4 research outputs found
Ischemic Heart Disease Selectively Modifies the Right Atrial Appendage Transcriptome
Background: Although many pathological changes have been associated with ischemic heart disease (IHD), molecular-level alterations specific to the ischemic myocardium and their potential to reflect disease severity or therapeutic outcome remain unclear. Currently, diagnosis occurs relatively late and evaluating disease severity is largely based on clinical symptoms, various imaging modalities, or the determination of risk factors. This study aims to identify IHD-associated signature RNAs from the atrial myocardium and evaluate their ability to reflect disease severity or cardiac surgery outcomes.Methods and Results: We collected right atrial appendage (RAA) biopsies from 40 patients with invasive coronary angiography (ICA)-positive IHD undergoing coronary artery bypass surgery and from 8 patients ICA-negative for IHD (non-IHD) undergoing valvular surgery. Following RNA sequencing, RAA transcriptomes were analyzed against 429 donors from the GTEx project without cardiac disease. The IHD transcriptome was characterized by repressed RNA expression in pathways for cell-cell contacts and mitochondrial dysfunction. Increased expressions of the CSRNP3, FUT10, SHD, NAV2-AS4, and hsa-mir-181 genes resulted in significance with the complexity of coronary artery obstructions or correlated with a functional cardiac benefit from bypass surgery.Conclusions: Our results provide an atrial myocardium-focused insight into IHD signature RNAs. The specific gene expression changes characterized here, pave the way for future disease mechanism-based identification of biomarkers for early detection and treatment of IHD.Peer reviewe
Epitranscriptomics of Ischemic Heart Disease—The IHD-EPITRAN Study Design and Objectives
Epitranscriptomic modifications in RNA can dramatically alter the way our genetic code is deciphered. Cells utilize these modifications not only to maintain physiological processes, but also to respond to extracellular cues and various stressors. Most often, adenosine residues in RNA are targeted, and result in modifications including methylation and deamination. Such modified residues as N-6-methyl-adenosine (m6A) and inosine, respectively, have been associated with cardiovascular diseases, and contribute to disease pathologies. The Ischemic Heart Disease Epitranscriptomics and Biomarkers (IHD-EPITRAN) study aims to provide a more comprehensive understanding to their nature and role in cardiovascular pathology. The study hypothesis is that pathological features of IHD are mirrored in the blood epitranscriptome. The IHD-EPITRAN study focuses on m6A and A-to-I modifications of RNA. Patients are recruited from four cohorts: (I) patients with IHD and myocardial infarction undergoing urgent revascularization; (II) patients with stable IHD undergoing coronary artery bypass grafting; (III) controls without coronary obstructions undergoing valve replacement due to aortic stenosis and (IV) controls with healthy coronaries verified by computed tomography. The abundance and distribution of m6A and A-to-I modifications in blood RNA are charted by quantitative and qualitative methods. Selected other modified nucleosides as well as IHD candidate protein and metabolic biomarkers are measured for reference. The results of the IHD-EPITRAN study can be expected to enable identification of epitranscriptomic IHD biomarker candidates and potential drug targets
Epitranscriptomics of Ischemic Heart Disease - The IHD-EPITRAN Study Design and Objectives
Epitranscriptomic modifications in RNA can dramatically alter the way our genetic code is deciphered. Cells utilize these modifications not only to maintain physiological processes, but also to respond to extracellular cues and various stressors. Most often, adenosine residues in RNA are targeted, and result in modifications including methylation and deamination. Such modified residues as N-6-methyl-adenosine (m(6)A) and inosine, respectively, have been associated with cardiovascular diseases, and contribute to disease pathologies. The Ischemic Heart Disease Epitranscriptomics and Biomarkers (IHD-EPITRAN) study aims to provide a more comprehensive understanding to their nature and role in cardiovascular pathology. The study hypothesis is that pathological features of IHD are mirrored in the blood epitranscriptome. The IHD-EPITRAN study focuses on m(6)A and A-to-I modifications of RNA. Patients are recruited from four cohorts: (I) patients with IHD and myocardial infarction undergoing urgent revascularization; (II) patients with stable IHD undergoing coronary artery bypass grafting; (III) controls without coronary obstructions undergoing valve replacement due to aortic stenosis and (IV) controls with healthy coronaries verified by computed tomography. The abundance and distribution of m(6)A and A-to-I modifications in blood RNA are charted by quantitative and qualitative methods. Selected other modified nucleosides as well as IHD candidate protein and metabolic biomarkers are measured for reference. The results of the IHD-EPITRAN study can be expected to enable identification of epitranscriptomic IHD biomarker candidates and potential drug targets.</p
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A comprehensive benchmarking of WGS-based deletion structural variant callers.
Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories