Connecting drugs to pathways using malaria parasite transcript profiles

数百万种专利和公开可用的化合物的高通量全细胞筛选导致了 发现数千个确认具有抗疟疾活性的点击量。这些化合物代表了一个丰富的开端 药物发现的关键。药物发现工作越来越依赖于全细胞筛选，大多是进行的 对抗疟疾寄生虫的无性血液期。全细胞筛选的优点是化合物 接触寄生虫，在那里他们可以击中简单或多因素的目标。例行公事，性价比高 阐明这些化合物的作用机制将极大地促进药物的发现。 进程。因此，迫切需要强大的、高通量的技术，并对以下方面敏感 在没有先验预期的情况下，识别、验证药物对寄生虫中靶点和途径的影响并确定其优先顺序 药物是如何起作用的。在这里，我们提出了一种系统的方法来揭示 通过生成来自两个不同寄生虫系的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点的药物/小分子进行治疗，并选择跨越广泛的 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 签名不依赖于任何一个或几个基因的大效应，而是可以细微地辨别 基于这些药物特异性途径指纹的药物效应之间的关系 转录反应(目标2)。这个收藏可以很容易地用新的药物来探测，放在这个 毒品--毒品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).