Metabolic engineering最新文献

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L⁻¹ in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

Microbial CO₂ fixation into lactic acid (LA) is an important approach for low-carbon biomanufacturing. Engineering microbes to utilize CO₂ and sugar as co-substrates can create efficient pathways through input of moderate reducing power to drive CO₂ fixation into product. However, to achieve complete conservation of organic carbon, how to engineer the CO₂-fixing modules compatible with native central metabolism and merge the processes for improving bioproduction of LA is a big challenge. In this study, we designed and constructed a solar formic acid/pentose (SFAP) pathway in Escherichia coli, which enabled CO₂ fixation merging into sugar catabolism to produce LA. In the SFAP pathway, adequate reducing equivalents from formate oxidation drive glucose metabolism shifting from glycolysis to the pentose phosphate pathway. The Rubisco-based CO₂ fixation and sequential reduction of C3 intermediates are conducted to produce LA stoichiometrically. CO₂ fixation theoretically can bring a 20% increase of LA production compared with sole glucose feedstock. This SFAP pathway in the integration of photoelectrochemical cell and an engineered Escherichia coli opens an efficient way for fixing CO₂ into value-added bioproducts.

Consolidated bioprocessing (CBP) of lignocellulosic biomass holds promise to realize economic production of second-generation biofuels/chemicals, and Clostridium thermocellum is a leading candidate for CBP due to it being one of the fastest degraders of crystalline cellulose and lignocellulosic biomass. However, CBP by C. thermocellum is approached with co-cultures, because C. thermocellum does not utilize hemicellulose. When compared with a single-species fermentation, the co-culture system introduces unnecessary process complexity that may compromise process robustness. In this study, we engineered C. thermocellum to co-utilize hemicellulose without the need for co-culture. By evolving our previously engineered xylose-utilizing strain in xylose, an evolved clonal isolate (KJC19-9) was obtained and showed improved specific growth rate on xylose by ∼3-fold and displayed comparable growth to a minimally engineered strain grown on the bacteria's naturally preferred substrate, cellobiose. To enable full xylan deconstruction to xylose, we recombinantly expressed three different β-xylosidase enzymes originating from Thermoanaerobacterium saccharolyticum into KJC19-9 and demonstrated growth on xylan with one of the enzymes. This recombinant strain was capable of co-utilizing cellulose and xylan simultaneously, and we integrated the β-xylosidase gene into the KJC19-9 genome, creating the KJCBXint strain. The strain, KJC19-9, consumed monomeric xylose but accumulated xylobiose when grown on pretreated corn stover, whereas the final KJCBXint strain showed significantly greater deconstruction of xylan and xylobiose. This is the first reported C. thermocellum strain capable of degrading and assimilating hemicellulose polysaccharide while retaining its cellulolytic capabilities, unlocking significant potential for CBP in advancing the bioeconomy.

Monoterpenes and monoterpenoids such as (S)-limonene and geraniol are valuable chemicals with a wide range of applications, including cosmetics, pharmaceuticals, and biofuels. Saccharomyces cerevisiae has proven to be an effective host to produce various terpenes and terpenoids. (S)-limonene and geraniol are produced from geranyl pyrophosphate (GPP) through the enzymatic actions of limonene synthase (LS) and geraniol synthase (GES), respectively. However, a major hurdle in their production arises from the dual functionality of the Erg20, a farnesyl pyrophosphate (FPP) synthase, responsible for generating GPP. Erg20 not only synthesizes GPP by condensing isopentenyl pyrophosphate (IPP) with dimethylallyl pyrophosphate but also catalyzes further condensation of IPP with GPP to produce FPP. In this study, we have tackled this issue by harnessing previously developed Erg20 mutants, Erg20^K197G (Erg20^G) and Erg20^{F96W, N127W} (Erg20^WW), which enhance GPP accumulation. Through a combination of these mutants, we generated a novel Erg20^WWG mutant with over four times higher GPP accumulating capability than Erg20^WW, as observed through geraniol production levels. The Erg20^WWG mutant was fused to the LS from Mentha spicata or the GES from Catharanthus roseus for efficient conversion of GPP to (S)-limonene and geraniol, respectively. Further improvements were achieved by localizing the entire mevalonate pathway and the Erg20^WWG-fused enzymes in peroxisomes, while simultaneously downregulating the essential ERG20 gene using the glucose-sensing HXT1 promoter. In the case of (S)-limonene production, additional Erg20^WWG-LS was expressed in the cytosol. As a result, the final strains produced 1063 mg/L of (S)-limonene and 1234 mg/L of geraniol by fed-batch biphasic fermentations with ethanol feeding. The newly identified Erg20^WWG mutant opens doors for the efficient production of various other GPP-derived chemicals including monoterpene derivatives and cannabinoids.

Polymyxin is a lipopeptide antibiotic that is effective against multidrug-resistant Gram-negative bacteria. However, its clinical development is limited due to low titer and the presence of homologs. To address this, the polymyxin gene cluster was integrated into Bacillus subtilis, and sfp from Paenibacillus polymyxa was expressed heterologously, enabling recombinant B. subtilis to synthesize polymyxin B. Regulating NRPS domain inhibited formation of polymyxin B2 and B3. The production of polymyxin B increased to 329.7 mg/L by replacing the native promoters of pmxA, pmxB, and pmxE with PfusA, C2up, and PfusA, respectively. Further enhancement in this production, up to 616.1 mg/L, was achieved by improving the synthesis ability of 6-methyloctanoic acid compared to the original strain expressing polymyxin heterologously. Additionally, incorporating an anikasin-derived domain into the hybrid nonribosomal peptide synthase of polymyxin increased the B1 ratio in polymyxin B from 57.5% to 62.2%. Through optimization of peptone supply in the fermentation medium and fermentation in a 5.0-L bioreactor, the final polymyxin B titer reached 962.1 mg/L, with a yield of 19.24 mg/g maltodextrin and a productivity of 10.02 mg/(L·h). This study demonstrates a successful approach for enhancing polymyxin B production and increasing the B1 ratio through combinatorial metabolic engineering.

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (¹³C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of ¹³C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current ¹³C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based ¹³C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

Especially for the production of artificial, difficult to express molecules a further development of the CHO production cell line is required to keep pace with the continuously increasing demands. However, the identification of novel targets for cell line engineering to improve CHO cells is a time and cost intensive process. Since plasma cells are evolutionary optimized for a high antibody expression in mammals, we performed a comprehensive multi-omics comparison between CHO and plasma cells to exploit optimized cellular production traits. Comparing the transcriptome, proteome, miRNome, surfaceome and secretome of both cell lines identified key differences including 392 potential overexpression targets for CHO cell engineering categorized in 15 functional classes like transcription factors, protein processing or secretory pathway. In addition, 3 protein classes including 209 potential knock-down/out targets for CHO engineering were determined likely to affect aggregation or proteolysis. For production phenotype engineering, several of these novel targets were successfully applied to transient and transposase mediated overexpression or knock-down strategies to efficiently improve productivity of CHO cells. Thus, substantial improvement of CHO productivity was achieved by taking nature as a blueprint for cell line engineering.

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO₂ and O₂, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO₂ fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

Precise control of gene expression is critical for optimizing cellular metabolism and improving the production of valuable biochemicals. However, hard-wired approaches to pathway engineering, such as optimizing promoters, can take time and effort. Moreover, limited tools exist for controlling gene regulation in non-conventional hosts. Here, we develop a two-channel chemically-regulated gene expression system for the multi-stress tolerant yeast Kluyveromyces marxianus and use it to tune ethyl acetate production, a native metabolite produced at high titers in this yeast. To achieve this, we repurposed the plant hormone sensing modules (PYR1^ABA/HAB1 and PYR1*^MANDI/HAB1*) for high dynamic-range gene activation and repression controlled by either abscisic acid (ABA) or mandipropamid (mandi). To redirect metabolic flux towards ethyl acetate biosynthesis, we simultaneously repress pyruvate dehydrogenase (PDA1) and activate pyruvate decarboxylase (PDC1) to enhance ethyl acetate titers. Thus, we have developed new tools for chemically tuning gene expression in K. marxianus and S. cerevisiae that should be deployable across many non-conventional eukaryotic hosts.