Mycoplasma pneumoniae-induced Kawasaki disease via PINK1/Parkin-mediated mitophagy.

Kawasaki disease (KD) is a systemic vasculitis with an unknown cause that primarily affects children. The objective of this study was to explore the function and underlying mechanism of mitophagy in Mycoplasma pneumoniae (MP)-induced KD. To create MP-induced KD models, Human coronary endothelial cells (HCAECs) and DBA/2 mice were employed and treated with Mp-Lipid-associated membrane proteins （LAMPs）. Lactate dehydrogenase (LDH) levels were tested to determine cellular damage or death. The inflammatory cytokines tumor necrosis factor (TNF)--α and interleukin (IL)-6 were measured using the Enzyme-Linked Immunosorbent Assay (ELISA) method. RT-qPCR and Western blotting were used to determine the expression of Intercellular Adhesion Molecule(ICAM)-1, vascular cell adhesion molecule (VCAM)-1, inducible nitric oxide synthase(iNOS), LC3, p62, PINK1(a mitochondrial serine/threonine-protein kinase), and PARKIN(a cytosolic E3-ubiquitin ligase). The adenosine triphosphate （ATP）, reactive oxygen species （ROS）, and mitochondrial membrane potential（MMP） levels were measured to determine mitochondrial function. Mitophagy was investigated using immunofluorescence and a mitophagy detection test. Autophagosome and mitochondrial morphology were examined using transmission electron microscopy. To identify inflammatory cell infiltration, hematoxylin and eosin staining was utilized. Mp-LAMPs increased the levels of TNF-α, IL-6, ICAM-1, VCAM-1, and iNOS in an HCAEC cell model, along with LDH release. After Mp-LAMPs exposure, there was a rise in LC3 and a reduction in p62. Meanwhile, the expression of PINK1 and Parkin was increased. Cyclosporin A dramatically increased ATP synthesis and MMP in HCAEC cells treated with Mp-LAMPs, while suppressing ROS generation, demonstrating excessive mitophagy-related mitochondrial dysfunction. Additionally, neither body weight nor artery tissue were affected due to PINK1 and Parkin suppression Cyclosporin A in Mp-LAMPs-treated mice. These findings indicated that PINK1/Parkin-mediated mitophagy inhibition may be a therapeutic target for MP-induced KD.

Single-Cell Analysis Reveals the Role of the Neuropeptide Receptor FPR2 in Monocytes in Kawasaki Disease: A Bioinformatic Study.

Exploring the role of neuropeptides in the communication between monocyte subtypes facilitates an investigation of the pathogenesis of Kawasaki disease (KD). We investigated the patterns of interaction between neuropeptide-associated ligands and receptors in monocyte subpopulations in KD patients. Single-cell analysis was employed for the identification of cell subpopulations in KD patients, and monocytes were classified into 3 subpopulations: classical monocytes (CMs), intermediate monocytes (IMs), and nonclassical monocytes (NCMs). Cell-cell communication and differential analyses were used to identify ligand-receptor interactions in monocytes. Five neuropeptide-related genes (SORL1, TNF, SORT1, FPR2, and ANXA1) were involved in cell-cell interactions, wherein FPR2, a neuropeptide receptor, was significantly highly expressed in KD. Weighted gene coexpression network analysis revealed a significant correlation between the yellow module and FPR2 (p < 0.001, CC = 0.43). Using the genes in the yellow module, we constructed a PPI network to assess the possible functions of the FPR2-associated gene network. Gene set enrichment analysis showed that increased FPR2 levels may be involved in immune system regulation. FPR2 in CMs mediates the control of inflammation in KD. The findings of this study may provide a novel target for the clinical treatment of KD.

Correction: A machine learning approach to predict intravenous immunoglobulin resistance in Kawasaki disease patients: A study based on a Southeast China population.

[This corrects the article DOI: 10.1371/journal.pone.0237321.].

A machine learning approach to predict intravenous immunoglobulin resistance in Kawasaki disease patients: A study based on a Southeast China population.

Kawasaki disease is the leading cause of pediatric acquired heart disease. Coronary artery abnormalities are the main complication of Kawasaki disease. Kawasaki disease patients with intravenous immunoglobulin resistance are at a greater risk of developing coronary artery abnormalities. Several scoring models have been established to predict resistance to intravenous immunoglobulin, but clinicians usually do not apply those models in patients because of their poor performance. To find a better model, we retrospectively collected data including 753 observations and 82 variables. A total of 644 observations were included in the analysis, and 124 of the patients observed were intravenous immunoglobulin resistant (19.25%). We considered 7 different linear and nonlinear machine learning algorithms, including logistic regression (L1 and L1 regularized), decision tree, random forest, AdaBoost, gradient boosting machine (GBM), and lightGBM, to predict the class of intravenous immunoglobulin resistance (binary classification). Data from patients who were discharged before Sep 2018 were included in the training set (n = 497), while all the data collected after 9/1/2018 were included in the test set (n = 147). We used the area under the ROC curve, accuracy, sensitivity, and specificity to evaluate the performances of each model. The gradient GBM had the best performance (area under the ROC curve 0.7423, accuracy 0.8844, sensitivity 0.3043, specificity 0.9919). Additionally, the feature importance was evaluated with SHapley Additive exPlanation (SHAP) values, and the clinical utility was assessed with decision curve analysis. We also compared our model with the Kobayashi score, Egami score, Formosa score and Kawamura score. Our machine learning model outperformed all of the aforementioned four scoring models. Our study demonstrates a novel and robust machine learning method to predict intravenous immunoglobulin resistance in Kawasaki disease patients. We believe this approach could be implemented in an electronic health record system as a form of clinical decision support in the near future.