首页> 美国卫生研究院文献>Frontiers in Genetics >Large-scale integration of small molecule-induced genome-wide transcriptional responses Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action
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Large-scale integration of small molecule-induced genome-wide transcriptional responses Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

机译:小分子诱导的全基因组转录反应全基因组结合亲和力和细胞生长抑制谱的大规模整合揭示了表征系统级药物作用的全球趋势

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摘要

The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.
机译:基于网络的集成细胞签名库(LINCS)项目是大规模的协调工作,旨在构建全面的系统生物学参考资源。该计划的目标包括生成非常大的多维数据矩阵以及集成和分析数据并使之易于访问的信息学和计算工具。 LINCS数据包括全基因组范围内的转录特征,生化蛋白结合图谱,细胞表型反应图谱以及各种其他数据集,适用于各种细胞模型系统以及分子和遗传扰动。在这里,我们介绍了由数据标准(尤其是强大的化合物标准化工作流程)促进的对这些数据的部分调查;我们整合了几种类型的LINCS签名,并重点研究了作用机理(MoA)和化合物,对结果进行了分析。我们说明了激酶靶点如何与疾病模型和相关药物相关。我们确定了一些基本趋势,这些趋势似乎将Kinome结合图谱和转录签名与化学信息联系起来,将生化结合图谱与转录响应联系起来,而与化学相似性无关。为了填补数据集中的空白,我们开发并应用了预测模型。可以基于大量的信号通路在系统级解释结果。我们可以确定清楚的全局关系,表明细胞对化学扰动的反应具有较强的鲁棒性。总体而言,结果表明化学相似性在系统水平上是有用的度量,这将支持表型药物优化工作。通过这项研究,我们证明了这种集成分析方法的潜力,并建议优先考虑进一步的实验以填补当前数据中的空白。

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