Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis.
Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis.
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DOI：
10.1109/tcbb.2016.2550444
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Caffrey CR
中科院分区：
文献类型：
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作者：
Singh R;Beasley R;Long T;Caffrey CR
被忽视的热带疾病，特别是由蠕虫引起的热带疾病，是世界上最贫穷人口最常见的传染病之一。其中，由血吸虫属血吸虫引起的血吸虫病（血吸虫病或“蜗牛热”）对人类的影响仅次于疟疾：2亿人感染，近8亿人面临感染风险。针对蠕虫的药物筛选带来了独特的挑战：寄生虫无法克隆，并且难以使用基因敲除或RNAi靶向。因此，铅的鉴定和验证都涉及表型筛选，其中寄生虫暴露于化合物，其效果通过分析随后的表型反应来确定。由此鉴定的先导化合物的功效源自一种或多种甚至未知的分子作用机制。两个最直接和最重要的挑战，面对国家的最先进的在这一领域是：自动化和定量表型筛选技术的发展和映射和定量表征的寄生虫的表型反应的整体。在本文中，我们调查并提出解决方案，后者的问题，在以下方面：（1）数学公式和算法，其允许寄生虫的表型响应空间的严格表示，（2）用于表型空间的定量建模和表征的图论和网络分析技术的应用，（3）应用上述方法分析S. mansoni -血吸虫病的病原体之一，由靶向其polo样激酶1（PLK 1）基因的化合物诱导-最近验证的药物靶点。在我们的方法中，首先，生物图像分析算法用于量化不同药物的表型反应。接下来，使用主成分分析（PCA）将这些响应线性映射到低维空间中。表型空间建模使用邻域图，用于表示表型之间的相似性。这些图的特点和探索使用网络分析算法。我们提出了一些结果有关的性质的表型空间的S。曼氏寄生虫以及在构建和分析表型反应空间中遇到的算法问题。特别是，寄生虫的表型分布被发现具有独特的形状和拓扑结构。我们还通过改变关键模型参数来定量表征表型空间。最后，这些表型空间的地图允许可视化和推理之间的复杂关系推定的药物和它们的系统范围内的影响，并可以作为一个高效的范例，用于吸收和统一的信息，从表型屏幕在铅识别和铅优化。
Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world’s poorest people. Amongst these, schistosomiasis (bilharzia or ‘snail fever’), caused by blood flukes of the genus Schistosoma, ranks second only to malaria in terms of human impact: two hundred million people are infected and close to 800 million are at risk of infection. Drug screening against helminths poses unique challenges: the parasite cannot be cloned and is difficult to target using gene knockouts or RNAi. Consequently, both lead identification and validation involve phenotypic screening, where parasites are exposed to compounds whose effects are determined through the analysis of the ensuing phenotypic responses. The efficacy of leads thus identified derives from one or more or even unknown molecular mechanisms of action. The two most immediate and significant challenges that confront the state-of-the-art in this area are: the development of automated and quantitative phenotypic screening techniques and the mapping and quantitative characterization of the totality of phenotypic responses of the parasite. In this paper we investigate and propose solutions for the latter problem in terms of the following: (1) mathematical formulation and algorithms that allow rigorous representation of the phenotypic response space of the parasite, (2) application of graph-theoretic and network analysis techniques for quantitative modeling and characterization of the phenotypic space, and (3) application of the aforementioned methodology to analyze the phenotypic space of S. mansoni – one of the etiological agents of schistosomiasis, induced by compounds that target its polo-like kinase 1 (PLK 1) gene – a recently validated drug target. In our approach, first, bio-image analysis algorithms are used to quantify the phenotypic responses of different drugs. Next, these responses are linearly mapped into a low-dimensional space using Principle Component Analysis (PCA). The phenotype space is modeled using neighborhood graphs which are used to represent the similarity amongst the phenotypes. These graphs are characterized and explored using network analysis algorithms. We present a number of results related to both the nature of the phenotypic space of the S. mansoni parasite as well as algorithmic issues encountered in constructing and analyzing the phenotypic-response space. In particular, the phenotype distribution of the parasite was found to have a distinct shape and topology. We have also quantitatively characterized the phenotypic space by varying critical model parameters. Finally, these maps of the phenotype space allows visualization and reasoning about complex relationships between putative drugs and their system-wide effects and can serve as a highly efficient paradigm for assimilating and unifying information from phenotypic screens both during lead identification and lead optimization.