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整合蛋白质组学-代谢组学策略研究血管性抑郁小鼠模型的病理机制

Integrative Proteomics-Metabolomics Strategy for Pathological Mechanism of Vascular Depression Mouse Model.

机构信息

School of Pharmacy, Second Military Medical University , Shanghai 200433, China.

Department of Pharmacy, Eastern Hepatobiliary Surgery Hospital , Shanghai 200433, China.

出版信息

J Proteome Res. 2018 Jan 5;17(1):656-669. doi: 10.1021/acs.jproteome.7b00724. Epub 2017 Dec 13.

Abstract

Vascular depression (VD), a subtype of depression, is caused by vascular diseases or cerebrovascular risk factors. Recently, the proportion of VD patients has increased significantly, which severely affects their quality of life. However, the current pathogenesis of VD has not yet been fully understood, and the basic research is not adequate. In this study, on the basis of the combination of LC-MS-based proteomics and metabolomics, we aimed to establish a protein metabolism regulatory network in a murine VD model to elucidate a more comprehensive impact of VD on organisms. We detected 44 metabolites and 304 proteins with different levels in the hippocampus samples from VD mice using a combination of metabolomic and proteomics analyses with an isobaric tags for relative and absolute quantification (iTRAQ) method. We constructed a protein-to-metabolic regulatory network by correlating and integrating the differential metabolites and proteins using ingenuity pathway analysis. Then we quantitatively validated the levels of the bimolecules shown in the bioinformatics analysis using LC-MS/MS and Western blotting. Validation results suggested changes in the regulation of neuroplasticity, transport of neurotransmitters, neuronal cell proliferation and apoptosis, and disorders of amino acids, lipids and energy metabolism. These proteins and metabolites involved in these dis-regulated pathways will provide a more targeted and credible direction to study the mechanism of VD. Therefore, this paper presents an approach and strategy that was applied in integrative proteomics and metabolomics for research and screening potential targets and biomarkers of VD, which could be more precise and credible in a field lacking adequate basic research.

摘要

血管性抑郁症(VD)是一种抑郁症亚型,由血管疾病或脑血管危险因素引起。最近,VD 患者的比例显著增加,严重影响了他们的生活质量。然而,VD 的发病机制尚未完全阐明,基础研究也不充分。在这项研究中,我们基于 LC-MS 蛋白质组学和代谢组学的结合,旨在建立一个在 VD 小鼠模型中的蛋白质代谢调控网络,以阐明 VD 对机体更全面的影响。我们使用代谢组学和蛋白质组学分析与同位标记相对和绝对定量(iTRAQ)方法相结合,检测到 VD 小鼠海马样本中 44 种代谢物和 304 种不同水平的蛋白质。我们通过关联和整合差异代谢物和蛋白质,使用 ingenuity pathway analysis 构建了蛋白质与代谢物的调控网络。然后,我们使用 LC-MS/MS 和 Western blotting 对生物信息学分析中显示的双分子的水平进行了定量验证。验证结果表明,神经可塑性、神经递质转运、神经元细胞增殖和凋亡以及氨基酸、脂质和能量代谢紊乱的调节发生了变化。这些参与失调途径的蛋白质和代谢物将为研究 VD 的机制提供一个更有针对性和更可信的方向。因此,本文提出了一种应用于整合蛋白质组学和代谢组学的方法和策略,用于研究和筛选 VD 的潜在靶点和生物标志物,这在基础研究不足的领域可能更加精确和可信。

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