Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, Sichuan, P.R. China.
Chengdu Shuangliu Hospital of Traditional Chinese Medicine, Chengdu, 610200, Sichuan, P.R. China.
Metab Brain Dis. 2024 Oct;39(7):1469-1480. doi: 10.1007/s11011-024-01396-7. Epub 2024 Aug 13.
Dysfunctional lactate metabolism in the brain has been implicated in neuroinflammation, Aβ deposition, and cell disturbance, all of which play a significant role in the pathogenesis of Alzheimer's disease (AD). In this study, we aimed to investigate the lactate metabolism-related genes (LMRGs) in AD via an integrated bulk RNA and single-nuclei RNA sequencing (snRNA-seq) analysis, with a specific focus on microglia. We obtained 26 HC and 24 AD snRNA-seq samples originated from human prefrontal cortex in Gene Expression Omnibus (GEO) database and collected 873 LMRGs from three databases, namely MSigDB, The Human Protein Atlas and GeneCards. Bulk RNA was analyzed with LMRG characteristics in AD by using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the protein-protein interaction (PPI), CytoHubba-MCC, Support Vector Machine (SVM) algorithms analyses. Then we conducted the Receiver Operating Characteristic (ROC) curve, correlation, and connection network analyses for biomarkers. Their differential expression validation was performed using AlzData database. The single-nuclei RNA analysis of microglia was applied to identify hub genes and pathways using cell-cell communication analysis and high dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Support Vector Machine (SVM) algorithm showed an AUC of 0.967, a sensitivity of 93.30% and a specificity of 100.00%. Our analysis identified biomarkers with LMRG characteristics, namely INSR, CDKL1, and PNISR. ROC analysis revealed that each of these biomarkers exhibited excellent diagnostic potential, as evidenced by their respective area under the curve (AUC) values: INSR (AUC: 0.679), CDKL1 (AUC: 0.788), and PNISR (AUC: 0.724). Correlation analysis showed that biomarkers exhibited a positive correlation with each other. Connection network illustrated their shared biological processes: aging, phosphorylation, metabolic process, and apoptosis. Cell-cell communication analysis revealed that GALECTIN signaling pathway was exclusively expressed in AD microglia, and only LGALS9 exhibited significant overexpression. HdWGCNA identified FTH1 as a hub gene enriched in ferroptosis and mineral absorption pathways within microglia. The roles of INSR, CDKL1, PNISR, LGALS9, and FTH1 should be taken into account to enhance our understanding of lactate metabolism in the context of AD.
脑内乳酸代谢功能障碍与神经炎症、Aβ 沉积和细胞紊乱有关，所有这些都在阿尔茨海默病（AD）的发病机制中起重要作用。在这项研究中，我们通过整合批量 RNA 和单细胞 RNA 测序（snRNA-seq）分析，旨在研究 AD 中的乳酸代谢相关基因（LMRGs），特别关注小胶质细胞。我们从基因表达综合数据库（GEO）中获得了 26 个健康对照（HC）和 24 个 AD 的 snRNA-seq 样本，并从三个数据库（MSigDB、人类蛋白质图谱和 GeneCards）中收集了 873 个 LMRGs。通过使用基因本体论（GO）、京都基因与基因组百科全书（KEGG）、蛋白质-蛋白质相互作用（PPI）、CytoHubba-MCC、支持向量机（SVM）算法分析，对 AD 中的 LMRG 特征进行了批量 RNA 分析。然后，我们对生物标志物进行了 ROC 曲线、相关性和连接网络分析。使用 AlzData 数据库对其差异表达进行了验证。通过细胞间通讯分析和高维加权基因共表达网络分析（hdWGCNA），对小胶质细胞的单细胞 RNA 分析进行了枢纽基因和途径的识别。SVM 算法的 AUC 为 0.967，灵敏度为 93.30%，特异性为 100.00%。我们的分析确定了具有 LMRG 特征的生物标志物，即 INSR、CDKL1 和 PNISR。ROC 分析表明，这些生物标志物各自的 AUC 值均显示出良好的诊断潜力：INSR（AUC：0.679）、CDKL1（AUC：0.788）和 PNISR（AUC：0.724）。相关性分析表明，生物标志物之间呈正相关。连接网络说明了它们的共同生物过程：衰老、磷酸化、代谢过程和细胞凋亡。细胞间通讯分析表明，GALECTIN 信号通路仅在 AD 小胶质细胞中表达，只有 LGALS9 表现出显著的过表达。hdWGCNA 确定 FTH1 是富含小胶质细胞中铁死亡和矿物质吸收途径的枢纽基因。应该考虑 INSR、CDKL1、PNISR、LGALS9 和 FTH1 的作用，以增强我们对 AD 中乳酸代谢的理解。
