Measuring the metabolome: current analytical technologies

Dunn, Warwick B.; Bailey, Nigel J.; Johnson, Helen E.

doi:10.1039/b418288j

Cited by 792 publications

(576 citation statements)

References 235 publications

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“…It benefits from being perhaps the most selective analytical technique, with its ability to provide unambiguous information about a molecule, an important aspect in terms of characterising components of complex mixtures (7,17) . NMR does not require extensive sample preparation time, has high reproducibility and little inter-laboratory variability (18) .…”

Section: Nmr Spectroscopymentioning

confidence: 99%

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The role of metabolomics in determination of new dietary biomarkers

O’Gorman

Brennan

2017

Proc. Nutr. Soc.

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Traditional methods for the assessment of dietary intake are prone to error; in order to improve and enhance these methods increasing interest in the identification of dietary biomarkers has materialised. Metabolomics has emerged as a key tool in the area of dietary biomarker discovery and to date the use of metabolomics has identified a number of putative biomarkers. Applications to identify novel biomarkers of intake have in general taken three approaches: (1) specific acute intervention studies to identify specific biomarkers of intake; (2) searching for biomarkers in cohort studies by correlating to self-reported intake of a specific food/food group(s); (3) analysing dietary patterns in conjunction with metabolomic profiles to identify biomarkers and nutritypes. A number of analytical technologies are employed in metabolomics as currently there is no single technique capable of measuring the entire metabolome. These approaches each have their own advantages and disadvantages. The present review will provide an overview of current technologies and applications of metabolomics in the determination of new dietary biomarkers. In addition, it will address some of the current challenges in the field and future outlooks.

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Section: Nmr Spectroscopymentioning

confidence: 99%

“…1. All stages should be carefully designed and executed in order to provide valid datasets and ultimately valid experimental conclusions and hypotheses (7) . Numerous comprehensive reviews on the experimental strategies in metabolomics are available elsewhere (8)(9)(10)(11) .…”

mentioning

confidence: 99%

The role of metabolomics in determination of new dietary biomarkers

O’Gorman

Brennan

2017

Proc. Nutr. Soc.

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“…As part of the emphasis on technology development above (and see Ref. [39]), the technologies that enable us to do metabolomics well have recently increased in power [17,[40][41][42][43]. They normally include a separation step (gas or liquid chromatographies or electrophoresis) coupled to an identification step (typically mass spectrometry).…”

Section: Metabolomics and Technologies For Its Measurementmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

264

161

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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“…The PMCMC sam- pler is further assessed by applying it to a data set with 8 samples acquired by in vitro 1D H-NMR on two neural cell types which are neurons and neural stem cells. The data were preprocessed after acquisition as usual [7], and the functional spectra is discretized by binning variables into bins of size 0.04 ppms resulting in 2394 variables totally. For each sample the baseline was removed and finally the data matrix X has the size 8 × 2394.…”

Section: Nuclear Magnetic Resonance Spectroscopy Datamentioning

confidence: 99%

Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization

Zhong

2016

Neural Process Lett

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A pseudo-marginal Markov chain Monte Carlo (PMCMC) method is proposed for nonnegative matrix factorization (NMF). The sampler jointly simulates the joint posterior distribution for the nonnegative matrices and the matrix dimensions which indicate the number of the nonnegative components in the NMF model. We show that the PMCMC sampler is a generalization of a version of the reversible jump Markov chain Monte Carlo (RJMCMC). An illustrative synthetic data was used to demonstrate the ability of the proposed PMCMC sampler in inferring the nonnegative matrices and as well as the matrix dimensions. The proposed sampler was also applied to a nuclear magnetic resonance (NMR) spectroscopy data to infer the number of nonnegative components.

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Measuring the metabolome: current analytical technologies

Cited by 792 publications

References 235 publications

The role of metabolomics in determination of new dietary biomarkers

The role of metabolomics in determination of new dietary biomarkers

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization

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