Dataset Information

Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme.

ABSTRACT:

Background

Heme has attracted much attention because of its wide applications in medicine and food. The products of genes hemBCDEFY convert 5-aminolevulinic acid to protoporphyrin IX (PPIX; the immediate precursor of heme); protoporphyrin ferrochelatase (FECH) inserts Fe²⁺ into PPIX to generate heme. Biosynthesis of heme is limited by the need for optimized expression levels of multiple genes, complex regulatory mechanisms, and low enzymatic activity; these problems need to be overcome in metabolic engineering to improve heme synthesis.

Results

We report a heme biosensor-guided screening strategy using the heme-responsive protein HrtR to regulate tcR expression in Escherichia coli, providing a quantifiable link between the intracellular heme concentration and cell survival in selective conditions (i.e., the presence of tetracycline). This system was used for rapid enrichment screening of heme-producing strains from a library with random ribosome binding site (RBS) variants and from a FECH mutant library. Through up to four rounds of iterative evolution, strains with optimal RBS intensities for the combination of hemBCDEFY were screened; we obtained a PPIX titer of 160.8 mg/L, the highest yield yet reported in shaken-flask fermentation. A high-activity FECH variant was obtained from the saturation mutagenesis library. Fed-batch fermentation of strain SH20C, harboring the optimized hemBCDEFY and the FECH mutant, produced 127.6 mg/L of heme.

Conclusion

We sequentially improved the multigene biosynthesis pathway of PPIX and performed in vivo directed evolution of FECH, based on a heme biosensor, which demonstrated the effectiveness of the heme biosensor-based pathway optimization strategy and broadens our understanding of the mechanism of heme synthesis.

SUBMITTER: Zhang J

PROVIDER: S-EPMC9979517 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme.

Zhang Jian J Li Qingbin Q Wang Qi Q Zhao Jingyu J Zhu Yuan Y Su Tianyuan T Qi Qingsheng Q Wang Qian Q

Biotechnology for biofuels and bioproducts 20230301 1

<h4>Background</h4>Heme has attracted much attention because of its wide applications in medicine and food. The products of genes hemBCDEFY convert 5-aminolevulinic acid to protoporphyrin IX (PPIX; the immediate precursor of heme); protoporphyrin ferrochelatase (FECH) inserts Fe<sup>2+</sup> into PPIX to generate heme. Biosynthesis of heme is limited by the need for optimized expression levels of multiple genes, complex regulatory mechanisms, and low enzymatic activity; these problems need to be ...[more]

PMID: 36859288

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Project description:BackgroundThe rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration.ResultsThe efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer.ConclusionsAn efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This "pathway architecture designer" workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.

Project description:Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Dataset Information

Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme.

Background

Results

Conclusion

Publications

Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme.

Similar Datasets

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