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. 2024 Feb 6;12(2):380.
doi: 10.3390/biomedicines12020380.

Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease

Borja Antequera-González  1 , Neus Martínez-Micaelo  1 , Carlos Sureda-Barbosa  2 , Laura Galian-Gay  3 , M Sol Siliato-Robles  2 , Carmen Ligero  1   4 , Artur Evangelista  3 , Josep M Alegret  1   4
Affiliations

Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease

Borja Antequera-González et al. Biomedicines. .

Abstract

Introduction and purpose: Bicuspid aortic valve (BAV) disease is associated with faster aortic valve degeneration and a high incidence of aortic stenosis (AS). In this study, we aimed to identify differences in the pathophysiology of AS between BAV and tricuspid aortic valve (TAV) patients in a multiomics study integrating metabolomics and transcriptomics as well as clinical data.

Methods: Eighteen patients underwent aortic valve replacement due to severe aortic stenosis: 8 of them had a TAV, while 10 of them had a BAV. RNA sequencing (RNA-seq) and proton nuclear magnetic resonance spectroscopy (1H-NMR) were performed on these tissue samples to obtain the RNA profile and lipid and low-molecular-weight metabolites. These results combined with clinical data were posteriorly compared, and a multiomic profile specific to AS in BAV disease was obtained.

Results: H-NMR results showed that BAV patients with AS had different metabolic profiles than TAV patients. RNA-seq also showed differential RNA expression between the groups. Functional analysis helped connect this RNA pattern to mitochondrial dysfunction. Integration of RNA-seq, 1H-NMR and clinical data helped create a multiomic profile that suggested that mitochondrial dysfunction and oxidative stress are key players in the pathophysiology of AS in BAV disease.

Conclusions: The pathophysiology of AS in BAV disease differs from patients with a TAV and has a specific RNA and metabolic profile. This profile was associated with mitochondrial dysfunction and increased oxidative stress.

Keywords: aortic stenosis; aortic valve; endothelial damage; metabolomics; mitochondrial dysfunction; oxidative stress; transcriptome.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Five most significant RNAs in the TAV vs. BAV RNA-seq comparison. Data are presented in (A) and can be easily visualized in the volcano plot (B), where the overexpressed RNAs are represented as red dots and the downregulated RNAs are represented as blue dots.
Figure 2
Figure 2
Visualization of RNA enrichment analysis with different databases. OXPHOS: oxidative phosphorylation; ATP: adenosine triphosphate; NADH: nicotinamide adenine dinucleotide (reduced).
Figure 3
Figure 3
Association between lipid profile and low-molecular-weight metabolites with age-adjusted valve morphology. Odds ratio for aortic valve morphology (95% CI) per 1-SD increment in metabolite concentration. The black circle indicates a p value < 0.05. EC: esterified cholesterol; FC: free cholesterol; TG: triglycerides; PL: phospholipids; PC: phosphocholine; SM: sphingomyelins; LPC: lysophosphatidylcholine; PUFA: polyunsaturated fatty acids; LA: linoleic acid; SFA: saturated fatty acids; w6 + w7: omega 6 and 7; w9: omega 9; ARA + EPA: arachnoid acid + eicosapentanoic acid.
Figure 4
Figure 4
Visualization of data integration. (A) Network with parameters symbolized with nodes classified by color and associations with each other, represented with red or green lines. (B) PLS-DA with X variables explaining 32% and 15% of the cases divided by aortic valve morphology.
Figure 5
Figure 5
Evaluation of the multiomics integration model. This model included the variables shown on the left with their contributions (VIP scores) and a ROC curve that classified aortic valve morphology on the right.
See this image and copyright information in PMC

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