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In Vitro and In Vivo Detection of Mitophagy in Human Cells, C. Elegans, and Mice
Published on: November 22, 2017
Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and
Yan Wang1,2, Ying Liu1, Nana Wang3
1Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, China.
Insights
This study identified CKAP4 and SRPK1 as key mitophagy-related genes in Kawasaki disease (KD). These biomarkers show high diagnostic potential and are linked to immune cell activity in KD patients.
Area of Science:
- Biomarker discovery
- Molecular mechanisms of disease
- Immunology
Background:
- Kawasaki disease (KD) is a pediatric vasculitis with unclear pathogenesis.
- Mitophagy's role in inflammation is established, but its significance in KD remains under-explored.
Purpose of the Study:
- To identify mitophagy-related genes (MRGs) as potential biomarkers for KD.
- To investigate the association of these biomarkers with immune cell infiltration in KD patients.
Main Methods:
- Analysis of four Gene Expression Omnibus (GEO) datasets.
- Identification of differentially expressed MRGs (DE-MRGs).
- Machine learning algorithms (RF-RFE, SVM-RFE) and WGCNA for hub gene identification.
- Immune cell infiltration analysis using CIBERSORT.
- Validation via quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).
Main Results:
- 306 DE-MRGs identified, linked to autophagy, mitochondrial function, and inflammation.
- CKAP4 and SRPK1 identified as critical hub genes with high diagnostic accuracy (AUC > 0.93).
- CKAP4 and SRPK1 expression correlated with specific immune cells, including CD4 memory T cells.
Conclusions:
- CKAP4 and SRPK1 are promising diagnostic biomarkers for Kawasaki disease.
- These genes may influence KD progression via mitophagy regulation.
Background:
Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood.
Methods:
This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).
Results:
Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls.
Conclusions:
CKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.
