Metabolic Engineering Communications最新文献

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.

The polyextremophilic Cyanidiophyceae are eukaryotic red microalgae with promising biotechnological properties arising from their low pH and elevated temperature requirements which can minimize culture contamination at scale. Cyanidioschyzon merolae 10D is a cell wall deficient species with a fully sequenced genome that is amenable to nuclear transgene integration by targeted homologous recombination. C. merolae maintains a minimal carotenoid profile and here, we sought to determine its capacity for ketocarotenoid accumulation mediated by heterologous expression of a green algal β-carotene ketolase (BKT) and hydroxylase (CHYB). To achieve this, a synthetic transgene expression cassette system was built to integrate and express Chlamydomonas reinhardtii (Cr) sourced enzymes by fusing native C. merolae transcription, translation and chloroplast targeting signals to codon-optimized coding sequences. Chloramphenicol resistance was used to select for the integration of synthetic linear DNAs into a neutral site within the host genome. CrBKT expression caused accumulation of canthaxanthin and adonirubin as major carotenoids while co-expression of CrBKT with CrCHYB generated astaxanthin as the major carotenoid in C. merolae. Unlike green algae and plants, ketocarotenoid accumulation in C. merolae did not reduce total carotenoid contents, but chlorophyll a reduction was observed. Light intensity affected global ratios of all pigments but not individual pigment compositions and phycocyanin contents were not markedly different between parental strain and transformants. Continuous illumination was found to encourage biomass accumulation and all strains could be cultivated in simulated summer conditions from two different extreme desert environments. Our findings present the first example of carotenoid metabolic engineering in a red eukaryotic microalga and open the possibility for use of C. merolae 10D for simultaneous production of phycocyanin and ketocarotenoid pigments.

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4–5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

Fatty acids are produced by eukaryotes like baker's yeast Saccharomyces cerevisiae mainly using a large multifunctional type I fatty acid synthase (FASI) where seven catalytic steps and a carrier domain are shared between one or two protein subunits. While this system may offer efficiency in catalysis, only a narrow range of fatty acids are produced. Prokaryotes, chloroplasts and mitochondria rely instead on a FAS type II (FASII) where each catalytic step is carried out by a monofunctional enzyme encoded by a separate gene. FASII is more flexible and capable of producing a wider range of fatty acid structures, such as the direct production of unsaturated fatty acids. An efficient FASII in the preferred industrial organism S. cerevisiae could provide a platform for developing sustainable production of specialized fatty acids. We functionally replaced either yeast FASI genes (FAS1 or FAS2) with a FASII consisting of nine genes from Escherichia coli (acpP, acpS and fab -A, -B, -D, -F, -G, -H, -Z) as well as three from Arabidopsis thaliana (MOD1, FATA1 and FATB). The genes were expressed from an autonomously replicating multicopy vector assembled using the Yeast Pathway Kit for in-vivo assembly in yeast. Two rounds of adaptation led to a strain with a maximum growth rate (μmax) of 0.19 h⁻¹ without exogenous fatty acids, twice the growth rate previously reported for a comparable strain. Additional copies of the MOD1 or fabH genes resulted in cultures with higher final cell densities and three times higher lipid content compared to the control.