Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Metabolic Engineering Communications Pub Date : 2023-06-16 DOI:10.1016/j.mec.2023.e00225
Paul Hanke , Bruce Parrello , Olga Vasieva , Chase Akins , Philippe Chlenski , Gyorgy Babnigg , Chris Henry , Fatima Foflonker , Thomas Brettin , Dionysios Antonopoulos , Rick Stevens , Michael Fonstein
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Abstract

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4–5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

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通过组合克隆和机器学习提高细菌L-苏氨酸产量
本研究的目标是使用综合合成生物学和机器学习(ML)方法开发细菌工程的通用策略。该策略是在增加大肠杆菌ATCC 21277中L-苏氨酸产量的背景下开发的。最初基于与苏氨酸生物合成相关的代谢途径选择一组16个基因,并用于组合克隆以构建一组385个菌株以生成训练数据(即,与每个特定基因组合相关的一系列L-苏氨酸滴度)。基于训练数据,开发了混合(回归/分类)深度学习(DL)模型,并用于预测后续几轮组合克隆中的额外基因组合,以增加L-苏氨酸产量。因此,仅经过三轮迭代组合克隆和模型预测后构建的大肠杆菌菌株产生的L-苏氨酸滴度(从2.7 g/L到8.4 g/L)高于用作对照的专利L-苏氨酸菌株(4-5 g/L)。L-苏氨酸生产中有趣的基因组合包括tdh、metL、dapA和dhaM基因的缺失以及pntAB、ppc和aspC基因的过表达。对性能最佳构建体的代谢系统约束的机制分析提供了通过调整特定基因组合的权重来改进模型的方法。成对基因修饰和相应L-苏氨酸产生水平的图论分析也提出了可以纳入未来ML模型的额外规则。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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