Biomarkers and key pathways in atrial fibrillation associated with mitral valve disease identified by multi-omics study

Figures

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  Figure 1. Analysis of proteomics in patients with AF compared to SR. (A) Unsupervised hierarchical clustering heatmap of proteins identified from the iTRAQ study (12 patients of AF and 12 patients of SR). The functional cluster analysis of DPs (left of heatmap) and the protein expression values (red: up-regulation, green: down-regulation) are demonstrated. (B,C,D) Classification of identified DPs based on their functional annotations using GO function and enrichment analysis: (B) biological process, (C) cell component, and (D) molecular function. (E) PPI network. Circular and square nodes represent the identified proteins and KEGG pathway terms, respectively. AF, atrial fibrillation; SR, sinus rhythm; iTRAQ, isobaric tags for relative and absolute quantitation; DPs, differential proteins; GO, Gene Ontology; PPI, protein-protein interaction; KEGG, Kyoto Encyclopedia of Genes and Genomes.

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  Figure 2. Validation of DPs identified from proteomics study. ELISA validation of DPs in AF patients (n=50) and SR patients (n=30). The figure shows that plasma levels of ApoA-I, angiotensinogen, CSF-1R, and VN were lower in AF patients compared to SR patients. Furthermore, the plasma level of LMO7 and prothrombin fragment 1+2 was higher in AF patients compared to SR patients. Data are shown as mean ± SEM. DPs, differential proteins; ELISA, enzyme-linked immunosorbent assay; AF, atrial fibrillation; SR, sinus rhythm; ApoA-I, apolipoprotein A-I; CSF-1R, colony-stimulating factor 1 receptor; VN, vitronectin; LMO7, LIM domain only protein 7; SEM, standard error of the mean.

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  Figure 3. Metabolomics analysis in patients with AF compared to SR. Patients of AF (n=30) or SR (n=26) were analyzed by GC-MS. (A) PCA, PLS-DA, and OPLS-DA of the data in this study. PCA scores plot displays the variability among AF, SR, and QC samples; PLS-DA plot and OPLS-DA plot compare the variability between AF and SR samples. The quality of the models is described by the the proportion of variance in the data explained by the X and Y models and indicates goodness of fit (R2X and R2Y) values. (B) Heatmap of the metabolites in the GC-MS study. Fifty-five metabolites (rows) distinguished AF patients from SR patients (columns) under the condition of VIP >1 in the PLS-DA model and P values <0.05 in the Student’s t-test. Unsupervised hierarchical clustering was performed using the R Programming Language (version 3.3.1). AF, atrial fibrillation; SR, sinus rhythm; GC-MS, gas chromatography-mass spectrometry; PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; OPLS-DA, orthogonal partial least-squares discriminant analysis; QC, quality control; VIP, variable importance in projection.

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  Figure 4. Integrated “-omics” analysis reveals global metabolic changes. (A) Cross-correlation analysis of identified proteins and metabolites from plasma samples. (B) PPI analysis of plasma proteomics findings. Note that PPAR pathway is now listed as one of the top pathways. Circular nodes represent the identified up- (red) and down-regulated (green) proteins, square nodes represent the KEGG pathway terms, and pentagonal nodes represent the identified metabolites. Solid or dashed grey lines between the molecules indicate the interaction with bigger and smaller confidence scores, respectively. (C) KEGG mapping tools were used to integrate the proteomics and metabolomics data sets into the human metabolic reference map (hsa01100). Red and blue represent up- and down-regulated, respectively. Lines represent reactions regulated by differentially expressed proteins and dots represent metabolites. Blue shading over lipid metabolism, galactose metabolism, nucleotide metabolism, and energy metabolism serve to highlight pathways that are highly represented. PPI, protein-protein interaction; PPAR, peroxisome proliferator-activated receptor; KEGG, Kyoto Encyclopedia of Genes and Genomes.

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  Figure 5. Schema of discovery of biomarkers and key pathways at the protein and metabolite levels in AF associated with MVD. The potential biomarkers and key pathways were investigated in the plasma of MVD patients with chronic AF by using multi-omics technologies. Dysregulation of the PPARα pathway (lower ApoA-I and ApoA-II) and upregulation of the galactose metabolism (higher lactose, sucrose, galactinol, and raffinose) indicates changes in the metabolism status in patients with MVD-associated AF. These proteins/metabolites may be developed as biomarkers to monitor the disease progress of MVD-associated AF. AF, atrial fibrillation; MVD, mitral valve disease; PPARα, peroxisome proliferators-activated receptor alpha; ApoA-I, apolipoprotein A-I; ApoA-II, apolipoprotein A-II.

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  Figure 6. PPARα plays a crucial role in the lipid metabolism in the process of AF. PPARα heterodimerizes in the nucleus with RXR, and then the dimer binds to the PPRE on the promoter region of target genes to activate their expression. Down-regulation of PPARα in the process of AF inhibits the expression of PPARα targeted genes, resulting in down-regulation of lipid uptake, fatty acid storage, fatty acid β-oxidation and TAG synthesis, as well as up-regulation of glucose oxidation. Therefore, the concentration of circulating unsaturated fatty acids (palmitic acid, arachidonic acid, linoleic acid) is increased due to decreased consumption, while the concentration of carbohydrates (sucrose, cellobiose, lactose, et al.) is increased due to complexed compensate mechanisms to fulfill the energy demands of the heart. PPARα, peroxisome proliferators-activated receptor alpha; AF, atrial fibrillation; RXR, retinoid X receptors; PPRE, PPAR response element; TAG, tricarboxylic acid cycle.