蛋白质聚集对酵母细胞氧化应激的影响评价
Özet
蛋白质聚集诱发细胞氧化应激。本协议描述了一种通过流式细胞仪监测 amyloidogenic 蛋白的细胞内状态和与之相关的氧化应激的方法。该方法用于研究淀粉样β肽的可溶性和聚集易变性的行为。
Abstract
蛋白质错误折叠和聚集到淀粉样蛋白构象已经与一些神经退行性疾病的发病和进展有关。然而, 对于不溶性蛋白聚合体在体内如何发挥其毒性作用, 仍有很少的信息。简单的原核和真核模型有机体, 如细菌和酵母, 已大大有助于我们目前对细胞内淀粉样形成的机制, 聚集繁殖和毒性的理解。在本议定书中, 酵母的使用被描述为一个模型来解剖蛋白质团聚体的形成和它们对细胞氧化应激的影响之间的关系。该方法将 amyloidogenic 蛋白的胞内可溶性/聚集态检测与流式细胞术 (FC) 表达的细胞氧化损伤的量化相结合。这种方法简单、快速、定量。本研究通过将一大组淀粉样β肽变异体所引起的细胞氧化应激与它们各自的内在聚集倾向进行关联来说明该技术。
Introduction
Proteostasis 是细胞适应和衰老过程的基本决定因素。在细胞中, 蛋白质的稳态是由复杂的蛋白质质量控制网络维持的, 目的是通过伴侣和/或他们的靶向蛋白质错误折叠, 以确保正确的复性蛋白构象1 ,2,3,4,5。大量的研究为人类疾病的发生和发展与 proteostasis 的失败之间的联系提供了支持, 导致蛋白质错误折叠和聚集。例如, 蛋白质矿床的存在被认为是许多神经退行性疾病的病理特征, 如阿尔茨海默氏症、帕金森氏病、亨廷顿疾病6、7、8、prionogenic 疾病, 非退行性 amyloidoses9。有人建议, 早期寡聚和 protofibrillar 聚集反应是细胞毒性的主要诱导, 与其他蛋白质在拥挤的细胞环境中建立异常的相互作用10。此外, 蛋白质包裹体 (PI) 可以在细胞间传播, 传播其毒性效应11,12。因此, 可以说, PI 的形成实际上可能构成一种解毒机制, 它将危险的聚集物种的存在限制在细胞中的特定位置, 在那里它们可以被加工或积累而不会产生重大的副作用。13,14。
标准的体外生化方法为不同物种的聚集反应及其性质15,16提供了重要的见解。然而, 这些化验所使用的条件明显不同于细胞内发生的情况, 因此, 质疑它们的生理相关性。由于细胞通路的显著保护, 如蛋白质质量控制, 自噬, 或调节细胞氧化还原状态17,18之间的真核生物19,20,21 ,22,23, 萌芽酵母酿酒酵母 (酵母) 已成为一个特权简单的细胞模型研究蛋白质聚集的分子决定因素及其相关的细胞毒性影响在生物相关的环境24,25,26。
蛋白质聚集倾向是在主序列中固有编码的特征。因此, 可以根据对多肽27中聚集促进区域的效力的鉴定和评价, 预测淀粉样结构的形成。然而, 尽管生物信息学算法成功地预测蛋白质序列的体外聚集特性, 但它们仍远未预测这些倾向如何转化为体内的细胞毒性影响。以系统的方式解决给定蛋白质的聚合状态与其相关的细胞损伤之间的联系的研究可能有助于规避这一计算限制。这个连接在本研究中被处理, 利用一大组变异的淀粉样β肽 Aβ42不同只在单一的残余, 但显示连续的范围的聚集倾向在体内28。特别是, 本文介绍了一种基于 FC 的方法, 用于识别酵母细胞中聚集易感蛋白引起的氧化损伤的构象物种。该方法提供了许多优点, 如简单性、高通量能力和精确的定量测量。这种方法使人们有可能确认 PI 对氧化应激起着保护作用。
Protocol
1.酵母培养和蛋白质表达 注: Aβ变种表现出不同的相对聚集倾向, 由于突变在一个单一的残留在位置 19 (Phe19) 的 Aβ42肽 (图 1A)。这些肽变体被标记为绿色荧光蛋白 (GFP), 它充当聚合报告员 (图 1)29。 将 20 Aβ42-GFP 变种的质粒编码转化成酵母细胞, 具有 BY4741 的父母背景 (他的 3Δ1列伊2Δ0满…
Representative Results
该协议描述如何使用 Aβ42肽的20变种的集合, 其中 Phe19 已经变异为所有自然 proteinogenic 氨基酸28。这些蛋白质的理论聚集倾向可以用两种不同的生物信息学算法 (AGGRESCAN 和探戈31,32) 来分析。在这两种情况下, 这种分析呈现出聚合倾向的渐进级化, 作为一般规则, 将最高值归结为疏水性残留物和最低的电荷和极性 (<…
Discussion
广泛的疾病与错误折叠蛋白质的积累联系在一起6,7,8,33的细胞沉积。已作出许多努力, 以解开分子机制, 触发这些疾病的开始使用计算方法, 不考虑蛋白质浓度, 或在体外的方法, 其中蛋白质浓度在反应过程中保持恒定。然而, 在细胞内, 蛋白质在拥挤和非均匀的环境中不断地合成和降解。这解释了…
Acknowledgements
Materials
| Yeast cells BY4741 | ATCC | 201388 | Genotype: MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 |
| pESC(-Ura) plasmid | Agilent Genomics | 217454 | Yeast expression plasmid with a Gal promotor. Selectable marker URA3 |
| Yeast Synthetic Drop-out Medium Supplements | Sigma | Y1501 | Powder |
| Yeast Nitrogen Base Without Amino Acids | Sigma | Y0626 | Powder |
| Raffinose | Sigma | R7630 | Powder |
| Glucose | Sigma | G7021 | Powder |
| Galactose | Sigma | G0750 | Powder |
| Phosphate Buffered Saline (PBS) | Fisher Scientific | BP3991 | Solution 10X |
| CellROX Deep Red Reagent | Life Technologies | C10422 | Free radical cell-permeant fluorescent sensor, non-fluorescent while in a reduced state, and exhibits bright fluorescence upon oxidation by reactive oxygen species (ROS), with absorption/emission maxima at 644/665 nm. |
| Y-PER protein extraction reagent | Thermo Scientific | 78990 | Liquid cell lysis buffer |
| Acrylamide/Bis-acrylamide | Sigma | A6050 | Solution |
| Bradford dye reagent | Bio-Rad | 5000205 | Dye reagent for one-step determination of protein concentration |
| β-amyloid antibody 6E10 | BioLegend | 803001 | Mouse IgG1. The epitope lies within amino acids 3-8 of beta amyloid (EFRHDS). |
| Goat anti-mouse IgG-HRP conjugate | Bio-Rad | 1721011 | |
| Membrane Immobilon-P, PVDF | Millipore | IPVH00010 | |
| Luminata forte | Merk | WBLUF0100 | Premixed, ready to use chemiluminescent HRP detection reagent |
| Phenylmethanesulfonyl fluoride solution (PMSF) | Sigma | 93482 | Protease inhibitor. Dissolved at 0.1 M in ethanol |
| FACSCanto flow cytometer | BD Biosciences | 657338 | Equipped with a 488 nm blue laser for the detection of GFP, and 635 nm red laser / 530/30 nm BP filter and 660/20 BP filter |
| Mini Trans-Blot Electrophoresis Transfer cell | Bio-Rad | 1703930 | Protein transference system |
| Mini-PROTEAN Tetra Handcast Systems | Bio-Rad | 1658000FC | Electrophoresis system |
Referanslar
- Frydman, J. Folding of newly translated proteins in vivo: the role of molecular chaperones. Annual Review of Biochemistry. 70, 603-647 (2001).
- Hartl, F. U., Bracher, A., Hayer-Hartl, M. Molecular chaperones in protein folding and proteostasis. Nature. 475, 324-332 (2011).
- Wong, E., et al. Molecular determinants of selective clearance of protein inclusions by autophagy. Nature Communications. 3, (2012).
- Winkler, J., Tyedmers, J., Bukau, B., Mogk, A. Chaperone networks in protein disaggregation and prion propagation. Journal of Structural Biology. 179, 152-160 (2012).
- Glickman, M. H., Ciechanover, A. The ubiquitin-proteasome proteolytic pathway: destruction for the sake of construction. Physiological Reviews. 82, 373-428 (2002).
- Dobson, C. M. Getting out of shape. Nature. 418, 729-730 (2002).
- Chiti, F., Dobson, C. M. Protein misfolding, functional amyloid, and human disease. Annual Review of Biochemistry. 75, 333-366 (2006).
- Aguzzi, A., O’Connor, T. Protein aggregation diseases: pathogenicity and therapeutic perspectives. Nature Reviews Drug Discovery. 9, 237-248 (2010).
- Rapezzi, C., et al. Transthyretin-related amyloidoses and the heart: a clinical overview. Nature Reviews Cardiology. 7, 398-408 (2010).
- Deas, E., et al. Alpha-synuclein oligomers interact with metal ions to induce oxidative stress and neuronal death in Parkinson’s disease. Antioxidants & Redox Signaling. 24, 376-391 (2016).
- Brundin, P., Melki, R., Kopito, R. Prion-like transmission of protein aggregates in neurodegenerative diseases. Nature Reviews Molecular Cell Biology. 11, 301-307 (2010).
- Frost, B., Diamond, M. I. Prion-like mechanisms in neurodegenerative diseases. Nature Reviews Neuroscience. 11, 155-159 (2009).
- Arrasate, M., Mitra, S., Schweitzer, E. S., Segal, M. R., Finkbeiner, S. Inclusion body formation reduces levels of mutant huntingtin and the risk of neuronal death. Nature. 431, 805-810 (2004).
- Ross, C. A., Poirier, M. A. Opinion: what is the role of protein aggregation in neurodegeneration?. Nature Reviews Molecular Cell Biology. 6, 891-898 (2005).
- Mishra, R., Sjölander, D., Hammarström, P. Spectroscopic characterization of diverse amyloid fibrils in vitro by the fluorescent dye Nile red. Molecular BioSystems. 7, 1232-1240 (2011).
- Klunk, W. E., Jacob, R. F., Mason, R. P. Quantifying amyloid by congo red spectral shift assay. Methods in Enzymology. 309, 285-305 (1999).
- Khurana, V., Lindquist, S. Modelling neurodegeneration in Saccharomyces cerevisiae: why cook with baker’s yeast?. Nature Reviews Neuroscience. 11, 436-449 (2010).
- Tenreiro, S., Outeiro, T. F. Simple is good: yeast models of neurodegeneration. FEMS Yeast Research. 10, 970-979 (2010).
- Moosavi, B., Mousavi, B., Macreadie, I. G. Yeast model of amyloid-β and Tau aggregation in Alzheimer’s disease. Journal of Alzheimer’s Disease. 47, 9-16 (2015).
- Tenreiro, S., Munder, M. C., Alberti, S., Outeiro, T. F. Harnessing the power of yeast to unravel the molecular basis of neurodegeneration. Journal of Neurochemistry. 127, 438-452 (2013).
- Yang, J., Hao, X., Cao, X., Liu, B., Nyström, T. Spatial sequestration and detoxification of huntingtin by the ribosome quality control complex. eLife. 5, (2016).
- Braun, R. J., Büttner, S., Ring, J., Kroemer, G., Madeo, F. Nervous yeast: modeling neurotoxic cell death. Trends in Biochemical Sciences. 35, 135-144 (2010).
- Figley, M. D., Gitler, A. D. Yeast genetic screen reveals novel therapeutic strategy for ALS. Rare Diseases. 1, e24420 (2013).
- Cooper, A. A., et al. Alpha-synuclein blocks ER-Golgi traffic and Rab1 rescues neuron loss in Parkinson’s models. Science. 313, 324-328 (2006).
- Johnson, B. S., McCaffery, J. M., Lindquist, S., Gitler, A. D. A yeast TDP-43 proteinopathy model: exploring the molecular determinants of TDP-43 aggregation and cellular toxicity. Proceedings of the National Academy of Sciences of the United States of America. 105, 6439-6444 (2008).
- Bharathi, V., et al. Use of ade1 and ade2 mutations for development of a versatile red/white color assay of amyloid-induced oxidative stress in saccharomyces cerevisiae. Yeast. 33, 607-620 (2016).
- Pallarès, I., Ventura, S. Advances in the prediction of protein aggregation propensity. Current Medicinal Chemistry. , (2017).
- Villar-Piqué, A., Ventura, S. Protein aggregation propensity is a crucial determinant of intracellular inclusion formation and quality control degradation. Biochimica et Biophysica Acta. 1833, 2714-2724 (2013).
- Navarro, S., Villar-Piqué, A., Ventura, S. Selection against toxic aggregation-prone protein sequences in bacteria. Biochimica et Biophysica Acta. 1843, 866-874 (2014).
- Morell, M., de Groot, N. S., Vendrell, J., Avilés, F. X., Ventura, S. Linking amyloid protein aggregation and yeast survival. Molecular BioSystems. 7, 1121-1128 (2011).
- Conchillo-Solé, O., et al. AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinformatics. 8, 65 (2007).
- Fernandez-Escamilla, A. M., Rousseau, F., Schymkowitz, J., Serrano, L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nature Biotechnology. 22, 1302-1306 (2004).
- Renner, M., Melki, R. Protein aggregation and prionopathies. Pathologie Biologie.(Paris). 62, 162-168 (2014).
- Carija, A., Navarro, S., de Groot, N. S., Ventura, S. Protein aggregation into insoluble deposits protects from oxidative stress. Redox Biology. 12, 699-711 (2017).
Bu Makaleden Alıntı Yapın
Carija, A., Ventura, S., Navarro, S. Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast. J. Vis. Exp. (136), e57470, doi:10.3791/57470 (2018).