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Quantitative measurements and modeling of cargo–motor interactions during fast transport in the living axon

机译:定量测量和货物电机相互作用的模型在客厅轴突快速运输过程中

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摘要

The kinesins have long been known to drive microtubule-based transport of sub-cellular components, yet the mechanisms of their attachment to cargo remain a mystery. Several different cargo-receptors have been proposed based on their in vitro binding affinities to kinesin-1. Only two of these—phosphatidyl inositol, a negatively charged lipid, and the carboxyl terminus of the amyloid precursor protein (APP-C), a trans-membrane protein—have been reported to mediate motility in living systems. A major question is how these many different cargo, receptors and motors interact to produce the complex choreography of vesicular transport within living cells. Here we describe an experimental assay that identifies cargo–motor receptors by their ability to recruit active motors and drive transport of exogenous cargo towards the synapse in living axons. Cargo is engineered by derivatizing the surface of polystyrene fluorescent nanospheres (100 nm diameter) with charged residues or with synthetic peptides derived from candidate motor receptor proteins, all designed to display a terminal COOH group. After injection into the squid giant axon, particle movements are imaged by laser-scanning confocal time-lapse microscopy. In this report we compare the motility of negatively charged beads with APP-C beads in the presence of glycine-conjugated non-motile beads using new strategies to measure bead movements. The ensuing quantitative analysis of time-lapse digital sequences reveals detailed information about bead movements: instantaneous and maximum velocities, run lengths, pause frequencies and pause durations. These measurements provide parameters for a mathematical model that predicts the spatiotemporal evolution of distribution of the two different types of bead cargo in the axon. The results reveal that negatively charged beads differ from APP-C beads in velocity and dispersion, and predict that at long time points APP-C will achieve greater progress towards the presynaptic terminal. The significance of this data and accompanying model pertains to the role transport plays in neuronal function, connectivity, and survival, and has implications in the pathogenesis of neurological disorders, such as Alzheimer’s, Huntington and Parkinson’s diseases.
机译:长期以来,驱动蛋白可驱动基于微管的亚细胞成分运输,但其与货物的附着机制仍是一个谜。基于它们与kinesin-1的体外结合亲和力,已经提出了几种不同的载物受体。据报道,其中只有两种-磷脂酰肌醇(带负电荷的脂质)和淀粉样前体蛋白(APP-C)(跨膜蛋白)的羧基末端-介导生命系统的运动。一个主要的问题是,这些许多不同的货物,受体和运动如何相互作用,从而在活细胞内产生复杂的水泡运输编排。在这里,我们描述了一种实验方法,该方法通过募集主动运动并驱动外源性货物向活轴突突触运输的能力来识别货物-运动受体。通过用带电残基或衍生自候选运动受体蛋白的合成肽衍生化聚苯乙烯荧光纳米球(直径为100 nm)的表面来衍生化货物,从而设计出一个末端COOH基团。注入鱿鱼巨轴突后,通过激光扫描共聚焦延时显微镜观察粒子运动。在此报告中,我们使用新方法测量珠运动,比较了在甘氨酸偶联的非运动性珠存在下带负电荷的珠与APP-C珠的运动性。随后对延时数字序列的定量分析揭示了有关磁珠运动的详细信息:瞬时和最大速度,运行长度,暂停频率和暂停持续时间。这些测量为数学模型提供了参数,该数学模型预测了轴突中两种不同类型的珠货物的分布的时空演变。结果表明,带负电荷的珠子在速度和分散性上与APP-C珠子不同,并预测在很长的时间点,APP-C将朝着突触前的末端取得更大的进展。该数据和随附模型的意义与运输在神经元功能,连通性和存活中所起的作用有关,并且对神经系统疾病(例如阿尔茨海默氏症,亨廷顿病和帕金森氏病)的发病机制具有影响。

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