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知识驱动的DBTL循环在优化大肠杆菌中多巴胺生成的同时提供了机制性见解。

The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli.

作者信息

Hägele Lorena, Trachtmann Natalia, Takors Ralf

机构信息

Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.

Laboratory of Molecular Genetics and Microbiology Methods, Kazan Scientific Center of Russian Academy of Sciences, 420111, Kazan, Russia.

出版信息

Microb Cell Fact. 2025 May 16;24(1):111. doi: 10.1186/s12934-025-02729-6.

DOI:10.1186/s12934-025-02729-6
PMID:40380156
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12084978/
Abstract

BACKGROUND

Dopamine is a promising organic compound with several key applications in emergency medicine, diagnosis and treatment of cancer, production of lithium anodes, and wastewater treatment. Since studies on in vivo dopamine production are limited, this study demonstrates the development and optimisation of a dopamine production strain by the help of the knowledge driven design-build-test-learn (DBTL) cycle for rational strain engineering.

RESULTS

The knowledge driven DBTL cycle, involving upstream in vitro investigation, is an automated workflow that enables both mechanistic understanding and efficient DBTL cycling. Following the in vitro cell lysate studies, the results were translated to the in vivo environment through high-throughput ribosome binding site (RBS) engineering. As a result, we developed a dopamine production strain capable of producing dopamine at concentrations of 69.03 ± 1.2 mg/L which equals 34.34 ± 0.59 mg/g. Compared to state-of-the-art in vivo dopamine production, our approach improved performance by 2.6 and 6.6-fold, respectively.

CONCLUSION

In essence, a highly efficient dopamine production strain was developed by implementing the knowledge driven DBTL cycle involving upstream in vitro investigation. The fine-tuning of the dopamine pathway by high-throughput RBS engineering clearly demonstrated the impact of GC content in the Shine-Dalgarno sequence on the RBS strength.

摘要

背景

多巴胺是一种有前景的有机化合物,在急诊医学、癌症诊断与治疗、锂阳极生产及废水处理等方面有若干关键应用。由于关于体内多巴胺生成的研究有限,本研究借助知识驱动的设计-构建-测试-学习(DBTL)循环进行理性菌株工程设计,展示了多巴胺生产菌株的开发与优化。

结果

涉及上游体外研究的知识驱动DBTL循环是一种自动化工作流程,既能实现机理理解,又能实现高效的DBTL循环。继体外细胞裂解物研究之后,通过高通量核糖体结合位点(RBS)工程将结果转化到体内环境。结果,我们开发出一种多巴胺生产菌株,其能够以69.03±1.2毫克/升的浓度生产多巴胺,相当于34.34±0.59毫克/克。与最先进的体内多巴胺生产相比,我们的方法分别将性能提高了2.6倍和6.6倍。

结论

本质上,通过实施涉及上游体外研究的知识驱动DBTL循环,开发出了一种高效的多巴胺生产菌株。通过高通量RBS工程对多巴胺途径进行微调,清楚地证明了Shine-Dalgarno序列中GC含量对RBS强度的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/5c3e6da098df/12934_2025_2729_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/fc5a9bf6b354/12934_2025_2729_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/b6b8dbc522a8/12934_2025_2729_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/db53316844cd/12934_2025_2729_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/3943b21cb932/12934_2025_2729_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/5c3e6da098df/12934_2025_2729_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/fc5a9bf6b354/12934_2025_2729_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/b6b8dbc522a8/12934_2025_2729_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/db53316844cd/12934_2025_2729_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/3943b21cb932/12934_2025_2729_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3551/12084978/5c3e6da098df/12934_2025_2729_Fig5_HTML.jpg

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2
AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering.AutoBioTech─一个用于自动化菌株工程的多功能生物铸造厂。
ACS Synth Biol. 2024 Jul 19;13(7):2227-2237. doi: 10.1021/acssynbio.4c00298. Epub 2024 Jul 8.
3
Metabolic Engineering of for the High-Level Production of l-Valine under Aerobic Conditions.
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4
Establishing an Artificial Pathway for the Biosynthesis of Octopamine and Synephrine.建立章鱼胺和辛弗林生物合成的人工途径。
ACS Synth Biol. 2024 Jun 21;13(6):1762-1772. doi: 10.1021/acssynbio.4c00082. Epub 2024 May 30.
5
teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.teemi:一种生物工程中迭代设计-构建-测试-学习循环的开源文学编程方法。
PLoS Comput Biol. 2024 Mar 8;20(3):e1011929. doi: 10.1371/journal.pcbi.1011929. eCollection 2024 Mar.
6
"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization"."使用实验室自动化和贝叶斯优化进行催化活性包涵体的高通量筛选"。
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7
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8
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ACS Synth Biol. 2024 Jan 19;13(1):206-219. doi: 10.1021/acssynbio.3c00441. Epub 2023 Dec 19.
9
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10
Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.模拟设计-构建-测试-学习循环,以在代谢工程中对机器学习方法进行一致比较。
ACS Synth Biol. 2023 Sep 15;12(9):2588-2599. doi: 10.1021/acssynbio.3c00186. Epub 2023 Aug 24.