FN1 governs global protein lactylation in acute myocardial infarction: a metabolic–epigenetic axis driving cardiac injury and impeding stem cell repair

Figures

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  FIGURE 1. Differential gene expression and weighted gene co-expression network analysis in acute myocardial infarction. (A) Volcano plot showing differentially expressed genes between the acute myocardial infarction and control groups in the GSE66360 dataset. Upregulated genes are shown in red, and downregulated genes in dark green. (B) Left panel: Selection of the soft-thresholding power (β) for constructing the adjacency matrix. The x-axis represents the power value, and the y-axis indicates the squared correlation coefficient between log(k) and log(p(k)) for the network, where higher values indicate a better fit to scale-free topology. The red horizontal line marks the threshold of 0.85. Right panel: Mean connectivity across different soft-thresholding powers. The red line highlights the average connectivity at the selected power. (C) Gene clustering dendrogram showing co-expression modules identified by dynamic tree cutting, with distinct colors assigned to each module. (D) Heatmap displaying correlations between module eigengenes and the AMI phenotype (AMI vs. Control). (E, F) Scatter plots of module membership versus gene significance for the yellow (E) and red (F) modules. (G, H)…

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  FIGURE 2. Screening and identification of lactylation-related functional core genes in hEnMSCs. (A) Venn diagram showing the overlap between AMI core-related genes and lactylation-related genes, identifying four differentially expressed lactylation genes. (B) Pearson correlation-based co-expression network (P < 0.01) between the four identified genes and hEnMSCs functional genes, revealing 16 core genes. (C) Protein-protein interaction network of the 16 core genes constructed using the STRING database. (D) Ten-fold cross-validation plot for tuning the regularization parameter (λ) in the LASSO model. (E) LASSO coefficient profile; the solid vertical line indicates the lambda value at the minimum partial likelihood deviance, and the dashed line marks the optimal lambda selected by cross-validation. (F) Final set of important features (genes) confirmed by the Boruta algorithm. (G) Plot of Z-scores representing the importance of all features during the Boruta feature selection process. (H) Plot of the out-of-bag (OOB) error rate against the number of trees in the Random Forest model. (I) Variable importance plot ranked by the mean decrease in the Gini index. (J) Venn diagram illustrating the…

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  FIGURE 3. Construction and validation of an acute myocardial infarction diagnostic model based on core signature genes. (A) SHAP force plot for a representative sample, illustrating the direction and magnitude of each feature gene’s contribution to the individual prediction. (B) Bar plot showing the global mean absolute SHAP value for each feature gene, representing its overall contribution to the model. (C) SHAP summary (bee swarm) plot displaying the distribution of SHAP values (contribution to predicted risk) for each feature gene across all samples, colored by gene expression level. (D) SHAP dependence plot for IL1B, depicting the non-linear relationship between its expression level and the corresponding SHAP value. (E) Diagnostic nomogram for AMI based on the expression levels of the five core genes. (F) Calibration curve of the nomogram in the training set (GSE66360), evaluating the agreement between predicted and observed probabilities. (G) Decision curve analysis assessing the clinical net benefit of the model across a range of threshold probabilities. (H) Receiver Operating Characteristic (ROC) curve for internal validation within the training set.

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  FIGURE 4. Identification of molecular subtypes in acute myocardial infarction based on core signature genes. (A) Unsupervised consensus clustering based on the expression of the five core genes identifies two stable molecular subtypes (Cluster 1 and Cluster 2). (B) Volcano plot of differentially expressed genes (DEGs) between the two subtypes. (C) Stacked bar chart showing the proportions of 22 immune cell types in all samples. (D) Stacked bar chart showing immune cell infiltration proportions in the two subtypes. (E, F) Functional enrichment analysis of DEGs between subtypes: bar plot of significant GO terms (E) and bubble plot of enriched KEGG pathways (F). ns P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001.

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  FIGURE 5. Single-cell transcriptomics reveals the post-myocardial infarction cardiac cell atlas and core gene expression patterns. (A) UMAP plot showing the clustering of all cells, annotated into 10 major cell types (e.g., cardiomyocytes, fibroblasts, endothelial cells) based on canonical marker expression. (B) Dot plot displaying the expression of classic marker genes for each identified cell type. (C) Split UMAP visualization comparing cell distribution between the control (left) and MI (right) groups. (D) Stacked bar chart illustrating the proportional changes of major cell types between MI and control conditions. (E) Dot plot showing the expression levels of the four candidate core genes (FN1, IL1B, ALDH1A1, GADD45B) across cell types and groups. (F) UMAP plot of re-clustered cardiomyocytes, revealing six distinct subsets. (G) Heatmap of characteristic marker gene expression for each cardiomyocyte subset. (H) Stacked bar chart comparing the proportions of cardiomyocyte subsets between MI and control groups. (I) Heatmap depicting the average expression of the four candidate genes across the six cardiomyocyte subsets.

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  FIGURE 6. Dynamic evolution of cardiomyocytes after myocardial infarction and changes in core gene expression. (A–C) Pseudotime trajectory analysis based on Monocle 2. (A) Trajectory plot of cells in the reduced - dimensional space (DDRTree), colored by cell subset. (B) Trajectory plot colored by pseudotime. Dark blue represents early stages, while yellow represents late stages. (C) Trajectory plot colored by group. Blue represents Control, and red represents Disease. (D) Curve plot showing the dynamic expression of the core gene FN1 along the pseudotime trajectory, with the shaded area representing the confidence interval.

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  FIGURE 7. FN1 regulates lactylation and mediates hypoxia/reoxygenation injury in cardiomyocytes. (A–D) Time-dependent effects of oxygen-glucose deprivation (OGD) treatment on AC16 cells. (A) Cell viability (CCK-8 assay). (B) Intracellular reactive oxygen species (ROS) levels (DCFH-DA assay). (C) Lactate dehydrogenase (LDH) release rate. (D) Western blot detection of global protein lactylation levels. (E) Western blot detection of FN1, Bax, Bcl-2, and global lactylated protein levels in different treatment groups (Control, OGD, OGD + sh-NC, OGD + sh-FN1). (F–J) Rescue experiments involving FN1 knockdown and exogenous lactate treatment. (F) Cell viability. (G) Intracellular ROS levels. (H) LDH release rate. (I) Mitochondrial membrane potential (JC-1 assay, red/green fluorescence ratio). (J) Cell apoptosis rate (Annexin V/PI flow cytometry). Data are presented as mean ± SEM, n = 3. ns P > 0.05, **P < 0.01, ***P < 0.001 (One-way ANOVA with Tukey’s post hoc test).

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  FIGURE 8. Targeting FN1 ameliorates myocardial infarction injury and counteracts the adverse effects of lactate in mice. (A–C) Assessment of cardiac function by echocardiography 28 days post-surgery. (A) Representative M-mode echocardiograms. (B) Statistical graph of left ventricular ejection fraction (LVEF). (C) Statistical graph of left ventricular fractional shortening (LVFS). (D) Representative image of H&E-stained myocardial tissue. Scale bars: 200 μm. (E) Representative image of Masson’s trichrome-stained tissue (collagen fibers in blue). Scale bars: 200 μm. (F) Representative immunohistochemistry images detecting FN1 and global protein lactylation (L-Lactyl Lysine) levels in myocardial tissue. Scale bars: 200 μm. Data are presented as mean ± SEM, n = 5. **P < 0.01, ***P < 0.001 (One-way ANOVA with Tukey’s post hoc test).

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  FIGURE 9. Knockdown of FN1 remodels the microenvironment and sensitizes the therapeutic efficacy of hEnMSCs in myocardial infarction. (A–C) Assessment of cardiac function by echocardiography 28 days post-surgery. (A) Representative M-mode echocardiograms. (B) Statistical graph of left ventricular ejection fraction (LVEF). (C) Statistical graph of left ventricular fractional shortening (LVFS). (D) Representative image of H&E-stained myocardial tissue. Scale bars: 200 μm. (E) Representative image of Masson’s trichrome-stained tissue (collagen fibers in blue). Scale bars: 200 μm. Data are presented as mean ± SEM, n = 5. **P < 0.01, ***P < 0.001 (One-way ANOVA with Tukey’s post hoc test).