Connecting drugs to pathways using malaria parasite transcript profiles

对数百万种专有和公开化合物的高通量全细胞筛选导致了 发现了数千个已证实具有抗疟活性的点击物。这些化合物代表了丰富的起始 药物发现的要点。药物发现工作越来越依赖于全细胞筛选，大部分进行 对抗疟疾寄生虫的无性血液阶段。全细胞筛选的优点是化合物 获得寄生虫的途径，它们可以攻击简单或多因素的目标。常规且划算 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，迫切需要稳健、高通量且具有敏感性的技术。 在没有先验预期的情况下，识别、验证和优先考虑药物对寄生虫目标和途径的影响 药物如何发挥作用。在这里，我们提出了一种系统方法来揭示之间的功能联系 通过从两个不同的寄生虫系生成基因表达谱的参考集合来发挥药物作用 用一系列具有已知或可疑靶标的药物/小分子进行治疗，并选择跨越广泛的范围 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 特征不依赖于任何一个或少数基因的巨大影响，而是可以辨别微妙的 基于源自这些药物特异性的途径指纹的药物作用之间的关系 转录反应（目标 2）。这个集合可以很容易地用放置在这个集合中的新药物进行探测 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).