Field Of Metabolic Engineering Research Articles

Book Review: Recent Advances in Yeast Metabolic Engineering

This book has been published to present in great detail the recent advances of yeast metabolic engineering, with a special focus on tools, technologies, assays, pathway engineering, selection, optimization, and screening. This book is written in an easy to understand language by eminent scientists in the field of metabolic engineering. It is a timely compilation of the recent advances and the future scope of the field. This book is an excellent and informative for yeast metabolic engineering experiments, as the editor has made an impressive compilation of almost of all the methods for the manipulation of yeast strains, and the consequent development of strains for industrial purposes with great simplicity. The central idea behind the book is to highlight the number of important aspects of metabolic engineering in a way that can help future investigators, researchers, students, and stakeholders to perform research with ease.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

BackgroundModeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.ResultsThe jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S-13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.ConclusionsjQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Co-culture engineering for microbial biosynthesis of 3-amino-benzoic acid in Escherichia coli.

3-amino-benzoic acid (3AB) is an important building block molecule for production of a wide range of important compounds such as natural products with various biological activities. In the present study, we established a microbial biosynthetic system for de novo 3AB production from the simple substrate glucose. First, the active 3AB biosynthetic pathway was reconstituted in the bacterium Escherichia coli, which resulted in the production of 1.5 mg/L 3AB. In an effort to improve the production, an E. coli-E. coli co-culture system was engineered to modularize the biosynthetic pathway between an upstream strain and an downstream strain. Specifically, the upstream biosynthetic module was contained in a fixed E. coli strain, whereas a series of E. coli strains were engineered to accommodate the downstream biosynthetic module and screened for optimal production performance. The best co-culture system was found to improve 3AB production by 15 fold, compared to the mono-culture approach. Further engineering of the co-culture system resulted in biosynthesis of 48 mg/L 3AB. Our results demonstrate co-culture engineering can be a powerful new approach in the broad field of metabolic engineering.

High-throughput evaluation of synthetic metabolic pathways.

A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.

Generation of structurally novel short carotenoids and study of their biological activity

Recent research interest in phytochemicals has consistently driven the efforts in the metabolic engineering field toward microbial production of various carotenoids. In spite of systematic studies, the possibility of using C30 carotenoids as biologically functional compounds has not been explored thus far. Here, we generated 13 novel structures of C30 carotenoids and one C35 carotenoid, including acyclic, monocyclic, and bicyclic structures, through directed evolution and combinatorial biosynthesis, in Escherichia coli. Measurement of radical scavenging activity of various C30 carotenoid structures revealed that acyclic C30 carotenoids showed higher radical scavenging activity than did DL-α-tocopherol. We could assume high potential biological activity of the novel structures of C30 carotenoids as well, based on the neuronal differentiation activity observed for the monocyclic C30 carotenoid 4,4′-diapotorulene on rat bone marrow mesenchymal stem cells. Our results demonstrate that a series of structurally novel carotenoids possessing biologically beneficial properties can be synthesized in E. coli.

Combinatorial Screening for Transgenic Yeasts with High Cellulase Activities in Combination with a Tunable Expression System.

Combinatorial screening used together with a broad library of gene expression cassettes is expected to produce a powerful tool for the optimization of the simultaneous expression of multiple enzymes. Recently, we proposed a highly tunable protein expression system that utilized multiple genome-integrated target genes to fine-tune enzyme expression in yeast cells. This tunable system included a library of expression cassettes each composed of three gene-expression control elements that in different combinations produced a wide range of protein expression levels. In this study, four gene expression cassettes with graded protein expression levels were applied to the expression of three cellulases: cellobiohydrolase 1, cellobiohydrolase 2, and endoglucanase 2. After combinatorial screening for transgenic yeasts simultaneously secreting these three cellulases, we obtained strains with higher cellulase expressions than a strain harboring three cellulase-expression constructs within one high-performance gene expression cassette. These results show that our method will be of broad use throughout the field of metabolic engineering.

The sweet branch of metabolic engineering: cherry-picking the low-hanging sugary fruits.

In the first science review on the then nascent Metabolic Engineering field in 1991, Dr. James E. Bailey described how improving erythropoietin (EPO) glycosylation can be achieved via metabolic engineering of Chinese hamster ovary (CHO) cells. In the intervening decades, metabolic engineering has brought sweet successes in glycoprotein engineering, including antibodies, vaccines, and other human therapeutics. Today, not only eukaryotes (CHO, plant, insect, yeast) are being used for manufacturing protein therapeutics with human-like glycosylation, newly elucidated bacterial glycosylation systems are enthusiastically embraced as potential breakthrough to revolutionize the biopharmaceutical industry. Notwithstanding these excitement in glycoprotein, the sweet metabolic engineering reaches far beyond glycoproteins. Many different types of oligo- and poly-saccharides are synthesized with metabolically engineered cells. For example, several recombinant hyaluronan bioprocesses are now in commercial production, and the titer of 2′-fucosyllactose, the most abundant fucosylated trisaccharide in human milk, reaches over 20 g/L with engineered E. coli cells. These successes represent only the first low hanging fruits, which have been appreciated scientifically, medically and fortunately, commercially as well. As one of the four building blocks of life, sugar molecules permeate almost all aspects of life. They are also unique in being intimately associated with all major types of biopolymers (including DNA/RNA, proteins, lipids) meanwhile they stand alone as bioactive polysaccharides, or free soluble oligosaccharides. As such, all sugar moieties in biological components, small or big and free or bound, are important targets for metabolic engineering. Opportunities abound at the interface of glycosciences and metabolic engineering. Continued investment and successes in this branch of metabolic engineering will make vastly diverse sugar-containing molecules (a.k.a. glycoconjugates) available for biomedical applications, sustainable technology development, and as invaluable tools for basic scientific research. This short review focuses on the most recent development in the field, with emphasis on the synthesis technology for glycoprotein, polysaccharide, and oligosaccharide.

13C NMR based profiling unveils different α-ketoglutarate pools involved into glutamate and lysine synthesis in the milk yeast Kluyveromyces lactis

BackgroundThe construction of efficient cell factories for the production of metabolites requires the rational improvement/engineering of the metabolism of microorganisms. The subject of this paper is directed towards the quantitative understanding of the respiratory/fermentative Kluyveromyces lactis yeast metabolism and its rag8 casein kinase mutant, taken as a model for all rag gene mutations. Methods13C NMR spectroscopy and [1,2-13C2]glucose were used as metabolic stable-isotope tracer to define the metabolic profiling of a K. lactis yeast and its derivative mutants. ResultsRag8 showed a decrease of all 13C glutamate fractional enrichments, except for [4-13C]glutamate that was higher than wild type ones. A decrease of TCA cycle flux in rag8 mutants and a contribution of a [4-13C]ketoglutarate pool not originating from mitochondria were suggested.13C lysine enrichments confirmed the presence of two compartmentalized α-ketoglutarate (α-KG) pools participating to glutamate and lysine synthesis.Moreover, an increased transaldolase, as compared to transketolase activity, was observed in the rag8 mutant by 13C NMR isotopomer analysis of alanine. Conclusions13C NMR-based isotopomer analysis showed the existence of different α-KG metabolic pools for glutamate and lysine biosynthesis. In the rag8 mutant, 13C labeled pentose phosphate intermediates participated in the synthesis of this compartmentalized α-KG pool. General significanceA compartmentalization of the α-KG pools involved in lysine biosynthesis has been revealed for the first time in K. lactis. Given its great impact in metabolic engineering field, its existence should be validated/compared with other yeasts and/or fungal species.

Direct metabolic activity measurement for unstable bioprocess experiment control

Paper deals with an algorithm which allows the automatic selection of the best operating point of biological system. This task is one of the subjects of research in the field of metabolic engineering, which deals with con trol related issues, in particular, with modelling of biological phenomena, as well as, monitoring of the unstable states of biomass growth. The possibility of using specific biosensors and microfluidic system for monitoring, optimizing and controlling of a bioreactor is presented in this paper. To ensure proper experiment control of the bioreactor, a real-time measurement of parameters at the macroscale level and metabolic activity of microorganism cells at the microscale level are relevant. Therefore, much attention has been paid to the description and modelling of cyclical changes in metabolic states. For the determination of key process parameters, a microcalorimeter for measuring the heat of reaction has been applied. The biosensor provides additional information, which is useful in development of an interface for monitoring the bioreactor by a decomposition of measurements including the scale of process. Finally, the paper discusses the problem of model selection describing the bioprocess at the microscale level.

Genome-scale modeling for metabolic engineering.

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

<i>In silico</i> Evaluation of the Effect of Pfl Gene Knockout on the Production of D-lactate by <i>Escherichia coli</i> Genome Scale Model Using the Optflux Software Platform

The increase availability of genome scale metabolic models of Escherichia coli and computational successes is revolutionizing the field of metabolic engineering and synthetic microbiology. E. coli has been experimentally established to produce D-lactate under micro-aerobic conditions when pyruvate formate lyase (PFL) genes are knocked out. However, investigation on the in silico prediction and for evaluation of the effect of PFL genes knockout on the production of D-lactate using E. coli genome scale metabolic model with regulatory on/off minimization (ROOM) under the OptFlux software platform remained under explored. Here, we demonstrate that metabolic engineering strategies using the OptFlux software platform by gene knockout simulation of pflA/b0902, pflB/b0903, pflC/b3952 and pflD/b3951 have been predicted to increase D-lactate production in E. coli and hence maintaining a growth rate that is 96% of the wild-type model. The deletion of the PFL genes have been established to increase D-lactate production in E. coli . The results obtained in this study is in agreement with the previously established experimental studies. These findings suggests that the OptFlux software platform using ROOM as the simulation algorithm, can prospectively and effectively predict future metabolic engineering targets for increased D-lactate production in E. coli and/or other microbial chemical syntheses.

Expanding metabolic engineering algorithms using feasible space and shadow price constraint modules

While numerous computational methods have been developed that use genome-scale models to propose mutants for the purpose of metabolic engineering, they generally compare mutants based on a single criteria (e.g., production rate at a mutant׳s maximum growth rate). As such, these approaches remain limited in their ability to include multiple complex engineering constraints. To address this shortcoming, we have developed feasible space and shadow price constraint (FaceCon and ShadowCon) modules that can be added to existing mixed integer linear adaptive evolution metabolic engineering algorithms, such as OptKnock and OptORF. These modules allow strain designs to be identified amongst a set of multiple metabolic engineering algorithm solutions that are capable of high chemical production while also satisfying additional design criteria. We describe the various module implementations and their potential applications to the field of metabolic engineering. We then incorporated these modules into the OptORF metabolic engineering algorithm. Using an Escherichia coli genome-scale model (iJO1366), we generated different strain designs for the anaerobic production of ethanol from glucose, thus demonstrating the tractability and potential utility of these modules in metabolic engineering algorithms.

Engineering the oxygen sensing regulation results in an enhanced recombinant human hemoglobin production by Saccharomyces cerevisiae.

Efficient production of appropriate oxygen carriers for transfusions (blood substitutes or artificial blood) has been pursued for many decades, and to date several strategies have been used, from synthetic polymers to cell-free hemoglobin carriers. The recent advances in the field of metabolic engineering also allowed the generation of different genetically modified organisms for the production of recombinant human hemoglobin. Several studies have showed very promising results using the bacterium Escherichia coli as a production platform, reporting hemoglobin titers above 5% of the total cell protein content. However, there are still certain limitations regarding the protein stability and functionality of the recombinant hemoglobin produced in bacterial systems. In order to overcome these limitations, yeast systems have been proposed as the eukaryal alternative. We recently reported the generation of a set of plasmids to produce functional human hemoglobin in Saccharomyces cerevisiae, with final titers of active hemoglobin exceeding 4% of the total cell protein. In this study, we propose a strategy for further engineering S. cerevisiae by altering the oxygen sensing pathway by deleting the transcription factor HAP1, which resulted in an increase of the final recombinant active hemoglobin titer exceeding 7% of the total cellular protein.

Effects of introducing heterologous pathways on microbial metabolism with respect to metabolic optimality

Although optimality of microbial metabolism under genetic and environmental perturbations is well studied, the effects of introducing heterologous reactions on the overall metabolism are not well understood. This point is important in the field of metabolic engineering because heterologous reactions are more frequently introduced into various microbial hosts. The genome-scale metabolic simulations of Escherichia coli strains engineered to produce 1,4-butanediol, 1,3-propanediol, and amorphadiene suggest that microbial metabolism shows much different responses to the introduced heterologous reactions in a strain-specific manner than typical gene knockouts in terms of the energetic status (e.g., ATP and biomass generation) and chemical production capacity. The 1,4-butanediol and 1,3-propanediol producers showed greater metabolic optimality than the wild-type strains and gene knockout mutants for the energetic status, while the amorphadiene producer was metabolically less optimal. For the optimal chemical production capacity, additional gene knockouts were most effective for the strain producing 1,3-propanediol, but not for the one producing 1,4-butanediol. These observations suggest that strains having heterologous metabolic reactions have metabolic characteristics significantly different from those of the wild-type strain and single gene knockout mutants. Finally, comparison of the theoretically predicted and 13C-based flux values pinpoints pathways with non-optimal flux values, which can be considered as engineering targets in systems metabolic engineering strategies. To our knowledge, this study is the first attempt to quantitatively characterize microbial metabolisms with different heterologous reactions. The suggested potential reasons behind each strain’s different metabolic responses to the introduced heterologous reactions should be carefully considered in strain designs.

Producing Succinic Acid in Yeast using A Hybrid of Differential Evolution and Flux Balance Analysis

In the field of metabolic engineering, one of the primary goals is to maximize the production of a desired substance. However, to identify the set of gene deletions that will result to the desired production goals is difficult. This is due to the complexity of the regulatory cellular and metabolic network and also lack of good modeling and optimization tools. In this study, the optimization algorithm from previous works was implemented to identify the gene knockout on the result. The previous works faced the problem of inability to provide single run with two goals to maximize the biomass and the desired products. Besides that, the previous work also showed long computational time. In this study, a hybrid of Differential Evolution and Flux Balance Analysis (DEFBA) is proposed to solve the long computational time problem and provide an optimal set of gene knockout with high yield of the desired product. The case study in this research involved the production of succinic acid (also called as succinate) in yeast Saccharomyces cerevisiae. The results from this experiment included the list of knockout genes and the growth rate after the deletion. DEFBA had shown better results compared to the other methods. The identified list suggested gene modifications over several pathways which may be useful in solving challenging genetic engineering problems.

IsoDesign: A software for optimizing the design of 13C‐metabolic flux analysis experiments

The growing demand for (13) C-metabolic flux analysis ((13) C-MFA) in the field of metabolic engineering and systems biology is driving the need to rationalize expensive and time-consuming (13) C-labeling experiments. Experimental design is a key step in improving both the number of fluxes that can be calculated from a set of isotopic data and the precision of flux values. We present IsoDesign, a software that enables these parameters to be maximized by optimizing the isotopic composition of the label input. It can be applied to (13) C-MFA investigations using a broad panel of analytical tools (MS, MS/MS, (1) H NMR, (13) C NMR, etc.) individually or in combination. It includes a visualization module to intuitively select the optimal label input depending on the biological question to be addressed. Applications of IsoDesign are described, with an example of the entire (13) C-MFA workflow from the experimental design to the flux map including important practical considerations. IsoDesign makes the experimental design of (13) C-MFA experiments more accessible to a wider biological community. IsoDesign is distributed under an open source license at http://metasys.insa-toulouse.fr/software/isodes/

By‐passing the refinery for production of high‐value BTX derivatives

By‐passing the refinery for production of high‐value BTX derivatives

Metabolic Engineering: Past and Future

We present here a broad overview of the field of metabolic engineering, describing in the first section the key fundamental principles that define and distinguish it, as well as the technological and intellectual developments over the past approximately 20 years that have led to the current state of the art. Discussion of concepts such as metabolic flux analysis, metabolic control analysis, and rational and combinatorial methods is facilitated by illustrative examples of their application drawn from the extensive metabolic engineering literature. In the second section, we present some of the rapidly emerging technologies that we think will play pivotal roles in the continued growth of the field, from improving production metrics to expanding the range of attainable compounds.

A Review of Computational Approaches for In Silico Metabolic Engineering for Microbial Fuel Production

High energy consumption nowadays alongside with concerns on the environment had caused rising demand for synthetic alternative fuels. These include biofuels that can be produced from a variety of engineered microbes such as Escherichia coli. In the metabolic engineering field, this is done by genetically modifying the target microbes to obtain optimal production of a particular biochemical. Conventional metabolic engineering approaches often intuitive, but with advancements in modern biology, vast amount of informative data generated from time to time to describe the metabolism system of the microbes more thoroughly. Discoveries from interpreting these available data using computational approaches are highly beneficial to metabolic engineers, especially professionals working in the in silico metabolic engineering field. Within the past decade, many computational approaches and routines have been proposed and developed in providing a platform to discover rational strategies to aid biologists in engineering the metabolic network. Here, efforts to find the optimal butanol production route in E. coli as well as several optimization algorithms currently available for finding optimal solution to enhance biochemical production in designated target microbe are discussed. This review aims to show different optimization algorithms developed for in silico metabolic engineering and their applications in microbial fuel production.

Plant and microbial sources of antioxidants

In recent years there has been growing interest in substances with antioxidative properties, which reduce or prevent harmful effects of free radicals on living tissues, and inhibit aging processes and the development of certain diseases. The objective of this paper is to review new methods of obtaining antioxidants of plant origin and new trends in research aiming to improve their quality and profitability on an industrial scale. Among the issues discussed, there are the methods that use techniques of plant and microbial genetic engineering. A brief description of antioxidants and natural sources of their occurrence is also presented in this paper. In view of the fact that the biosynthesis of flavonoids and isoflavonoids is probably the best-known metabolic pathway of natural plant products, the review of achievements of recent years in the field of metabolic engineering was shown with the example of flavonoids. The modifications of flavonoid biosynthetic pathways were related to changes in the expression level of structural or regulatory genes, silencing of competitive genes or modifying catalytic properties of enzymes using techniques of protein engineering. The paper also presents the achievements of microorganism engineering in the field of application of fermentation processes as a source of specific flavonoid compounds, which was possible by designing the phenylpropanoid biosynthetic pathway in cells of microorganisms such as the bacterium E. coli or S. cerevisiae, baker's yeast. Both approaches can be used in the production of flavonoids attractive in terms of application.