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Research Article

Vascular Calcification and not Arrhythmia in Idiopathic Atrial Fibrillation Associates with Sex Differences in Diabetic Microvascular Injury miRNA Profiles

Author(s): Elton Dudink*, Barend Florijn, Bob Weijs, Jacques Duijs, Justin Luermans, Frederique Peeters, Leon Schurgers, Joachim Wildberger, Ulrich Schotten, Roel Bijkerk, Harry J. Crijns and Anton Jan van Zonneveld

Volume 8, Issue 2, 2019

Page: [127 - 134] Pages: 8

DOI: 10.2174/2211536608666181122125208

Abstract

Background: Atrial Fibrillation (AF) in patients without concomitant cardiovascular pathophysiological disease, is called idiopathic Atrial Fibrillation (iAF). Nonetheless, iAF patients have often times subclinical coronary (micro) vascular dysfunction and, particularly in women, a higher prevalence of subsequent cardiovascular comorbidities. Previously, we identified a plasma miRNA association with diabetes and microvascular injury in Diabetic Nephropathy (DN) patients. Therefore, in this study we assessed whether plasma levels of these diabetic, microvascular injury associated miRNAs reflect microvascular integrity in iAF patients, associated with the presence of paroxysmal arrhythmia or instead are determined by concealed coronary artery disease.

Methods: Circulating levels of a pre-selected set of diabetic, (micro) vascular injury associated miRNAs, were measured in 59 iAF patients compared to 176 Sinus Rhythm (SR) controls. Furthermore, the presence of coronary artery and aortic calcification in each patient was assessed using Cardiac Computed Tomography Angiography (CCTA).

Results: Paroxysmal arrhythmia in iAF patients did not result in significant miRNA expression profile differences in iAF patients compared to SR controls. Nonetheless, coronary artery calcification (CAC) was associated with higher levels of miRNAs-103, -125a-5p, -221 and -223 in men. In women, CAC was associated with higher plasma levels of miRNA-27a and miRNA-126 and correlated with Agatston scores. Within the total population, ascending Aortic Calcification (AsAC) patients displayed increased plasma levels of miRNA-221, while women, in particular, demonstrated a Descending Aorta Calcification (DAC) associated increase in miRNA-212 levels.

Conclusions: Diabetic microvascular injury associated miRNAs in iAF are associated with subclinical coronary artery disease in a sex-specific way and confirm the notion that biological sex identifies iAF subgroups that may require dedicated clinical care.

Keywords: Atrial fibrillation, microRNA, sex-differences, vascular calcification, plasma, paroxysmal arrhythmia.

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[1]
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116: 281-97.
[2]
Chen X. MicroRNA biogenesis and function in plants. FEBS Lett 2005; 579: 5923-31.
[3]
Voinnet O. Origin, biogenesis, and activity of plant microRNAs. Cell 2009; 136: 669-87.
[4]
Mattick JS, Makunin IV. Non-coding RNA. Hum Mol Genet 2006; 15(Spec No 1): R17-29.
[5]
Iwakawa HO, Tomari Y. Molecular insights into microRNA-mediated translational repression in plants. Mol Cell 2013; 52: 591-601.
[6]
Reinhart BJ, Weinstein EG, Rhoades MW, et al. MicroRNAs in plants. Genes Dev 2002; 16: 1616-26.
[7]
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009; 136: 215-33.
[8]
Jones-Rhoades MW, Bartel DP, Bartel B. MicroRNAs and their regulatory roles in plants. Annu Rev Plant Biol 2006; 57: 19-53.
[9]
Sunkar R, Girke T, Jain PK, et al. Cloning and characterization of microRNAs from rice. Plant Cell 2005; 17: 1397-411.
[10]
Sunkar R, Zhu JK. Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell 2004; 16: 2001-19.
[11]
Lu S, Sun Y-H, Shi R, et al. Novel and mechanical stress-responsive microRNAs in Populus trichocarpa that are absent from Arabidopsis. Plant Cell 2005; 17: 2186-203.
[12]
Lai EC, Tomancak P, Williams RW, et al. Computational identification of Drosophila microRNA genes. Genome Biol 2003; 4: R42.
[13]
Berezikov E, Guryev V, van de Belt J, et al. Phylogenetic shadowing and computational identification of human microRNA genes. Cell 2005; 120: 21-4.
[14]
Grad Y, Aach J, Hayes GD, et al. Computational and experimental identification of C. elegans microRNAs. Mol Cell 2003; 11: 1253-63.
[15]
Barh D, Khan MS, Davies E. PlantOmics: the omics of plant scienceEd 1, Springer, 2015, pp XXV, 825.
[16]
Dhandapani V, Ramchiary N, Paul P, et al. Identification of potential microRNAs and their targets in Brassica rapa L. Mol Cells 2011; 32: 21-37.
[17]
Wang J, Yang X, Xu H, et al. Identification and characterization of microRNAs and their target genes in Brassica oleracea. Gene 2012; 505: 300-8.
[18]
Zhang B, Wang Q, Wang K, et al. Identification of cotton microRNAs and their targets. Gene 2007; 397: 26-37.
[19]
Zhang B, Pan X, Stellwag EJ. Identification of soybean microRNAs and their targets. Planta 2008; 229: 161-82.
[20]
Xie F, Frazier TP, Zhang B. Identification, characterization and expression analysis of microRNAs and their targets in the potato (Solanum tuberosum). Gene 2011; 473: 8-22.
[21]
Jin W, Li N, Zhang B, et al. Identification and verification of microRNA in wheat (Triticum aestivum). J Plant Res 2008; 121: 351-5.
[22]
Song C, Jia Q, Fang J, et al. Computational identification of citrus microRNAs and target analysis in citrus expressed sequence tags. Plant Biol 2010; 12: 927-34.
[23]
Xie F, Frazier TP, Zhang B. Identification and characterization of microRNAs and their targets in the bioenergy plant switch grass (Panicum virgatum). Planta 2010; 232: 417-34.
[24]
Wang J, Hou X, Yang X. Identification of conserved microRNAs and their targets in Chinese cabbage (Brassica rapa subsp. pekinensis). Genome 2011; 54: 1029-40.
[25]
Kochert G, Thomas Stalker H, Gimenes M, et al. RFLP and cytogenetic evidence on the origin and evolution of allotetraploid domesticated peanut, Arachis hypogaea (Leguminosae). Am J Bot 1996; 83: 1282-91.
[26]
Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42: D68-73.
[27]
Li W, Godzik A. CD-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006; 22: 1658-9.
[28]
Altschul SF, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol 1990; 215: 403-10.
[29]
Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol 1981; 147: 195-7.
[30]
Xue C, Li F, He T, et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005; 6: 310.
[31]
Gao D, Middleton R, Rasko JE, et al. miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 2013; 29: 3225-6.
[32]
Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res 2003; 31: 3429-31.
[33]
Fahlgren N, Carrington JC. miRNA target prediction in plants. Methods Mol Biol 2010; 592: 51-7.
[34]
Dai X, Zhao PX. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 2011; 39: W155-9.
[35]
Conesa A, Götz S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics 2008; 2008: 619832.
[36]
Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2: 1-27.
[37]
Zhang BH, Pan XP, Cox SB, et al. Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci 2006; 63: 246-54.
[38]
Ambros V, Bartel B, Bartel DP, et al. A uniform system for microRNA annotation. RNA 2003; 9: 277-9.
[39]
Seffens W, Digby D. mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res 1999; 27: 1578-84.
[40]
Chi X, Yang Q, Chen X, et al. Identification and characterization of microRNAs from peanut (Arachis hypogaea L.) by high-throughput sequencing. PLoS One 2011; 6: e27530.
[41]
Zhao CZ, Xia H, Frazier TP, et al. Deep sequencing identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.). BMC Plant Biol 2010; 10: 3.
[42]
Srivastava PK, Moturu TR, Pandey P, et al. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics 2014; 15: 348.
[43]
Sreevidya VS, Srinivasa Rao C, Sullia SB, et al. Metabolic engineering of rice with soybean isoflavone synthase for promoting nodulation gene expression in rhizobia. J Exp Bot 2006; 57: 1957-69.
[44]
Moore KM, Knauft DA. The inheritance of high oleic acid in peanut. J Hered 1989; 80: 252-3.

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