Abstract
Direct-injection mass spectrometry (DIMS) is a means of rapidly obtaining metabolomic phenotype data in both prokaryotes and eukaryotes. Given our generally poor understanding of Campylobacter metabolism, the high-throughput and relatively simple sample preparation of DIMS has made this an attractive technique for metabolism-related studies and hypothesis generation, especially when attempting to analyze metabolic mutants with no clear phenotype. Here we describe a metabolomic fingerprinting approach with sampling and extraction methodologies optimized for direct-injection electrospray ionization mass spectrometry (ESI-MS), which we have used as a means of comparing wild-type and isogenic mutant strains of C. jejuni with various metabolic blocks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Howlett R, Davey M, Quick WP et al (2014) Metabolomic analysis of the food-borne pathogen Campylobacter jejuni: application of direct injection mass spectrometry for mutant characterisation. Metabolomics 10:887–896
Dunn WB, Overy S, Quick WP (2005) Evaluation of automated electrospray-TOF mass spectrometry for metabolic fingerprinting of the plant metabolome. Metabolomics 1:137–148
Goodacre R, Timmins EM, Burton R et al (1998) Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 144:1157–1170
Vaidyanathan S, Rowland JJ, Kell DB et al (2001) Discrimination of aerobic endospore-forming bacteria via electrospray-ionization mass spectrometry of whole cell suspensions. Anal Chem 73:4134–4144
Kaderbhai NN, Broadhurst DI, Ellis DI et al (2003) Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp Funct Genomics 4:376–391
Li H, Xia X, Li X et al (2015) Untargeted metabolomic profiling of amphenicol-resistant Campylobacter jejuni by ultra-high-performance liquid chromatography−mass spectrometry. J Proteome Res 14:1060–1068
Howlett RM (2013) Analysis of Campylobacter jejuni amino acid metabolism and solute transport systems. PhD Dissertation. The University of Sheffield
Faijes M, Mars AE, Smid EJ (2007) Comparison of quenching and extraction methodologies for metabolome analysis of Lactobacillus plantarum. Microb Cell Fact 6:27
Park C, Yun S, Lee SY et al (2012) Metabolic profiling of Klebsiella oxytoca: evaluation of methods for extraction of intracellular metabolites using UPLC/Q-TOF-MS. Appl Biochem Biotechnol 167:425–438
Maharjan RP, Ferenci T (2003) Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli. Anal Biochem 313:145–154
Overy SA, Walker HJ, Malone S et al (2005) Application of metabolite profiling to the identification of traits in a population of tomato introgression lines. J Exp Bot 56:287–296
Walker H (2011) Metabolic profiling of plant tissues by electrospray mass spectrometry. In: de Bruijn FJ (ed) Handbook of molecular microbial ecology I—Metagenomics and complementary approaches. Wiley, Hoboken. ISBN 9780470644799
Xia J, Wishart D (2011) Web based inference of biological patterns, functions and pathways from metabolomics data using MetaboAnalyst. Nat Protoc 6:743–760
Jolliffe I (2002) Principal component analysis. Wiley Stats, New York
Eriksson L, Byrne T, Johansson E, et al. (2013) Multi- and megavariate data analysis basic principles and applications. MKS Umetrics AB. ISBN-10: 9197373028
Davey MP (2011) Metabolite identification, pathways, and omic integration using online databases and tools. In: de Bruijn FJ (ed) Handbook of molecular microbial ecology: Metagenomics and complementary approaches. Wiley, Hoboken. ISBN 9780470644799
Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2:171–196
Raamsdonk LM, Teusink B, Broadhurst D et al (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19:45–50
Bessede E, Solecki O, Sifre E et al (2011) Identification of Campylobacter species and related organisms by matrix assisted laser desorption ionization–time of flight (MALDI‐TOF) mass spectrometry. Clin Microbiol 17:1735–1739
Acknowledgments
This work was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) Doctoral Training Award, to R.M.H. M.P.D. acknowledges the support of a Wellcome Trust Value in People award—reference 083772/Z/07/Z. Work in D.J.K.’s laboratory is supported by a grant from the BBSRC. We thank Prof. Paul Quick and Prof. Mike Burrell for help with data processing and Heather Walker for technical help.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this protocol
Cite this protocol
Howlett, R.M., Davey, M.P., Kelly, D.J. (2017). Metabolomic Analysis of Campylobacter jejuni by Direct-Injection Electrospray Ionization Mass Spectrometry. In: Butcher, J., Stintzi, A. (eds) Campylobacter jejuni. Methods in Molecular Biology, vol 1512. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6536-6_16
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6536-6_16
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6534-2
Online ISBN: 978-1-4939-6536-6
eBook Packages: Springer Protocols