Exploration of SOCS3 and HSPA1A in PANoptosis during acute myocardial infarction and chronic kidney disease

Figures

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  Figure 1. Flow chart of this study. First, MR showed that CKD is a risk factor for CAD. Second, transcriptomics data were used to search for genes shared by both diseases and associated with PANoptosis. Functional enrichment, immune microenvironment analysis, single-cell localization, and diagnostic performance validation of the candidate genes in a separate cohort of independent samples were carried out. Finally, gene loss experiments were carried out on animal models. Dataset selection based on (1) tissue relevance (PBMC/whole blood), (2) sample size >20 per group, (3) platform compatibility (Affymetrix U133 Plus 2.0), and (4) prior validation in peer-reviewed studies. Detailed sample sizes are provided in Supplementary Table 1.

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  Figure 2. Shared gene signatures and functional enrichment among AMI, CKD, and PANoptosis. (A) Venn diagram showing two key DEGs shared by AMI (GSE59867, GSE62646), CKD (GSE66494), and PANoptosis. (B) Venn diagram showing 10 key module genes from positively correlated WGCNA modules in AMI (GSE59867, GSE62646), CKD (GSE66494), and PANoptosis. (C) List of genes shared between WGCNA modules and DEGs. (D) Bar chart of the top 12 significantly enriched GO and KEGG signaling pathways. (E) Bubble chart of the top 20 diseases from DO enrichment analysis. (F) Raincloud plot presenting Friends analysis of the core genes.

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  Figure 3. Identification of candidate biomarkers by machine learning. (A, D) Coefficient trajectories for variables in the LASSO regression model for AMI (A) and CKD (D). (B, E) Tenfold cross-validation of tuning parameters for AMI (B) and CKD (E). (C, F) Rankings of gene importance scores from the random forest algorithm for AMI (C) and CKD (F). (G, H) Venn diagrams for AMI (G) and CKD (H) showing four hub genes obtained by intersecting results from LASSO regression and the random forest algorithm. (I) List of hub genes in AMI and CKD.

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  Figure 4. Immune cell infiltration and correlations with key immune-related genes. (A, B) Stacked bar charts of immune cell proportions in tissues from patients with AMI (A) and CKD (B). (C, D) Box plots showing correlations between SOCS3 and various immune cell subsets in AMI (C) and CKD (D). (E, F) Box plots showing correlations between HSPA1A and various immune cell subsets in AMI (E) and CKD (F).

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  Figure 5. Single-cell transcriptional profiles in patients with AMI and CKD. (A) UMAP representation of 4,400 cells from the AMI scRNA-seq dataset. (B) UMAP representation of 12,837 cells from the CKD scRNA-seq dataset. (C, D) Violin plots of SOCS3 and HSPA1A expression across cell populations in AMI (C) and CKD (D). (E, F) Single-cell co-localization of SOCS3 and HSPA1A in AMI (E) and CKD (F).

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  Figure 6. Single-cell transcriptional features of disease and control groups in AMI and CKD. (A, C) UMAP plots showing cell group distributions in AMI (A) and CKD (C). (B, D) UMAP overlays of SOCS3 and HSPA1A expression in AMI (B) and CKD (D). (E, F) Violin plots of SOCS3 and HSPA1A expression in AMI (E) and CKD (F).

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  Figure 7. SOCS3 knockdown reduces MI-induced PANoptosis and improves cardiac function. (A) SOCS3 expression differed across monocyte subsets in AMI. (B) ELISA showed that secretion of key inflammatory cytokines (IL-18, IL-1β, and TNF-α) increased significantly in the MI group. (C) qPCR revealed that MI surgery significantly elevated mRNA levels of Casp1, Cxcl10, Zbp1, and SOCS3 in the MI + AAV9-scramble group. (D) Immunohistochemical staining assessed the expression of SOCS3, IL-18, IL-1β, and TNF-α proteins in mouse heart tissues (scale bar: 500 μm). (E) Echocardiography indicated that ejection fraction and fractional shortening were higher in SOCS3-knockdown mice than in the MI + AAV9-scramble group. ** : P < 0.01, *** : P < 0.001