Special Issue "Metabolic Engineering and Synthetic Biology Volume 2"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Microbiology and Metabolic Engineering".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Lars M. Blank
Website
Guest Editor
Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
Interests: synthetic biology; metabolomics; metabolic engineering; metabolic flux analysis; bioeconomy
Dr. An N.T. Phan
Website
Guest Editor
Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
Interests: Ustilago; metabolomics; metabolic engineering; metabolic flux analysis; mass spectrometry

Special Issue Information

Dear Colleagues,

In times of ever-increasing demand for chemicals and the subsequent increase of CO2 in the atmosphere, we have to intensify our efforts to establish a circular (bio)economy. To reduce fossil resource use, alternative carbon sources including biomass and CO2 itself need to be used. The (bio)catalysts of choice that are able to convert these carbon sources into valuable chemicals often have to be tailored to meet the industrial requirements in titer, rate, and yield, and, hence, ultimately in cost. While exciting examples exist, from vitamins to plastic monomers and bioplastics, the metabolic engineering of such biocatalysts is still time and cost consuming. With the improvement of genetic tools and ideas for genetic standardization, creating and/or building new whole-cell biocatalysts becomes an ever more rapid task. However, the two other aspects of the design/build/test cycle are to some extent still very cumbersome. While computational tools support whole-cell biocatalyst design, parallelization and miniaturization speed up the characterization of mutants. Still, the goal has to be a knowledge-based design and a high information content phenotyping.

In this Special Issue, we ask for contributions of metabolic engineering and synthetic biology that are driven by flux and/or metabolome approaches. We would like to emphasize the importance of sample preparation and data evaluation. Fluxes of all intracellular biochemical reaction steps are the ultimate outcome of genetic and environmental alterations. We are convinced that quantitative approaches in metabolite analysis will help to reduce the time required to establish an efficient whole-cell biocatalyst. While the concentrations of intracellular metabolites can highlight enzymatic bottlenecks, the intracellular fluxes might help decipher redox cofactor imbalances, futile cycles, and the use of alternative pathways. Thermodynamically feasible reaction conditions can not only explain the phenotype observed but may also lead to genetic targets for further strain improvement and to new biochemical network designs.

Prof. Dr. Lars M. Blank
Dr. An N. T. Phan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolic engineering
  • synthetic biology
  • metabolomics
  • metabolic flux analysis
  • cell factory
  • bioeconomy

Published Papers (9 papers)

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Research

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Open AccessArticle
Photosynthetic Co-production of Succinate and Ethylene in a Fast-Growing Cyanobacterium, Synechococcus elongatus PCC 11801
Metabolites 2020, 10(6), 250; https://doi.org/10.3390/metabo10060250 - 16 Jun 2020
Abstract
Cyanobacteria are emerging as hosts for photoautotrophic production of chemicals. Recent studies have attempted to stretch the limits of photosynthetic production, typically focusing on one product at a time, possibly to minimise the additional burden of product separation. Here, we explore the simultaneous [...] Read more.
Cyanobacteria are emerging as hosts for photoautotrophic production of chemicals. Recent studies have attempted to stretch the limits of photosynthetic production, typically focusing on one product at a time, possibly to minimise the additional burden of product separation. Here, we explore the simultaneous production of two products that can be easily separated: ethylene, a gaseous product, and succinate, an organic acid that accumulates in the culture medium. This was achieved by expressing a single copy of the ethylene forming enzyme (efe) under the control of PcpcB, the inducer-free super-strong promoter of phycocyanin β subunit. We chose the recently reported, fast-growing and robust cyanobacterium, Synechococcus elongatus PCC 11801, as the host strain. A stable recombinant strain was constructed using CRISPR-Cpf1 in a first report of markerless genome editing of this cyanobacterium. Under photoautotrophic conditions, the recombinant strain shows specific productivities of 338.26 and 1044.18 μmole/g dry cell weight/h for ethylene and succinate, respectively. These results compare favourably with the reported productivities for individual products in cyanobacteria that are highly engineered. Metabolome profiling and 13C labelling studies indicate carbon flux redistribution and suggest avenues for further improvement. Our results show that S. elongatus PCC 11801 is a promising candidate for metabolic engineering. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessArticle
Heterologous Production of 6-Deoxyerythronolide B in Escherichia coli through the Wood Werkman Cycle
Metabolites 2020, 10(6), 228; https://doi.org/10.3390/metabo10060228 - 01 Jun 2020
Abstract
Polyketides are a remarkable class of natural products with diverse functional and structural diversity. The class includes many medicinally important molecules with antiviral, antimicrobial, antifungal and anticancer properties. Native bacterial, fungal and plant hosts are often difficult to cultivate and coax into producing [...] Read more.
Polyketides are a remarkable class of natural products with diverse functional and structural diversity. The class includes many medicinally important molecules with antiviral, antimicrobial, antifungal and anticancer properties. Native bacterial, fungal and plant hosts are often difficult to cultivate and coax into producing the desired product. As a result, Escherichia coli has been used for the heterologous production of polyketides, with the production of 6-deoxyerythronolide B (6-dEB) being the first example. Current strategies for production in E. coli require feeding of exogenous propionate as a source for the precursors propionyl-CoA and S-methylmalonyl-CoA. Here, we show that heterologous polyketide production is possible from glucose as the sole carbon source. The heterologous expression of eight genes from the Wood-Werkman cycle found in Propionibacteria, in combination with expression of the 6-dEB synthases DEBS1, DEBS2 and DEBS3 resulted in 6-dEB formation from glucose as the sole carbon source. Our results show that the Wood-Werkman cycle provides the required propionyl-CoA and the extender unit S-methylmalonyl-CoA to produce up to 0.81 mg/L of 6-dEB in a chemically defined media. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessArticle
Using Metabolomics to Identify Cell Line-Independent Indicators of Growth Inhibition for Chinese Hamster Ovary Cell-Based Bioprocesses
Metabolites 2020, 10(5), 199; https://doi.org/10.3390/metabo10050199 - 15 May 2020
Abstract
Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition [...] Read more.
Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition to ammonia and lactate, many other metabolites accumulate in CHO cell cultures, although their effects remain largely unknown. Elucidating these effects has the potential to further improve the productivity of CHO cell-based bioprocesses. This study used untargeted metabolomics to identify metabolites that accumulate in fed-batch cultures of monoclonal antibody (mAb) producing CHO cells. The metabolomics experiments profiled six cell lines that are derived from two different hosts, produce different mAbs, and exhibit different growth profiles. Comparing the cell lines’ metabolite profiles at different growth stages, we found a strong negative correlation between peak viable cell density (VCD) and a tryptophan metabolite, putatively identified as 5-hydroxyindoleacetaldehyde (5-HIAAld). Amino acid supplementation experiments showed strong growth inhibition of all cell lines by excess tryptophan, which correlated with the accumulation of 5-HIAAld in the culture medium. Prospectively, the approach presented in this study could be used to identify cell line- and host-independent metabolite markers for clone selection and bioprocess development. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessArticle
Metabolomics Analysis Reveals Global Metabolic Changes in the Evolved E. coli Strain with Improved Growth and 1-Butanol Production in Minimal Medium
Metabolites 2020, 10(5), 192; https://doi.org/10.3390/metabo10050192 - 13 May 2020
Abstract
Production of 1-butanol from microorganisms has garnered significant interest due to its prospect as a drop-in biofuel and precursor for a variety of commercially relevant chemicals. Previously, high 1-butanol titer has been reported in Escherichia coli strain JCL166, which contains a modified clostridial [...] Read more.
Production of 1-butanol from microorganisms has garnered significant interest due to its prospect as a drop-in biofuel and precursor for a variety of commercially relevant chemicals. Previously, high 1-butanol titer has been reported in Escherichia coli strain JCL166, which contains a modified clostridial 1-butanol pathway. Although conventional and metabolomics-based strain improvement strategies of E. coli strain JCL166 have been successful in improving production in rich medium, 1-butanol titer was severely limited in minimal medium. To further improve growth and consequently 1-butanol production in minimal medium, adaptive laboratory evolution (ALE) using mutD5 mutator plasmid was done on JCL166. Comparative metabolomics analysis of JCL166 and BP1 revealed global perturbations in the evolved strain BP1 compared to JCL166 (44 out of 64 metabolites), encompassing major metabolic pathways such as glycolysis, nucleotide biosynthesis, and CoA-related processes. Collectively, these metabolic changes in BP1 result in improved growth and, consequently, 1-butanol production in minimal medium. Furthermore, we found that the mutation in ihfB caused by ALE had a significant effect on the metabolome profile of the evolved strain. This study demonstrates how metabolomics was utilized for characterization of ALE-developed strains to understand the overall effect of mutations acquired through evolution. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessArticle
In Vivo Rate of Formaldehyde Condensation with Tetrahydrofolate
Metabolites 2020, 10(2), 65; https://doi.org/10.3390/metabo10020065 - 12 Feb 2020
Cited by 1
Abstract
Formaldehyde is a highly reactive compound that participates in multiple spontaneous reactions, but these are mostly deleterious and damage cellular components. In contrast, the spontaneous condensation of formaldehyde with tetrahydrofolate (THF) has been proposed to contribute to the assimilation of this intermediate during [...] Read more.
Formaldehyde is a highly reactive compound that participates in multiple spontaneous reactions, but these are mostly deleterious and damage cellular components. In contrast, the spontaneous condensation of formaldehyde with tetrahydrofolate (THF) has been proposed to contribute to the assimilation of this intermediate during growth on C1 carbon sources such as methanol. However, the in vivo rate of this condensation reaction is unknown and its possible contribution to growth remains elusive. Here, we used microbial platforms to assess the rate of this condensation in the cellular environment. We constructed Escherichia coli strains lacking the enzymes that naturally produce 5,10-methylene-THF. These strains were able to grow on minimal medium only when equipped with a sarcosine (N-methyl-glycine) oxidation pathway that sustained a high cellular concentration of formaldehyde, which spontaneously reacts with THF to produce 5,10-methylene-THF. We used flux balance analysis to derive the rate of the spontaneous condensation from the observed growth rate. According to this, we calculated that a microorganism obtaining its entire biomass via the spontaneous condensation of formaldehyde with THF would have a doubling time of more than three weeks. Hence, this spontaneous reaction is unlikely to serve as an effective route for formaldehyde assimilation. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Review

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Open AccessReview
Promoter Architecture and Promoter Engineering in Saccharomyces cerevisiae
Metabolites 2020, 10(8), 320; https://doi.org/10.3390/metabo10080320 - 06 Aug 2020
Abstract
Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations [...] Read more.
Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessReview
Machine Learning Applications for Mass Spectrometry-Based Metabolomics
Metabolites 2020, 10(6), 243; https://doi.org/10.3390/metabo10060243 - 13 Jun 2020
Abstract
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass [...] Read more.
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessReview
Metabolic Engineering Design Strategies for Increasing Acetyl-CoA Flux
Metabolites 2020, 10(4), 166; https://doi.org/10.3390/metabo10040166 - 23 Apr 2020
Abstract
Acetyl-CoA is a key metabolite precursor for the biosynthesis of lipids, polyketides, isoprenoids, amino acids, and numerous other bioproducts which are used in various industries. Metabolic engineering efforts aim to increase carbon flux towards acetyl-CoA in order to achieve higher productivities of its [...] Read more.
Acetyl-CoA is a key metabolite precursor for the biosynthesis of lipids, polyketides, isoprenoids, amino acids, and numerous other bioproducts which are used in various industries. Metabolic engineering efforts aim to increase carbon flux towards acetyl-CoA in order to achieve higher productivities of its downstream products. In this review, we summarize the strategies that have been implemented for increasing acetyl-CoA flux and concentration, and discuss their effects. Furthermore, recent works have developed synthetic acetyl-CoA biosynthesis routes that achieve higher stoichiometric yield of acetyl-CoA from glycolytic substrates. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessReview
Enzyme Assembly for Compartmentalized Metabolic Flux Control
Metabolites 2020, 10(4), 125; https://doi.org/10.3390/metabo10040125 - 26 Mar 2020
Abstract
Enzyme assembly by ligand binding or physically sequestrating enzymes, substrates, or metabolites into isolated compartments can bring key molecules closer to enhance the flux of a metabolic pathway. The emergence of enzyme assembly has provided both opportunities and challenges for metabolic engineering. At [...] Read more.
Enzyme assembly by ligand binding or physically sequestrating enzymes, substrates, or metabolites into isolated compartments can bring key molecules closer to enhance the flux of a metabolic pathway. The emergence of enzyme assembly has provided both opportunities and challenges for metabolic engineering. At present, with the development of synthetic biology and systems biology, a variety of enzyme assembly strategies have been proposed, from the initial direct enzyme fusion to scaffold-free assembly, as well as artificial scaffolds, such as nucleic acid/protein scaffolds, and even some more complex physical compartments. These assembly strategies have been explored and applied to the synthesis of various important bio-based products, and have achieved different degrees of success. Despite some achievements, enzyme assembly, especially in vivo, still has many problems that have attracted significant attention from researchers. Here, we focus on some selected examples to review recent research on scaffold-free strategies, synthetic artificial scaffolds, and physical compartments for enzyme assembly or pathway sequestration, and we discuss their notable advances. In addition, the potential applications and challenges in the applications are highlighted. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Machine learning supported metabolomics analysis for biotechnological applications
Authors: Ulf W. Liebal; Lars M. Blank
Affiliation: Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
Abstract: Metabolomics measures the ultimate level in an information chain that integrates genetic and environmental inputs. The representing metabolic networks are closely related to the phenotype of an organism and the manipulation of the metabolome is of prime interest in biotechnology. New analytic techniques enhance the output from metabolomics measurements and machine learning approaches provide new strategies for data analysis. In this review, we show recent developments of combining metabolomics data with machine learning to understand metabolism with a biotechnological perspective. Apart from upscaling existing analyses, machine learning extends the interpretation of metabolomics. For example, it is possible to associate metabolites with high level phenotypes, or identify unknown metabolic pathways. Among the most promising developments for biotechnology is the combination of metabolomics with genome scale metabolic models. Fluxomics as a subset of metabolomics is pivotal to constrain genome scale flux simulations and continue to be developed. Of particular relevance is the combination of different Omics levels with genome scale models. Connected by different machine learning approaches, these models provide compelling phenotype predictions. Metabolomics will remain an important part in an integrated methodology toolbox for understanding and engineering metabolism.

Title: Analysis of Dynamic Labeling Patterns to Debottleneck L-Histidine Production in C. glutamicum
Authors: Ralf Takors
Affiliation: Institute of Biochemical Engineering, University of Stuttgart, Germany

Title: Assessing the in vivo rate of formaldehyde condensation with tetrahydrofolate
Authors: Arren Bar-Even
Affiliation: Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm-Potsdam, Germany

Title: Comparative fluxome and metabolome analysis of trehalose-mediated metabolic response in glucose‐limited cultivation of Penicillium chrysogenum
Authors: Guan Wang
Affiliation: East China University Of Science And Technology Shanghai, China
Abstract: Trehalose cycling has been confirmed as a double-edged sword in the high-yielding Penicillium chrysogenum strain; this cycling helps to cope with sudden glucose availability, facilitating the maintenance of metabolic balanced state, but it consumes extra amount of ATP responsible for repeated breakdown and formation of trehalose molecule in response to extracellular glucose perturbations. This loss of ATP would be in competition with the high ATP-demanding penicillin biosynthesis. Hence, it is of great importance to study the role of trehalose metabolism. Through interfering with the genes (tps1 and tps2) in the trehalose metabolism, our latest findings have shown that trehalose metabolism plays an important role in metabolic regulation and is central to maintaining higher penicillin productivity under glucose-limited chemostat cultures. However, due to insufficient mass transfer and mixing issues, cells in the industrial-scale bioreactor are forced to experience glucose feast/famine cycles, mostly resulting in the reduced commercial metrics (titer, yield and productivity). In this upcoming paper, the metabolite levels and fluxes in response to continuous feeding (control) and intermittent feeding (mimicking glucose gradients present in an industrial-scale fermentor) in glucose‐limited chemostat cultures at the dilution rate (D) of 0.05 h−1 are determined to evaluate the quantitative impact of trehalose cycling on the physiology of a high‐yielding P. chrysogenum strain. Based on this, we will as a whole evaluate the role of trehalose cycling in both steady state and dynamic conditions.

Title: Pathway enzymes assembly for metabolic flux control and optimization
Authors: Long Liu
Affiliation: Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, China
Abstract: Enzyme assembling by ligand binding or physically sequestrating enzymes, substrates or metabolites into the isolated compartments can bring the key molecules closer to enhance metabolic pathway flux. The emergence of enzymatic assembly provides both opportunities and challenges for metabolic engineering. In recent years, enzyme assembly via the synthetic scaffold strategy involving in nucleic acid, proteins or micro-compartments and scaffold-free self-assembly strategy has been explored and applied to the synthesis of various important bio-based products, and has also achieved varying degrees of success. Despite some achievements, the pathway enzyme assembly, especially in vivo, still has many problems that have attracted wide attention of researchers. Here, we review recent research on the scaffold-free self-assembly and synthetic scaffold strategy for enzyme assembly and discuss their notable advances. In addition, the potential applications and the challenges in the applications are also highlighted.

Title: Metabolic engineering design strategies for increasing acetyl-CoA flux
Authors: Ethan I Lan
Affiliation: Department of Biological Science and Technology, NCTU

Title: Heterologous production of 6-deoxyerythronolide B in Escherichia coli through the Wood Werkman cycle
Authors: Ricardo Axayacatl Gonzalez-Garcia, Lars K. Nielsen, Esteban Marcellin*
Affiliation: 1 Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD 4072, Australia. 2 Queensland Node of Metabolomics Australia, The University of Queensland, Brisbane, QLD, Australia *Corresponding author: Dr Esteban Marcellin, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia. Phone: +61 7 334 64298, Fax: +61 7 3346 3973, Email: [email protected]

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