Understanding and application of Bacillus nitrogen regulation: A synthetic biology perspective

He, Hehe; Li, Youran; Zhang, Liang; Ding, Zhongyang; Shi, Guiyang

doi:10.1016/j.jare.2022.09.003

Cited by 21 publications

(14 citation statements)

References 105 publications

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“…These are the predictions of the machine learning model for the abundance of each metabolite across the carbon and nitrogen concentration ranges. We found that L -arginine, which is linked with 2-oxoglutarate and is in the same pathway with glutamate synthesis, an amino acid in surfactin, possesses high abundancy with higher carbon-nitrogen concentration, suggesting higher activity in the glutamine synthetase-glutamate synthase (GOGAT) cycle and overflowing of this compound to the medium [65] , [66] .…”

Section: Resultsmentioning

confidence: 95%

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry

Albornoz,

Oyarzún,

Burgess

2024

Computational and Structural Biotechnology Journal

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Section: Resultsmentioning

confidence: 95%

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry

Albornoz,

Oyarzún,

Burgess

2024

Computational and Structural Biotechnology Journal

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“…These are the predictions of the machine learning model for the abundance of each metabolite across the carbon and nitrogen concentration ranges. We found that L-arginine, which is linked with 2-oxoglutarate and is in the same pathway with glutamate synthesis, an amino acid in surfactin, possesses high abundancy with higher carbon-nitrogen concentration, suggesting higher activity in the glutamine synthetase-glutamate synthase (GOGAT) cycle and overflowing of this compound to the medium [64, 65].…”

Section: Resultsmentioning

confidence: 99%

Optimisation of surfactin yield inBacillususing active learning and high-throughput mass spectrometry

Albornoz,

Oyarzún,

Burgess

2024

Preprint

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Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160% yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies.

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“…In contrast, the glutamine utilization genes glnJ and glnT , annotated as components of the two-component system (TCS), were upregulated in sample C15 to consume glutamine and accumulate glutamate. Glutamate and glutamine are the primary amino group suppliers for all nitrogen-containing molecules, including other amino acids and building blocks for DNA and RNA synthesis [ 26 ]. In B. subtilis , glutamate serves as the amino group donor in most transamination reactions, including branched-chain amino acid [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

Regulation mechanism and bioactivity characteristic of surfactin homologues with C14 and C15 fatty acid chains

Su,

Gao,

et al. 2024

Microb Cell Fact

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Background Surfactin, a green lipopeptide bio-surfactant, exhibits excellent surface, hemolytic, antibacterial, and emulsifying activities. However, a lack of clear understanding of the synthesis regulation mechanism of surfactin homologue components has hindered the customized production of surfactin products with different biological activities. Results In this study, exogenous valine and 2-methylbutyric acid supplementation significantly facilitated the production of C14–C15 surfactin proportions (up to 75% or more), with a positive correlation between the homologue proportion and fortified concentration. Subsequently, the branched-chain amino acid degradation pathway and the glutamate synthesis pathway are identified as critical pathways in regulating C14–C15 surfactin synthesis by transcriptome analysis. Overexpression of genes bkdAB and glnA resulted in a 1.4-fold and 1.3-fold increase in C14 surfactin, respectively. Finally, the C14-rich surfactin was observed to significantly enhance emulsification activity, achieving an EI24 exceeding 60% against hexadecane, while simultaneously reducing hemolytic activity. Conversely, the C15-rich surfactin demonstrated an increase in both hemolytic and antibacterial activities. Conclusion This study presents the first evidence of a potential connection between surfactin homologue synthesis and the conversion of glutamate and glutamine, providing a theoretical basis for targeting the synthesis regulation and structure–activity relationships of surfactin and other lipopeptide compounds. Graphical Abstract

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Understanding and application of Bacillus nitrogen regulation: A synthetic biology perspective

Cited by 21 publications

References 105 publications

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry

Optimisation of surfactin yield inBacillususing active learning and high-throughput mass spectrometry

Regulation mechanism and bioactivity characteristic of surfactin homologues with C14 and C15 fatty acid chains

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