Jiao Yuanyuan, Liu Xinze, Shi Jingxuan, An Jiaqi, Yu Tianyu, Zou Guming, Li Wenge, Zhuo Li
Department of Nephrology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037, Beijing, China.
Department of Nephrology, China-Japan Friendship Hospital, 100029, Beijing, China.
Diabetol Metab Syndr. 2024 Apr 20;16(1):86. doi: 10.1186/s13098-024-01316-w.
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.
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.
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.
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.
糖尿病肾病（DKD）是糖尿病的一种主要微血管并发症，治疗效果有限。深入探究DKD的发病机制并确定新的治疗靶点至关重要。新兴研究揭示了铁死亡和免疫失调在DKD发病机制中的作用，然而，它们之间的确切关系仍未完全阐明。研究它们之间的相互作用对于揭示糖尿病肾病的发病机制至关重要，为靶向干预和改善患者预后提供关键见解。
对DKD肾小球mRNA测序进行综合分析、共识聚类、机器学习，包括广义线性模型（GLM）、随机森林（RF）、支持向量机（SVM）和极端梯度提升（xGB）、人工神经网络（ANN）方法，以筛选与DKD相关的铁死亡基因。使用CIBERSORT、ESTIMATE和ssGSEA算法评估DKD组与对照组之间以及两种不同铁死亡表型中免疫细胞的浸润情况。通过免疫组织化学和蛋白质印迹分析，在高糖（HG）条件下的小鼠足细胞MPC5和系膜细胞SV40-MES-13中，对DKD患者和db/db自发性2型糖尿病小鼠模型中的铁死亡关键基因进行验证。
我们获得了16个差异表达的铁死亡相关基因，通过共识聚类将DKD患者分为两个亚组。鉴定出五个铁死亡基因（DUSP1、ZFP36、PDK4、CD44和RGS4），构建了一个在外部验证中具有良好诊断性能的诊断模型。免疫浸润分析揭示了DKD患者与对照组之间的免疫异质性。此外，在两个铁死亡相关基因（FRG）簇之间观察到由免疫细胞、ESTIMATE评分、免疫评分和基质评分组成的免疫格局存在显著差异。基因集变异分析（GSVA）表明自噬、凋亡和补体激活可参与铁死亡表型的调节。实验结果表明，ZFP36在组织和细胞中均显著过表达，而CD44则相反。同时，Spearman分析表明ZFP36和CD44均与不同免疫细胞，尤其是巨噬细胞有很强的相关性。
DKD中免疫格局的调节受到铁死亡焦点的显著影响。新鉴定的铁死亡标志物CD44和ZFP36有望通过与巨噬细胞的相互作用发挥重要作用，为这一调节格局增添重要价值。
