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.
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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DOI：
10.3389/fgene.2014.00342
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发表时间：
2014
影响因子：
3.7
通讯作者：
Schürer SC
中科院分区：
文献类型：
--
作者：
Vidović D;Koleti A;Schürer SC
基于集成网络的细胞特征库（LINCS）项目是一项大规模的协调努力，旨在建立全面的系统生物学参考资源。该计划的目标包括生成一个非常大的多维数据矩阵和信息学和计算工具，以整合，分析和使数据易于访问。LINCS数据包括全基因组转录特征、生化蛋白结合谱、细胞表型反应谱以及各种细胞模型系统和分子及遗传扰动的各种其他数据集。在这里，我们提出了一个由数据标准，特别是一个强大的化合物标准化工作流程促进的数据的部分调查;我们集成了几种类型的LINCS签名，并分析了结果，重点是作用机制（MoA）和化合物。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的趋势，似乎连接Kinome结合档案和转录签名的化学信息和生化结合档案的转录反应独立的化学相似性。为了填补数据集的空白，我们开发并应用了预测模型。结果可以在系统水平上解释，如基于大量信号通路所证明的。我们可以确定明确的全球关系，表明细胞反应的化学扰动的鲁棒性。总的来说，结果表明，化学相似性是一个有用的措施，在系统水平上，这将支持表型药物优化的努力。通过这项研究，我们展示了这种综合分析方法的潜力，并建议优先考虑进一步的实验，以填补目前数据中的空白。
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.