An arrhythmogenic metabolite in atrial fibrillation

Abstract Background Long-chain acyl-carnitines (ACs) are potential arrhythmogenic metabolites. Their role in atrial fibrillation (AF) remains incompletely understood. Using a systems medicine approach, we assessed the contribution of C18:1AC to AF by analysing its in vitro effects on cardiac electrophysiology and metabolism, and translated our findings into the human setting. Methods and results Human iPSC-derived engineered heart tissue was exposed to C18:1AC. A biphasic effect on contractile force was observed: short exposure enhanced contractile force, but elicited spontaneous contractions and impaired Ca2+ handling. Continuous exposure provoked an impairment of contractile force. In human atrial mitochondria from AF individuals, C18:1AC inhibited respiration. In a population-based cohort as well as a cohort of patients, high C18:1AC serum concentrations were associated with the incidence and prevalence of AF. Conclusion Our data provide evidence for an arrhythmogenic potential of the metabolite C18:1AC. The metabolite interferes with mitochondrial metabolism, thereby contributing to contractile dysfunction and shows predictive potential as novel circulating biomarker for risk of AF

Extended data: Tissue-specific multi-omics analysis of atrial fibrillation

Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation: Assum, I., Krause, J., Scheinhardt, M.O. et al. Tissue-specific multi-omics analysis of atrial fibrillation. Nat Commun 13, 441 (2022). https://doi.org/10.1038/s41467-022-27953-1 For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial. Ines Assum1,2,†, Julia Krause3,4,†, Markus O. Scheinhardt5, Christian Müller3,4, Elke Hammer6,7, Christin S. Börschel4,8, Uwe Vöker6,7, Lenard Conradi9, Bastiaan Geelhoed4,8,10, Tanja Zeller3,4,*, Renate B. Schnabel4,8,*, Matthias Heinig1,2,11,* † ,* These authors contributed equally. 1 Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany. 2 Department of Informatics, Technical University Munich, München, Germany. 3 University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany. 4 Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany. 5 Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany. 6 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. 7 Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany. 8 Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany. 9 Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany. 10 Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 11Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany. ABSTRACT: Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization. This version adds a reference file identifying effect alleles for all QTL results. TABLE OF CONTENTS: Reference for effect alleles map_AFHRI_B_effect_alleles.txt Single-omic cis-QTL results cis-eQTLs (all pairs, incl. LD clump info) eQTL_right_atrial_appendage_allpairs_clump.txt cis-pQTLs (all pairs, incl. LD clump info) pQTL_right_atrial_appendage_allpairs_clump.txt cis-res eQTLs (all pairs, incl. LD clump info) res_eQTL_right_atrial_appendage_allpairs_clump.txt cis-res pQTLs (all pairs, incl. LD clump info) res_pQTL_right_atrial_appendage_allpairs_clump.txt cis-ratioQTLs (all pairs, incl. LD clump info) ratioQTL_right_atrial_appendage_allpairs_clump.txt Functional cis-QTL categories and eQTL/pQTL overlap: All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR<0.05) Fig2a_source_data_Shared_eQTL_pQTL_clump.txt All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR<0.05) Fig2b_source_data_Independent_eQTL_clump.txt All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR<0.05) Fig2c_source_data_Independent_pQTL_clump.txt QTS rankings and enrichment results eQTS rankings and enrichments TableS6_source_data_eQTS_ranking.txt TableS7_source_data_eQTS_GSEA_results.txt pQTS rankings and enrichments TableS8_source_data_pQTS_ranking.txt TableS9_source_data_pQTS_GSEA_results.txt Trans-QTLs all tested pairs including trans-pQTLs for trans-eQTLs and trans-eQTLs for trans-pQTLs Table2_source_data_Trans-QTL_results.tx

Extended data: Tissue-specific multi-omics analysis of atrial fibrillation

Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation (https://doi.org/10.1101/2020.04.06.021527). For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial. ABSTRACT: Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization