Unraveling the interplay of ferroptosis and immune dysregulation in diabetic kidney disease: a comprehensive molecular analysis

被引:4
|
作者
Jiao, Yuanyuan [1 ,2 ]
Liu, Xinze [2 ,3 ]
Shi, Jingxuan [2 ,4 ]
An, Jiaqi [2 ,5 ]
Yu, Tianyu [2 ]
Zou, Guming [2 ]
Li, Wenge [2 ]
Zhuo, Li [2 ,6 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Fuwai Hosp, Natl Ctr Cardiovasc Dis, Dept Nephrol, Beijing 100037, Peoples R China
[2] China Japan Friendship Hosp, Dept Nephrol, Beijing 100029, Peoples R China
[3] Beijing Univ Chinese Med, China Japan Friendship Clin Med Coll, Beijing 100029, Peoples R China
[4] China Japan Friendship Hosp, Inst Clin Med Sci, Beijing 100029, Peoples R China
[5] Peking Univ, China Japan Friendship Clin Med Coll, Beijing 100191, Peoples R China
[6] China Japan Friendship Hosp, Dept Nephrol, 2 East Yinghuayuan St, Beijing 100029, Peoples R China
来源
DIABETOLOGY & METABOLIC SYNDROME | 2024年 / 16卷 / 01期
基金
中国国家自然科学基金;
关键词
Diabetic kidney disease; Ferroptosis; Artificial intelligence; Immune landscape; PROTECTS; PATHWAY;
D O I
10.1186/s13098-024-01316-w
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Diabetic kidney disease (DKD) is a primary microvascular complication of diabetes with limited therapeutic effects. Delving into the pathogenic mechanisms of DKD and identifying new therapeutic targets is crucial. Emerging studies reveal the implication of ferroptosis and immune dysregulation in the pathogenesis of DKD, however, the precise relationship between them remains not fully elucidated. Investigating their interplay is pivotal to unraveling the pathogenesis of diabetic kidney disease, offering insights crucial for targeted interventions and improved patient outcomes.Methods Integrated analysis, Consensus clustering, Machine learning including Generalized Linear Models (GLM), RandomForest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (xGB), Artificial neural network (ANN) methods of DKD glomerular mRNA sequencing were performed to screen DKD-related ferroptosis genes.CIBERSORT, ESTIMATE and ssGSEA algorithm were used to assess the infiltration of immune cells between DKD and control groups and in two distinct ferroptosis phenotypes. The ferroptosis hub genes were verified in patients with DKD and in the db/db spontaneous type 2 diabetes mouse model via immunohistochemical and Western blotting analyses in mouse podocyte MPC5 and mesangial SV40-MES-13 cells under high-glucose (HG) conditions.Results We obtained 16 differentially expressed ferroptosis related genes and patients with DKD were clustered into two subgroups by consensus clustering. Five ferroptosis genes (DUSP1,ZFP36,PDK4,CD44 and RGS4) were identified to construct a diagnostic model with a good diagnosis performance in external validation. Analysis of immune infiltration revealed immune heterogeneity between DKD patients and controls.Moreover, a notable differentiation in immune landscape, comprised of Immune cells, ESTIMATE Score, Immune Score and Stromal Score was observed between two FRG clusters. GSVA analysis indicated that autophagy, apoptosis and complement activation can participate in the regulation of ferroptosis phenotypes. Experiment results showed that ZFP36 was significantly overexpressed in both tissue and cells while CD44 was on the contrary.Meanwhile,spearman analysis showed both ZFP36 and CD44 has a strong correlation with different immune cells,especially macrophage.Conclusion The regulation of the immune landscape in DKD is significantly influenced by the focal point on ferroptosis. Newly identified ferroptosis markers, CD44 and ZFP36, are poised to play essential roles through their interactions with macrophages, adding substantial value to this regulatory landscape.
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页数:14
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