Connecting drugs to pathways using malaria parasite transcript profiles

高通量全细胞筛选数百万种专有和公开可用的化合物， 发现了数千个经证实具有抗疟疾活性的命中物。这些化合物代表了一个丰富的开始 药物发现的关键药物发现工作越来越依赖于全细胞筛选， 对抗血液中无性繁殖的疟原虫全细胞筛选的优势在于， 获得接触寄生虫的机会，在那里它们可以攻击简单或多因素的目标。一个常规和成本效益 阐明这些命中的作用机制（MoA）的方法将极大地促进药物发现 过程因此，迫切需要具有以下灵敏度的稳健的高通量技术： 确定、验证和优先考虑药物对寄生虫中靶点和途径的影响，而没有先验期望 药物的作用原理在这里，我们提出了一个系统的方法来揭示功能之间的联系， 通过从两种不同的寄生虫系中产生参考基因表达谱来产生药物作用 用一系列具有已知或疑似靶点的药物/小分子治疗，并选择广泛的 生物空间范围（目标1）。然后，我们将捕获这些诱导的转录状态作为响应 这些特征不依赖于任何一个或几个基因的大效应，而是可以识别微妙的 基于来自这些药物特异性的途径指纹， 转录反应（Aim 2）。这个集合可以很容易地探测与新的药物被放置在这个 药物-药物网络作为验证和假设检验的框架（目标3）。

High-throughput whole-cell screens of millions of proprietary and publicly available compounds have led to the discovery of thousands of hits with confirmed anti-malarial activity. These compounds represent a rich starting point for drug discovery. Drug discovery efforts increasingly rely on whole-cell screening, mostly conducted against the asexual blood-stages of malaria parasites. The advantage of whole-cell screens is that compounds gain access to the parasite where they can hit simple or multifactorial targets. A routine and cost-effective method to elucidating mechanisms of action (MoA) of these hits would greatly enhance the drug discovery process. Consequently, there is an urgent need for robust, high-throughput technologies with the sensitivity to identify, validate and prioritize drug effects on targets and pathways in the parasite, with no a priori expectation of how the drug works. Here we present a systematic approach to uncover the functional connections among drug actions by generating a reference collection of gene expression profiles from two different parasite lines treated with an array of drugs/small molecules with known or suspected targets and chosen to span a wide range of biological space (Aim 1). We will then capture these induced transcriptional states as response signatures that do not depend on large effects by any one or few genes, but rather can discern subtle relationships among drug effects based on the pathway fingerprints derived from these drug-specific transcriptional responses (Aim 2). This collection can be easily probed with new drugs to be placed in this drug-drug network as a framework for validation and hypothesis testing (Aim 3).