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淀粉样配体能让我们了解分子多态性和疾病的哪些方面。

What amyloid ligands can tell us about molecular polymorphism and disease.

作者信息

LeVine Harry, Walker Lary C

机构信息

Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, USA; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA; Department of Molecular and Cellular Biochemistry, Center for Structural Biology, University of Kentucky, Lexington, KY, USA.

Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA; Department of Neurology, Emory University, Atlanta, GA, USA.

出版信息

Neurobiol Aging. 2016 Jun;42:205-12. doi: 10.1016/j.neurobiolaging.2016.03.019. Epub 2016 Mar 24.

Abstract

Brain-penetrant positron emission tomography imaging ligands selective for amyloid pathology in living subjects have sparked a revolution in presymptomatic biomarkers for Alzheimer's disease progression. As additional chemical structures were investigated, the heterogeneity of ligand-binding sites became apparent, as did discrepancies in binding of some ligands between human disease and animal models. These differences and their implications have received little attention. This review discusses the impact of different ligand-binding sites and misfolded protein conformational polymorphism on the interpretation of imaging data acquired with different ligands. Investigation of the differences in binding in animal models may identify pathologic processes informing improvements to these models for more faithful recapitulation of this uniquely human disease. The differential selectivity for binding of particular ligands to different conformational states could potentially be harnessed to better define disease progression and improve the prediction of clinical outcomes.

摘要

对活体受试者中淀粉样病理具有选择性的脑渗透性正电子发射断层扫描成像配体,引发了阿尔茨海默病进展的症状前生物标志物的一场革命。随着对更多化学结构的研究,配体结合位点的异质性变得明显,一些配体在人类疾病和动物模型之间的结合差异也同样明显。这些差异及其影响很少受到关注。本综述讨论了不同配体结合位点和错误折叠蛋白构象多态性对使用不同配体获取的成像数据解释的影响。对动物模型中结合差异的研究可能会识别出病理过程,从而为改进这些模型提供信息,以便更忠实地重现这种独特的人类疾病。特定配体与不同构象状态结合的差异选择性,有可能被用于更好地定义疾病进展并改善临床结果的预测。

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