Determination of minimal transcriptional signatures of compounds for target prediction.

Florian Nigsch, Janna Hutz, Ben Cornett, Douglas W Selinger, Gregory McAllister, Somnath Bandyopadhyay, Joseph Loureiro, Jeremy L Jenkins
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引用次数: 10

Abstract

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

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测定化合物的最小转录特征用于靶标预测。
化合物的分子靶点和作用机制的确定是药物开发的关键环节。基于头部表达谱的多路复用技术允许在高通量模式下测量化合物处理细胞的转录特征。这样的轮廓可以用来深入了解化合物的作用模式和它们调节的蛋白质目标。通过这些基因特征的靶预测代理,我们探索了使用转录谱来捕获受干扰细胞测定的生物学变异性的重要方面。我们发现来自表达数据的签名和来自生物相互作用网络的签名表现同样好,并且我们表明基因签名可以使用遗传算法进行优化。大约128个基因的基因特征似乎是最通用的,捕获了通过复合处理对细胞施加的最大扰动。此外,我们发现氧化磷酸化是捕获化合物扰动的最一般方法之一的证据。
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