Identifying nexilin as a central gene in neutrophil-driven abdominal aortic aneurysm pathogenesis

Abstract

Objectives: Abdominal aortic aneurysm (AAA) is an inflammation-driven disease in which neutrophil infiltration is critical to its progression. This study aims to explore the molecular mechanisms behind neutrophil infiltration in AAA and identify key regulatory genes.

Methods: We utilized weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis to compare AAA and healthy abdominal aortic tissues. Functional enrichment analysis and a protein-protein interaction (PPI) network were constructed to understand gene functions. Machine learning algorithms were applied to identify key hub genes, followed by in vivo validation using an ApoE-/- mouse model.

Results: Neutrophils, NK cells, and pDCs were significantly increased in AAA tissues. WGCNA identified 234 genes associated with neutrophil infiltration, of which 39 were significantly differentially expressed. Functional enrichment analysis highlighted roles in actin-related processes and pathways. Nexilin (NEXN) was consistently identified as a key hub gene negatively correlated with immune cell infiltration. In vivo validation confirmed that NEXN inhibits AAA progression in ApoE-/- mice by regulating immune cell infiltration.

Conclusion: NEXN plays a crucial role in modulating neutrophil infiltration in AAA. These findings provide new molecular insights into AAA pathogenesis and suggest NEXN as a potential target for AAA therapy.

Keywords: Abdominal aortic aneurysm; Immune cell infiltration; Machine learning; Neutrophils; Nexilin; Weighted gene co-expression network analysis.

Fig. 1

Screening of Genes Associated with Neutrophil Infiltration and WGCNA Analysis. A Comparison of immune cell infiltration scores between AAA and normal tissues (AAA group: n = 14, Normal group: n = 8). B Average connectivity at various soft threshold powers (power) in WGCNA analysis. C Topological overlap measure index (scale-free fit index) at various soft threshold powers (power) in WGCNA analysis. D Correlation analysis of gene co-expression modules associated with neutrophil infiltration

Fig. 2

Screening of Differentially Expressed Genes Associated with Neutrophil Infiltration. Note: A Volcano plot of differentially expressed genes between AAA tissues and control tissues. B Heatmap of differentially expressed genes, showing the gene expression levels in AAA tissues compared to normal tissues (AAA group: n = 14, Normal group: n = 8). C Venn diagram of the intersection of genes selected from WGCNA and differential expression analyses

Fig. 3

Functional Enrichment Analysis and PPI Network. A Results of GO enrichment analysis for BP, MF, and CC. B KEGG pathway enrichment analysis results. C PPI network of 39 genes generated according to the String database. D Protein interaction network related to the 39 genes displayed through GENEMANIA

Fig. 4

Machine Learning-Based Selection of Hub Genes Related to Neutrophils in AAA. A Relationship between the number of gene features and model accuracy in the SVM-REF algorithm. B Relationship between the number of gene features and model error rate in the SVM-REF algorithm. C LASSO regression analysis displaying regression coefficients of genes under different Lambda values. D Importance ranking of genes in the RF model, with a visualization of the top 20 genes. E Three machine learning algorithms selected the intersection of feature genes (SVM-REF, LASSO, RF)

Fig. 5

Expression, Diagnostic, and Functional Analysis of NEXN in AAA. A Comparison of NEXN expression levels in AAA and normal tissues. B ROC curve for NEXN distinguishing between AAA and normal tissues, with an AUC value of 0.725. C GSEA enrichment analysis displaying pathways associated with upregulation and downregulation of NEXN

Fig. 6

Analysis of the Negative Correlation Between NEXN and Immune Cell Infiltration in AAA. Spearman correlation analysis calculates the correlation of NEXN with various immune cells in AAA tissues

Fig. 7

Expression of NEXN in AAA Tissues and Its Association with Immune Cell Infiltration. A qRT-PCR detection of mRNA expression levels of NEXN in AAA tissues (n = 5) and healthy control tissues (n = 5). B WB analysis of protein expression levels of NEXN in AAA and healthy control tissues. C FACS analysis of the relationship between NEXN expression levels and immune cell infiltration. *p < 0.05

Fig. 8

Impact of NEXN on Cell Functions. A CCK-8 assay detecting the effect of NEXN overexpression on the proliferation ability of HUVECs and HAoSMCs. B CCK-8 assay assessing the impact of NEXN knockout on the proliferation ability of HUVECs and HAoSMCs. C Transwell migration and invasion experiments evaluate NEXN overexpression's influence on cell migration and invasion capabilities. D Transwell migration and invasion assays investigating the effect of NEXN knockdown on cell migration and invasion abilities. *p < 0.05, **p < 0.01, ***p < 0.001. Cell experiments were performed in triplicate

Fig. 9

Role of NEXN in the AAA Mouse Model. A qRT-PCR assessment of NEXN mRNA expression levels in the AAA mouse model. B Morphology of the AAA model. C WB analysis of NEXN protein expression levels in the AAA mouse model. D WB analysis of NEXN protein expression levels in the NEXN knockout normal group. E Morphology of the NEXN knockout normal group. F WB analysis of NEXN protein expression levels in the AAA group with NEXN overexpression. G Morphology of the AAA model with NEXN overexpression. *p < 0.05. N = 6

Fig. 10

Relationship Between NEXN Expression Levels and Immune Cell Infiltration Analyzed by FACS. A Flow cytometry analysis of the proportions of neutrophils (CD16), NK cells (CD56), effector T cells (CD8), and effector B cells (CD4) in the control group (Ctrl) and NEXN knockdown group (sgNEXN). B Flow cytometry analysis of the proportions of CD16, CD56, CD8, and CD4 cells in the NEXN control group (AAA-NC) and NEXN overexpression group (AAA-NEXN). Each group with n = 6. Data presented as mean ± standard error analyzed using a t-test, with * indicating p < 0.05

Fig. 11

Revealing the Key Role of NEXN in AAA and Its Association with Immune Cell Infiltration