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囊性纤维化功能格局治疗管理的特征

Hallmarks of therapeutic management of the cystic fibrosis functional landscape.

作者信息

Amaral Margarida D, Balch William E

机构信息

University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Lisboa, Portugal.

Department of Chemical Physiology, Department of Cell and Molecular Biology, The Skaggs Institute of Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA.

出版信息

J Cyst Fibros. 2015 Nov;14(6):687-99. doi: 10.1016/j.jcf.2015.09.006. Epub 2015 Oct 29.

Abstract

The cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein does not operate in isolation, rather in a dynamic network of interacting components that impact its synthesis, folding, stability, intracellular location and function, referred to herein as the 'CFTR Functional Landscape (CFFL)'. For the prominent F508del mutation, many of these interactors are deeply connected to a protein fold management system, the proteostasis network (PN). However, CF encompasses an additional 2000 CFTR variants distributed along its entire coding sequence (referred to as CFTR2), and each variant contributes a differential liability to PN management of CFTR and to a protein 'social network' (SN) that directs the probability of the (patho)physiologic events that impact ion transport in each cell, tissue and patient in health and disease. Recognition of the importance of the PN and SN in driving the unique patient CFFL leading to disease highlights the importance of precision medicine in therapeutic management of disease progression. We take the view herein that it is not CFTR, rather the PN/SN, and their impact on the CFFL, that are the key physiologic forces driving onset and clinical progression of CF. We posit that a deep understanding of each patients PN/SN gained by merging genomic, proteomic (mass spectrometry (MS)), and high-content microscopy (HCM) technologies in the context of novel network learning algorithms will lead to a paradigm shift in CF clinical management. This should allow for generation of new classes of patient specific PN/SN directed therapeutics for personalized management of the CFFL in the clinic.

摘要

囊性纤维化(CF)跨膜传导调节因子(CFTR)蛋白并非独立发挥作用,而是处于一个相互作用成分的动态网络中,这些成分会影响其合成、折叠、稳定性、细胞内定位和功能,本文将其称为“CFTR功能格局(CFFL)”。对于常见的F508del突变,许多这些相互作用分子与一个蛋白质折叠管理系统——蛋白质稳态网络(PN)紧密相连。然而,CF还包括沿其整个编码序列分布的另外2000种CFTR变体(称为CFTR2),每种变体对CFTR的PN管理以及对一个蛋白质“社交网络”(SN)的影响各不相同,该“社交网络”决定了在健康和疾病状态下影响每个细胞、组织和患者离子转运的(病理)生理事件发生的概率。认识到PN和SN在驱动导致疾病的独特患者CFFL中的重要性,凸显了精准医学在疾病进展治疗管理中的重要性。我们在此认为,驱动CF发病和临床进展的关键生理力量不是CFTR,而是PN/SN及其对CFFL的影响。我们假定,通过在新型网络学习算法的背景下融合基因组学、蛋白质组学(质谱(MS))和高内涵显微镜(HCM)技术,深入了解每个患者的PN/SN,将导致CF临床管理的范式转变。这应该能够产生针对患者特定PN/SN的新型疗法,用于临床中CFFL的个性化管理。

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