Reconciling high-throughput gene essentiality data with metabolic network reconstructions
Reconciling high-throughput gene essentiality data with metabolic network reconstructions
复制标题
DOI：
10.1371/journal.pcbi.1006507
复制
发表时间：
2019-04-01
影响因子：
4.3
通讯作者：
Papin, Jason A.
中科院分区：
文献类型：
--
作者：
Blazier, Anna S.;Papin, Jason A.
鉴定细菌生长和存活所必需的基因代表了发现抗菌靶标的有希望的策略。使用转座子诱变方法可以在基因组规模上鉴定必需基因;然而，筛选之间的变异性和对必需性数据解释的挑战阻碍了条件无关性和条件依赖性必需基因的鉴定。为了说明这些挑战的范围，我们对多个已发表的铜绿假单胞菌基因必要性数据集进行了大规模比较，揭示了屏幕之间的实质性差异。然后，我们使用基因组规模的代谢网络重建的背景下的必要性，并证明了这种方法的实用性，在提供功能解释的必要性和协调屏幕之间的差异。基因组规模的代谢网络重建还能够进行高通量的定量分析，以评估培养基条件对鉴定条件无关的必需基因的影响。我们的计算模型驱动的分析提供了对必要性的机制性洞察，并为未来基因必要性筛选的设计和核心代谢过程的识别提供了新的见解。作者总结随着抗生素耐药性的上升，越来越需要发现新的治疗靶点来治疗细菌感染。一个有吸引力的策略是靶向对生长和生存至关重要的基因。必需基因可以用转座子诱变方法鉴定;然而，筛选之间的可变性和对必需性数据解释的挑战阻碍了必需基因的鉴定和分析。我们进行了一个大规模的比较，多个基因的必要性筛选的微生物病原体铜绿假单胞菌。我们实现了一种计算模型驱动的方法，为必要性提供功能解释，并协调屏幕之间的差异。将计算建模与高通量实验筛选相结合，可以高置信度地识别药物靶点，并为开发新的治疗策略提供更好的理解。
The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.Author summary With the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.