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整合分子医学与功能蛋白质组学:现状与期望

Integrating molecular medicine with functional proteomics: realities and expectations.

作者信息

Miklos G L, Maleszka R

机构信息

GenetixXpress Proprietary Limited, 78 Pacific Road, Palm Beach, Sydney, NSW, Australia, 2108.

出版信息

Proteomics. 2001 Jan;1(1):30-41. doi: 10.1002/1615-9861(200101)1:1<30::AID-PROT30>3.0.CO;2-X.

Abstract

We analyze key proteomic issues and cutting-edge technologies that will spearhead inroads into functional interpretations of human diseases and their therapeutic rectification, following the availability of the predicted human proteome. We contrast the distinctions between high quality data that are low throughput, (e.g., 3-D proteomic reconstructions in embryogenic and nervous system contexts, and multigenerational transgenic studies), versus automated data harvesting that is more distant from human disease phenotypes and currently fulfills a diagnostic role, (e.g., molecular portraits of human diseases via transcriptomic analyses). We examine the extent to which these approaches impinge upon a realistic understanding of human diseases, namely how close they come to revealing the causal events involved in the initiation of disease. While tissue sources from human embryogenesis, foetal development and the brain remain the absolute priority, the pragmatic approaches utilize judicious data integration from selected proteomic studies of model organisms. The role of genome-wide disease-related screens, "humanized" transgenic analyses, multigenerational gene interference methods, and analyses of post-translational modifications in epigenetic contexts from Drosophila will be crucial, since these avenues are far too slow and transgenically cumbersome in mammals. Finally, the implementation of multi compartment electrolyzers (MCE) and multi photon detection (MPD) systems will be pivotal for the proteomic profiling of human tissue samples.

摘要

在预测的人类蛋白质组可用之后,我们分析了关键的蛋白质组学问题和前沿技术,这些将引领对人类疾病的功能解释及其治疗矫正。我们对比了低通量高质量数据(例如胚胎发生和神经系统背景下的三维蛋白质组重建以及多代转基因研究)与自动化数据收集之间的区别,自动化数据收集与人类疾病表型的距离更远,目前发挥着诊断作用(例如通过转录组分析绘制人类疾病的分子图谱)。我们研究了这些方法在多大程度上影响对人类疾病的现实理解,即它们在揭示疾病发生所涉及的因果事件方面有多接近。虽然来自人类胚胎发生、胎儿发育和大脑的组织来源仍然是绝对优先事项,但务实的方法利用从选定的模式生物蛋白质组学研究中进行明智的数据整合。全基因组疾病相关筛选、“人源化”转基因分析、多代基因干扰方法以及果蝇表观遗传背景下翻译后修饰分析的作用将至关重要,因为这些途径在哺乳动物中太慢且转基因操作过于繁琐。最后,多隔室电解槽(MCE)和多光子检测(MPD)系统的实施对于人类组织样本的蛋白质组分析至关重要。

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