Reasoning with chemically induced dynamic phenotypes in whole-organism assays

在整个生物体分析中用化学诱导的动态表型进行推理

• 批准号: 9810003
• 负责人: Conor Caffrey
• 金额: $ 20.48万
• 依托单位: SAN FRANCISCO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9810003
• 关键词: Academia; Address; Algorithmic Software; Algorithms; Appearance; Area; Biological Assay; Cells; Chemical Structure; Chemicals; Chemistry; Coenzyme A; Collection; Complex; Computer software; Computers; Data; Data Set; Databases; Detection; Development; Disease; Drug Targeting; Drug effect disorder; Drug resistance; Epigenetic Process; Etiology; Exhibits; Exposure to; Gender; Gene Expression Profiling; Generations; Genetic; Graph; Helminths; Image; Image Analysis; Individual; Industry; Measures; Methods; Mining; Molecular Mechanisms of Action; Molecular Target; Motion; Noise; Online Systems; Output; Oxidoreductase; Parasites; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Phase; Phenotype; Platyhelminths; Population; Poverty; Praziquantel; Proteomics; Publications; Quantitative Reasoning; Research; Research Personnel; Resistance; Resources; Risk; Schistosoma; Schistosome Parasite; Schistosomiasis; Scientist; Series; Shapes; Structure; Structure-Activity Relationship; Systems Development; Techniques; Therapeutic; Time; Translating; Whole Organism; World Health Organization; algorithmic methodologies; base; data resource; design; disorder control; drug discovery; high dimensionality; innovation; interest; metabolomics; multicatalytic endopeptidase complex; novel; novel therapeutics; phenome; phenotypic data; response; screening; small molecule; software development; software systems; technology development; time use; tool; web services

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.

摘要 血吸虫病是由寄生的扁形虫引起的，是穷人的一种疾病。它感染了全世界超过2亿人， 又有8亿人处于危险之中目前对该病的治疗和控制仅依赖于一种药物吡喹酮（PZQ） - 一旦出现抗药性，情况将岌岌可危。PZQ的治疗概况也不理想。世界卫生 因此，世界卫生组织宣布血吸虫病是一种急需新疗法的疾病。 特别是血吸虫病（以及一般的蠕虫病）的药物发现传统上是基于 表型筛选，由此使寄生虫暴露于化合物及其全身反应，如 形状、外观和运动被分析以识别命中。分析时变的、高维的、 然而，来自复杂大型寄生虫的表型筛选的信息丰富的输出是不平凡的。这一事实 强调指出，完全没有任何数据库或分析工具， 动态表型数据的分析和推理。为了满足这一需要，我们制定了以下两个目标： 在目标1下，我们建议开发第一个定量和公开可用的数据库，以了解人类的时间- 对化学探针的不同反应。该数据库将支持基于内容的动态表型查询， 时间序列匹配该数据库中的信息将支持结构-活性关系（SAR）研究， 我们已经验证的药物靶点和相关的小分子化学。这种表型记录也将有助于 了解各种化学物质的分子作用机制（MMoA），并作为 全世界的研究人员使用其他化合物引发的表型。 根据目标2，我们将开发算法方法，用于分析患者的时变表型反应。 可怕的寄生虫这些方法将使科学家能够匹配，比较，聚类和定量推理， 动态（即随时间变化的）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，以确定类似的影响，即使他们发生由于结构不同的化合物，（2）有关 在不同条件下进行的不同研究中观察到的表型效应，（3）对表型变异进行分层 在寄生虫种群内和跨寄生虫种群，以及（4）基于动态和动态的定量推理对化合物进行优先排序， 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全世界的生物学家 由我们开发。我们的建议构成了一个创新点的进展，在（a）发展算法的方法， 用于推理和理解血吸虫病病原体的表型并利用它的数据集 （B）建立严格的分析框架和可应用的公共资源 其他复杂的致病大型寄生虫。