Aida Honoka, Uchida Keisuke, Nagai Motoki, Hashizume Takamasa, Masuo Shunsuke, Takaya Naoki, Ying Bei-Wen
School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan.
Microbiology Research Center for Sustainability, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan.
Comput Struct Biotechnol J. 2023 Apr 20;21:2654-2663. doi: 10.1016/j.csbj.2023.04.020. eCollection 2023.
The composition of medium components is crucial for achieving the best performance of synthetic construction in genetically engineered cells. Which and how medium components determine the performance, e.g., productivity, remain poorly investigated. To address the questions, a comparative survey with two genetically engineered strains was performed. As a case study, the strains carried the synthetic pathways for producing the aromatic compounds of 4-aminophenylalanine (4APhe) or tyrosine (Tyr), common in the upstream but differentiated in the downstream metabolism. Bacterial growth and compound production were examined in hundreds of medium combinations that comprised 48 pure chemicals. The resultant data sets linking the medium composition to bacterial growth and production were subjected to machine learning for improved production. Intriguingly, the primary medium components determining the production of 4PheA and Tyr were differentiated, which were the initial resource (glucose) of the synthetic pathway and the inducer (IPTG) of the synthetic construction, respectively. Fine-tuning of the primary component significantly increased the yields of 4APhe and Tyr, indicating that a single component could be crucial for the performance of synthetic construction. Transcriptome analysis observed the local and global changes in gene expression for improved production of 4APhe and Tyr, respectively, revealing divergent metabolic strategies for producing the foreign and native metabolites. The study demonstrated that ML-assisted medium optimization could provide a novel point of view on how to make the synthetic construction meet the designed working principle and achieve the expected biological function.
培养基成分的组成对于在基因工程细胞中实现合成构建体的最佳性能至关重要。培养基成分如何以及哪些成分决定其性能，例如生产力，仍鲜为人知。为了解决这些问题，我们对两种基因工程菌株进行了比较研究。作为一个案例研究，这些菌株携带了用于生产4-氨基苯丙氨酸（4APhe）或酪氨酸（Tyr）芳香族化合物的合成途径，它们在上游是常见的，但在下游代谢中有所不同。我们在由48种纯化学品组成的数百种培养基组合中检测了细菌生长和化合物生产情况。将培养基成分与细菌生长和生产联系起来的所得数据集用于机器学习以提高产量。有趣的是，决定4PheA和Tyr产量的主要培养基成分有所不同，分别是合成途径的初始资源（葡萄糖）和合成构建体的诱导剂（IPTG）。对主要成分的微调显著提高了4APhe和Tyr的产量，这表明单一成分可能对合成构建体的性能至关重要。转录组分析分别观察到了基因表达的局部和全局变化，以提高4APhe和Tyr的产量，揭示了产生外源和内源代谢物的不同代谢策略。该研究表明，机器学习辅助的培养基优化可以为如何使合成构建体符合设计工作原理并实现预期生物学功能提供新的视角。
