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引用次数: 0
摘要
低密度脂蛋白受体(LDLR)是一种调节胆固醇平衡的主要脂蛋白受体。LDLR 缺乏会诱发海马突触病,从而导致认知障碍。尽管 LDLR 与神经退行性疾病关系密切,但针对 LDLR 基因敲除(KO)模型的蛋白质组学研究仍然不足。因此,了解 LDLR KO 介导的海马内差异蛋白表达对于阐明 LDLR 在神经退行性疾病中的作用至关重要。在这项研究中,我们首次使用基于串联质量标签(TMT)的质谱分析方法对 LDLR KO 小鼠的海马组织进行了蛋白质组分析。LDLR 缺乏会导致与海马内多种分子的转运、激酶活性和催化剂相关的蛋白质发生变化。此外,研究还发现,主要突触通路中成分的表达发生了显著变化。此外,这些突触效应还通过一种基于数据独立采集(DIA)的蛋白质组学方法得到了验证。我们的数据将成为进一步研究发现 LDLR 在神经退行性疾病中分子功能的宝贵资源。
In-Depth Proteome Profiling of the Hippocampus of LDLR Knockout Mice Reveals Alternation in Synaptic Signaling Pathway.
The low-density lipoprotein receptor (LDLR) is a major apolipoprotein receptor that regulates cholesterol homeostasis. LDLR deficiency is associated with cognitive impairment by the induction of synaptopathy in the hippocampus. Despite the close relationship between LDLR and neurodegenerative disorders, proteomics research for protein profiling in the LDLR knockout (KO) model remains insufficient. Therefore, understanding LDLR KO-mediated differential protein expression within the hippocampus is crucial for elucidating a role of LDLR in neurodegenerative disorders. In this study, we conducted first-time proteomic profiling of hippocampus tissue from LDLR KO mice using tandem mass tag (TMT)-based MS analysis. LDLR deficiency induces changes in proteins associated with the transport of diverse molecules, and activity of kinase and catalyst within the hippocampus. Additionally, significant alterations in the expression of components in the major synaptic pathways were found. Furthermore, these synaptic effects were verified using a data-independent acquisition (DIA)-based proteomic method. Our data will serve as a valuable resource for further studies to discover the molecular function of LDLR in neurodegenerative disorders.
期刊介绍:
PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.