Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.
Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.
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连通性同源性使种间间的综合杀伤力网络模型。
DOI：
10.1371/journal.pcbi.1004506
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发表时间：
2015-10
影响因子：
4.3
通讯作者：
Tatonetti NP
中科院分区：
文献类型：
--
作者：
Jacunski A;Dixon SJ;Tatonetti NP
合成致死性是一种基因相互作用，其中两个非必需基因同时被敲除时导致细胞丧失活力。药物可以模拟基因敲除效应；因此，我们对混杂药物、多种药理学相关的药物不良反应、多药物治疗，特别是癌症联合治疗的理解，可能需要对合成致死率有更深入的了解。然而，巨大的人体实验负担需要用计算机方法来指导合成致死对的鉴定。在这里，我们提出了SINaTRA（物种独立翻译），这是一种基于网络的方法，可以发现物种之间翻译的全基因组合成致死率。SINaTRA使用连接同源性，定义为跨物种持续存在的生物连接模式，来识别合成的致命配对。重要的是，我们的方法不依赖于遗传同源性或结构和功能相似性，并且它明显优于利用这些数据的模型。我们利用S. cerevisiae的数据，通过预测S. pombe的合成致死性来验证SINaTRA，然后鉴定出超过100万对假定的人类合成致死性对，以指导实验方法。我们强调了我们的算法在药物发现方面的转化应用，通过识别单药物和多药物癌症治疗显著富集的基因簇。合成致死性是一种基因相互作用，对提供新的癌症治疗方法具有重要意义。要确定人类中所有的成对合成致死相互作用，将需要超过2亿次成对试验——目前，这是一个不可能的巨大实验负担。为了简化这一过程，我们开发了一种方法，利用两种物种的蛋白质-蛋白质相互作用网络，预测人类合成致死对从一个研究充分的物种翻译到一个合成致死对研究不足的物种。在此，我们探讨了该模型在S. cerevisiae到S. pombe翻译中的成功。然后，我们预测人类的合成致死率，并建议研究癌症治疗的新领域。
Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies. Synthetic lethality is a genetic interaction that has promising implications for informing novel cancer therapies. Over 200 million pairwise tests would be required to identify all pairwise synthetic lethal interactions in humans–currently, an impossibly large experimental burden. To simplify the process, we have developed a method to predict human synthetic lethal pairs in translation from a well-studied species to one in which synthetic lethality is understudied using both species’ protein-protein interaction networks. Here, we explore the model’s success in translation from S. cerevisiae to S. pombe. We then predict human synthetic lethality and suggest novel areas of inquiry for cancer therapies.