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• W4287149897 abstract "Atrial fibrillation (AF) is defined as the high-frequency excitation of the atrium, resulting in both dyssynchronous atrial contraction and the irregularity of ventricular excitation.1 According to its condition, AF disease is divided into two sub-types: paroxysmal and persistent. In contrast to persistent AF, paroxysmal AF is diagnosed in the first phase of the disease, which later progresses to persistent AF.1 Furthermore, AF includes risk factors such as obesity, diabetes, smoking and a sedentary lifestyle and is prevalent in the older males of European ancestry. Previous studies have shown that both heart failure (HF) and cardiovascular diseases (CVD) contribute to an increased risk of AF.1 In this study, we investigated genes responsible for AF with sub-disease groups through transcriptomic analysis (Additional file 1: High-resolution figures). It was conducted as a continuation of our thorough CVD research focusing on HF performed on 61 CVD patients (Sample IDs: 1058–1118) and 10 patients without CVD (Control IDs: 648–658) (Additional file 2: population details). When grouped by gender and race, there were 40 males and 21 females, 42 Whites, 7 Blacks (Blacks or African Americans), 1 Asian, 1 Decline to Answer, 2 others, and 8 NA (Table 1 and Figure 1A). Peripheral blood samples were used for RNA extraction, and sequencing was performed using Illumina NovaSeq 6000-S4 to assess the RNA quality.2 An efficient data management system (PROMIS-LCR) with data extraction, transfer and loader system (ETL), created by the authors,3 was used for patient recruitment and consent tracking as well as dealing with the multi-omics data, respectively.4 We also created a publicly available gene-disease database, PAS-Gen, which includes over 59 000 protein-coding and non-coding genes, and over 90 000 classified gene-disease associations, to ease the gene-disease visualization for researchers, medical practitioners and pharmacists. First, the transcriptomic data analysis involved the development of an RNA-seq processing pipeline that contained four operating parts: (I) data pre-processing, (II) data quality checking, (III) data storage and management and (IV) data visualization (Additional file 1: High-resolution figures).2 The analysis of transcripts per million (TPM) was performed to normalize the RNA-seq data by using the visualizing genes with disease-causing variants environment with the findable, accessible, intelligent and reproducible approach (Additional file 4: AF analysis - gene expression data). It reveals all genes annotated with their associated clinical AF phenotype using gene–disease association.2, 5 This expression analysis was expanded to visualize the classification of protein- and non-coding genes in detail as gender- and race-based. First, we looked across the AF-annotated genes to identify protein- and non-coding genes together and found 71 genes related to AF and relative diseases (Additional file 3: Complete Gene List). Next, we observed expression in protein-coding genes and found 22 genes associated with direct and relative AF diseases, which are denominated as AF phenotypes (SCN1B, NPPA-AS1, KCNQ1, KCNE1, VKORC1, ATF7, KCNH2, SELP, PDE4D, ACE, PRKAR1B, NUP155, CYP4F2, ABCC9, KCNJ2-AS1, CFAP20, KCNJ2, MYBPC3, KCNE3, PF4, PPBP, MYL4) (Figure 1B and Table 2). After the initial analysis, differential gene expression analysis was implemented to further investigate AF genes. Of the protein-coding genes, seven AF-associated genes (MYL4, PPBP, PF4, KCNE3, VKORC1, KCNQ1 and CYP4F2) showed differentially regulated expression (Figure 1C). A previous study has reported some of these genes (GJA5, KCNA5, KCNE2, KCNJ2, KCNQ1, KCNH2, NPPA and SCN5A) as novel genes for familial AF in the absence of mutations, whereas mutations in MYL4 have been strongly associated with AF disease in humans.6 With a deeper investigation of the normalized expression analysis, we found that PF4, PPBP, MYL4, KCNE3, VKORC1, KCNQ1 and CYP4F2 genes are highly expressed in AF (Figure 1B) with relative diseases as AF phenotypes; AF (both for PF4 and PPBP); AF familial 18; familial AF, AF, AF familial 3, AF, respectively (Additional file 5: Information about AF phenotypes). The phenotypes represent different subsets of how the disease presents when it is inherited based on the gene of interest. Additionally, these findings were supported by another study in which two long non-coding RNAs genes were found to interact with protein-coding genes associated with AF.7 A subsequent analysis was performed based on two groupings: race- and gender-based. The race-based analysis involved Black, White and all other races in which PF4, PPBP and MYL4 were found to be highly expressed protein-coding genes in AF in all different race groups (Figure 2A–C). Although KCN3 appeared in the analysis, it did not show consistent expression across the patients. In addition, the PPBP gene, which is one of the three immune-related genes (CXCL12, CCL4), has been found to have a positive relationship with the infiltration of immune cells (e.g. neutrophils, plasma cells and resting dendritic cells) and plays a role in the development of AF disease.8 Furthermore, the gene expression analysis based on gender segregation showed similar results, with PF4, PPBP and MYL4 genes as highly expressed with AF disease in both female and male groups (Figure 2D,E). In summary, we performed the systematic transcriptomic characterization of AF-associated genes. Our findings report three highly expressed genes and their associated diseases as AF phenotypes; PF4: AF; PPBP: AF and MYL4: AF familial 18, with a similar expression pattern across races and genders. Moreover, when we compared the genes associated with HF from our previous CVD/HF study2 with those associated with AF, we discovered that two genes (ACE and MYBPC3) were associated with both diseases (HF and AF). These findings are valuable for future research studies as they signify the potential to further investigate these genes for mutations and disease-specific variants. This will provide a new path focusing on a more personalized approach to therapy and treatment. In the future, we seek to evaluate the causal basis for AF by moving beyond the one gene-one disease model through the integration of the expressed genome, characterization of mutations derived from genomic signatures and mapping them on phenotypic traits in the electronic medical records. We aim to contribute to the paradigm shift in the application and interpretation of genetic and genomically informed medicine for AF, moving from a deterministic conceptualization to a probabilistic interpretation of genetic risk. This will support diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice. We aim to construct machine learning models to identify a baseline transcriptional signature highly predictive of response across these indications.9 This might accelerate our ability to leverage and extend the information contained within the original data and to model patient-specific genomics and clinical data for significant transcriptional correlations, highlighting the association of genetic variants to clinical outcomes of treatment in AF and other CVD.5, 9, 10 We appreciate great support by the Rutgers Institute for Health, Health Care Policy, and Aging Research (IFH); Department of Medicine, Rutgers Robert Wood Johnson Medical School (RWJMS); and Rutgers Biomedical and Health Sciences (RBHS), at the Rutgers, The State University of New Jersey. We thank members and collaborators of Ahmed Lab at the Rutgers (IFH, RWJMS and RBHS) for their support, participation and contribution to this study. This study was supported by the Institute for Health, Health Care Policy and Aging Research (IFH); Rutgers Robert Wood Johnson Medical School (RWJMS) and Rutgers Biomedical and Health Sciences (RBHS) at the Rutgers, The State University of New Jersey. The authors declare no conflict of interests regarding financial or non-financial aspects. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article." @default.
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• W4287149897 date "2022-07-01" @default.
• W4287149897 modified "2023-10-16" @default.
• W4287149897 title "RNA‐seq‐driven expression analysis to investigate cardiovascular disease genes with associated phenotypes among atrial fibrillation patients" @default.
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• W4287149897 doi "https://doi.org/10.1002/ctm2.974" @default.
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