Interplay between α-, β-, and γ-secretases determines biphasic amyloid-β protein level in the presence of a γ-secretase inhibitor.
Interplay between α-, β-, and γ-secretases determines biphasic amyloid-β protein level in the presence of a γ-secretase inhibitor.
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DOI：
10.1074/jbc.m112.419135
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发表时间：
2013-01-11
期刊：
影响因子：
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通讯作者：
Bendtsen C
中科院分区：
文献类型：
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作者：
Ortega F;Stott J;Visser SA;Bendtsen C
背景：在从细胞系到人类的不同情况下，中等浓度的γ分泌酶抑制剂会增加Aβ的产生。结果：建立了包括α-，β-、γ-分泌酶和β-分泌酶的数学模型。结论：A-β升高是由三种分泌酶相互作用决定的，而不是γ-分泌酶单独作用的结果。意义：这对开发针对阿尔茨海默病Aβ产生的药物具有重要意义。淀粉样蛋白(A-β，A-β)是由淀粉样前体蛋白(APP)在β-分泌酶作用下连续裂解，生成C99，再由γ-分泌酶作用产生。APP也被α-分泌酶切割。据推测，减少大脑中Aβ的产生可能会减缓阿尔茨海默病的进展。因此，人们开发了不同的γ分泌酶抑制剂来减少A-β的产生。矛盾的是，已有研究表明，低到中等浓度的抑制剂会导致不同细胞系、不同动物模型以及人类中Aβ产量的增加。对Aβ上涨的机械性理解仍然难以捉摸。在这里，已经开发了一个最小数学模型，该模型定量地描述了出现上升的细胞系以及没有上升的细胞系中的Aβ动力学。该模型包括通过所谓的淀粉样变通路和所谓的非淀粉样变通路处理APP的步骤。结果表明，这两条通路之间的串扰是Aβ产生增加的原因，即C99的增加将抑制非淀粉样变途径，使APP被β-分泌酶切割，导致C99的额外增加，从而克服γ-分泌酶活性的丧失。稍作扩展，该模型还描述了在人类服用β分泌酶抑制剂后观察到的血浆Aγ图谱。总而言之，这个机制模型使一系列实验结果合理化，这些实验结果涵盖了从体外到体内和人体的范围。这对开发针对阿尔茨海默病Aβ产生的药物具有重要意义。
Background: Moderate concentrations of γ-secretase inhibitor increase Aβ production in different scenarios from cell lines to humans. Results: A mathematical model, including α-, β-, and γ-secretases, is proposed describing Aβ rise. Conclusion: The Aβ rise is decided by the interplay between the three secretases and not γ-secretase alone. Significance: This has important implications for the development of drugs targeting Aβ production in Alzheimer disease. Amyloid-β (Aβ) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by β-secretase, generating C99, and then by γ-secretase. APP is also cleaved by α-secretase. It is hypothesized that reducing the production of Aβ in the brain may slow the progression of Alzheimer disease. Therefore, different γ-secretase inhibitors have been developed to reduce Aβ production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in Aβ production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the Aβ rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the Aβ dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in Aβ production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by β-secretase, leading to an additional increase in C99 that overcomes the loss in γ-secretase activity. With a minor extension, the model also describes plasma Aβ profiles observed in humans upon dosing with a γ-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting Aβ production in Alzheimer disease.