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Metabolites
Special Issues
Metabolomics-Driven Biotechnology
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Metabolomics-Driven Biotechnology

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 18026

Special Issue Editors

Prof. Dr. Hiroshi Shimizu

E-Mail Website
Guest Editor
Department of Bioinformatic Engineering, Osaka University, Osaka 565-0871, Japan
Interests: flux balance analysis; dynamic modeling; bioprocess development
Dr. Yoshihiro Toya

E-Mail Website
Guest Editor
Department of Bioinfomatic Engineering, Osaka University, Suita, Japan
Interests: 13c metabolic flux analysis; molecular tools for flux control; elementary mode analysis

Special Issue Information

Dear Colleagues,

Bio-productions of useful compounds including foods, fermentation beverages, pharmaceuticals, chemicals, and fuels have been performed around the world. Metabolism is a sequence of chemical reactions in cells, and is the most important phenotype directly related to cellular activities. Redox and co-factor balance in the metabolism is also important to effectively increase the metabolic flow to target compounds. Marker metabolites are very useful to diagnose diseases in many medial applications. Precise and accurate analyses of metabolites and the development of control methods for the metabolism are key issues in biotechnology.

Recent big advances in comprehensive and specific analysis technology—including mass spectrometry and NMR—and in information technology with big metabolomics data are providing a new concept for “metabolomics-driven biotechnology”. This Special Issue of Metabolites is dedicated not only to the new analytical techniques, but also to the metabolic engineering strategies driven by metabolomics data. The topics of the Special Issue include cutting edge experimental and computational researches in the related area: target and non-target metabolome analyses, isotope labeling techniques, metabolic flux analysis, identification techniques for important genes to confer the superior phenotypes of cells, genome-wide and local metabolic modeling with static and dynamic metabolism data, and applications of metabolome analysis to the development of bio-production processes and medical treatments.

Prof. Hiroshi Shimizu
Dr. Yoshihiro Toya
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 submissions that pass pre-check are 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 2700 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

  • Metabolomics
  • 13C Metabolic Flux Analysis
  • Flux Balance Analysis
  • Kinetic Modelling
  • Flux Analysis in Mammalian Cells

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Published Papers (4 papers)

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Research

12 pages, 2137 KiB  
Article
Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli
by Yuki Kuriya and Michihiro Araki
Metabolites 2020, 10(5), 198; https://doi.org/10.3390/metabo10050198 - 15 May 2020
Cited by 15 | Viewed by 3536
Abstract
Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is [...] Read more.
Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement. Full article
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
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17 pages, 4137 KiB  
Article
Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana
by Ratklao Siriwach, Fumio Matsuda, Kentaro Yano and Masami Yokota Hirai
Metabolites 2020, 10(4), 159; https://doi.org/10.3390/metabo10040159 - 18 Apr 2020
Cited by 14 | Viewed by 4825
Abstract
Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the [...] Read more.
Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs. Full article
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
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18 pages, 3458 KiB  
Article
Short-Term Temporal Metabolic Behavior in Halophilic Cyanobacterium Synechococcus sp. Strain PCC 7002 after Salt Shock
by Shimpei Aikawa, Atsumi Nishida, Tomohisa Hasunuma, Jo-Shu Chang and Akihiko Kondo
Metabolites 2019, 9(12), 297; https://doi.org/10.3390/metabo9120297 - 5 Dec 2019
Cited by 13 | Viewed by 4046
Abstract
In response to salt stress, cyanobacteria increases the gene expression of Na+/H+ antiporter and K+ uptake system proteins and subsequently accumulate compatible solutes. However, alterations in the concentrations of metabolic intermediates functionally related to the early stage of the [...] Read more.
In response to salt stress, cyanobacteria increases the gene expression of Na+/H+ antiporter and K+ uptake system proteins and subsequently accumulate compatible solutes. However, alterations in the concentrations of metabolic intermediates functionally related to the early stage of the salt stress response have not been investigated. The halophilic cyanobacterium Synechococcus sp. PCC 7002 was subjected to salt shock with 0.5 and 1 M NaCl, then we performed metabolomics analysis by capillary electrophoresis/mass spectrometry (CE/MS) and gas chromatography/mass spectrometry (GC/MS) after cultivation for 1, 3, 10, and 24 h. Gene expression profiling using a microarray after 1 h of salt shock was also conducted. We observed suppression of the Calvin cycle and activation of glycolysis at both NaCl concentrations. However, there were several differences in the metabolic changes after salt shock following exposure to 0.5 M and 1 M NaCl: (i): the main compatible solute, glucosylglycerol, accumulated quickly at 0.5 M NaCl after 1 h but increased gradually for 10 h at 1 M NaCl; (ii) the oxidative pentose phosphate pathway and the tricarboxylic acid cycle were activated at 0.5 M NaCl; and (iii) the multi-functional compound spermidine greatly accumulated at 1 M NaCl. Our results show that Synechococcus sp. PCC 7002 acclimated to different levels of salt through a salt stress response involving the activation of different metabolic pathways. Full article
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
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12 pages, 1636 KiB  
Article
Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
by Yoshihiro Izumi, Fumio Matsuda, Akiyoshi Hirayama, Kazutaka Ikeda, Yoshihiro Kita, Kanta Horie, Daisuke Saigusa, Kosuke Saito, Yuji Sawada, Hiroki Nakanishi, Nobuyuki Okahashi, Masatomo Takahashi, Motonao Nakao, Kosuke Hata, Yutaro Hoshi, Motohiko Morihara, Kazuhiro Tanabe, Takeshi Bamba and Yoshiya Oda
Metabolites 2019, 9(11), 257; https://doi.org/10.3390/metabo9110257 - 31 Oct 2019
Cited by 29 | Viewed by 5145
Abstract
Background: One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory. Methods: In this study, we [...] Read more.
Background: One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory. Methods: In this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory. Results: The results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference. Conclusion: The results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization. Full article
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
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