Guzmán Gabriela I, Utrilla José, Nurk Sergey, Brunk Elizabeth, Monk Jonathan M, Ebrahim Ali, Palsson Bernhard O, Feist Adam M
Departments of Bioengineering.
Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia;
Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):929-34. doi: 10.1073/pnas.1414218112. Epub 2015 Jan 6.
Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli--aspC, argD, and gltA--are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.
从进化的角度来看，酶对底物的选择性已被讨论为提供了适应新环境的灵活性。在本研究中，我们描述了一种在代谢网络空间中探索这种酶选择性的方法。这种方法利用了基因组规模模型，该模型已被广泛用于预测各种环境中的生长表型或跟踪基因扰动；然而，这些预测偶尔会失败。基因必需性的错误预测为靶向生物学发现提供了机会，这表明存在源于酶交叉反应性的未知潜在途径。我们展示了一种工作流程，该流程将基于约束的建模和生物信息学工具与基因敲除菌株分析和适应性实验室进化相结合，以在基因组规模上预测选择性。研究了在大肠杆菌中被错误预测为必需基因的三个案例——aspC、argD和gltA——并在不同程度上揭示了每个基因的同工酶功能。基于遗传和转录证据，在aspC和tyrB、argD和astC、gabT和puuE以及gltA和prpC基因之间发现了七种同工酶功能。这项研究展示了一种有针对性的模型驱动发现方法如何能够系统地填补知识空白、表征潜在代谢并阐明响应基因敲除扰动的适应性调节机制。
