Reasoning with chemically induced dynamic phenotypes in whole-organism assays

摘要 血吸虫病是由寄生的扁虫引起的，是穷人的疾病。它感染了全球超过2亿人， 将另外8亿人置于危险之中。目前这种疾病的治疗和控制仅依靠一种药物--吡喹酮(PZQ)。 -如果出现抗药性，局势将岌岌可危。PZQ的治疗概况也不理想。《世界卫生》 因此，该组织宣布血吸虫病是一种迫切需要新疗法的疾病。 特别是血吸虫病(和一般的蠕虫疾病)的药物发现传统上是基于 表型筛选，使寄生虫(S)暴露于化合物及其系统反应，如 对形状、外观和运动进行分析，以识别命中。分析时间变化的、高维的和 然而，从复杂的大型寄生虫的表型筛选中获得信息丰富的输出并不是微不足道的。这一事实是 强调完全没有任何数据库(S)或致病蠕虫的分析工具，这将使 利用动态表型数据进行分析和推理。为了满足这一需要，我们制定了以下两个目标： 在目标1下，我们建议建立首个量化和公开提供的血吸虫时间数据库- 对化学探针的反应各不相同。该数据库将支持基于内容的动态表型查询，使用 时间序列匹配。该数据库中的信息将为结构-活性关系(SAR)研究提供支持 我们已经确认的药物靶点和相关的小分子化学。这一表型记录也将有助于 了解各种化学物质的分子作用机理，并为 世界各地的研究人员使用其他化合物引起的表型。 在目标2下，我们将开发算法方法来分析 血吸虫寄生虫。这些方法将允许科学家匹配、比较、分类和定量推理-与 动态的(即时间变化的)表型。特别是，科学家将能够：(1)客观地比较表型 寄生虫的反应以识别相似的影响，即使它们是由于结构不同的化合物而发生的，(2)相关 在不同条件下进行的不同研究中观察到的表型效应，(3)将表型变异性分层 在寄生虫种群内部和之间，以及(4)基于动态和动态的定量推理来确定化合物的优先级 复杂的表型反应。 这两个目标的结果将通过公共数据库和软件免费提供给世界各地的生物学家 由我们开发的。我们的建议构成了(A)开发算法方法和 用于推理和理解血吸虫病病原体的表现组并利用它的数据集 用于药物发现和(B)建立严格的分析框架和可供公众使用的资源 其他复杂的致病大型寄生虫。

ABSTRACT Schistosomiasis, caused by a parasitic flatworm, is a disease of the poor. It infects over 200 million people worldwide and places another 800 million at risk. Current treatment and control of this disease relies on just one drug, praziquantel (PZQ) - a precarious situation should drug resistance emerge. The therapeutic profile of PZQ is also not ideal. The World Health Organization has therefore declared schistosomiasis a disease for which new therapies are urgently needed. Drug discovery for schistosomiasis in particular (and helmintic diseases in general) is traditionally based on phenotypic screening, whereby the parasite(s) are exposed to compounds and their systemic responses, such as changes in shape, appearance, and motion, are analyzed to identify hits. Analyzing the temporally varying, high-dimensional, and information-rich output from phenotypic screening of a complex macroparasite is, however, non-trivial. This fact is underlined by the complete absence of any database(s) or analysis tools for disease-causing helminths that would allow analysis and reasoning with dynamic phenotypic data. To address this need, we formulate the following two aims: Under Aim 1, we propose to develop the first quantitative and publicly available database of the schistosome’s time- varying response to chemical probes. The database will support content-based querying of dynamic phenotypes using time-series matching. The information in this database will underpin structure-activity relationship (SAR) studies with the drug targets and associated small molecule chemistries that we have validated. This phenotypic record will also aid understanding of the molecular mechanism of action (MMoA) of various chemistries and serve as a reference for phenotypes elicited using other compounds by researchers worldwide. Under Aim 2, we will develop algorithmic methods for analyzing the time-varying phenotypic responses of the schistosome parasite. These methods will allow scientists to match, compare, cluster, and quantitatively reason-with dynamic (i.e. temporally varying) phenotypes. In particular, scientists will be able to: (1) objectively compare phenotypic responses of parasites to identify similar effects, even when they occur due to structurally distinct compounds, (2) relate phenotypic effects observed in different studies conducted under varying conditions, (3) stratify the phenotypic variability within and across parasite populations, and (4) prioritize compounds based on quantitative reasoning with dynamic and complex phenotypic responses. Results from both aims will be made freely available to biologists worldwide through a public database and software developed by us. Our proposal constitutes an innovative point of progress in (a) developing algorithmic methods and datasets for reasoning-with and understanding the phenome of the etiological agent of schistosomiasis and leveraging it for drug discovery and (b) establishing a rigorous analysis framework and publicly available resources that can be applied to other complex disease-causing macroparasites.