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用于肌萎缩侧索硬化症和额颞叶痴呆生物标志物及药物靶点发现的蛋白质组学方法

Proteomics Approaches for Biomarker and Drug Target Discovery in ALS and FTD.

作者信息

Hedl Thomas J, San Gil Rebecca, Cheng Flora, Rayner Stephanie L, Davidson Jennilee M, De Luca Alana, Villalva Maria D, Ecroyd Heath, Walker Adam K, Lee Albert

机构信息

Neurodegeneration Pathobiology Laboratory, Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia.

Centre for Motor Neuron Disease Research, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, North Ryde, NSW, Australia.

出版信息

Front Neurosci. 2019 Jun 11;13:548. doi: 10.3389/fnins.2019.00548. eCollection 2019.

Abstract

Neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are increasing in prevalence but lack targeted therapeutics. Although the pathological mechanisms behind these diseases remain unclear, both ALS and FTD are characterized pathologically by aberrant protein aggregation and inclusion formation within neurons, which correlates with neurodegeneration. Notably, aggregation of several key proteins, including TAR DNA binding protein of 43 kDa (TDP-43), superoxide dismutase 1 (SOD1), and tau, have been implicated in these diseases. Proteomics methods are being increasingly applied to better understand disease-related mechanisms and to identify biomarkers of disease, using model systems as well as human samples. Proteomics-based approaches offer unbiased, high-throughput, and quantitative results with numerous applications for investigating proteins of interest. Here, we review recent advances in the understanding of ALS and FTD pathophysiology obtained using proteomics approaches, and we assess technical and experimental limitations. We compare findings from various mass spectrometry (MS) approaches including quantitative proteomics methods such as stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tagging (TMT) to approaches such as label-free quantitation (LFQ) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) in studies of ALS and FTD. Similarly, we describe disease-related protein-protein interaction (PPI) studies using approaches including immunoprecipitation mass spectrometry (IP-MS) and proximity-dependent biotin identification (BioID) and discuss future application of new techniques including proximity-dependent ascorbic acid peroxidase labeling (APEX), and biotinylation by antibody recognition (BAR). Furthermore, we explore the use of MS to detect post-translational modifications (PTMs), such as ubiquitination and phosphorylation, of disease-relevant proteins in ALS and FTD. We also discuss upstream technologies that enable enrichment of proteins of interest, highlighting the contributions of new techniques to isolate disease-relevant protein inclusions including flow cytometric analysis of inclusions and trafficking (FloIT). These recently developed approaches, as well as related advances yet to be applied to studies of these neurodegenerative diseases, offer numerous opportunities for discovery of potential therapeutic targets and biomarkers for ALS and FTD.

摘要

诸如肌萎缩侧索硬化症(ALS)和额颞叶痴呆(FTD)等神经退行性疾病的患病率正在上升,但缺乏针对性的治疗方法。尽管这些疾病背后的病理机制尚不清楚,但ALS和FTD在病理上均以神经元内异常蛋白质聚集和包涵体形成为特征,这与神经退行性变相关。值得注意的是,包括43 kDa的TAR DNA结合蛋白(TDP-43)、超氧化物歧化酶1(SOD1)和tau在内的几种关键蛋白质的聚集与这些疾病有关。蛋白质组学方法正越来越多地应用于利用模型系统以及人类样本更好地理解疾病相关机制并识别疾病生物标志物。基于蛋白质组学的方法提供无偏见、高通量和定量的结果,在研究感兴趣的蛋白质方面有众多应用。在这里,我们综述了使用蛋白质组学方法在理解ALS和FTD病理生理学方面的最新进展,并评估技术和实验局限性。我们比较了各种质谱(MS)方法的研究结果,包括定量蛋白质组学方法,如细胞培养中氨基酸稳定同位素标记(SILAC)和串联质量标签(TMT),与无标记定量(LFQ)和所有理论碎片离子质谱的顺序窗口采集(SWATH-MS)等方法在ALS和FTD研究中的应用。同样,我们描述了使用包括免疫沉淀质谱(IP-MS)和邻近依赖性生物素识别(BioID)在内的方法进行的疾病相关蛋白质-蛋白质相互作用(PPI)研究,并讨论了包括邻近依赖性抗坏血酸过氧化物酶标记(APEX)和抗体识别生物素化(BAR)在内的新技术的未来应用。此外,我们探索了使用MS检测ALS和FTD中与疾病相关蛋白质的翻译后修饰(PTM),如泛素化和磷酸化。我们还讨论了能够富集感兴趣蛋白质的上游技术,强调了新技术在分离与疾病相关的蛋白质包涵体方面的贡献,包括包涵体和运输的流式细胞术分析(FloIT)。这些最近开发的方法,以及尚未应用于这些神经退行性疾病研究的相关进展,为发现ALS和FTD的潜在治疗靶点和生物标志物提供了众多机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2d9/6579929/eb24c933a9a9/fnins-13-00548-g001.jpg

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