Metabolic Engineering Communications最新文献

Phenylpropenes are a class of natural products that are synthesised by a vast range of plant species and hold considerable promise in the flavour and fragrance industries. Many in vitro studies have been carried out to elucidate and characterise the enzymes responsible for the production of these volatile compounds. However, there is a scarcity of studies demonstrating the in vivo production of phenylpropenes in microbial cell factories. In this study, we engineered Escherichia coli to produce methylchavicol, methyleugenol and isoeugenol from their respective phenylacrylic acid precursors. We achieved this by extending and modifying a previously optimised heterologous pathway for the biosynthesis of chavicol and eugenol. We explored the potential of six S-adenosyl l-methionine (SAM)-dependent O-methyltransferases to produce methylchavicol and methyleugenol from chavicol and eugenol, respectively. Additionally, we examined two isoeugenol synthases for the production of isoeugenol from coniferyl acetate. The best-performing strains in this study were able to achieve titres of 13 mg L⁻¹ methylchavicol, 59 mg L⁻¹ methyleugenol and 361 mg L⁻¹ isoeugenol after feeding with their appropriate phenylacrylic acid substrates. We were able to further increase the methyleugenol titre to 117 mg L⁻¹ by supplementation with methionine to facilitate SAM recycling. Moreover, we report the biosynthesis of methylchavicol and methyleugenol from l-tyrosine through pathways involving six and eight enzymatic steps, respectively.

Methionine biosynthesis relies on the sequential catalysis of multiple enzymes. Escherichia coli, the main bacteria used in research and industry for protein production and engineering, utilizes the three-step trans-sulfurylation pathway catalyzed by L-homoserine O-succinyl transferase, cystathionine gamma synthase and cystathionine beta lyase to convert L-homoserine to L-homocysteine. However, most bacteria employ the two-step direct-sulfurylation pathway involving L-homoserine O-acetyltransferases and O-acetyl homoserine sulfhydrylase. We previously showed that a methionine-auxotroph Escherichia coli strain (MG1655) with deletion of metA, encoding for L-homoserine O-succinyl transferase, and metB, encoding for cystathionine gamma synthase, could be complemented by introducing the genes metX, encoding for L-homoserine O-acetyltransferases and metY, encoding for O-acetyl homoserine sulfhydrylase, from various sources, thus altering the Escherichia coli methionine biosynthesis metabolic pathway to direct-sulfurylation. However, introducing metX and metY from Corynebacterium glutamicum failed to complement methionine auxotrophy. Herein, we generated a randomized genetic library based on the metX and metY of Corynebacterium glutamicum and transformed it into a methionine-auxotrophic Escherichia coli strain lacking the metA and metB genes. Through multiple enrichment cycles, we successfully isolated active clones capable of growing in M9 minimal media. The dominant metX mutations in the evolved methionine-autotrophs Escherichia coli were L315P and H46R. Interestingly, we found that a metY gene encoding only the N-terminus 106 out of 438 amino acids of the wild-type MetY enzyme is functional and supports the growth of the methionine auxotroph. Recloning the new genes into the original plasmid and transforming them to methionine auxotroph Escherichia coli validated their functionality. These results show that directed enzyme-evolution enables fast and simultaneous engineering of new active variants within the Escherichia coli methionine direct-sulfurylation pathway, leading to efficient complementation.

The aldehyde 5-(hydroxymethyl)furfural (HMF) is of great importance for a circular bioeconomy. It is a renewable platform chemical that can be converted into a range of useful compounds to replace petroleum-based products such as the green plastic monomer 2,5-furandicarboxylic acid (FDCA). However, it also exhibits microbial toxicity for example hindering the efficient biotechnological valorization of lignocellulosic hydrolysates. Thus, there is an urgent need for tolerance-improved organisms applicable to whole-cell biocatalysis. Here, we engineer an oxidation-deficient derivative of the naturally robust and emerging biotechnological workhorse P. taiwanensis VLB120 by robotics-assisted adaptive laboratory evolution (ALE). The deletion of HMF-oxidizing enzymes enabled for the first time evolution under constant selection pressure by the aldehyde, yielding strains with consistently improved growth characteristics in presence of the toxicant. Genome sequencing of evolved clones revealed loss-of function mutations in the LysR-type transcriptional regulator-encoding mexT preventing expression of the associated efflux pump mexEF-oprN. This knowledge allowed reverse engineering of strains with enhanced aldehyde tolerance, even in a background of active or overexpressed HMF oxidation machinery, demonstrating a synergistic effect of two distinct tolerance mechanisms.

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including K_m, k_cat, K_i, K_eq, and n_h, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Cell based factories can be engineered to produce a wide variety of products. Advances in DNA synthesis and genome editing have greatly simplified the design and construction of these factories. It has never been easier to generate hundreds or even thousands of cell factory strain variants for evaluation. These advances have amplified the need for standardized, higher throughput means of evaluating these designs. Toward this goal, we have previously reported the development of engineered E. coli strains and associated 2-stage production processes to simplify and standardize strain engineering, evaluation and scale up. This approach relies on decoupling growth (stage 1), from production, which occurs in stationary phase (stage 2). Phosphate depletion is used as the trigger to stop growth as well as induce heterologous expression. Here, we describe in detail the development of protocols for the evaluation of engineered E. coli strains in 2-stage microfermentations. These protocols are readily adaptable to the evaluation of strains producing a wide variety of protein as well as small molecule products. Additionally, by detailing the approach to protocol development, these methods are also adaptable to additional cellular hosts, as well as other 2-stage processes with various additional triggers.

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Rhodococcus strains were designed as model biocatalysts (BCs) for the production of acrylic acid and mixtures of acrylic monomers consisting of acrylamide, acrylic acid, and N-alkylacrylamide (N-isopropylacrylamide). To obtain BC strains, we used, among other approaches, adaptive laboratory evolution (ALE), based on the use of the metabolic pathway of amide utilization. Whole genome sequencing of the strains obtained after ALE, as well as subsequent targeted gene disruption, identified candidate genes for three new amidases that are promising for the development of BCs for the production of acrylic acid from acrylamide. New BCs had two types of amidase activities, acrylamide-hydrolyzing and acrylamide-transferring, and by varying the ratio of these activities in BCs, it is possible to influence the ratio of monomers in the resulting mixtures. Based on these strains, a prototype of a new technological concept for the biocatalytic synthesis of acrylic monomers was developed for the production of water-soluble acrylic heteropolymers containing valuable N-alkylacrylamide units. In addition to the possibility of obtaining mixtures of different compositions, the advantages of the concept are a single starting reagent (acrylamide), more unification of processes (all processes are based on the same type of biocatalyst), and potentially greater safety for personnel and the environment compared to existing chemical technologies.

Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.

Geraniol is a monoterpene with wide applications in the food, cosmetics, and pharmaceutical industries. Microbial production has largely used model organisms lacking favorable properties for monoterpene production. In this work, we produced geraniol in metabolically engineered Yarrowia lipolytica. First, two plant-derived geraniol synthases (GES) from Catharanthus roseus (Cr) and Valeriana officinalis (Vo) were tested based on previous reports of activity. Both wild type and truncated mutants of GES (without signal peptide targeting chloroplast) were examined by co-expressing with MVA pathway enzymes tHMG1 and IDI1. Truncated CrGES (tCrGES) produced the most geraniol and thus was used for further experimentation. The initial strain was obtained by overexpression of the truncated HMG1, IDI and tCrGES. The acetyl-CoA precursor pool was enhanced by overexpressing mevalonate pathway genes such as ERG10, HMGS or MVK, PMK. The final strain overexpressing 3 copies of tCrGES and single copies of ERG10, HMGS, tHMG1, IDI produced approximately 1 g/L in shake-flask fermentation. This is the first demonstration of geraniol production in Yarrowia lipolytica and the highest de novo titer reported to date in yeast.

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to ‘peak shift’ to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.