Gastrointestinal metabolites: a potential bridge connecting gut microbiota and myocardial metabolic reprogramming in acute myocardial infarction

Figures

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  Figure 1. AMI group mice exhibit decreased cardiac function, myocardial inflammation, and fibrosis. (A) Schematic overview of the multi-omics study design in a mouse model of AMI; (B) (a-b) Representative echocardiography; (c) Photoacoustic imaging of the left ventricular anterior wall; (d) Representative electrocardiogram (ECG); (C) Changes in the body weight at 7 days after surgery; (D) (a) Left ventricular ejection fraction (LVEF%); (b) Left ventricular fractional shortening (LVFS%) (E) Heart rate (HR); (F) Blood oxygen content in the left ventricular anterior wall; (G-J) QTc, QT intervals, QRS intervals, and PR intervals; (K) (a) H&E-staining; (b) Masson staining (scale bars = 1000 μm, 200 μm, and 50 μm); (c-d) Inflammatory infiltration and collagen deposition analysis; (L-M) Serum CK-MB and LDH level. * p < 0.05 vs. Sham; 6 mice per group; Error bars represent standard deviation.

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  Figure 2. The AMI group mice show significant alterations in the composition of the gut microbiota. (A-C) Differences in the gut microbiota between the AMI and Sham group mice at the phylum, genus, and species levels; (D) Venn diagram shows the number of shared, unique, and depleted ASVs between the AMI and Sham groups of mice at the genus level; (E) Phylogenetic tree of gut microbiota at the genus level in the AMI and Sham group of mice; (F) Circos plot shows the sample-gut microbiota species relationships at the genus level in the AMI and Sham group of mice; (G) Clustering heatmap shows relative abundances of the gut microbial genera in the AMI and Sham group of mice. (H, I) MetaNet species modular association network diagram of gut microbiota; The node represents a genus, and the size of the node indicates the relative abundance of the genus. The edge represents the functional-based positive or negative association between genera, with red edges indicating positive correlation and blue edges indicating negative correlation. The overall network topology coefficients are shown in Supplementary Table 1.

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  Figure 3. The α-diversity and β-diversity indices of gut microbiota. (A-D) The α-diversity indices (Chao1, Pielou_e, Shannon, Simpson) of gut microbiota in the AMI and Sham groups of mice; (E) The species accumulation boxplots for the AMI and Sham groups of mice; (F) The rank abundance curves for the AMI and Sham groups of mice; (G) Heatmaps of the Bray-Curtis distance matrices for the AMI and Sham groups of mice; (H) Boxplots of the β-diversity index distribution between the AMI and Sham groups of mice; (I) The β-diversity Principal Coordinate Analysis (PCoA) based on the Bray-Curtis distance for the AMI and Sham groups of mice; (J) UPGMA clustering tree at the genus level for the AMI and Sham groups of mice. T-test and Wilcoxon rank-sum test were performed between the two groups. *p < 0.05 vs. Sham; 6 mice per group; Error bars represent standard deviation.

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  Figure 4. Statistical tests to evaluate differences in the gut microbiota community structure. (A) Simper bubble plot at the genus level for the AMI and Sham groups of mice; (B) LEfSe evolutionary branch diagram for the AMI and Sham groups of mice; (C) Bar chart of LDA value distribution for the AMI and Sham groups of mice; (D) MetagenomeSeq complex heatmap at the genus level for the AMI and Sham groups of mice.

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  Figure 5. PICRUSt2 functional prediction of gut microbiota. (A, B) T-test of PICRUSt2 functional annotation of the gut microbiota based on the KEGG pathways between the AMI and Sham groups of mice. Adjusted p is shown in Supplementary Data.

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  Figure 6. Quantitative analysis and pathway annotation of differential metabolites from gut intestinal contents. (A) Cluster analysis of differential metabolites; (B) Pie chart of metabolite Class I classification; (C) PLS-DA/OPLS-DA modeling results; (D) Volcano plot of differential metabolites; (E) Matchstick plot of differential metabolites; (F-J) Violin plots of differential metabolites; (K, L) Bubble chart (K) and bar chart (L) of KEGG pathway enrichment and annotation of differential metabolites.

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  Figure 7. Quantitative analysis and pathway annotation of DEGs from damaged heart tissues. (A) Cluster analysis of differential genes; (B) Volcano plot of differential genes; (C) Gene up/down-regulation counts in the top 30 GO functional enrichment pathways; (D, E) Bubble plot of functional annotations for AMI up/down-regulated pathways.

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  Figure 8. Correlation analysis of differential microbiota, metabolites and genes. (A) Heatmap for correlation analysis between differential gut microbiota and differential metabolites; (B-M) Scatter plot shows the top 12 pairs (largest |r| values) of differential genera and differential metabolites based on the Pearson correlation coefficient (r) and P-value. The value range of the correlation coefficient was (-1, 1). A correlation coefficient of less than 0 indicated negative correlation, whereas a correlation co-efficient greater than 0 indicated. The legend represents the correlation coefficient, with red indicating positive correlation and blue indicating negative correlation; the asterisk (*) mark indicates statistical significance; (N) Bubble plot of KEGG enrichment annotation for co-enriched pathways. Red indicates stronger significance; triangles represent enrichment of DEGs, dots represent enrichment of differential metabolites, and the shape size corresponds to the count of enriched genes/metabolites; (O) Pathview visualization of the cGMP-PKG signaling pathway. Green nodes represent downregulated expression, and red nodes represent upregulated expression.