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Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
Department of Obstetrics and Gynecology, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China.
Front Immunol. 2025 Aug 29;16:1627173. doi: 10.3389/fimmu.2025.1627173. eCollection 2025.
Diabetic nephropathy (DN), a major complication of type 2 diabetes mellitus (DM), is driven by complex mechanisms involving mitochondrial dysfunction, senescence, and chronic inflammation. Despite therapeutic advances, interventions specifically targeting mitochondrial dysfunction, senescence, and inflammation remain elusive.
An integrative analysis was performed on bulk RNA-seq data from DN and normal kidney samples to identify differentially expressed genes (DEGs) associated with the disease. Weighted gene co-expression network analysis (WGCNA) was utilized to reveal gene modules linked to DN, mitochondrial dysfunction, and senescence. The key genes were determined using multiple machine learning approaches, and their diagnostic value was verified using external datasets. At single-cell resolution, the cellular landscape of DN was explored and the distinct expression patterns across different cell types were explored. Key genes and markers associated with mitochondrial dysfunction and senescence were validated through single-cell RNA sequencing (scRNA-seq) data and high-glucose-induced HK-2 cell models. Finally, functional studies were conducted using Small interfering RNA (siRNA)-mediated gene knockdown to predict the biological roles of selected targets.
We identified 2,176 DEGs between DN and normal kidney tissues, among which 259 mitochondrial-related genes (MRGs) and 273 senescence-related genes (SRGs) were significantly enriched in inflammatory and metabolic pathways. WGCNA revealed DN-associated gene modules strongly linked to mitochondrial dysfunction and senescence. Through integrated machine learning, five hub genes-CLDN1, TYROBP, HDAC9, CASP3, and RCN1-were selected, with the support vector machine (SVM) model achieving high diagnostic accuracy. ScRNA-seq revealed 13 distinct kidney cell types, with proximal tubule (PT) cells emerging as key contributors to the signaling pathway associated with mitochondrial dysfunction and senescence. These transcriptomic findings were corroborated by functional assays, in which HDAC9 upregulation in high-glucose-stimulated HK-2 cells was accompanied by mitochondrial impairment and increased levels of p53, p21, p16, and senescence associated secretory phenotype (SASP) factors. Conversely, HDAC9 knockdown mitigated these effects, underscoring its pathogenic role in DN.
Mitochondrial dysfunction and senescence-associated inflammation contribute to DN progression. The five identified hub genes demonstrate strong diagnostic potential, and HDAC9 is likely to be a potential therapeutic target for reducing mitochondrial injury, senescence, and inflammation in DN.
糖尿病肾病（DN）是2型糖尿病（DM）的主要并发症，其发病机制复杂，涉及线粒体功能障碍、细胞衰老和慢性炎症。尽管治疗取得了进展，但针对线粒体功能障碍、细胞衰老和炎症的特异性干预措施仍然难以捉摸。
对糖尿病肾病和正常肾脏样本的批量RNA测序数据进行综合分析，以鉴定与该疾病相关的差异表达基因（DEG）。利用加权基因共表达网络分析（WGCNA）揭示与糖尿病肾病、线粒体功能障碍和细胞衰老相关的基因模块。使用多种机器学习方法确定关键基因，并使用外部数据集验证其诊断价值。在单细胞分辨率下，探索糖尿病肾病的细胞图谱，并探索不同细胞类型之间的独特表达模式。通过单细胞RNA测序（scRNA-seq）数据和高糖诱导的HK-2细胞模型验证与线粒体功能障碍和细胞衰老相关的关键基因和标志物。最后，使用小干扰RNA（siRNA）介导的基因敲低进行功能研究，以预测所选靶点的生物学作用。
我们在糖尿病肾病和正常肾组织之间鉴定出2176个差异表达基因，其中259个线粒体相关基因（MRG）和273个衰老相关基因（SRG）在炎症和代谢途径中显著富集。WGCNA揭示了与糖尿病肾病相关的基因模块与线粒体功能障碍和细胞衰老密切相关。通过综合机器学习，选择了五个枢纽基因——紧密连接蛋白1（CLDN1）、酪氨酸蛋白激酶结合蛋白（TYROBP）、组蛋白去乙酰化酶9（HDAC9）、半胱天冬酶3（CASP3）和 reticulocalbin 1（RCN1），支持向量机（SVM）模型具有较高的诊断准确性。scRNA-seq揭示了13种不同的肾细胞类型，近端小管（PT）细胞是与线粒体功能障碍和细胞衰老相关信号通路的关键贡献者。这些转录组学发现得到了功能试验的证实，在高糖刺激的HK-2细胞中，HDAC9上调伴随着线粒体损伤以及p53、p21、p16和衰老相关分泌表型（SASP）因子水平的增加。相反，HDAC9敲低减轻了这些影响，突出了其在糖尿病肾病中的致病作用。
线粒体功能障碍和衰老相关炎症促进糖尿病肾病进展。鉴定出的五个枢纽基因具有很强的诊断潜力，HDAC9可能是减轻糖尿病肾病中线粒体损伤、细胞衰老和炎症的潜在治疗靶点。
