Transcript networks and crowdsourcing to predict drug combinations in malaria par

描述(由申请人提供)：全球实施基于青蒿素(ART)的联合疗法(ACTs)显著减轻了疾病负担，并被世界卫生组织确认为在所有流行地区治疗疟疾寄生虫恶性疟原虫的一线疗法。然而，最近有关艺术抵抗的报道为这最后一道防线的福祉敲响了极端的警钟。随着传统疗法失败的前景迫在眉睫，迫切的挑战是扩大概念和战略，以支持抗药性疟疾菌株的抗逆转录病毒疗法的有效性和持久性。从历史上看，这种寻找伴侣的过程一直是临时的，依赖于现有的少数已知对寄生虫有效的化合物。合理和精确的药物组合对于提高疗效、最大限度地减少非靶标效应、降低人类病原体的耐药性出现率具有非常重要的价值。传统的经验方法遇到了巨大的挑战，而新的基因组/系统生物学提供了预测能力，以最佳地确定目标和重点经验测试。我们建议形成一个概念框架，利用30个不同基因的全球分离株的ART转录反应，并将由此产生的ART反应网络与3个分离株对30种不同药物的转录反应整合在一起，以预测ART的最佳药物协同效应。I)该方法将基因组数据集与来自基因敲除系的生长表型和通量平衡分析(FBA)相结合，以预测ART受损的基因相互作用，这些基因相互作用可能增加对二次药物的敏感性。Ii)该方法将本提案中产生的数据集扩展到反向工程评估和方法对话(DREAM)，这是一个已建立的系统生物学开放创新平台，以使国际数据分析人员社区参与开发新的方法，以预测常规方法无法实现的大规模药物协同效应。拟议的项目有可能推进ART伙伴药物的搜索，同时有助于建立将基因组数据集与临床相关表型联系起来的新方法。

DESCRIPTION (provided by applicant): The global implementation of Artemisinin (ART)-based combination therapies (ACTs) has significantly reduced disease burden and is recognized by the World Health Organization as the first line treatment against the malaria parasite, Plasmodium falciparum, in all endemic regions. However, recent reports of ART resistance raise extreme alarms for the well-being of this last line of defense. With the looming prospect of the failure of traditional ACTs, the urgent challenge is to expand concepts and strategies to bolster the effectiveness and longevity of ART against multi-drug resistant malaria strains. Historically, this process of finding partners has been ad hoc, relying on a narrow existing array of compounds known to be individually effective against parasites. Rational and precise drug combinations are extraordinarily valuable for improving efficacy, minimizing off-target effects, decreasing the rate of resistance emergence in human pathogens. Traditional empirical approaches encounter significant challenges and new genomic/systems biology offers predictive power to optimally identify targets and focused empirical testing. We propose to formalize a conceptual framework that utilizes ART transcriptional responses of 30 genotypically diverse global isolates and integrates the resulting ART response networks with transcriptional responses to 30 diverse drugs across 3 isolates to predict optimal drug synergies for ART. i) The approach integrates genomic datasets with growth phenotypes from knockout lines and flux-balance analysis (FBA) to predict ART compromised gene interactions that potentially enhance susceptibility to secondary drugs. ii) The approach extends datasets generated in this proposal to the Dialogue for Reverse Engineering Assessments and Methods (DREAM), an established open innovation platform for systems biology, to engage an international community of data analysts in developing novel methods for predicting drug synergy at scale that is not attainable by conventional methods. The proposed project has the potential to advance the search for ART partner drugs while at the same time contributing to novel methods for linking genomic datasets to clinically relevant phenotypes.