Special Issue "Microbial Metabolomics Volume 2"
A special issue of Metabolites (ISSN 2218-1989).
Deadline for manuscript submissions: closed (31 October 2017)
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
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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
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
Manuscript Submission Information
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- metabolic models
- microbial population
- secondary metabolites
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.
Author: Per Bruheim
Affiliation: Department of Biotechnology, Norwegian University of Science and Technology, 7034 Trondheim, Norway
Author: Farhana Pinu
Affiliation: School of Biological Sciences, University of Auckland, New Zealand
Author: Raphael B M Aggio
Affiliation: School of Biological Sciences, The University of Auckland, Auckland, New Zealand
Author: Silas G. Villas-Boas
Affiliation: Centre for Microbial Innovation, School of Biological Sciences, The University of Auckland, 3A Symonds Street, Auckland 1142, New Zealand
Tentative title: Enhanced Isotopic Ratio Outlier Analysis (IROA) peak detection and identification with ultrahigh resolution GC-Orbitrap-MS: Potential Application for Investigation of Model Organism Metabolomes
Authors: Irwin J. Kurland and Yunping Qiu
Affiliation: Albert Einstein College of Medicine
Abstract: Identification of the metabolome is critical for discovering the maximum amount of phenotypic information regarding biochemical pathways from metabolomics datasets. Detailed experimental characterization of any organismal metabolome has only just begun, and unknown metabolite identification is problematic in general. For both known and unknown metabolite identification, we have developed a high mass accuracy GC/MS approach, using Isotopic Ratio Outlier Analysis (IROA) technology (Qiu et al Anal. Chem 2016). As an example, we have characterized the yeast metabolome, and this method is also applicable to any prokaryotic model organism. IROA uses two different 13C enriched carbon sources (randomized 5% 13C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n) to aid identification for unknown endogenous metabolites. The ratio of M0+n/M0+n+1 CI intensities estimates the number of reactive groups silylated, which further enables accurate chemical formula generation (CFG) for unknown metabolites. With accurate m/z, n and derivatization information, we developed a workflow to identify unknown metabolites. In this study, we were able to mine more information using the same IROA yeast samples with ultrahigh resolution GC-Orbitrap-MS.
We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs with Waters GCTOFMS machine, Qiu et al 2016), with 10% ammonium in methane as CI reagent gas. the average mass derivation for GC-Orbitrap is 1.48ppm, however, the average mass derivation is 32.2 ppm for GC-TOF/MS. The increased mass accuracy aided formula identification, and we further aided unknown identification using an in silico approach based on finding all possible structures for the formula for the MINE database, then processing them with MetaboloDerivatizer (RIKEN) and CFM-ID. We feel this approach can be combined with in silico LC/MS to aid screening of unknown metabolome identifications for model organisms.
Keywords: Isotopic Ratio Outlier Analysis; GC-Orbitrap; positive chemical ionization, S. cerevisiae.