Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology

Mapelli, Valeria; Olsson, Lisbeth; Nielsen, Jens

doi:10.1016/j.tibtech.2008.05.008

Cited by 127 publications

(85 citation statements)

References 41 publications

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“…Formate had a particularly surprising utilization profile, with depletion followed by subsequent excretion; the precise reason is not clear at this moment. Compound excretion is a well-known phenomenon for biotechnologically interesting compounds in bacteria such as Corynebacterium glutamicum (11,40). A number of fundamental principles that lead to compound excretion have been formulated (11).…”

Section: Discussionmentioning

confidence: 99%

“…However, sampling intracellular metabolites without either changing their relative concentrations or introducing contamination from supernatant metabolites is not straightforward, and research methods are still under active development by different groups (7,12,15,59,62). In contrast, exometabolome or supernatant profiling ("metabolic footprinting") is simple, and extracellular metabolites can exhibit very large changes in pool size (1,27,40,45). These multiple advantages mean that exometabolome analysis has already been used for a number of diverse applications, such as phenotyping of both single-gene deletion mutants and isolates from natural populations, although thus far mostly for fungi rather than bacteria (1,2,9,25,40,48).…”

mentioning

confidence: 99%

“…In contrast, exometabolome or supernatant profiling ("metabolic footprinting") is simple, and extracellular metabolites can exhibit very large changes in pool size (1,27,40,45). These multiple advantages mean that exometabolome analysis has already been used for a number of diverse applications, such as phenotyping of both single-gene deletion mutants and isolates from natural populations, although thus far mostly for fungi rather than bacteria (1,2,9,25,40,48).…”

mentioning

confidence: 99%

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Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

Behrends

Ebbels

Williams

et al. 2009

Appl Environ Microbiol

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Untargeted profiling of small-molecule metabolites from microbial culture supernatants (metabolic footprinting) has great potential as a phenotyping tool. We used time-resolved metabolic footprinting to compare one Escherichia coli and three Pseudomonas aeruginosa strains growing on complex media and show that considering metabolite changes over the whole course of growth provides much more information than analyses based on data from a single time point. Most strikingly, there was pronounced selectivity in metabolite uptake, even when the bacteria were growing apparently exponentially, with certain groups of metabolites not taken up until others had been entirely depleted from the medium. In addition, metabolite excretion showed some complex patterns. Fitting nonlinear equations (four-parameter sigmoids) to individual metabolite data allowed us to model these changes for metabolite uptake and visualize them by back-projecting the curve-fit parameters onto the original growth curves. These "uptake window" plots clearly demonstrated strain differences, with the uptake of some compounds being reversed in order between different strains. Comparison of an undefined rich medium with a defined complex medium designed to mimic cystic fibrosis sputum showed many differences, both qualitative and quantitative, with a greater proportion of excreted to utilized metabolites in the defined medium. Extending the strain comparison to a more closely related set of isolates showed that it was possible to discriminate two species of the Burkholderia cepacia complex based on uptake dynamics alone. We believe time-resolved metabolic footprinting could be a valuable tool for many questions in bacteriology, including isolate comparisons, phenotyping deletion mutants, and as a functional complement to taxonomic classifications.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

Behrends

Ebbels

Williams

et al. 2009

Appl Environ Microbiol

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“…On the other hand, microorganisms are known to rapidly adjust their metabolism in response to environmental changes such as the presence of a contaminant organism (10,11). Changes in microbial metabolism are quickly detected through changes in their metabolic footprint profile.…”

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confidence: 99%

An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

Sue

Obolonkin

Griffiths³

et al. 2011

Appl Environ Microbiol

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The early detection of microbial contamination is crucial to avoid process failure and costly delays in fermentation industries. However, traditional detection methods such as plate counting and microscopy are labor-intensive, insensitive, and time-consuming. Modern techniques that can detect microbial contamination rapidly and cost-effectively are therefore sought. In the present study, we propose gas chromatography-mass spectrometry (GC-MS)-based metabolic footprint analysis as a rapid and reliable method for the detection of microbial contamination in fermentation processes. Our metabolic footprint analysis detected statistically significant differences in metabolite profiles of axenic and contaminated batch cultures of microalgae as early as 3 h after contamination was introduced, while classical detection methods could detect contamination only after 24 h. The data were analyzed by discriminant function analysis and were validated by leave-one-out cross-validation. We obtained a 97% success rate in correctly classifying samples coming from contaminated or axenic cultures. Therefore, metabolic footprint analysis combined with discriminant function analysis presents a rapid and cost-effective approach to monitor microbial contamination in industrial fermentation processes.

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“…Among these comprehensive analyses, metabolomics is effective in the attempt to understand cellular metabolic states for use in metabolic engineering. 38,39) In metabolomics, various chemical analysis systems have been utilized, including high performance liquid chromatography, gas chromatography, and capillary electrophoresis (CE), combined with mass spectrometry (MS), for the detection, quantification, and identification of the chemical structures of metabolites. Among these metabolomic analysis systems, CE-MS is used in the analysis of charged metabolites, such as sugar phosphates and organic acids.…”

Section: )mentioning

confidence: 99%

Metabolic engineering of Saccharomyces cerevisiae to improve succinic acid production based on metabolic profiling

Ito

Hirasawa

Shimizu

2014

Bioscience, Biotechnology, and Biochemistry

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We performed metabolic engineering on the budding yeast Saccharomyces cerevisiae for enhanced production of succinic acid. Aerobic succinic acid production in S. cerevisiae was achieved by disrupting the SDH1 and SDH2 genes, which encode the catalytic subunits of succinic acid dehydrogenase. Increased succinic acid production was achieved by eliminating the ethanol biosynthesis pathways. Metabolic profiling analysis revealed that succinic acid accumulated intracellularly following disruption of the SDH1 and SDH2 genes, which suggests that enhancing the export of intracellular succinic acid outside of cells increases succinic acid production in S. cerevisiae. The mae1 gene encoding the Schizosaccharomyces pombe malic acid transporter was introduced into S. cerevisiae, and as a result, succinic acid production was successfully improved. Metabolic profiling analysis is useful in producing chemicals for metabolic engineering of microorganisms.

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Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology

Cited by 127 publications

References 41 publications

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

An Exometabolomics Approach to Monitoring Microbial Contamination in Microalgal Fermentation Processes by Using Metabolic Footprint Analysis

Metabolic engineering of Saccharomyces cerevisiae to improve succinic acid production based on metabolic profiling

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