文章基本信息

标题：Model-driven discovery of underground metabolic functions in Escherichia coli
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作者：Gabriela I. Guzmán ; José Utrilla ; Sergey Nurk 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2015
卷号：112
期号：3
页码：929-934
DOI：10.1073/pnas.1414218112
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceOrganisms have evolved to take advantage of their environment. Enzymes drive this adaptability by displaying flexibility in terms of substrate specificity and catalytic promiscuity. This enzyme promiscuity has been observed in a limited number of laboratory experiments; however, a larger underground network of reactions may occur within a cell below the level of detection. It is not until a cell's metabolic capabilities are probed that these novel functions come to light. In this study, a workflow is presented for probing promiscuous activity at the genome scale. This workflow combines genome-scale reconstructions of metabolic networks with gene KOs and adaptive laboratory evolution. Such tools become increasingly important when designing drugs targeting pathogenic bacteria or engineering enzymes and bacteria for biotechnology applications. 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.
关键词：underground metabolism ; substrate promiscuity ; systems biology ; isozyme discovery ; genome-scale modeling