.2 in the comparisons of baseline

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characteristics between the PAF and control group were included as covariates. Four lncRNA expression levels were entered into the model regardless of their P values.

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The following variables were included in the multivariable model: NST00000559960,

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uc004aef.3, ENST00000561094, uc002nvy.3, age and LAD. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the lncRNAs to detect PAF. Data were considered to be statistically significant if P < 0.05. Data analyses were performed with SPSS 13.0 (SPSS Inc.).

3. Results 3.1. Baseline Characteristics

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ACCEPTED MANUSCRIPT The detailed characteristics of the study participants are listed in Table 1. Between May 2014 and December 2015, a total of 67 patients, including 34 patients with PAF and 33 patients without PAF, were recruited in this study. Of these participants, 3 PAF patients and 3 controls were used for the microarray analysis and a

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separate cohort (31 PAF patients and 30 controls) was used for the qRT-PCR

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validation. In the microarray analysis cohort, the left atrial diameter in the PAF group

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was larger than the control group (42.4 ± 1.21 mm vs. 33.8 ± 1.08 mm, respectively; P < 0.05). There was no significant difference in sex, age, diabetes mellitus,

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hypertension, coronary heart disease, hyperlipidemia, smoking habits, medication

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history and left ventricular ejection fraction between the PAF and control patients. In another separate cohort, PAF was comparable in terms of sex, age, diabetes mellitus,

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hypertension, coronary heart disease, hyperlipidemia, smoking habits, medication

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history, left atrial diameter and left ventricular ejection fraction. 3.2. Overview of LncRNA Expression Profiles

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The results showed that 2095 lncRNAs were consistently differentially expressed.

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Among them, 986 lncRNAs were upregulated in PAF, while 1109 lncRNAs were downregulated (P<0.05; Fig. 1 and Online Table S2). The 20 most significant differentially expressed lncRNAs are shown in Table 2. 3.3. Overview of mRNA Expression Profiles The microarray data showed that 1584 mRNAs were differentially expressed in the PAF group compared with the controls (P<0.05; Fig. 2 and Online Table S3). Among them, 565 mRNAs were upregulated more than two-fold in PAF, while 1019

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ACCEPTED MANUSCRIPT mRNAs were downregulated. 3.4. LncRNA Classification and Subgroup Analysis 3.4.1 Genomic Location of Differently Expressed LncRNAs The relationships between the lncRNAs and its nearby coding gene can be

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classified into six types: exon sense-overlapping, intron sense-overlapping, intronic

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antisense, natural antisense, bidirectional and intergenic (Derrien et al., 2012;

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Scheuermann and Boyer, 2013). The distribution of the differentially expressed lncRNAs between PAF and controls are shown in Fig. 3.

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3.4.2 HOX cluster profiling

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Numerous lncRNAs were found to be transcribed from the human homeobox transcription factors (HOX) clusters(Rinn et al., 2007). In this study, profiling the data

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from all the probes in the four HOX loci targeting 82 lncRNAs and coding transcripts

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are shown in Online Table S4.

3.4.3 Large intergenic non-coding RNA profiling

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Large intergenic non-coding RNAs (lincRNAs) have recently been identified with

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clear evolutionary conservation and their function is associated with diverse biological processes, including embryonic stem cell pluripotency and differentiation, chromatin states, epigenetic inheritance regulation, cell proliferation, immune surveillance and cell-cycle regulation (Khalil et al., 2009; Tsai et al., 2010; Guttman et al., 2011). The profiling data for the lincRNAs is shown in Online Table S5, which contains 9671 probes for lincRNAs calculated by genomic coordinates. Between PAF and the controls, 407 differentially expressed lincRNAs were in nearby coding genes

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ACCEPTED MANUSCRIPT (distance < 300 kb, Online Table S5). A total of 233 lincRNAs were downregulated, and 174 lincRNAs were upregulated (Online Table S6). 3.4.4 LncRNAs with enhancer-like function profiling Recent studies have confirmed a set of lncRNAs in human cell lines as lncRNAs

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with enhancer-like function (Harrow et al., 2006; Orom et al., 2010). Depletion of

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those lncRNAs led to reduced expression of their neighboring protein-coding genes.

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Between PAF and the controls, there were 77 differentially expressed enhancer-like lncRNAs and nearby coding genes (distance < 300 kb; Online Table S7). Among

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them, 33 were upregulated and 44 were downregulated (Online Table S8).

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3.5. Functional Analysis

To associate the differentially expressed mRNAs with GO terms, we performed

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GO analysis based on the GO categories. Among the upregulated mRNAs in PAF,

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there were 465 groups of genes involved in biological processes, 31 groups of genes involved in cellular component and 55 groups of genes involved in molecular function.

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Among the downregulated mRNAs, there were 432 groups of genes involved in

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biological process, 44 groups of genes involved in cellular components and 57 groups of genes involved in molecular function (Online Table S9-10). Among these pathways, voltage-gated potassium channel activity and calcium channel activity were implicated in atrial electrical remodeling. Pathway analysis is a functional analysis that maps genes to KEGG pathways. The differentially expressed mRNAs in PAF were clustered into 46 KEGG terms (Online Table S11). Among them, 22 pathways were identified in the upregulated

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ACCEPTED MANUSCRIPT mRNAs, including the Calcium signaling pathway and Dilated cardiomyopathy. Twenty-four pathways were identified in the downregulated mRNAs, including NF-kappa B signaling pathway. 3.6. Construction of Co-expression Network

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To analyze the correlation of the differentially lncRNAs and mRNAs, we

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constructed the coding-noncoding (CNC) gene co-expression networks as previously

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described(Zhu et al., 2014). LncRNAs with more than a ten-fold change and differentially expressed mRNAs were selected for CNC analysis (Fig. 4). The results

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suggested that the co-expression network was composed of 299 network nodes and

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400 connections between the 9 lncRNAs and 290 coding genes (Fig. 4). Within this network, 177 connections were shown as positive and 223 were negative.

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GO and KEGG analyses were also conducted for 290 coding genes. A total of 56 GO

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terms were enriched for 290 coding genes in the co-expression network (Table S12 ). Between these pathways, delayed rectifier potassium channel activity and

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voltage-gated cation channel activity are implicated in atrial electrical remodeling.

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Nineteen KEGG terms were identified for 290 coding genes (Table S13). Acute myeloid leukemia is the maximum enrichment score pathway. 3.7. Validation of LncRNAs To validate the data from the microarray profiling, four lncRNAs (uc002nvy.3, ENST00000561094, uc004aef.3, ENST00000559960) from CNC co-expression networks were randomly selected for qRT-PCR. The results showed that uc002nvy.3 and ENST00000559960 were upregulated, and uc004aef.3 and ENST00000561094

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ACCEPTED MANUSCRIPT were downregulated in the PAF group (Fig. 5A). The qRT-PCR results were consistent with the microarray data. Subsequently, four lncRNAs were evaluated in a separate cohort (31 PAF patients and 30 controls). The results demonstrated that ENST00000559960 was upregulated and uc004aef.3 was downregulated in the PAF

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group (P<0.05 for each lncRNA; Fig. 5B), while uc002nvy.3 and ENST00000561094

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showed no significant difference between PAF and the controls (P>0.05 for each

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lncRNA; Fig. 5B).

In the logistic regression analysis, ENST00000559960 (OR 1.47; 95% CI 1.09 to

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2.00; P = 0.01), uc004aef.3 (OR 0.63; 95% CI 0.41 to 0.96; P = 0.03) and LAD (OR

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1.35; 95% CI 1.08 to 1.69; P < 0.01) were independently associated with PAF, while uc002nvy.3 (OR 1.06; 95% CI 0.91 to 1.22; P = 0.46), ENST00000561094 (OR 0.96;

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were not associated with PAF.

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95% CI 0.76 to 1.21; P = 0.72) and age (OR 1.07; 95% CI 0.99 to 1.15; P = 0.72)

Receiver operating characteristic (ROC) curves were used to evaluate the

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accuracy of the lncRNAs level to detect PAF. The AUC, optimal cutoff value,

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sensitivity, and specificity of each variable were presented in Table 3. ENST00000559960 had a modest power for predicting PAF as suggested by AUC of 0.70 ± 0.07 (95%CI 0.56-0.83; P < 0.01) which was greater than that of uc004aef.3 (0.67 ± 0.07 , 95% CI 0.54–0.81; P = 0.02 ). Other variables such as uc002nvy.3, ENST00000561094, LAD and age showed no significant difference (Fig.6 and Table 3). The optimal cut-off value of ENST00000559960 fold-change was 2.60 and provided a sensitivity of 55% and a specificity of 83%. The cut-off value of

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ACCEPTED MANUSCRIPT uc004aef.3 fold-change was 0.73 and yielded a sensitivity of 81% and a specificity of 53%.

4. Discussion

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In this study, blood leukocyte lncRNAs were discovered to be differentially

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expressed in PAF patients, with lower expression of uc004aef.3 and higher expression

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of ENST00000559960.

Considering that heart tissue is unavailable for analysis, we chose blood

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leukocytes as a template to measure the expression of lncRNAs. Blood leukocytes are

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not only more easily available compared to a tissue biopsy but also mechanistically associated with various cardiovascular pathophysiology processes (Aziz et al., 2007).

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A recent report revealed that RNA expression profiles of blood cells share common

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features with cardiomyocytes during the preclinical and pathologic stages of aldosterone/salt treatment in hypertensive rats (Gerling et al., 2013). In addition,

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studies have proved that the expression profiles of the peripheral blood cells are

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valuable for correlation with coronary disease severity and myocardial infarction (Wingrove et al., 2008; Vausort et al., 2014). We examined lncRNA and mRNA expression profiles of leukocytes in PAF patients. The microarray data showed that 2095 lncRNAs and 1584 mRNAs were consistently differentially expressed between PAF and the controls. Most of the differentially expressed lncRNAs have not been functionally defined. The differentially expressed lncRNAs may be involved in the development of PAF and

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ACCEPTED MANUSCRIPT serve as possible biomarkers for PAF in the future. Increasing evidence suggests that lncRNAs act as key regulators at various levels of gene expression, including chromatin organization, transcriptional regulation, and post-transcriptional control (Angrand et al., 2015). In cardiovascular systems,

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lncRNAs have been widely investigated under pathophysiology states. For example,

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levels of KCNQ1OT1, a metastasis-associated lung adenocarcinoma transcript

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1(MALAT1), and hypoxia inducible factor 1A antisense RNA 2(HIF-1 AS) in peripheral blood mononuclear cells were higher in patients with myocardial infarction

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(MI) than in healthy controls, and levels of ANRIL were reduced in patients with MI

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(Vausort et al., 2014). Two studies demonstrate that lncRNAs can regulate hypertrophy and cardiomyocyte death by disturbing miRNAs. Wang et al found that

inhibited

apoptosis

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functions,

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cardiac-apoptosis related lncRNA (CARL), acting as a sponge to block miR-539 and

mitochondrial

fission

and

reduced

ischemia/reperfusion injury (Vausort et al., 2014; Wang et al., 2014). An lncRNA

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named cardiac hypertrophy–related factor (CHRF) has been shown to regulate cardiac

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hypertrophy and apoptosis by interfering with miR-489 (Wang et al., 2014). Two reports have revealed that lncRNAs were differentially expressed in atrial fibrillation (Ruan et al., 2015; Xu et al., 2016). Ruan et al. examined the lncRNA expression profiles of atrial tissues by microarray analyses in AF patients with rheumatic heart disease and discovered 219 differentially expressed lncRNAs(Ruan et al., 2015). BC064139 was upregulated and TCONS_00006371 was downregulated in atrial tissues. However, our study showed a reverse trend in PAF leukocytes. Xu et al.

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ACCEPTED MANUSCRIPT detected the expression levels of serum lncRNAs in AF patients and identified that lncRNA NONHSAT040387 was upregulated, and lncRNA NONHSAT098586 was downregulated (Xu et al., 2016). As these two lncRNAs were not included in our microarray chip, we could not compare the difference between PAF and the controls.

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Our microarray data demonstrated that other lncRNAs showed no significant

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difference in the leukocytes between PAF and the controls. The discrepancy may

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result from tissue specificity, various underlying diseases and different AF stage. GO analysis indicated that several functional pathways were enriched. Among

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these pathways, voltage-gated potassium channel activity and calcium channel

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activity were implicated in atrial electrical remodeling. Pathway analysis showed that several pathways, including the calcium signaling pathway, dilated cardiomyopathy

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and NF-kappa B signaling pathway might play vital roles in the occurrence and

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development of atrial fibrillation. However, functional studies are required to elucidate their roles in PAF.

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To obtain new insights into the function of lncRNAs in PAF, nine lncRNAs were

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selected for CNC network construction (Fig. 4). From the network, we found that one lncRNA was associated with various protein-coding genes. Conversely, one mRNA was also associated with a multitude of lncRNAs. KCNA5, which encodes the potassium channel α-subunit KV1.5, underlies the atrial-specific ultra-rapid delayed rectifier potassium current IKur (Christophersen et al., 2013). Genetic studies reveal that gene mutations (gain and loss-of-function) and polymorphisms for IKur enhance AF susceptibility (Olson et al., 2006; Yang et al.,

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ACCEPTED MANUSCRIPT 2009; Christophersen et al., 2013). Though the remodeling of IKur in AF is conflicting, with reports of decreased IKur density or no change, it is widely accepted that Ikur is involved in electrical remodeling of paroxysmal AF (Brundel et al., 2001; Brundel et al., 2001) and persistent AF (Bosch et al., 1999; Brandt et al., 2000; Workman et al.,

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2001a; Workman et al., 2001b). Our data showed that KCNA5 mRNA was

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downregulated in PAF compared to the controls. The CNC network indicated that the

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upregulated lncRNA ENST00000559960 was negatively corrected with KCNA5 mRNA, whereas downregulated lncRNA uc004aef.3 was positively correlated with

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KCNA5. Therefore, it is hypothesized that KCNA5 may be the direct or indirect target

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gene of uc004aef.3 and ENST00000559960. Though CNC network provides a valuable method for predicting the function of lncRNAs, the detailed mechanisms still

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need to be explored.

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Further, we verified four lncRNAs (uc002nvy.3, ENST00000561094, uc004aef.3, ENST00000559960) from CNC network using qRT-PCR in a separate cohort.

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ENST00000559960 is a 552-bp intergenic lncRNA located on chr15. Uc004aef.3 is a

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1028 bp lncRNA transcribed from the natural antisense strand of the gene RP11-262H14.4, whose function is still unknown. The results demonstrated that ENST00000559960 was upregulated and uc004aef.3 was downregulated in the PAF group, while uc002nvy.3 and ENST00000561094 showed no significant difference between PAF and the controls (Fig. 5B). Multiple logistic regression analyses revealed ENST00000559960, uc004aef.3 and LAD were independently associated with PAF, while uc002nvy.3, ENST00000561094 and age were not associated with

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ACCEPTED MANUSCRIPT PAF. ROC curves also validated that ENST00000559960 and uc004aef.3 were modest predictors of PAF (Fig. 6 and Table 3). The two lncRNAs (ENST00000559960 and uc004aef.3) may help in prediction of PAF in the future. In this study, we recruited PAF patients without complicated comorbidities to

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validate the association of lncRNAs with PAF. Strict inclusion criteria were conducted

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to avoid imbalances in the baseline characteristics between the groups. However, the

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research is mainly restricted by the small sample size, which limits our ability to confirm the diagnostic power of the lncRNA signatures. This study is also limited by

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the subjective selection of the lncRNAs and single-center design. Therefore, selection

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bias cannot be excluded. Other differentially expressed lncRNAs may also possess prognostic value. Future prospective trials on larger cohorts are needed to establish

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the roles of lncRNAs in PAF. Though blood leukocytes are easily available and partly

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reflect the pathological conditions (Wingrove et al., 2008; Gerling et al., 2013; Li et al., 2013; Vausort et al., 2014), they are not equivalent to the cardiomyocytes. Atrial

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tissue biopsy is still needed for further study. In addition, the study is limited by the

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lack of leukocyte counts and classification of leukocyte subtypes that prevented the adjustment of the levels of lncRNAs to the inflammatory status for comparison between PAF patients and controls. In conclusion, our results demonstrated that lncRNA profiles were differentially expressed in the leukocytes of PAF, and two lncRNAs (ENST00000559960 and uc004aef.3) may help in prediction of PAF.

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ACCEPTED MANUSCRIPT Supporting information Table S1 Primers used for qRT-PCR. Table S2 Differentially expressed lncRNAs Table S3 Differentially expressed mRNAs

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Table S4 HOX cluster profiling

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Table S5 LincRNA profiling

Table S7 Enhancer lncRNA profiling

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Table S8 Enhancer lncRNAs nearby coding genes

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Table S6 Data for lincRNAs nearby coding gene

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Table S9 GO analysis (upregulated mRNAs)

Table S10 GO analysis (downregulated mRNAs)

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Table S11 Pathway analysis

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Table S12 GO analysis for CNC coding genes

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Table S13 Pathway analysis for CNC coding genes

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by microarray analysis.

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Fig. 1. Hierarchical clustering of lncRNA profiles between PAF patients and controls. Fig. 2. Hierarchical clustering of mRNA profiles between PAF patients and controls. Fig. 3. Distribution of differentially expressed lncRNAs between PAF and the control group. All differentially expressed lncRNAs were classified into six types: exon sense-overlapping,

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intron sense-overlapping, intronic antisense, natural antisense, bidirectional and intergenic. (A)

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Fig. 4. Coding-non-coding gene co-expression networks.

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Distribution of upregulated lncRNAs (B) Distribution of downregulated lncRNAs.

The network was composed of 299 network nodes and 400 connections between 9 lncRNAs

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and 290 coding genes. Within this network, 177 connections were shown as positive, and 223

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were negative. Circular nodes represent coding RNA and arrow nodes represent non-coding RNA. Red color indicates upregulated RNAs and green color indicates downregulated RNAs. A positive

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correlation is shown by solid lines, and a negative correlation is shown by a dashed line.

microarray

profiling,

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Fig. 5. (A) Comparison between microarray data and qPCR result. To validate the data from four

lncRNAs

(uc002nvy.3,

ENST00000561094,

uc004aef.3,

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ENST00000559960) were randomly selected for qRT-PCR. The results showed that uc002nvy.3

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and ENST00000559960 were upregulated, and uc004aef.3 and ENST00000561094 were downregulated in the PAF group. The qRT-PCR results were consistent with the microarray data. The bars represent the interquartile range. (B) qRT-PCR verification of four differentially expressed lncRNAs in a separate cohort. Four lncRNAs were evaluated in a separate cohort (31 PAF patients and 30 controls). The results demonstrated that ENST00000559960 was upregulated and uc004aef.3 was downregulated in the PAF group (P < 0.05 for each lncRNA), while uc002nvy.3 and ENST00000561094 showed no

27

ACCEPTED MANUSCRIPT significant difference between the PAF and control group (P > 0.05 for each lncRNA). Fig. 6. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the lncRNAs level to detect PAF.

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Table 1. Characteristics of the study participants.

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Table 2. Top 20 most significantly differentially expressed lncRNAs from the microarray

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data.

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Table 3. Accuracy of variables in predicting paroxysmal atrial fibrillation

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Table 1 Characteristics of the study participants Patients for Microarray Analysis

Patients for qRT-PCR

T P

Characteristics

Control group(n=3)

PAF group (n=3)

P

Gender( male)

2

2

1.00

Age

62±6.56

63.33±4.04

0.77

Diabetes mellitus

0

0

1.00

Hypertension

2

3

1.00

Coronary artery disease

2

2

Hyperlipidemia

2

3

Smoking habit

0

ACEI/ARB, n

2

Calcium ion antagonist, n

1

PAF group (n=31)

P

23

0.72

58.67±10.11

61.97±8.75

0.18

6

5

0.69

17

21

0.37

1.00

10

13

0.49

1.00

8

12

0.32

1.00

15

13

0.53

3

1.00

14

18

0.37

2

1.00

3

7

0.30

Beta-blocker, n

0

1

1.00

2

5

0.43

D E

T P E

C C 0

A

I R

Control group(n=30)

C S U 21

N A

M

1

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Diuretics, n

1

2

1.00

4

6

0.732

Digitalis, n

0

0

1.00

0

0

NS

Lipid-lowering drugs, n

2

3

1.00

8

11

0.46

LAD, mm

33.8±1.08

42.4±1.21*

<0.01

37.48±3.24

39.06±3.09

0.06

LVEF, %

68.33±0.58

65.33±2.08

0.07

65.0±4.63

64.9±4.43

0.95

C S U

I R

T P

N A

M

ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; LAD, left atrial diameter; LVEF, left ventricular ejection fraction; qRT-PCR , quantitative real-time PCR; PAF, paroxysmal atrial fibrillation.

D E

T P E

Values are presented as number of patients or meanSD. *P<0.05 values from nonpaired Student’s t test for continuous variables and from 2 test for categorical

C C

variables.

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Table 2 Top 20 most significantly differentially expressed lncRNAs from the microarray data seqname

FCAbsolute

p-value

regulation

GeneSymbol

RNAlength

chrom

strand

txStart

txEnd

ENST00000433329

32.81567

0.00785071

up

HBBP1

439

-

5263349

5264767

ENST00000533670

28.226313

0.0000543

up

RP11-119D9.1

I R

chr11

613

chr11

+

67654060

67673821

ENST00000559960

17.415098

0.000066

up

RP11-519G16.3

552

chr15

+

45751207

45753154

uc003myx.3

14.210849

0.04933318

up

LOC100130275

1868

chr6

+

10414299

10416402

TCONS_00015370

13.104703

0.020487187

up

XLOC_006934

2120

chr8

+

135768683

135779490

uc002nvy.3

11.430042

0.027870344

up

DQ583499

410

chr19

-

35304687

35305097

ENST00000572343

9.948105

0.016689371

up

AC139099.4

641

chr17

+

81091899

81125376

TCONS_00012422

9.473

0.000083

up

XLOC_005160

247

chr6

+

8906331

8926157

ENST00000496414

9.072429

0.023908233

up

RPL29P30

828

chr15

-

71088995

71095027

ENST00000584688

8.394365

0.042117063

up

RP11-40A13.1

557

chr17

+

31438875

31458761

ENST00000584688

8.394365

0.042117063

up

RP11-40A13.1

557

chr17

+

31438875

31458761

PT

E C

C A

D E

C S U

N A

M

3

T P

ACCEPTED MANUSCRIPT

ENST00000419104

8.374279

0.038137503

up

AC073135.3

559

chr3

+

197836982

197838437

ENST00000400371

8.059673

0.036173474

up

RP5-981L23.1

2722

chr20

-

45042866

45087915

NR_027074

7.9532785

0.02214191

up

LOC283761

2366

-

90048160

90067265

ENST00000534756

7.8205504

0.046668995

up

RP11-64I17.1

I R

chr15

509

chr11

+

38670407

38676802

TCONS_00005377

7.149473

0.01070326

up

XLOC_002560

1208

chr2

-

242999928

243001371

ENST00000510200

6.9410014

0.001776443

up

RP11-328K4.1

417

chr4

+

104346198

104360885

uc003azm.3

6.8758597

0.045278504

up

BC040700

1377

chr22

-

41581218

41593505

ENST00000429947

6.5689225

0.022868332

up

AC131097.3

470

chr2

+

242824098

242901084

ENST00000561386

6.536664

0.04091391

up

RP11-162I7.1

963

chr15

+

62022048

62023588

ENST00000561094

25.040436

0.0000479

down

RP11-605F22.2

518

chr15

-

48481316

48483157

TCONS_00016323

21.220142

0.000216

down

XLOC_007697

909

chr9

-

44181549

44183417

ENST00000536317

19.820847

0.0000937

down

RP11-1060J15.4

1110

chr12

-

27849320

27863703

ENST00000491562

17.072594

0.03547826

down

PI4KAP2

209

chr22

-

21846264

21868512

PT

E C

C A

D E

C S U

N A

M

4

T P

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uc004aef.3

13.194102

0.00000113

down

AK000451

1028

chr9

-

66523813

66553911

ENST00000567795

9.554755

0.010588785

down

CTD-2574D22.4

2270

chr16

-

29930956

29933226

uc010nxf.2

8.682964

0.000529

down

TTTY6

593

+

24291112

24292981

ENST00000502680

8.642875

0.007865913

down

RP1-251I12.1

I R

chrY

822

chr5

-

15112485

15117116

TCONS_00012218

8.110877

0.001435578

down

XLOC_005775

254

chr6

-

83386874

83387744

TCONS_00021993

8.044323

0.00865163

down

XLOC_010596

527

chr13

-

46850581

46851832

uc003onw.3

8.025822

0.003184849

down

AY927499

1100

chr6

-

37618994

37620094

chr12:53675775-53689275-

7.790785

0.0000352

down

chr12:53675775-53689275

13500

chr12

-

55389508

55403008

uc010ciy.1

7.7877364

0.0000183

down

BC160930

2079

chr16

+

89978911

89981576

NR_040017

7.770939

0.001874807

down

RNF157-AS1

2177

chr17

+

74136636

74150729

NR_040017

7.770939

0.001874807

down

RNF157-AS1

2177

chr17

+

74136636

74150729

TCONS_00016406

7.3463883

0.000239

down

XLOC_007777

698

chr9

-

94246564

94249578

NR_028386

7.2148795

0.000272

down

LOC375196

1068

chr2

-

39186428

39187485

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E C

C A

D E

C S U

N A

M

5

T P

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NR_073012

7.214039

0.004593383

down

PRSS21

1044

chr16

+

2867163

2871723

NR_073012

7.214039

0.004593383

down

PRSS21

1044

chr16

+

2867163

2871723

NR_073012

7.214039

0.004593383

down

PRSS21

1044

chr16

+

2867163

2871723

C S U

I R

N A

D E

M

T P E

C C

A

6

T P

ACCEPTED MANUSCRIPT Table 3. Accuracy of variables in predicting paroxysmal atrial fibrillation Variables

AUC

ROC

P Value

Cut-off value

(95% CI) uc004aef.3

0.67

±

0.07

0.81

0.53

0.009**

2.60

0.07

0.11

2.57

±0.08

0.72

PT

±0.07

0.83

0.61

0.63

0.53

1.08

0.58

0.57

MA

(0.48-0.76) ENST00000561094

0.62

0.12

35.8

0.90

0.43

0.18

56.5

0.74

0.47

D

±0.07

PT E

(0.38-0.67) LAD

0.55

SC

±

NU

0.62

(%)

0.73

(0.56-0.83) uc002nvy.3

(%)

RI

0.70

Specificity

0.02*

(0.54-0.81) ENST00000559960

Sensitivity

(0.47-0.76) Age

0.60

±

0.07

AC

CE

(0.46-0.74)

LAD, left atrial diameter. Significant differences are marked by *(P < 0.05) and **( P < 0.01).

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ACCEPTED MANUSCRIPT Abbreviations: lncRNAs, long noncoding RNAs; PAF, paroxysmal atrial fibrillation; qRT-PCR, quantitative real-time PCR; lincRNAs, large intergenic non-coding RNAs; CARL, cardiac-apoptosis related lncRNA; MALAT1, metastasis-associated lung

PT

adenocarcinoma transcript 1; HIF-1 AS, hypoxia inducible factor 1A antisense RNA 2;

AC

CE

PT E

D

MA

NU

SC

RI

CHRF, cardiac hypertrophy–related factor.

2

ACCEPTED MANUSCRIPT

Highlights

1. LncRNAs profiles were differentially expressed in leukocytes of paroxysmal AF patients. 2. ENST00000559960 was upregulated and uc004aef.3 was downregulated in

PT

paroxysmal AF patients.

RI

3. ENST00000559960 and uc004aef.3 had a modest power for predicting PAF

SC

4. uc002nvy.3 and ENST00000561094 showed no difference between paroxysmal AF

AC

CE

PT E

D

MA

NU

and controls.

3