Integrated pathway mining and selection of an artificial CYP79-mediated bypass to improve benzylisoquinoline alkaloid biosynthesis.

BACKGROUND: Computational mining of useful enzymes and biosynthesis pathways is a powerful strategy for metabolic engineering. Through systematic exploration of all conceivable combinations of enzyme reactions, including both known compounds and those inferred from the chemical structures of established reactions, we can uncover previously undiscovered enzymatic processes. The application of the novel alternative pathways enables us to improve microbial bioproduction by bypassing or reinforcing metabolic bottlenecks. Benzylisoquinoline alkaloids (BIAs) are a diverse group of plant-derived compounds with important pharmaceutical properties. BIA biosynthesis has developed into a prime example of metabolic engineering and microbial bioproduction. The early bottleneck of BIA production in Escherichia coli consists of 3,4-dihydroxyphenylacetaldehyde (DHPAA) production and conversion to tetrahydropapaveroline (THP). Previous studies have selected monoamine oxidase (MAO) and DHPAA synthase (DHPAAS) to produce DHPAA from dopamine and oxygen; however, both of these enzymes produce toxic hydrogen peroxide as a byproduct. RESULTS: In the current study, in silico pathway design is applied to relieve the bottleneck of DHPAA production in the synthetic BIA pathway. Specifically, the cytochrome P450 enzyme, tyrosine N-monooxygenase (CYP79), is identified to bypass the established MAO- and DHPAAS-mediated pathways in an alternative arylacetaldoxime route to DHPAA with a peroxide-independent mechanism. The application of this pathway is proposed to result in less formation of toxic byproducts, leading to improved production of reticuline (up to 60 mg/L at the flask scale) when compared with that from the conventional MAO pathway. CONCLUSIONS: This study showed improved reticuline production using the bypass pathway predicted by the M-path computational platform. Reticuline production in E. coli exceeded that of the conventional MAO-mediated pathway. The study provides a clear example of the integration of pathway mining and enzyme design in creating artificial metabolic pathways and suggests further potential applications of this strategy in metabolic engineering.

Machine learning discovery of missing links that mediate alternative branches to plant alkaloids.

Engineering the microbial production of secondary metabolites is limited by the known reactions of correctly annotated enzymes. Therefore, the machine learning discovery of specialized enzymes offers great potential to expand the range of biosynthesis pathways. Benzylisoquinoline alkaloid production is a model example of metabolic engineering with potential to revolutionize the paradigm of sustainable biomanufacturing. Existing bacterial studies utilize a norlaudanosoline pathway, whereas plants contain a more stable norcoclaurine pathway, which is exploited in yeast. However, committed aromatic precursors are still produced using microbial enzymes that remain elusive in plants, and additional downstream missing links remain hidden within highly duplicated plant gene families. In the current study, machine learning is applied to predict and select plant missing link enzymes from homologous candidate sequences. Metabolomics-based characterization of the selected sequences reveals potential aromatic acetaldehyde synthases and phenylpyruvate decarboxylases in reconstructed plant gene-only benzylisoquinoline alkaloid pathways from tyrosine. Synergistic application of the aryl acetaldehyde producing enzymes results in enhanced benzylisoquinoline alkaloid production through hybrid norcoclaurine and norlaudanosoline pathways.

An ion-pair free LC-MS/MS method for quantitative metabolite profiling of microbial bioproduction systems.

Data-driven engineering of microbes has been demonstrated for the sustainable production of high-performance chemicals. Metabolic profiling analysis is essential to increase the productivity of target compounds. However, improvement of comprehensive analysis methodologies is required for the high demands of metabolic engineering. Therefore, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodology was designed and applied to cover a wide target range with high precision. Ion-pair free separation of metabolites on a pentafluorophenyl propyl column enabled high-precision quantification of 113 metabolites. The method was further evaluated for high reproducibility and robustness. Target analytes consisted of primary metabolites and intermediate metabolites for microbial production of high-performance chemicals. 95 metabolites could be detected with high reproducibility of peak area (intraday data: CV<15%), and 53 metabolites could be sensitively determined within a wide dynamic linear range (3-4 orders of magnitude). The developed system was further applied to the metabolomic analysis of various prokaryotic and eukaryotic microorganisms. Differences due to culture media and metabolic phenotypes could be observed when comparing the metabolomes of conventional and non-conventional yeast. Furthermore, almost all Kluyveromyces marxianus metabolites could be detected with moderate reproducibility (CV<40%, among independent extractions), where 41 metabolites were detected with very high reproducibility (CV<15%). In addition, the accuracy was validated via a spike-and-recovery test,and 78 metabolites were detected with analyte recovery in the 80-120% range. Together these results establish ion-pair free metabolic profiling as a comprehensive and precise tool for data-driven bioengineering applications.

Efficient and Supplementary Enzyme Cocktail from Actinobacteria and Plant Biomass Induction.

Actinobacteria plays a key role in the cycling of organic matter in soils. They secret biomass-degrading enzymes that allow it to produce the unique metabolites that originate in plant biomass. Although past studies have focused on these unique metabolites, a large-scale screening of Actinobacteria is yet to be reported to focus on their biomass-degrading ability. In the present study, a rapid and simple method is constructed for a large-scale screening, and the novel resources that form the plant biomass-degrading enzyme cocktail are identified from 850 isolates of Actinobacteria. As a result, Nonomuraea fastidiosa secretes a biomass degrading enzyme cocktail with the highest enzyme titer, although cellulase activities are lower than a commercially available enzyme. So the rich accessory enzymes are suggested to contribute to the high enzyme titer for a pretreated bagasse with a synergistic effect. Additionally, an optimized cultivation method of biomass induction caused to produce the improved enzyme cocktail indicated strong enzyme titers and a strong synergistic effect. Therefore, the novel enzyme cocktails are selected via the optimized method for large-scale screening, and then the enzyme cocktail can be improved via the optimized production with biomass-induction.

DNA-duplex linker for AFM-SELEX of DNA aptamer against human serum albumin.

DNA-duplex interactions in thymines and adenins are used as a linker for the novel methodology of Atomic Force Microscope-Systematic Evolution of Ligands by EXpotential enrichment (AFM-SELEX). This study used the hydrogen bonds in 10 mer of both thymines (T10) and adenines (A10). Initially, the interactive force in T10-A10 was measured by AFM, which returned an average interactive force of approximately 350pN. Based on this result, DNA aptamers against human serum albumin could be selected in the 4th round, and 15 different clones could be sequenced. The lowest dissociation constant of the selected aptamer was identified via surface plasmon resonance, and it proved to be identical to that of the commercial aptamer. Therefore, specific hydrogen bonds in DNA can be useful linkers for AFM-SELEX.

Mapping of endoglucanases displayed on yeast cell surface using atomic force microscopy.

The surface of yeast cells has been an attractive interface for the effective use of cellulose. Surface enzymes, however, are difficult to visualize and evaluate. In this study, two kinds of unique anchoring regions were used to display the cellulase, endoglucanase (EG), on a yeast cell surface. Differences in the display level and the localization of EG were observed by atomic force microscopy. By surveying the yeast cell surface with a chemically modified cantilever, the interactive force between the cellulose and EG was measured. Force curve mapping revealed differences in the display levels and the localization of EG according to anchoring regions. The proposed methodology enables visualization of displayed enzymes such as EG on the yeast cell surface.

The mapping of yeast's G-protein coupled receptor with an atomic force microscope.

An atomic force microscope (AFM) can measure the adhesion force between a sample and a cantilever while simultaneously applying a rupture force during the imaging of a sample. An AFM should be useful in targeting specific proteins on a cell surface. The present study proposes the use of an AFM to measure the adhesion force between targeting receptors and their ligands, and to map the targeting receptors. In this study, Ste2p, one of the G protein-coupled receptors (GPCRs), was chosen as the target receptor. The specific force between Ste2p on a yeast cell surface and a cantilever modified with its ligand, α-factor, was measured and found to be approximately 250 pN. In addition, through continuous measuring of the cell surface, a mapping of the receptors on the cell surface could be performed, which indicated the differences in the Ste2p expression levels. Therefore, the proposed AFM system is accurate for cell diagnosis.