Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis
Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis
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DOI：
10.1186/1471-2164-11-493
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发表时间：
2010-09-10
期刊：
影响因子：
4.4
通讯作者：
Kaern, Mads
中科院分区：
文献类型：
--
作者：
Batenchuk, Cory;Tepliakova, Lioudmila;Kaern, Mads
背景资料：高通量基因组学使得基于组合遗传扰动的表型影响的遗传相互作用的全局映射成为可能。下一步重要的是了解这些网络如何动态地重塑以响应环境刺激。在这里，我们报告的方法来识别这种相互作用的开发和测试。该方法是从第一原理开发的，通过处理环境扰动对细胞生长的影响等同于基因缺失。这使我们能够建立一个新的中性功能，标志着不存在上位性的敏感性表型，而不是健身。我们测试了该方法，通过确定适合度和敏感性为基础的相互作用，参与响应药物诱导的DNA损伤的芽殖酵母酿酒酵母使用两个突变库，一个包含转录因子缺失，另一个包含DNA修复基因的缺失。在转录因子缺失突变体文库中，我们观察到显着差异的遗传相互作用的适应性和敏感性为基础的方法确定的集合。值得注意的是，在最可能的相互作用中，两种方法仅识别出50%的相似性。虽然仅通过基于敏感性的方法确定的相互作用响应于药物诱导的DNA损伤而被调节，但仅通过基于适应度的方法确定的相互作用对治疗保持不变。所确定的相互作用的转录谱和蛋白质-DNA相互作用数据的比较表明，基于灵敏度的方法提高了识别的DNA损伤反应中涉及的相互作用。此外，对于含有DNA修复突变体的文库，我们观察到基于灵敏度的方法改善了功能相关基因的分组，以及参与DNA修复的蛋白质复合物的鉴定。我们的研究结果表明，响应的识别-调节的遗传相互作用可以通过将变化的环境的影响直接纳入标记缺乏的中性函数来改善。上位性我们期望这种传统上位性分析的扩展将有助于从遗传相互作用的定量测量中发展基因网络的动态模型。虽然该方法是为生长表型开发的，但它应该同样适用于其他表型，包括荧光报告基因的表达。
Background: High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness-and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries one containing transcription factor deletions, and the other containing deletions of DNA repair genes.Results: Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness-and sensitivity-based approaches. Notably, among the most likely interactions, only similar to 50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair.Conclusion: Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.