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.

抽象的 血吸虫病是由寄生扁虫引起的，是一种感染全世界 2 亿多人的疾病。 使另外 8 亿人面临风险，目前这种疾病的治疗和控制仅依赖于一种药物：吡喹酮 (PZQ)。 - 如果出现耐药性，PZQ 的治疗效果也并不理想。 因此，该组织宣布血吸虫病是一种迫切需要新疗法的疾病。 特别是针对血吸虫病（以及一般蠕虫病）的药物发现传统上基于 表型筛选，即寄生虫暴露于化合物及其全身反应，例如 分析形状、外观和运动，以分析随时间变化的、高维度的和动态的。 然而，复杂大型寄生虫表型筛选的丰富信息并非无足轻重。 强调的是完全缺乏任何致病蠕虫数据库或分析工具， 为了满足这一需求，我们制定了以下两个目标： 根据目标 1，我们建议开发第一个血吸虫发生时间的定量且公开的数据库—— 该数据库将支持使用基于内容的动态表型查询。 该数据库中的信息将支持结构-活动关系（SAR）研究。 我们已经验证的药物靶点和相关的小分子化学物质也将有所帮助。 了解各种化学物质的分子作用机制 (MMoA)，并作为参考 世界各地的研究人员使用其他化合物引发了表型。 在目标 2 下，我们将开发算法方法来分析随时间变化的表型响应 这些方法将使科学家能够对血吸虫寄生虫进行匹配、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。 寄生虫对识别类似效应的反应，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）对表型变异进行分层 寄生虫种群内部和之间，以及（4）基于动态和定量推理的优先化合物 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 我们的提案构成了以下方面的创新进展：（a）开发算法方法和 用于推理和理解血吸虫病病原体表型并加以利用的数据集 药物发现和（b）建立严格的分析框架和可应用的公开可用资源 其他复杂的致病大型寄生虫。