Cai Lijun, Liu Yin, Tang Shuang, Deng Song, Zhang Li, Liao Xin, Zhang Bei, Han Bing, Xie Rujia
Department of Rehabilitation, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People's Republic of China.
Department of Pathophysiology, College of Basic Medical Sciences, Guizhou Medical University, Guiyang, Guizhou Province, China.
FASEB J. 2025 Aug 15;39(15):e70779. doi: 10.1096/fj.202500823R.
Parkinson's disease (PD) is a complex neurodegenerative disorder with a growing body of evidence suggesting the involvement of immune responses. To better understand the interplay between lactylation-related genes and immune reactions in PD, we conducted an integrated bioinformatics analysis. Utilizing publicly available PD gene expression datasets, we performed a detailed analysis employing Single-sample Gene Set Enrichment Analysis and Weighted Gene Co-expression Network Analysis. Diagnostic models were constructed using Support Vector Machine (SVM) and LASSO+SVM to evaluate the performance of four candidate genes (PAK6, LMO3, SPTBN2, FA2H). We also investigated the correlations between these genes and immune cells to elucidate their roles in the immune microenvironment. Animal models and immunohistochemistry were used to validate the findings. Our analysis revealed that differentially expressed genes (DEGs) were primarily enriched in pathways associated with neurological diseases, such as Alzheimer's disease and Huntington's disease. Among the four candidate genes, PAK6 exhibited the best predictive performance. Significant correlations were found between these genes and "resting memory CD4 T cells," highlighting their potential involvement in the immune microenvironment. This study provides new insights into the roles of lactylation-related genes, specifically those involved in the biochemical process of lactylation, particularly PAK6, in the context of immune responses in PD. While our pathway enrichment analysis highlights commonalities with other neurodegenerative diseases, our focus on lactylation-related genes offers novel perspectives on how these genes might influence immune regulation in PD. The findings suggest potential therapeutic targets and open avenues for future research into the mechanisms underlying PD and its immune interactions.
帕金森病(PD)是一种复杂的神经退行性疾病,越来越多的证据表明免疫反应参与其中。为了更好地理解与乳酰化相关基因和PD免疫反应之间的相互作用,我们进行了综合生物信息学分析。利用公开可用的PD基因表达数据集,我们采用单样本基因集富集分析和加权基因共表达网络分析进行了详细分析。使用支持向量机(SVM)和LASSO+SVM构建诊断模型,以评估四个候选基因(PAK6、LMO3、SPTBN2、FA2H)的性能。我们还研究了这些基因与免疫细胞之间的相关性,以阐明它们在免疫微环境中的作用。使用动物模型和免疫组织化学来验证研究结果。我们的分析表明,差异表达基因(DEG)主要富集在与神经疾病相关的通路中,如阿尔茨海默病和亨廷顿病。在这四个候选基因中,PAK6表现出最佳的预测性能。发现这些基因与“静息记忆CD4 T细胞”之间存在显著相关性,突出了它们在免疫微环境中的潜在参与。这项研究为与乳酰化相关基因的作用提供了新的见解,特别是那些参与乳酰化生化过程的基因,尤其是PAK6,在PD免疫反应的背景下。虽然我们的通路富集分析突出了与其他神经退行性疾病的共性,但我们对与乳酰化相关基因的关注为这些基因如何影响PD免疫调节提供了新的视角。研究结果表明了潜在的治疗靶点,并为未来研究PD及其免疫相互作用的潜在机制开辟了途径。