Li Huahua, Zou Lingling, Long Zhaowei, Zhan Junkun
Department of Geriatric, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
Department of Geriatric, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Front Immunol. 2025 Jan 14;15:1537909. doi: 10.3389/fimmu.2024.1537909. eCollection 2024.
Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving systemic inflammation in T2DM; however, the underlying single-cell mechanisms remain inadequately defined.
Single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from 37 patients with T2DM and 11 healthy controls (HC) was conducted. Immune cell types were identified through clustering analysis, followed by differential expression and pathway analysis. Metabolic heterogeneity within T cell subpopulations was evaluated using Gene Set Variation Analysis (GSVA). Machine learning models were constructed to classify T2DM subtypes based on metabolic signatures, and T-cell-monocyte interactions were explored to assess immune crosstalk. Transcription factor (TF) activity was analyzed, and drug enrichment analysis was performed to identify potential therapeutic targets.
In patients with T2DM, a marked increase in monocytes and a decrease in CD4+ T cells were observed, indicating immune dysregulation. Significant metabolic diversity within T cell subpopulations led to the classification of patients with T2DM into three distinct subtypes (A-C), with HC grouped as D. Enhanced intercellular communication, particularly through the MHC-I pathway, was evident in T2DM subtypes. Machine learning models effectively classified T2DM subtypes based on metabolic signatures, achieving an AUC > 0.84. Analysis of TF activity identified pivotal regulators, including NF-kB, STAT3, and FOXO1, associated with immune and metabolic disturbances in T2DM. Drug enrichment analysis highlighted potential therapeutic agents targeting these TFs and related pathways, including Suloctidil, Chlorpropamide, and other compounds modulating inflammatory and metabolic pathways.
This study underscores significant immunometabolic dysfunction in T2DM, characterized by alterations in immune cell composition, metabolic pathways, and intercellular communication. The identification of critical TFs and the development of drug enrichment profiles highlight the potential for personalized therapeutic strategies, emphasizing the need for integrated immunological and metabolic approaches in T2DM management.
2型糖尿病(T2DM)是一项重大的全球健康挑战,其特征为慢性高血糖、胰岛素抵抗和免疫系统功能障碍。包括T细胞和单核细胞在内的免疫细胞在T2DM系统性炎症的发生中起关键作用;然而,潜在的单细胞机制仍未得到充分阐明。
对37例T2DM患者和11例健康对照(HC)的外周血单个核细胞(PBMC)进行单细胞RNA测序。通过聚类分析鉴定免疫细胞类型,随后进行差异表达和通路分析。使用基因集变异分析(GSVA)评估T细胞亚群内的代谢异质性。构建机器学习模型以基于代谢特征对T2DM亚型进行分类,并探索T细胞-单核细胞相互作用以评估免疫串扰。分析转录因子(TF)活性,并进行药物富集分析以确定潜在的治疗靶点。
在T2DM患者中,观察到单核细胞显著增加,CD4+T细胞减少,表明免疫失调。T细胞亚群内显著的代谢多样性导致T2DM患者被分为三种不同亚型(A-C),HC归为D型。在T2DM亚型中,细胞间通讯增强,尤其是通过MHC-I途径。机器学习模型基于代谢特征有效地对T2DM亚型进行分类,曲线下面积(AUC)>0.84。TF活性分析确定了关键调节因子,包括NF-kB、STAT3和FOXO1,它们与T2DM中的免疫和代谢紊乱相关。药物富集分析突出了靶向这些TF和相关通路(包括舒洛地尔、氯磺丙脲和其他调节炎症和代谢通路的化合物)的潜在治疗药物。
本研究强调了T2DM中显著的免疫代谢功能障碍,其特征为免疫细胞组成、代谢途径和细胞间通讯的改变。关键TF的鉴定和药物富集图谱的开发突出了个性化治疗策略的潜力,强调了在T2DM管理中采用综合免疫和代谢方法的必要性。