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通过分析关键事件的时间模式揭示成骨细胞力学转导中的隐藏信息。

Revealing hidden information in osteoblast's mechanotransduction through analysis of time patterns of critical events.

机构信息

Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK.

Insigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK.

出版信息

BMC Bioinformatics. 2020 Mar 18;21(1):114. doi: 10.1186/s12859-020-3394-0.

Abstract

BACKGROUND

Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis.

RESULTS

A hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system's state.

CONCLUSIONS

The behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation.

摘要

背景

骨细胞中的机械转导在成骨细胞分化和骨重塑中起着关键作用。机械转导提供了细胞外基质通过机械负荷和细胞内活动调节之间的联系。通过控制细胞内和细胞外区域之间的平衡,机械转导决定了骨骼动力学的最佳功能。这种关系的失败被认为是导致骨质疏松等与骨骼相关疾病的原因之一。

结果

利用模拟单个成骨细胞中机械转导的混合力学和基于代理的模型(Mech-ABM),模拟和研究机械转导中分子的激活动力学在细胞对机械刺激的反应中的调制。从分子信号中推导出重复事件的分布,对分子数量及其波动进行了分析。开发了一种数值方法来反转从属过程,并从分子信号中提取方向过程,以提取重复事件的分布。这些预测表明,存在大量包含隐藏在噪声中的信息的波动,这些波动超出了分子基线的动态变化。此外,研究不同机械负荷状态和改变的反馈回路动力学下的系统,表明每个分子的等待时间分布是系统状态的特征。

结论

分子等待时间的行为随着机械负荷状态和改变的反馈回路动力学的变化而变化,对于类似相互作用的分子呈现相同的变化模式,并确定机械转导中关键分子的特定变化。这种方法可用于提供一种新工具来识别潜在的分子候选物以调节机械转导,从而加速针对骨量上调的治疗靶点的药物发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380e/7079370/4d6ef2fa22df/12859_2020_3394_Fig1_HTML.jpg

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