School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
J Phys Chem B. 2021 May 20;125(19):4955-4963. doi: 10.1021/acs.jpcb.0c11250. Epub 2021 May 7.
Understanding the aggregation mechanism of amyloid proteins, such as Sup35NM, is essential to understanding amyloid diseases. Significant recent work has focused on using the fluorescence of thioflavin T (ThT), which undergoes a red shift when bound to amyloid aggregates, to monitor amyloid fibril formation. In the present study, the progression of the total mass of aggregates during fibril formation is monitored for initial monomer concentrations in order to infer the relevant aggregation mechanisms. This workflow was implemented using the amyloid-forming fragment Sup35NM under different agitation conditions and for initial monomer concentrations spanning 2 orders of magnitude. The analysis suggests that primary nucleation, monomeric elongation, secondary nucleation, and fragmentation might all be relevant, but their relative importance could not be determined unambiguously, despite the large set of high-quality data. Discriminating between the fibril-generating processes is shown to require additional information, such as a fibril length distribution. Using Sup35NM as a case study, a framework for fitting the parameters of arbitrary amyloid aggregation kinetics is developed based on a population balance model (PBM), which resolves not only the total aggregate mass (monitored experimentally via ThT fluorescence) but the entire fibril length distribution over time. In addition to the rich new set of ThT fluorescence data, we have reanalyzed a previously published aggregate size distribution using this method. With the size distribution, it was determined that in the reanalyzed experiment, secondary nucleation generated significantly fewer new Sup35NM fibrils than fragmentation. The proposed strategy of applying the same PBM to a combination of kinetic data from fluorescence monitoring and experimental fibril length distributions will allow the inference of aggregation mechanisms with far greater confidence than fluorescence studies alone.
理解淀粉样蛋白的聚集机制，如 Sup35NM，对于理解淀粉样疾病至关重要。最近的大量工作集中在使用硫黄素 T（ThT）的荧光，当与淀粉样聚集物结合时，ThT 会发生红移，从而监测淀粉样纤维的形成。在本研究中，监测了纤维形成过程中总聚集物质量的进展，以推断相关的聚集机制。该工作流程在不同搅拌条件下使用淀粉样形成片段 Sup35NM 并针对跨越 2 个数量级的初始单体浓度进行了实施。分析表明，初级成核、单体延伸、次级成核和片段化都可能相关，但尽管有大量高质量的数据，仍不能明确确定它们的相对重要性。结果表明，区分纤维生成过程需要额外的信息，例如纤维长度分布。以 Sup35NM 为例，我们基于群体平衡模型（PBM）开发了一种用于拟合任意淀粉样聚集动力学参数的框架，该模型不仅可以解析总聚集物质量（通过 ThT 荧光实验监测），还可以解析随时间变化的整个纤维长度分布。除了丰富的新 ThT 荧光数据集外，我们还使用这种方法重新分析了以前发表的聚集物尺寸分布。通过尺寸分布，确定在重新分析的实验中，次级成核产生的新 Sup35NM 纤维比片段化产生的纤维少得多。该方法将相同的 PBM 应用于荧光监测和实验纤维长度分布的动力学数据组合，将比单独的荧光研究更有信心地推断聚集机制。
