Special Issue "Microbial Metabolomics Volume 2"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 October 2017)

Special Issue Editor

Guest Editor
A/Prof. Silas G. Villas-Boas

Centre for Microbial Innovation, School of Biological Sciences, The University of Auckland, 3A Symonds Street, Auckland 1142, New Zealand
Website | E-Mail
Fax: +64 9 373 7416
Interests: microbial metabolomics; central carbon metabolism; microbial physiology; secondary metabolites; metabolic pathway analysis; mass spectrometry; gas chromatography; metabolic flux analysis; sample preparation for metabolome analysis; metabolic engineering

Special Issue Information

Dear Colleagues,

Metabolomics is a relatively young and exciting field enabling new scientific discoveries in the life sciences that make it of increasing interest in modern biology. Focusing on analysing different small molecules produced or modified by living cells in a high throughput fashion, metabolomics plays an essential role in functional genomics and systems biology studies.

Within the system-wide framework approach of metabolomics, microbial systems are most likely to benefit from recent advances in this field because they are less complex systems to be studied and exploited; the majority of genomes sequenced to date are from microorganisms, and we know more about gene regulation, metabolic network, and physiology of microbial cells than of higher eukaryote systems. Therefore, this special issue of Metabolites is tailored for the microbiology community, combining timely reviews discussing the unique challenges associated with microbial metabolomics with research articles presenting new findings and results related to microorganisms and their metabolites.

Assoc. Prof. Dr. Silas Villas-Boas
Guest Editor

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 quarterly 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 850 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

  • microorganisms
  • bacteria
  • fungi
  • yeast
  • protozoa
  • algae
  • archeae
  • fermentation
  • metabolic models
  • microbial population
  • metabolomics
  • secondary metabolites
  • metabolites
  • lipidomics

Related Special Issue

Published Papers (4 papers)

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Research

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Open AccessArticle Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes
Metabolites 2018, 8(1), 9; https://doi.org/10.3390/metabo8010009
Received: 7 November 2017 / Revised: 10 January 2018 / Accepted: 10 January 2018 / Published: 18 January 2018
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Abstract
Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA)
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Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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Review

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Open AccessReview Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols
Metabolites 2017, 7(4), 53; https://doi.org/10.3390/metabo7040053
Received: 1 September 2017 / Revised: 11 October 2017 / Accepted: 21 October 2017 / Published: 23 October 2017
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Abstract
Sample preparation is one of the most important steps in metabolome analysis. The challenges of determining microbial metabolome have been well discussed within the research community and many improvements have already been achieved in last decade. The analysis of intracellular metabolites is particularly
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Sample preparation is one of the most important steps in metabolome analysis. The challenges of determining microbial metabolome have been well discussed within the research community and many improvements have already been achieved in last decade. The analysis of intracellular metabolites is particularly challenging. Environmental perturbations may considerably affect microbial metabolism, which results in intracellular metabolites being rapidly degraded or metabolized by enzymatic reactions. Therefore, quenching or the complete stop of cell metabolism is a pre-requisite for accurate intracellular metabolite analysis. After quenching, metabolites need to be extracted from the intracellular compartment. The choice of the most suitable metabolite extraction method/s is another crucial step. The literature indicates that specific classes of metabolites are better extracted by different extraction protocols. In this review, we discuss the technical aspects and advancements of quenching and extraction of intracellular metabolite analysis from microbial cells. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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Open AccessReview Extracellular Microbial Metabolomics: The State of the Art
Metabolites 2017, 7(3), 43; https://doi.org/10.3390/metabo7030043
Received: 31 July 2017 / Revised: 21 August 2017 / Accepted: 22 August 2017 / Published: 22 August 2017
Cited by 2 | PDF Full-text (1010 KB) | HTML Full-text | XML Full-text
Abstract
Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than
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Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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Open AccessReview Quantification of Microbial Phenotypes
Metabolites 2016, 6(4), 45; https://doi.org/10.3390/metabo6040045
Received: 31 October 2016 / Revised: 5 December 2016 / Accepted: 6 December 2016 / Published: 9 December 2016
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Abstract
Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current
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Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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