Design Of Metabolic Pathways Research Articles

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

An Automated Cell-Free Workflow for Transcription Factor Engineering.

The design and optimization of metabolic pathways, genetic systems, and engineered proteins rely on high-throughput assays to streamline design-build-test-learn cycles. However, assay development is a time-consuming and laborious process. Here, we create a generalizable approach for the tailored optimization of automated cell-free gene expression (CFE)-based workflows, which offers distinct advantages over in vivo assays in reaction flexibility, control, and time to data. Centered around designing highly accurate and precise transfers on the Echo Acoustic Liquid Handler, we introduce pilot assays and validation strategies for each stage of protocol development. We then demonstrate the efficacy of our platform by engineering transcription factor-based biosensors. As a model, we rapidly generate and assay libraries of 127 MerR and 134 CadR transcription factor variants in 3682 unique CFE reactions in less than 48 h to improve limit of detection, selectivity, and dynamic range for mercury and cadmium detection. This was achieved by assessing a panel of ligand conditions for sensitivity (to 0.1, 1, 10 μM Hg and 0, 1, 10, 100 μM Cd for MerR and CadR, respectively) and selectivity (against Ag, As, Cd, Co, Cu, Hg, Ni, Pb, and Zn). We anticipate that our Echo-based, cell-free approach can be used to accelerate multiple design workflows in synthetic biology.

Design and construction of artificial metabolic pathways for the bioproduction of useful compounds

Design and construction of artificial metabolic pathways for the bioproduction of useful compounds

Perspectives for Using CO2 as a Feedstock for Biomanufacturing of Fuels and Chemicals.

Microbial cell factories offer an eco-friendly alternative for transforming raw materials into commercially valuable products because of their reduced carbon impact compared to conventional industrial procedures. These systems often depend on lignocellulosic feedstocks, mainly pentose and hexose sugars. One major hurdle when utilizing these sugars, especially glucose, is balancing carbon allocation to satisfy energy, cofactor, and other essential component needs for cellular proliferation while maintaining a robust yield. Nearly half or more of this carbon is inevitably lost as CO2 during the biosynthesis of regular metabolic necessities. This loss lowers the production yield and compromises the benefit of reducing greenhouse gas emissions-a fundamental advantage of biomanufacturing. This review paper posits the perspectives of using CO2 from the atmosphere, industrial wastes, or the exhausted gases generated in microbial fermentation as a feedstock for biomanufacturing. Achieving the carbon-neutral or -negative goals is addressed under two main strategies. The one-step strategy uses novel metabolic pathway design and engineering approaches to directly fix the CO2 toward the synthesis of the desired products. Due to the limitation of the yield and efficiency in one-step fixation, the two-step strategy aims to integrate firstly the electrochemical conversion of the exhausted CO2 into C1/C2 products such as formate, methanol, acetate, and ethanol, and a second fermentation process to utilize the CO2-derived C1/C2 chemicals or co-utilize C5/C6 sugars and C1/C2 chemicals for product formation. The potential and challenges of using CO2 as a feedstock for future biomanufacturing of fuels and chemicals are also discussed.

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.

MooSeeker: A Metabolic Pathway Design Tool Based on Multi-Objective Optimization Algorithm.

Recently, metabolic pathway design has attracted considerable attention and become an increasingly important area in metabolic engineering. Manual or computational methods have been introduced to retrieve the metabolic pathway. These methods model metabolic pathway design as a single-objective optimization problem with the weighted sum of a variety of criteria as the final score. While these methods have demonstrated promising results, the majority of current methods do not account for comparisons and competition among criteria. Here, we propose MooSeeker, a metabolic pathway design tool based on the multi-objective optimization algorithm that aims to trade off all the criteria optimally. The metabolic pathway design problem is characterized as a multi-objective optimization problem with three objectives including pathway length, thermodynamic feasibility and theoretical yield. In order to digitize the continuous metabolic pathway, MooSeeker develops the encoding strategy, BioCrossover and BioMutation operators to search for the candidate pathways. Finally, MooSeeker outputs the Pareto optimal solutions of the candidate metabolic pathways with three criterion values. The experiment results show that MooSeeker is capable of constructing the experimentally validated pathways and finding the higher-performance pathway than the single-objective-based methods.

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference.

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact: jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn.

Recent Progress in Metabolic Engineering of Escherichia coli for the Production of Various C4 and C5-Dicarboxylic Acids.

As an alternative to petrochemical synthesis, well-established industrial microbes, such as Escherichia coli, are employed to produce a wide range of chemicals, including dicarboxylic acids (DCAs), which have significant potential in diverse areas including biodegradable polymers. The demand for biodegradable polymers has been steadily rising, prompting the development of efficient production pathways on four- (C4) and five-carbon (C5) DCAs derived from central carbon metabolism to meet the increased demand via the biosynthesis. In this context, E. coli is utilized to produce these DCAs through various metabolic engineering strategies, including the design or selection of metabolic pathways, pathway optimization, and enhancement of catalytic activity. This review aims to highlight the recent advancements in metabolic engineering techniques for the production of C4 and C5 DCAs in E. coli.

Modular Cloning by Golden Gate Assembly and Possible Application in Pathway Design.

Preparation of expression vectors using conventional cloning strategies is laborious and not suitable for the design of metabolic pathways or enzyme cascades, which usually requires the preparation of a vector library to identify productive clones. Recently, Modular Cloning as a novel cloning technique in synthetic biology has been developed. Modular Cloning relies on Golden Gate assembly and supports preparation of individual expression vectors in one-step and one-pot reactions, thus allowing rapid generation of vector libraries. A number of Modular Cloning toolkits for specific applications has been established, providing a collection of distinct genetic elements such as promoters, ribosome binding sites and tags, that can be combined individually in one-step using defined fusion sites. Modular Cloning has been successfully applied to generate various strains for producing value-added compounds. This was achieved by orchestrating complex pathways involving up to 20 enzymes. Due to the novelty of the genetic approach, industrial applications are still rare. In addition, some applications are limited due to the lack of high-throughput screening methods. This shifts the bottleneck from library preparation to screening capacity and needs to be addressed by future developments to pave the path for the establishment of Modular Cloning in industrial applications.

Omics data for sampling thermodynamically feasible kinetic models

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable.In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism.Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

An update on the review of microbial synthesis of glucosamine and N-acetylglucosamine.

Glucosamine (GlcN) is a natural amino monosaccharide in which a hydroxyl group of glucose is substituted by an amino group. It belongs to functional amino sugar compounds. In the traditional preparation process, GlcN and GlcNAc are obtained by hydrolyzing the cell wall of shrimp and crab. There are many potential problems with this method, such as geographical and seasonal restrictions on the supply of raw materials, serious environmental pollution and potential allergic reactions. Microbial fermentation has the advantages of mild conditions, low environmental pollution, high production intensity, and product safety. It can effectively solve the problem of shrimp and crab hydrolysis process, attracting many researchers to participate in the research of microbial fermentation production of GlcN. This paper mainly summarizes the research on strain construction method, metabolic pathway design and fermentation condition optimization in microbial fermentation, which has certain guiding significance for the further production, research and production of glucosamine.

Investigation of two metabolic engineering approaches for (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis

BackgroundFlux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in Bacillus subtilis to enhance (R,R)-2,3-butanediol (2,3-BD) production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis.ResultsSingle-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings.ConclusionsWe showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.

Cell-free metabolic engineering enables selective biotransformation of fatty acids to value-added chemicals

Fatty acid-derived products such as alkanes, fatty aldehydes, and fatty alcohols have many applications in the chemical industry. These products are predominately produced from fossil resources, but their production processes are often not environmentally friendly. While microbes like Escherichia coli have been engineered to convert fatty acids to corresponding products, the design and optimization of metabolic pathways in cells for high productivity is challenging due to low mass transfer, heavy metabolic burden, and intermediate/product toxicity. Here, we describe an E. coli-based cell-free protein synthesis (CFPS) platform for in vitro conversion of long-chain fatty acids to value-added chemicals with product selectivity, which can also avoid the above issues when using microbial production systems. We achieve the selective biotransformation by cell-free expression of different enzymes and the use of different conditions (e.g., light and heating) to drive the biocatalysis toward different final products. Specifically, in response to blue light, cell-free expressed fatty acid photodecarboxylase (CvFAP, a photoenzyme) was able to convert fatty acids to alkanes with approximately 90% conversion. When the expressed enzyme was switched to carboxylic acid reductase (CAR), fatty acids were reduced to corresponding fatty aldehydes, which, however, could be further reduced to fatty alcohols by endogenous reductases in the cell-free system. By using a thermostable CAR and a heating treatment, the endogenous reductases were deactivated and fatty aldehydes could be selectively accumulated (>97% in the product mixture) without over-reduction to alcohols. Overall, our cell-free platform provides a new strategy to convert fatty acids to valuable chemicals with notable properties of operation flexibility, reaction controllability, and product selectivity.

Simple N-Glycan Profile Analysis Using Lectin Staining, Mass Spectrometry, and GlycoMaple.

The Golgi apparatus is one of the major sites of protein and lipid glycosylation and processing. Protein N-glycosylation plays critical roles in protein folding, transport, stability, and activity. Various glycosyltransferases and glycoside hydrolases are localized at each cisterna in the Golgi apparatus and synthesize a large variety of N-glycan structures. The biosynthetic pathways of N-glycans are complicated, which hiders the rational design of glycan metabolic pathways. In addition, the analysis of glycan structure requires specialized instruments for analyses such as mass spectrometry, high-performance liquid chromatography, and nuclear magnetic resonance spectroscopy, which are not familiar to all laboratories. Here, we introduce relatively simple methods for N-glycan analysis, including disruption of genes encoding glycosyltransferases or glycoside hydrolases, glycan structural analysis using lectins and mass spectrometry, and visualization of glycan metabolic pathways in silico.

The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.

Biosynthetic approaches to efficient assimilation of CO2 via photorespiration modification in plant chassis.

Plant chassis has emerged as the platform with great potential for bioproduction of high value-added products such as recombinant protein, vaccine and natural product. However, as the primary metabolic pathway, photorespiration results in the loss of photosynthetically fixed carbon compounds and limits the exploration of plant chassis. People are endeavored to reduce the photorespiration energy or carbon loss based on variation screening or genetic engineering. Insomuch as protein engineering of Rubisco has not resulted in the significant improvement of Rubisco specificity which is linked to the direct CO2 fixation, the biosynthetic approaches of photorespiration bypass are gaining much more attention and manifested great potentiality in conferring efficient assimilation of CO2 in plant chassis. In this review, we summarize the recent studies on the metabolic pathway design and implementation of photorespiration alternative pathway aiming to provide clues to efficiently enhance carbon fixation via the modification of photorespiration in plant chassis for bioproduction. These will benefit the development of plant synthetic metabolism for biorefineries via improvement of artificial carbon sequestration cycle, particularly for the mitigation of serious challenges such as extreme climate change, food and energy shortages in the future.

Metabolic pathway design for growth-associated phenylalanine production using synthetically designed mutualism.

Combination of growth-associated pathway engineering based on flux balance analysis (FBA) and adaptive laboratory evolution (ALE) is a powerful approach to enhance the production of useful compounds. However, the feasibility of such growth-associated pathway designs depends on the type of target compound. In the present study, FBA predicted a set of gene deletions (pykA, pykF, ppc, zwf, and adhE) that leads to growth-associated phenylalanine production in Escherichia coli. The knockout strain is theoretically enforced to produce phenylalanine only at high growth yields, and could not be applied to the ALE experiment because of a severe growth defect. To overcome this challenge, we propose a novel approach for ALE based on mutualistic co-culture for coupling growth and production, regardless of the growth rate. We designed a synthetic mutualism of a phenylalanine-producing leucine-auxotrophic strain (KF strain) and a leucine-producing phenylalanine-auxotrophic strain (KL strain) and performed an ALE experiment for approximately 160 generations. The evolved KF strain (KF-E strain) grew in a synthetic medium (with glucose as the main carbon source) supplemented with leucine, while severe growth defects were observed in the parental KF strain. The phenylalanine yield of the KF-E strain was 2.3 times higher than that of the KF strain.

Development of a dedicated Golden Gate Assembly Platform (RtGGA) for Rhodotorula toruloides

Rhodotorula toruloides is a potential chassis for microbial cell factories as this yeast can metabolise different substrates into a diverse range of natural products, but the lack of efficient synthetic biology tools hinders its applicability. In this study, the modular, versatile and efficient Golden Gate DNA assembly system (RtGGA) was adapted to the first basidiomycete, an oleaginous yeast R. toruloides. R. toruloides CCT 0783 was sequenced, and used for the GGA design. The DNA fragments were assembled with predesigned 4-nt overhangs and a library of standardized parts was created containing promoters, genes, terminators, insertional regions, and resistance genes. The library was combined to create cassettes for the characterization of promoters strength and to overexpress the carotenoid production pathway. A variety of reagents, plasmids, and strategies were used and the RtGGA proved to be robust. The RtGGA was used to build three versions of the carotenoid overexpression cassette by using different promoter combinations. The cassettes were transformed into R. toruloides and the three new strains were characterized. Total carotenoid concentration increased by 41%. The dedicated GGA platform fills a gap in the advanced genome engineering toolkit for R. toruloides, enabling the efficient design of complex metabolic pathways.

Graph-based and constraint-based heterologous metabolic pathway design methods and application

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.

PyMiner: A method for metabolic pathway design based on the uniform similarity of substrate-product pairs and conditional search

Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.