Metabolic engineering最新文献

Rosavin is the characteristic component of Rhodiola rosea L., an important medicinal plant used widely in the world that has been reported to possess multiple biological activities. However, the endangered status of wild Rhodiola has limited the supply of rosavin. In this work, we successfully engineered an Escherichia coli strain to efficiently produce rosavin as an alternative production method. Firstly, cinnamate: CoA ligase from Hypericum calycinum, cinnamoyl-CoA reductase from Lolium perenne, and uridine diphosphate (UDP)-glycosyltransferase (UGT) from Bacillus subtilis (Bs-YjiC) were selected to improve the titer of rosin in E. coli. Subsequently, four UGTs from the UGT91R subfamily were identified to catalyze the formation of rosavin from rosin, with SlUGT91R1 from Solanum lycopersicum showing the highest activity level. Secondly, production of rosavin was achieved for the first time in E. coli by incorporating the SlUGT91R1 and UDP-arabinose pathway, including UDP-glucose dehydrogenase, UDP-xylose synthase, and UDP-xylose 4-epimerase, into the rosin-producing stain, and the titer reached 430.5 ± 91.4 mg/L. Thirdly, a two-step pathway derived from L-arabinose, composed of L-arabinokinase and UDP-sugar pyrophosphorylase, was developed in E. coli to further optimize the supply of the precursor UDP-arabinose. Furthermore, 1203.7 ± 32.1 mg/L of rosavin was produced from D-glucose and L-arabinose using shake-flask fermentation. Finally, the production of rosavin reached 7539.1 ± 228.7 mg/L by fed-batch fermentation in a 5-L bioreactor. Thus, the microbe-based production of rosavin shows great potential for commercialization. This work provides an effective strategy for the biosynthesis of other valuable natural products with arabinose-containing units from D-glucose and L-arabinose.

Curcumin is a polyphenolic natural product from the roots of turmeric (Curcuma longa). It has been a popular coloring and flavoring agent in food industries with known health benefits. The conventional phenylpropanoid pathway is known to proceed from phenylalanine via p-coumaroyl-CoA intermediate. Although hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyl transferase (HCT) plays a key catalysis in the biosynthesis of phenylpropanoid products at the downstream of p-coumaric acid, a recent discovery of caffeoyl-shikimate esterase (CSE) showed that an alternative pathway exists. Here, the biosynthetic efficiency of the conventional and the alternative pathway in producing feruloyl-CoA was examined using curcumin production in yeast. A novel modular multiplex genome-edit (MMG)-CRISPR platform was developed to facilitate rapid integrations of up to eight genes into the yeast genome in two steps. Using this MMG-CRISPR platform and metabolic engineering strategies, the alternative CSE phenylpropanoid pathway consistently showed higher titers (2–19 folds) of curcumin production than the conventional pathway in engineered yeast strains. In shake flask cultures using a synthetic minimal medium without phenylalanine, the curcumin production titer reached up to 1.5 mg/L, which is three orders of magnitude (∼4800-fold) improvement over non-engineered base strain. This is the first demonstration of de novo curcumin biosynthesis in yeast. Our work shows the critical role of CSE in improving the metabolic flux in yeast towards the phenylpropanoid biosynthetic pathway. In addition, we showcased the convenience and reliability of modular multiplex CRISPR/Cas9 genome editing in constructing complex synthetic pathways in yeast.

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin–Benson–Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.

Diterpenoids form a diverse group of natural products, many of which are or could become pharmaceuticals or industrial chemicals. The modular character of diterpene biosynthesis and the promiscuity of the enzymes involved make combinatorial biosynthesis a promising approach to generate libraries of diverse diterpenoids. Here, we report on the combinatorial assembly in yeast of ten diterpene synthases producing (+)-copalyl diphosphate-derived backbones and four cytochrome P450 oxygenases (CYPs) in diverse combinations. This resulted in the production of over 200 diterpenoids. Based on literature and chemical database searches, 162 of these compounds can be considered new-to-Nature. The CYPs accepted most substrates they were given but remained regioselective with few exceptions. Our results provide the basis for the systematic exploration of the diterpenoid chemical space in yeast using sequence databases.

Cyclo-diphenylalanine (cFF) is a symmetrical aromatic diketopiperazine (DKP) found wide-spread in microbes, plants, and resulting food products. As different bioactivities continue being discovered and relevant food and pharmaceutical applications gradually emerge for cFF, there is a growing need for establishing convenient and efficient methods to access this type of compound. Here, we present a robust cFF production system which entailed stepwise engineering of the filamentous fungal strain Aspergillus nidulans A1145 as a heterologous expression host. We first established a preliminary cFF producing strain by introducing the heterologous nonribosomal peptide synthetase (NRPS) gene penP1 to A. nidulans A1145. Key metabolic pathways involving shikimate and aromatic amino acid biosynthetic support were then engineered through a combination of gene deletions of competitive pathway steps, over-expressing feedback-insensitive enzymes in phenylalanine biosynthesis, and introducing a phosphoketolase-based pathway, which diverted glycolytic flux toward the formation of erythrose 4-phosphate (E4P). Through the stepwise engineering of A. nidulans A1145 outlined above, involving both heterologous pathway addition and native pathway metabolic engineering, we were able to produce cFF with titers reaching 611 mg/L in shake flask culture and 2.5 g/L in bench-scale fed-batch bioreactor culture. Our study establishes a production platform for cFF biosynthesis and successfully demonstrates engineering of phenylalanine derived diketopiperazines in a filamentous fungal host.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Protocatechuic acid (3, 4-dihydroxybenzoic acid, PCA) is widely used in the pharmaceuticals, health food, and cosmetics industries owing to its diverse biological activities. However, the inhibition of 3-dehydroshikimate dehydratase (AroZ) by PCA and its toxicity to cells limit the efficient production of PCA in Escherichia coli. In this study, a high-level strain of 3-dehydroshikimate, E. coli DHS01, was developed by blocking the carbon flow from the shikimate-overproducing strain E. coli SA09. Additionally, the PCA biosynthetic pathway was established in DHS01 by introducing the high-activity ApAroZ. Subsequently, the protein structure and catalytic mechanism of 3-dehydroshikimate dehydratase from Acinetobacter pittii PHEA-2 (ApAroZ) were clarified. The variant ApAroZ^R363A, achieved by modulating the conformational dynamics of ApAroZ, effectively relieved product inhibition. Additionally, the tolerance of the strain E. coli PCA04 to PCA was enhanced by adaptive laboratory evolution, and a biosensor-assisted high-throughput screening method was designed and implemented to expedite the identification of high-performance PCA-producing strains. Finally, in a 5 L bioreactor, the final strain PCA05 achieved the highest PCA titer of 46.65 g/L, a yield of 0.23 g/g, and a productivity of 1.46 g/L/h for PCA synthesis from glucose using normal fed-batch fermentation. The strategies described herein serve as valuable guidelines for the production of other high-value and toxic products.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the “IPP-bypass” pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.