Post-operative atrial fibrillation examined using whole-genome RNA sequencing in human left atrial tissue

Martin I Sigurdsson, Louis Saddic, Mahyar Heydarpour, Tzuu-Wang Chang, Prem Shekar, Sary Aranki, Gregory S Couper, Stanton K Shernan, Jochen D Muehlschlegel, Simon C Body, Martin I Sigurdsson, Louis Saddic, Mahyar Heydarpour, Tzuu-Wang Chang, Prem Shekar, Sary Aranki, Gregory S Couper, Stanton K Shernan, Jochen D Muehlschlegel, Simon C Body

Abstract

Background: Both ambulatory atrial fibrillation (AF) and post-operative AF (poAF) are associated with substantial morbidity and mortality. Analyzing the tissue-specific gene expression in the left atrium (LA) can identify novel genes associated with AF and further the understanding of the mechanism by which previously identified genetic variants associated with AF mediate their effects.

Methods: LA free wall samples were obtained intraoperatively immediately prior to mitral valve surgery in 62 Caucasian individuals. Gene expression was quantified on mRNA harvested from these samples using RNA sequencing. An expression quantitative trait loci (eQTL) analysis was performed, comparing gene expression between different genotypes of 1.0 million genetic markers, emphasizing genomic regions and genes associated with AF.

Results: Comparison of whole-genome expression between patients who later developed poAF and those who did not identified 23 differentially expressed genes. These included genes associated with the resting membrane potential modified by potassium currents, as well as genes within Wnt signaling and cyclic GMP metabolism. The eQTL analysis identified 16,139 cis eQTL relationships in the LA, including several involving genes and single nucleotide polymorphisms (SNPs) linked to AF. A previous relationship between rs3744029 and MYOZ1 expression was confirmed, and a novel relationship between rs6795970 and the expression of the SCN10A gene was identified.

Conclusions: The current study is the first analysis of the human LA expression landscape using high-throughput RNA sequencing. Several novel genes and variants likely involved in AF pathogenesis were identified, thus furthering the understanding of how variants associated with AF mediate their effects via altered gene expression.

Trial registration: ClinicalTrials.gov ID: NCT00833313 , registered 5. January 2009.

Keywords: Atrial fibrillation; Expression quantitative trait loci; Mitral valve surgery; eQTL.

Figures

Fig. 1
Fig. 1
Differentially expressed genes in patients with post-operative atrial fibrillation (poAF). A volcano plot comparing the expression of all genes in the human left atria between patients who had post-operative atrial fibrillation compared to those who did not. The x-axis shows the log2 fold change and the y-axis shows the –log10 of the p-value adjusted for multiple testing. Dotted lines mark predetermined levels of significance; absolute log2 ratio >0.5 (x-axis) or p-value adjusted for multiple testing <0.1 (y-axis). Green dots indicate genes that fulfill one significance criteria, red dost indicate genes that fulfill both
Fig. 2
Fig. 2
Expression of PITX2, KCNN3, and ZFHX3 in the LA of patients with and without post-operative atrial fibrillation (poAF). Boxplots of the normalized expression of aPITX2, bKCNN3 and cZFHX3 between patients with and without poAF. The number above each box shows the number of patients in each group. FPM – Fraction per million mapped fragments
Fig. 3
Fig. 3
Gene Network Analysis. Result of the functional network analysis of the list of genes with differential expression in poAF by the GeneMANIA algorithm [19]. Connections were assessed using the list of differentially expressed genes (black, black stripes) in the context of a left atrial co-expression network (gray connections) and the default GeneMANIA networks on genetic interactions (green) or shared protein domains (yellow). Two gene function categories were overrepresented in the output, the Wnt Signaling pathway (red) and the cGMP metabolic process (blue)
Fig. 4
Fig. 4
Genomic location of cis-eQTL relationships in the human LA. A Manhattan plot of the genomic location of all significant cis-eQTL (at false discovery adjusted p < 0.05) relationships in the human left atrium
Fig. 5
Fig. 5
cis eQTL relationships of SNPs and genes associated with AF. Boxplots of the normalized expression of each gene (y-axis) and the genotype (allele counts of minor allele) for aMYOZ1 and rs3740293 and bSCN10A gene and rs6795970. The number above each box shows the number of patients with each genotype. FPM – Fraction fragments per million mapped fragments

References

    1. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007;448(7151):353–357. doi: 10.1038/nature06007.
    1. Ellinor PT, Lunetta KL, Glazer NL, Pfeufer A, Alonso A, Chung MK, Sinner MF, de Bakker PI, Mueller M, Lubitz SA, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet. 2010;42(3):240–244. doi: 10.1038/ng.537.
    1. Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, Schnabel RB, Bis JC, Boerwinkle E, Sinner MF, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009;41(8):879–881. doi: 10.1038/ng.416.
    1. Olesen MS, Nielsen MW, Haunso S, Svendsen JH. Atrial fibrillation: the role of common and rare genetic variants. Eur J Hum Genet. 2014;22(3):297–306. doi: 10.1038/ejhg.2013.139.
    1. Sinner MF, Ellinor PT, Meitinger T, Benjamin EJ, Kaab S. Genome-wide association studies of atrial fibrillation: past, present, and future. Cardiovasc Res. 2011;89(4):701–709. doi: 10.1093/cvr/cvr001.
    1. Jabbari J, Olesen MS, Yuan L, Nielsen JB, Liang B, Macri V, Christophersen IE, Nielsen N, Sajadieh A, Ellinor PT, et al. Common and rare variants in SCN10A modulate the risk of atrial fibrillation. Circ Cardiovasc Genet. 2015;8(1):64–73. doi: 10.1161/HCG.0000000000000022.
    1. Body SC, Collard CD, Shernan SK, Fox AA, Liu KY, Ritchie MD, Perry TE, Muehlschlegel JD, Aranki S, Donahue BS, et al. Variation in the 4q25 chromosomal locus predicts atrial fibrillation after coronary artery bypass graft surgery. Circ Cardiovasc Genet. 2009;2(5):499–506. doi: 10.1161/CIRCGENETICS.109.849075.
    1. Sigurdsson MI, Muehlschlegel JD, Fox AA, Heydarpour M, Lichtner P, Meitinger T, Collard CD, Shernan SK, Body SC. Genetic variants associated with atrial fibrillation and PR interval following cardiac surgery. J Cardiothorac Vasc Anesth. 2015;29(3):605–610. doi: 10.1053/j.jvca.2014.10.028.
    1. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M. Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009;10(3):184–194. doi: 10.1038/nrg2537.
    1. Khankirawatana B, Khankirawatana S, Porter T. How should left atrial size be reported? Comparative assessment with use of multiple echocardiographic methods. Am Heart J. 2004;147(2):369–374. doi: 10.1016/j.ahj.2003.03.001.
    1. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36. doi: 10.1186/gb-2013-14-4-r36.
    1. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. doi: 10.1186/gb-2009-10-3-r25.
    1. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. Genome project data processing S: the sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–2079. doi: 10.1093/bioinformatics/btp352.
    1. Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–169. doi: 10.1093/bioinformatics/btu638.
    1. R Development Core Team . R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2011.
    1. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol. 2014;15(12):550.
    1. Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP. Calculating sample size estimates for RNA sequencing data. J Comput Biol. 2013;20(12):970–978. doi: 10.1089/cmb.2012.0283.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful aproach to multiple testing. J R Stat Soc B. 1995;57(1):289–300.
    1. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Web Server issue):W214–220. doi: 10.1093/nar/gkq537.
    1. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353–1358. doi: 10.1093/bioinformatics/bts163.
    1. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–575. doi: 10.1086/519795.
    1. Aune E, Baekkevar M, Roislien J, Rodevand O, Otterstad JE. Normal reference ranges for left and right atrial volume indexes and ejection fractions obtained with real-time three-dimensional echocardiography. Eur J Echocardiogr. 2009;10(6):738–744. doi: 10.1093/ejechocard/jep054.
    1. Beck T, Hastings RK, Gollapudi S, Free RC, Brookes AJ. GWAS central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur J Hum Genet. 2014;22(7):949–952. doi: 10.1038/ejhg.2013.274.
    1. Lin H, Dolmatova EV, Morley MP, Lunetta KL, McManus DD, Magnani JW, Margulies KB, Hakonarson H, del Monte F, Benjamin EJ, et al. Gene expression and genetic variation in human atria. Heart Rhythm. 2014;11(2):266–271. doi: 10.1016/j.hrthm.2013.10.051.
    1. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, Arking DE, Muller-Nurasyid M, Krijthe BP, Lubitz SA, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44(6):670–675. doi: 10.1038/ng.2261.
    1. Wang J, Wang Y, Han J, Li Y, Xie C, Xie L, Shi J, Zhang J, Yang B, Chen D, et al. Integrated analysis of microRNA and mRNA expression profiles in the left atrium of patients with nonvalvular paroxysmal atrial fibrillation: Role of miR-146b-5p in atrial fibrosis. Heart Rhythm. 2015;12(5):1018–1026. doi: 10.1016/j.hrthm.2015.01.026.
    1. Liu H, Qin H, Chen GX, Liang MY, Rong J, Yao JP, Wu ZK. Comparative expression profiles of microRNA in left and right atrial appendages from patients with rheumatic mitral valve disease exhibiting sinus rhythm or atrial fibrillation. J Transl Med. 2014;12:90. doi: 10.1186/1479-5876-12-90.
    1. Deshmukh A, Barnard J, Sun H, Newton D, Castel L, Pettersson G, Johnston D, Roselli E, Gillinov AM, McCurry K, et al. Left atrial transcriptional changes associated with atrial fibrillation susceptibility and persistence. Circ Arrhythm Electrophysiol. 2015;8(1):32–41. doi: 10.1161/CIRCEP.114.001632.
    1. Oliver PL, Chodroff RA, Gosal A, Edwards B, Cheung AF, Gomez-Rodriguez J, Elliot G, Garrett LJ, Lickiss T, Szele F, et al. Disruption of Visc-2, a brain-expressed conserved long noncoding RNA, does not elicit an overt anatomical or behavioral phenotype. Cereb Cortex. 2014;25:3572. doi: 10.1093/cercor/bhu196.
    1. Hattori K, Nakamura K, Hisatomi Y, Matsumoto S, Suzuki M, Harvey RP, Kurihara H, Hattori S, Yamamoto T, Michalak M, et al. Arrhythmia induced by spatiotemporal overexpression of calreticulin in the heart. Mol Genet Metab. 2007;91(3):285–293. doi: 10.1016/j.ymgme.2007.02.003.
    1. Bardien-Kruger S, Wulff H, Arieff Z, Brink P, Chandy KG, Corfield V. Characterisation of the human voltage-gated potassium channel gene, KCNA7, a candidate gene for inherited cardiac disorders, and its exclusion as cause of progressive familial heart block I (PFHBI) Eur J Hum Genet. 2002;10(1):36–43. doi: 10.1038/sj.ejhg.5200739.
    1. Zou A, Lin Z, Humble M, Creech CD, Wagoner PK, Krafte D, Jegla TJ, Wickenden AD. Distribution and functional properties of human KCNH8 (Elk1) potassium channels. Am J Physiol Cell Physiol. 2003;285(6):C1356–1366. doi: 10.1152/ajpcell.00179.2003.
    1. Decher N, Maier M, Dittrich W, Gassenhuber J, Bruggemann A, Busch AE, Steinmeyer K. Characterization of TASK-4, a novel member of the pH-sensitive, two-pore domain potassium channel family. FEBS Lett. 2001;492(1-2):84–89. doi: 10.1016/S0014-5793(01)02222-0.
    1. Syeda F, Holmes AP, Yu TY, Tull S, Kuhlmann SM, Pavlovic D, Betney D, Riley G, Kucera JP, Jousset F, et al. PITX2 modulates atrial membrane potential and the antiarrhythmic effects of sodium-channel blockers. J Am Coll Cardiol. 2016;68(17):1881–1894. doi: 10.1016/j.jacc.2016.07.766.
    1. Gore-Panter SR, Hsu J, Hanna P, Gillinov AM, Pettersson G, Newton DW, Moravec CS, Van Wagoner DR, Chung MK, Barnard J, et al. Atrial Fibrillation associated chromosome 4q25 variants are not associated with PITX2c expression in human adult left atrial appendages. PLoS One. 2014;9(1):e86245. doi: 10.1371/journal.pone.0086245.
    1. Rao TP, Kuhl M. An updated overview on Wnt signaling pathways: a prelude for more. Circ Res. 2010;106(12):1798–1806. doi: 10.1161/CIRCRESAHA.110.219840.
    1. Gessert S, Kuhl M. The multiple phases and faces of wnt signaling during cardiac differentiation and development. Circ Res. 2010;107(2):186–199. doi: 10.1161/CIRCRESAHA.110.221531.
    1. Naito AT, Shiojima I, Komuro I. Wnt signaling and aging-related heart disorders. Circ Res. 2010;107(11):1295–1303. doi: 10.1161/CIRCRESAHA.110.223776.
    1. Lozano-Velasco E, Hernandez-Torres F, Daimi H, Serra SA, Herraiz A, Hove-Madsen L, Aranega A, Franco D. Pitx2 impairs calcium handling in a dose-dependent manner by modulating Wnt signalling. Cardiovasc Res. 2015;109:55. doi: 10.1093/cvr/cvv207.
    1. Minamino T, Kitakaze M, Sato H, Asanuma H, Funaya H, Koretsune Y, Hori M. Plasma levels of nitrite/nitrate and platelet cGMP levels are decreased in patients with atrial fibrillation. Arterioscler Thromb Vasc Biol. 1997;17(11):3191–3195. doi: 10.1161/01.ATV.17.11.3191.
    1. Tamargo J, Caballero R, Gomez R, Delpon E. Cardiac electrophysiological effects of nitric oxide. Cardiovasc Res. 2010;87(4):593–600. doi: 10.1093/cvr/cvq214.
    1. Vandiedonck C, Knight JC. The human major histocompatibility complex as a paradigm in genomics research. Brief Funct Genomic Proteomic. 2009;8(5):379–394. doi: 10.1093/bfgp/elp010.
    1. Ter Keurs HE, Shinozaki T, Zhang YM, Wakayama Y, Sugai Y, Kagaya Y, Miura M, Boyden PA, Stuyvers BD, Landesberg A. Sarcomere mechanics in uniform and nonuniform cardiac muscle: a link between pump function and arrhythmias. Ann N Y Acad Sci. 2008;1123:79–95. doi: 10.1196/annals.1420.010.
    1. Akopian AN, Sivilotti L, Wood JN. A tetrodotoxin-resistant voltage-gated sodium channel expressed by sensory neurons. Nature. 1996;379(6562):257–262. doi: 10.1038/379257a0.
    1. Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, Smith AV, Tarasov KV, Muller M, Sotoodehnia N, Sinner MF, et al. Genome-wide association study of PR interval. Nat Genet. 2010;42(2):153–159. doi: 10.1038/ng.517.

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