Metabolomics: from pattern recognition to biological interpretation

Weckwerth, Wolfram; Morgenthal, Katja

doi:10.1016/s1359-6446(05)03609-3

Cited by 261 publications

(184 citation statements)

References 96 publications

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“…3B). Notably, substantial line broadening of 13 C resonances of C1, C2, and C4 in mutaxanthenes A-C, ostensibly due to dynamic chemical exchange of the enol tautomers, appears as sharp singlets in D and E (SI Appendix, Fig. S35).…”

Section: Isolation and Identification Of Selected Secondary Metabolicmentioning

confidence: 99%

“…Comparative analysis of metabolomic data are greatly facilitated by using multivariate statistical analysis methods [e.g., principal component analysis (PCA), orthogonal projection to latent structures-discriminant analysis (OPLS-DA)], which convert multidimensional microbial extract ultra-performance liquid chromatography (UPLC)-MS data into lower-dimensional plots of maximally distinguishing eigenvectors (13). Critical loadings evaluation (14) efficiently validates the observed changes and identifies subsets of metabolites that are unique or shared within the datasets of a given antibiotic-resistant mutant and its progenitor strain.…”

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confidence: 99%

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Antimicrobial drug resistance affects broad changes in metabolomic phenotype in addition to secondary metabolism

Derewacz

Goodwin

McNees

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Bacteria develop resistance to many classes of antibiotics vertically, by engendering mutations in genes encoding transcriptional and translational apparatus. These severe adaptations affect global transcription, translation, and the correspondingly affected metabolism. Here, we characterize metabolome scale changes in transcriptional and translational mutants in a genomically characterized Nocardiopsis, a soil-derived actinomycete, in stationary phase. Analysis of ultra-performance liquid chromatography-ion mobility-mass spectrometry metabolomic features from a cohort of streptomycin-and rifampicin-resistant mutants grown in the absence of antibiotics exhibits clear metabolomic speciation, and loadings analysis catalogs a marked change in metabolic phenotype. Consistent with derepression, up to 311 features are observed in antibiotic-resistant mutants that are not detected in their progenitors. Mutants demonstrate changes in primary metabolism, such as modulation of fatty acid composition and the increased production of the osmoprotectant ectoine, in addition to the presence of abundant emergent potential secondary metabolites. Isolation of three of these metabolites followed by structure elucidation demonstrates them to be an unusual polyketide family with a previously uncharacterized xanthene framework resulting from sequential oxidative carbon skeletal rearrangements. Designated as "mutaxanthenes," this family can be correlated to a type II polyketide gene cluster in the producing organism. Taken together, these data suggest that biosynthetic pathway derepression is a general consequence of some antibiotic resistance mutations.natural products | metabolite discovery | multivariate statistical analysis

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Section: Isolation and Identification Of Selected Secondary Metabolicmentioning

confidence: 99%

mentioning

confidence: 99%

Antimicrobial drug resistance affects broad changes in metabolomic phenotype in addition to secondary metabolism

Derewacz

Goodwin

McNees

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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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

266

163

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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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“…Metabolomics were initially defined as the global analysis of all metabolites present in a sample (metabolomics) and the analysis of metabolic responses to drugs and diseases (metabonomics) [Nicholson et al, 1999, Fiehn 2002, Weckwerth and Morgenthal 2005. These two definitions have an historical origin, while metabolomics has its foundations in microbial and plant sciences metabonomics originated in toxicology studies of mamalian systems.…”

Section: Introductionmentioning

confidence: 99%