{
  "abstract": "Introduction Severe aortic stenosis (AS) is associated with marked myocardial remodelling, which can be quantified with cardiac magnetic resonance (CMR) imaging. This provides detailed functional and structural data, but their molecular correlates are poorly understood.Methods RNA was extracted from discarded right atrial appendage (RAA) material of patients undergoing surgical aortic valve replacement for severe AS (n=34). Bulk RNA-sequencing (RNA-seq) of single-end reads was performed using Illumina NextSeq 2000, followed by alignment to the human reference genome (STAR aligner) and processing with PCAtools. RNA-seq deconvolution was conducted with CIBERSORTx using signature matrices developed from single-cell RNA-seq data from the Heart Cell Atlas to estimate the cellular composition of each participant’s RAA. Linear regression was used to correlate CMR parameters with RNA-seq principal components (PCs) or deconvoluted cell lineage abundances. Pre-operative CMR was performed 1-month pre-operatively. The CMR protocol, consisted of cine imaging using a steady-state free precession sequence, pre- and post-contrast T1 mapping, quantitative adenosine stress and rest perfusion, and late gadolinium enhancement (LGE) imaging at 3T.Results Of the 34 participants, 25 (73%) were men, and the mean age was 68 years (95% CI 66, 71). Mean (95% CI) CMR metrics included: left ventricular (LV) ejection fraction (EF) 64% (61–66%); LV mass 155g (140–171g); LV global longitudinal strain (GLS) 15% (14%-16%); left atrial (LA) EF 39% (34–45%); LV end-diastolic volume indexed to body surface area 76 mL/m 2 (71–80 mL/m2); native T1 time 1301 ms (1286–1316 ms); LGE area 2.2% (0.9–6.5%); myocardial perfusion reserve 2.3 (2.0, 2.6).PC1 (by definition, the PC accounting for the greatest transcriptional variance), correlated strongly with the abundance of atrial cardiomyocytes and vascular endothelial cells (both R2>0.6 and p<0.01), whilst PC2 was strongly correlated with sex. PC3 was significantly correlated with measures of cardiac function/stress, including NT pro-BNP, LAEF and LVEF (all with R2>0.3 and p<0.05).Of all deconvoluted cell lineages, the abundance of neuronal cells demonstrated the greatest number of statistically significant associations with CMR parameters, including LV and LA end diastolic volumes and LVEF (figure 1). Of all CMR parameters, LV GLS demonstrated the greatest number of statistically significant associations with cell lineage abundances, including atrial cardiomyocytes, fibroblasts and vascular endothelial cells.Abstract BS17 Figure 1Eigencor plot displaying relationship between clinical variables and RNA-seq deconvolution-predicted cell lineage proportions, quantified as R2 in linear regression adjusted for age, sex and body mass index. Benjamini-Hochberg adjusted p values are indicated as * = p<0.05 and ** = p<0.01. AC – atrial cardiomyocyte; BNP – NT-pro Brain Natriuretic Peptide; ED – end diastolic; EDVi – end diastolic volume indexed to body surface area; ES – end systolic; ESVi – end systolic volume indexed to body surface area; LV mass EDV – LV mass indexed to LVEDV; MBF – myocardial blood flow; SMC – vascular smooth muscle cellConclusions The atrial myocardial transcriptome is associated with cardiac imaging phenotypes in people with severe AS. Our data suggest an unappreciated correlation of myocardial neuronal abundance with CMR biomarkers of myocardial remodelling and a blood biomarker of myocardial stress, which warrants further assessment to pursue its role as a biomarker and therapeutic target.",
  "authors": [
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Marcella Conning-Rowland"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Marilena Giannoudi"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Nicholas Jex"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Henry Procter"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Sindhoora Kotha"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Anna McGrane"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Amanda MacCannell"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "David J Beech"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Peter Swoboda"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Peter Kellman"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Sven Plein"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Mark T Kearney"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Lee D Roberts"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Kathryn J Griffin"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Eylem Levelt"
    },
    {
      "affiliations": [
        "Leeds Institution of Molecular and Metabolic Medicine, University of Leeds"
      ],
      "name": "Richard M Cubbon"
    }
  ],
  "title": "BS17 Using the myocardial transcriptome to explore cardiac imaging phenotypes in patients with severe aortic stenosis",
  "uid": "36f3703a-bde0-5521-9d94-1aad56e0d25f"
}