Human aortic dissection specimens and non-dissected vascular tissues (harvested distant from the dissection site) were obtained from patients undergoing surgery for type A aortic dissection. Samples were collected during aortic root and ascending aorta replacement surgeries at either the Department of Thoracic and Cardiovascular Surgery of Chongqing University Central Hospital and the First Affiliated Hospital of Chongqing Medical University from April 2024 to June 2024. All procedures received prior approval from the hospital’s ethics committee and for this retrospective analysis of anonymized clinical data, the requirement for written informed consent was waived by the Ethics Committee (2024-173-02) and all procedures conducted in accordance with the Declaration of Helsinki. Protein was extracted from aortic tissue samples and digested with trypsin. The resulting peptides underwent liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using high-resolution mass spectrometer. The acquired spectra were processed using a database to identify and quantify the proteins. Label-free quantification (LFQ) was used to analyze differential protein expression between dissection and control tissues.

Male C57BL/6J mice and global S100a9 knockout (KO) mice were purchased from Cyagen Biosciences Inc (Suzhou, China) and housed under specific pathogen-free conditions at the Chongqing Medical University Animal Research Center. Aortic dissection was induced in both wild-type (WT) and S100a9 KO mice. Starting from week 6, all mice were received daily $\beta$-aminopropionitrile (BAPN, Cat: A3134-5G, Sigma, Milano, Italy) dissolved in drinking water at a dose of 1 g/kg/day. During the aortic diameter examination, mice were anesthetized and maintained with isoflurane (3% for induction, followed by 1.5% for maintenance), and aortic diameter was assessed using VINNO 6 VET ultrasound system for small animals. For euthanasia, intravenous thiopental sodium (150 mg/kg) was administered. The drug was prepared at a concentration of 25 mg/mL in sterile saline and injected via the tail vein. Then thoracic aortas were harvested, snap-frozen in liquid nitrogen, and stored at –80 °C for subsequent analysis. All animal procedures were approved by the Animal Care and Use Committee of Chongqing Medical University (IACUC-CQMU-2024-04098).

Protein lysates were resolved by SDS-PAGE (Cat: PG212, EpiZyme, Shanghai, China) at 80–120 V constant voltage. Separated proteins were electrophoretically transferred onto PVDF membranes (Cat: ISEQ00010, Millipore, Billerica, MA, USA) using a rapid transfer solution at 400 mA for 30 minutes. The membranes were blocked with 5% skim milk (Cat: D8340, Solarbio, Beijing, China) for 1.5 hours and washed three times with TBST (Cat: G0001-2L, Servicebio, Wuhan, Hubei, China) for 3 minutes each. Primary antibodies S100A9 (1:1000, Cat: ab288715, Abcam, Cambridge, UK), GAPDH (1:10,000, Cat: GB11002, Servicebio, Wuhan, Hubei, China) were incubated overnight at 4 °C, followed by incubation with secondary antibodies (1:7000, Cat: AB-228395, Invitrogen, Carlsbad, CA, USA) the next day. The signal was visualized by enhanced chemiluminescence (Cat: BL520B, Biosharp, Beijing, China).

Total RNA was extracted from tissue samples using Trizol (Takara, Beijing, China). The RNA was then reverse-transcribed into cDNA using a reverse transcription kit (Cat: RK20433, ABclonal, Wuhan, Hubei, China). Quantitative PCR was performed using SYBR Green Master Mix with specific primers (Cat: RK21203, ABclonal, Wuhan, Hubei, China) on a CFX96™ Real-Time system (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The mRNA expression levels were normalized to $\beta$-ACTIN using the delta-delta Ct (2^{-Δ⁢Δ⁢Ct}) relative quantification method. The PCR primer sequences used for measuring mRNA expression are presented below (F, R): $\beta$-ACTIN human: (CCTTCCTGGGCATGGAGTC, TGATCTTCATTGTGCTGGGTG); S100A9 human: (ATACTCTAGGAAGGAAGGACACC, TCCATGATGTCATTTATGAGGGC); MT-CO1 human: (CCTACTCCTGCTCGCATCTG, AGAGGGGCGTTTGGTATTGG); MT-ND1 human: (CGATTCCGCTACGACCAACT, AGGTTTGAGGGGGAATGCTG); MT-ND2 human: (ACCATCTTTGCAGGCACACT, GCTTCTGTGGAACGAGGGT); MT-ND3 human: (GCGGCTTCGACCCTATATCC, AGGGCTCATGGTAGGGGTAA); MT-ND4 human: (GCTCCATCTGCCTACGACAA, GCTTCAGGGGGTTTGGATGA); MT-ND5 human: (CGGAAGCCTATTCGCAGGAT, ATAGGGGATTGTGCGGTGTG); MT-CYB human: (CCACCCCATCCAACATCTCC, GCGTCTGGTGAGTAGTGCAT); MT-ATP8 human: (CCTACCTCCCTCACCAAAGC, AGGATTGTGGGGGCAATGAAT).

The human acute monocytic cell line Tohoku Hospital Pediatrics-1 (THP-1) was purchased from the Procell system (CL-0233, Wuhan, China), authenticated by STR profiling with no evidence of cross-contamination and tested negative for mycoplasma contamination. The cells were cultured in Complete Medium For Cell Culture Tested (Cat: TCH-G361-C, HyCyte, Suzhou, China) and supplemented with 10% fetal Bovine serum (FBS) (Cat: FBP-S005, HyCyte, Suzhou, China) in a humidified atmosphere at 37 °C under 5% CO₂. A working cell bank was produced from one of the master cell banks and the cells in the second or third passage were used for experiments. The cells were diluted to 2 $\times$ 10⁵ cells/mL three times a week and were in the exponential growth phase for all experiments.

Data were analyzed using GraphPad Prism software (v8.0.2, GraphPad Software, LLC, San Diego, CA, USA). Results are presented as mean $\pm$ standard deviation (SD). Statistical comparisons between groups were made using one-way Analysis of Variance (ANOVA), with a p value 0.05 considered statistically significant.

Paired aortic tissue specimens (five dissected and five matched non-dissected vascular samples) were obtained from patients undergoing surgical repair for acute Type A aortic dissection. All ten samples underwent proteomic profiling via LC-MS/MS. Following stringent quality control, 4608 proteins were confidently identified. Principal component analysis (PCA) validated dataset integrity, showing a significant separation between dissection and control groups (Fig. 1A). Differential expression analysis identified 190 significantly altered proteins ($|$log₂FC$|$ $>$1.2, p.adj 0.05), with 120 proteins upregulated and 70 downregulated proteins relative to controls (Fig. 1B,C). GO enrichment analysis revealed significant enrichment (p.adj 0.05) of differentially expressed proteins in biological processes related to the response to lipopolysaccharides, molecular functions such as glycosaminoglycan and Toll-like receptor binding, and cellular components associated with secretory granules (Fig. 1D–F, Supplementary Table 1). Further GSEA further confirmed significant activation (Nonparametric Estimation Statistics (NES) $>$1.8, p.adj 0.05) of inflammatory and lipopolysaccharide responses (Fig. 1G,H, Supplementary Table 2).

Fig. 1.

Proteomic sequencing and analysis of type A aortic dissection (n = 5 for normal and dissection vascular samples). (A) Principal Component Analysis (PCA) of the proteomic data. (B) Volcano plot illustrating variable proteins (total proteins analyzed = 4608). (C) Heatmap depicting identified differential proteins. (D–F) Gene Ontology (GO) enrichment analysis of the differential proteins identified in proteomics (p.adj 0.05). (G,H) Gene Set Enrichment Analysis (GSEA) for the differential proteins (NES $>$1.8, p.adj 0.05). NES, Nonparametric Estimation Statistics.

To characterize functional interactions among differentially expressed proteins, we conducted a PPI network using the STRING database (v12.0). Among the 190 input proteins, 52 lacked predicted interactions, while five formed disconnected singleton pairs (Fig. 2A, Supplementary Table 3). After filtering out these non-networked proteins, the remaining 133 proteins were ranked by topological importance in Cytoscape (v3.10.2), with the highest-scoring hubs visualized at the network core (Fig. 2B, Supplementary Table 4). Topological analysis revealed high-scoring hub proteins concentrated at the network core, indicating functional centrality. Subsequent MCODE clustering identified 17 potential hub proteins from densely interconnected modules (Fig. 2C, Supplementary Table 5). To pinpoint the most significant hub protein, we calculated semantic similarity (GOSemSim v3.8, Bioconductor Core Team, Guangzhou, Guangdong, China) across three GO domains: biological process, molecular functions, and cellular components. S100A9 demonstrated the highest functional coherence score (Fig. 2D). Correlation heatmap analysis showed that S100A9 was closely associated with S100A8 and exhibited interactions with other genes (Fig. 2E). Visualization through heatmap and volcano plot confirmed significant upregulation of S100A9 in the aortic dissection group (Fig. 2F,G). These findings position S100A9 as a central regulator in aortic dissection.

Fig. 2.

Hub protein screening for type A aortic dissection. (A) Interaction network of differential proteins analyzed using the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) database. (B) Importing protein interaction results into Cytoscape and ranking the proteins. (C) Identification of 17 hub proteins from the analysis of 138 proteins using the MCODE plugin. (D,E) Box plot and correlation heatmap of the final ranking for the 17 hub proteins from GOsensim analysis. (F) Volcano plot of the 17 hub proteins. (G) Heatmap of the 17 hub proteins, displaying each group separately. *p 0.05; **p 0.01; ***p 0.001.

Following the identification of S100A9 as a hub protein, we validated its expression in dissection vessels. Tissue proteins were extracted from both aortic dissection samples and normal vascular tissues. Western blot analysis revealed a significant increase in S100A9 expression in the aortic dissection group, consistent with proteomic data (Fig. 3A,B). Quantitative PCR further confirmed a marked upregulation of S100a9 mRNA levels (Fig. 3C).

Fig. 3.

Role of S100A9 in aortic dissection animal models. (A,B) Protein expression of S100A9 in aortic tissues from AD patients evaluated through Western blotting, with statistical analysis presented in (B) (n = 3). (C) S100a9 mRNA expression in vascular tissues (n = 6). (D) Protein expression of S100A9 in aortic tissues evaluated through Western blotting (n = 3). (E) Survival analysis of WT and S100a9 KO AD mice (45% vs 80%; p value = 0.0412). (F,G) Aortic diameter of mice across groups and the statistical result (p value 0.0001). The data are presented as the mean $\pm$ standard error of the mean. ns, no significant difference; **p 0.01; ***p 0.001; ****p 0.0001.

To explore the effects of S100A9 on aortic dissection development, we utilized wild-type mice and S100a9 KO mice to induce aortic dissection model. Western blot analysis revealed that S100A9 levels were significantly elevated in the aortic vessels of BAPN-treated WT mice, while expression was nearly undetectable in S100a9 KO mice treated with BAPN (Fig. 3D). S100a9 KO significantly increased survival rates of AD mice (Hazard ratio S100a9^-⁣/-+BAPN/WT+BAPN: 0.3236; 95% CI of ratio: 0.1143–0.9155, p value = 0.0412; Fig. 3E). Doppler ultrasound examinations showed that S100a9 deficiency also decreased the aortic diameter in AD mice (p value 0.0001; Fig. 3F,G). These findings indicate that S100A9 elevation drives AD pathogenesis in mice, while its inhibition markedly improves the condition, suggesting it could be a potential therapeutic intervention for aortic dissection.

To further investigate the mechanisms by which S100A9 influences aortic dissection, we analyzed single-cell sequencing data of aortic tissues from both aortic dissection models and ABR (paquinimod)-treated dissection models (ABR, an S100A9-specific inhibitor, intragastric administration, 10 mg/kg/d, single-cell dataset GEO247260) [11]. Using the Seurat package, we performed rigorous quality control, dimensionality reduction, and unsupervised clustering, identifying 15 distinct clusters (Fig. 4A). Through manual cell type annotation based on literature and marker databases (Fig. 4B,C). The results indicated that the proportions of B cells, T cells, Granulocyte, Macrophage, and Endothelial cells were significantly elevated in the dissection group compared to the treated group, while Fibrocyte and Smooth muscle cells (SMC) were more prevalent in the treated group (Fig. 4D). This suggests that inhibiting S100A9 expression leads to an increase in cell populations involved in vascular structure while reducing the numbers of immune and inflammatory cells. These findings align with our observation that deletion of S100a9 improved the aortic dissection phenotype in BAPN-treated mice.

Fig. 4.

Single-cell transcriptomic analysis of aortic dissection animal models with S100A9 intervention. (A) UMAP plot showing dimensionality reduction and clustering analysis of the GSE247260 dataset. (B) Bubble plot displaying the markers for each cluster. (C) UMAP plot illustrating the clustering based on marker expression. (D) Bar graph comparing the cell populations between the AD and AD+ABR groups (ABR, S100A9 inhibitor; 10 mg/kg/d).

Our analysis confirmed that S100a9 is predominantly expressed in granulocyte (Fig. 5A). To elucidate the mechanisms involved, we performed single-cell Gene Set Variation Analysis (GSVA; v1.50.2, https://bioconductor.org/packages/GSVA/) specifically on granulocyte. We found that the oxidative phosphorylation pathway was significantly downregulated in granulocyte from the aortic dissection group compared to the treated group, indicating that inhibiting S100A9 expression enhances mitochondrial function (Fig. 5B). GO enrichment analysis for the granulocyte population revealed significant enrichment of the reactive oxygen species (ROS) pathway in the dissection group (p.adj 0.05; Fig. 5C). Further examination of core mitochondrial genes demonstrated S100A9 inhibition (Fig. 5D,E) significantly increases in the expression levels of mt-Nd1, mt-Nd2, mt-Nd3, mt-Nd4, mt-Nd5, mt-Cytb, mt-Co1, and mt-Atp8 following (Fig. 5F–M). These results suggest that enhanced mitochondrial quality may be responsible for the protective effects of S100A9 knockout.

Fig. 5.

Inhibition of S100a9 mitochondrial quality in granulocytes. (A) UMAP plots displaying the separate visualization of S100A9 and S100A8. (B) GSVA on granulocyte, with the visualized heatmap was reflecting changes in AD group (p.adj 0.05). Related pathways marked by red boxes mean that the oxidative phosphorylation pathway was significantly downregulated in granulocytes from the aortic dissection group compared to the treated group. (C) Bar graph showing GO enrichment analysis for the granulocyte population (p.adj 0.05). The related pathway marked by a red box means that GO enrichment analysis for the granulocyte population revealed significant enrichment of the reactive oxygen species (ROS) pathway in the dissection group. (D–M) Violin plots illustrating the differential expression of key mitochondrial genes in granulocyte population after S100A9 inhibition (p.adj 0.0001).

To validate the single-cell analysis, we measured mRNA levels of key mitochondrial genes. MT-CO1, MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND5, MT-CYB, and MT-ATP8 were significantly downregulated in human AD tissues (Fig. 6A–H). Using THP-1 cells, we further assessed the role of S100A9 in mitochondrial function. JC-1 staining showed reduced mitochondrial membrane potential, and MitoSOX staining revealed increased oxidative stress after treatment with Ang-II (Fig. 6I) and recombinant human S100A9 (rhS100A9) (Fig. 6J). These findings indicate that S100A9 deteriorate mitochondrial quality, targeting S100A9 to improve mitochondrial quality may offer a therapeutic strategy for aortic dissection.

Fig. 6.

S100A9 deteriorate mitochondrial quality. (A–H) The core mitochondrial genes mRNA expression in vascular tissues (n = 4). (I) JC-1 fluorescence images of THP-1 cells treated with Ang-II or recombinant human S100A9 (rhS100A9) for 24 hours (scale bar: 200 µm). (J) MitoSOX fluorescence images of THP-1 cells treated with Ang-II or rhS100A9 for 24 hours (scale bar: 200 µm). The data are presented as the mean $\pm$ standard error of the mean. ns, no significant difference; *p 0.05; **p 0.01; ***p 0.001.