Serum proteins may facilitate the identification of Kawasaki disease and promote in vitro neutrophil infiltration

Abstract

Kawasaki disease (KD) usually affects the children younger than 5 years of age and subsequently causes coronary artery lesions (CALs) without timely identification and treatment. Developing a robust and fast prediction method may facilitate the timely diagnosis of KD, significantly reducing the risk of CALs in KD patients. The levels of inflammatory serum proteins dramatically vary during the onsets of many immune diseases, including in KD. However, our understanding of their pathogenic roles in KD is behind satisfaction. The purpose of this study was to evaluate candidate diagnostic serum proteins and the potential mechanism in KD using iTRAQ gel-free proteomics. We enrolled subjects and conducted iTRAQ gel-free proteomics to globally screen serum proteins followed by specific validation with ELISA. Further in vitro leukocyte trans-endothelial model was also applied to investigate the pathogenesis roles of inflammatory serum proteins. We identified six KD protein biomarkers, including Protein S100-A8 (S100A8), Protein S100-A9 (S100A9), Protein S100-A12 (S100A12), Peroxiredoxin-2 (PRDX2), Neutrophil defensin 1 (DEFA1) and Alpha-1-acid glycoprotein 1 (ORM1). They enabled us to develop a high-performance KD prediction model with an auROC value of 0.94, facilitating the timely identification of KD. Further assays concluded that recombinant S100A12 protein treatment activated neutrophil surface adhesion molecules responsible for adhesion to endothelial cells. Therefore, S100A12 promoted both freshly clinically isolated neutrophils and neutrophil-like cells to infiltrate through the endothelial layer in vitro. Finally, the antibody against S100A12 may attenuate the infiltration promoted by S100A12. Our result demonstrated that evaluating S100A8, S100A9, S100A12, PRDX2, DEFA1 and ORM1 levels may be a good diagnostic tool of KD. Further in vitro study implied that S100A12 could be a potential therapeutic target for KD.

Figure 1

The serum protein profiles in samples. (a) We used iTRAQ gel-free proteomics to determine the relative intensities of serum proteins. Only 101 proteins with an average variation larger than 1.25-fold were presented in this heat map. (b) The FC and KD subjects were subjected to drawing blood after evident diagnosis and before treatment. Among these varied proteins, S100A8, A9 and A12 were selected for further ELISA validation owing to their inflammatory roles. DEFA1, PRDX2 and ORM1 were also included, since they were linked to the GO functions (Table 2) and their abundance tendencies were reproducible for the two pooled FC and two pooled KD serum samples. *, **, *** and **** denoted p-values < 0.05, 0.01, 0.001 and 0.0001 according to t-tests, respectively.

Figure 2

The performances of the derived KD prediction models. We used a type of machine learning algorithm, support vector machine (SVM), to derive the prediction models. The KD subjects were used to discriminate from (a) HC, (b) HC + FC and (c) FC subjects, resulting in different auROC values.

Figure 3

The variations of the six serum proteins before and after IVIG administration. Most of the KD subjects were subjected to ELISA for three times: at the acute phase before IVIG administration (pre-IVIG phase), three days after IVIG administration (post-IVIG phase) and three weeks after IVIG administration (convalescent phase). Each line presented the three ELISA values of one subject at the three phases. The sample size is 78. *** and **** denoted p-values < 0.001 and 0.0001 according to pair-wised t-tests, respectively.

Figure 4

The variations of S100A12 gene expression and the promoter methylation assay. (a) We used qPCR to examine the relative mRNA levels of S100A12 in total WBCs as suggested by a previous study. The variation tendency of the S100A12 gene in the WBCs was consistent with that of the S100A12 protein in serum. Data was presented as 2^-ΔΔCt with 18S gene as internal control. (b) Compared with empty pGL3 vector, pGL3-S100A12 demonstrated promoter activity. (c) The construct carrying the S100A12 promoter and luciferase was treated with M.SssI (totally methylated) or untreated (non-methylated), followed by transfection and luciferase assays. Data were presented as the mean ± SD. ** and *** denoted p-values < 0.01 and 0.001 according to t-tests, respectively.

Figure 5

The variations of the surface adhesion molecules anchored on the surfaces of leukocytes. We used flow cytometry to determine the intensities (geo-means) of the surface adhesion molecules of leukocytes. (a) Neutrophils were treated with S100A12 or not for 24 h and then measured with flow cytometry. (b) Neutrophils were treated with KD serum (evenly pooled serum samples) or HC serum (evenly pooled serum samples) for 24 h and then measured with flow cytometry. Data were presented as mean ± SD. *, ** and *** denoted p-values < 0.05, 0.01 and 0.001 according to t-tests, respectively.

Figure 6

The results of LTEM assays with neutrophils manipulated. (a,b) Clinically freshly isolated neutrophils were left untreated or treated with S100A12, followed by LTEM assays. Compared with the control, S100A12 treatment allowed more neutrophils to penetrate the endothelial layer (2,857 vs. 2,051). (c) By three independent assays, S100A12 treatment significantly promoted freshly isolated neutrophils (donated by a healthy male adult) to infiltrate and to migrate through the endothelial layer. (d) KD serum promoted neutrophils (cell line) infiltration by approximately 1.7-fold, which allowed us to mimic KD disease by using serum treatment (n = 3). (e) KD serum-treated neutrophils (cell line) were simultaneously treated with a control IgG, S100A12 antibody or IVIG, followed by LTEM assays (n = 3). Data were presented as the mean ± SD. *, **, *** and **** denoted p-values < 0.05, 0.01, 0.001 and 0.0001 according to t-tests, respectively.