High Resolution “Ultra Performance” Liquid Chromatography Coupled to oa-TOF Mass Spectrometry as a Tool for Differential Metabolic Pathway Profiling in Functional Genomic Studies

Wilson, Ian D.; Nicholson, Jeremy K.; Castro-Perez, José; Granger, Jennifer H.; Johnson, Kent C.; Smith, Brian; Plumb, Robert S.

doi:10.1021/pr049769r

Cited by 403 publications

(297 citation statements)

References 18 publications

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“…UPLC operates with 1.7 μm chromatographic particles and a fluid system capable of operating at pressures in the 6000-15000 psi range, providing an increased chromatographic selectivity compared with conventional high performance liquid chromatography (HPLC) which uses larger particles [10]. Due to a reduction of peak width, there will also be a greater S/N ratio and an increasing sensitivity compared with the conventional HPLC.…”

Section: Uplc-ms Techniquementioning

confidence: 99%

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UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Zhao

Cheng

Vaziri

et al. 2014

Clinical Biochemistry

118

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a b s t r a c t a r t i c l e i n f oObjectives: Metabonomics is a powerful and promising analytic tool that allows assessment of global lowmolecular-weight metabolites in biological systems. It has a great potential for identifying useful biomarkers for early diagnosis, prognosis and assessment of therapeutic interventions in clinical practice. The aim of this review is to provide a brief summary of the recent advances in UPLC-based metabonomic approach for biomarker discovery in a variety of diseases, and to discuss their significance in clinical chemistry.Design and methods: All the available information on UPLC-based metabonomic applications for discovering biomarkers of diseases were collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Springer, Google Scholar, etc.).Results: Metabonomics has been used in clinical chemistry to identify and evaluate potential biomarkers and therapeutic targets in various diseases affecting the liver (hepatocarcinoma and liver cirrhosis), lung (lung cancer and pneumonia), gastrointestinal tract (colorectal cancer) and urogenital tract (prostate cancer, ovarian cancer and chronic kidney disease), as well as metabolic diseases (diabetes) and neuropsychiatric disorders (Alzheimer's disease and schizophrenia), etc.Conclusions: The information provided highlights the potential value of determination of endogenous lowmolecular-weight metabolites and the advantages and potential drawbacks of the application of UPLC-based metabonomics in clinical setting.

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Section: Uplc-ms Techniquementioning

confidence: 99%

“…This can provide better peak resolution and higher sensitivity and speed for complex mixture separation. Because of the superior UPLC resolution, the problem of ion suppression is greatly reduced [10].…”

Section: Uplc-ms Techniquementioning

confidence: 99%

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Zhao

Cheng

Vaziri

et al. 2014

Clinical Biochemistry

118

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“…Among the various LC platforms, ultra-performance LC (UPLC) is considered suitable for metabolite profiling and metabolomics [17 -28], especially, for large-scale untargeted metabolic profiling due to its enhanced reproducibility in retention time [24]. The ability to generate high peak capacities in short time by UPLC has facilitated the simultaneous analysis of the complex samples (e. g., herbal extracts and biological samples) with diverse chemical characteristics [22,24,25]. Several profound reports have covered the application of UPLC to pharmaceutical analyses [26 -31] particularly in the field of metabolism or metabolomic studies [17,22,23,26,27,32].…”

Section: Introductionmentioning

confidence: 99%

“…The ability to generate high peak capacities in short time by UPLC has facilitated the simultaneous analysis of the complex samples (e. g., herbal extracts and biological samples) with diverse chemical characteristics [22,24,25]. Several profound reports have covered the application of UPLC to pharmaceutical analyses [26 -31] particularly in the field of metabolism or metabolomic studies [17,22,23,26,27,32]. Different MS analyzers have been used in metabolite profiling or metabolic fingerprinting including triple quadrupole, ITs, and quadrupole TOF MS (QTOFMS).…”

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confidence: 99%

Ultra‐performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research

Xie

Plumb

et al. 2008

J of Separation Science

156

108

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Original Paper Ultra-performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic researchIn this study, metabolite profiling of five medicinal Panax herbs including Panax ginseng (Chinese ginseng), Panax notoginseng (Sanchi), Panax japonicus (Rhizoma Panacis Majoris), Panax quinquefolium L. (American ginseng), and P. ginseng (Korean ginseng) were performed using ultra-performance LC-quadrupole TOF MS (UPLC-QTOFMS) and multivariate statistical analysis technique. Principal component analysis (PCA) of the analytical data showed that the five Panax herbs could be separated into five different groups of phytochemicals. The chemical markers such as ginsenoside Rf, 20(S)-pseudoginsenoside F11, malonyl gisenoside Rb1, and gisenoside Rb2 accountable for such variations were identified through the loadings plot of PCA, and were identified tentatively by the accurate mass of TOFMS and partially verified by the available reference standards. Results from this study indicate that the proposed method is reliable for the rapid analysis of a group of metabolites present in herbal medicines and other natural products and applicable in the differentiation of complex samples that share similar chemical ingredients. IntroductionMetabolomics aims to identify and quantify the full complement of low-molecular-weight, soluble metabolites in actively metabolizing tissues [1 -4], which has aroused extensive awareness and interests, especially in the botanical studies such as quality control of plants [5 -9], plant engineering [10, 11] using a number of techniques including NMR or different combinations of LC, GC, and MS. Also, based on the multivariate analysis of complex biological profiles, metabolomics has been successfully applied to plant genotype discrimination and differentiation [1, 12 -14]. Plants are rich in chemically diverse metabolites, which cannot be completely detected by a single analytical method currently available in phytochemical laboratories. Over the past 20 years, HPLC/MS (LC/MS) has become one of the main analytical techniques used in controlling the quality and consistency of both the chemical markers and active substances of traditional Chinese medicines (TCMs). Despite early claims that MS, and particularly MS/MS, is able to remove the need for good chromatographic separation in the analysis of low abundance samples, a poor LC run prior to MS analysis will lead to issues of ion suppression and isobaric interferences that cannot be overcome by a spectrometer alone. More recently, the desire for significantly reduced analysis time with increased sample throughput, resolution, and sensitivity has resulted in the development of ultrafast separation and identification using LC-MS techniques [15,16]. Among the various LC platforms, ultra-performance LC (UPLC) is considered suitable for metabolite profiling and metabolomics [17 -28], especially, for large-scale untargeted metabolic profiling due to its enhanced reproducibility in retention time [24]. The ability to generate high peak capacities in short...

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“…Two-dimensional GCxGC-tof-MS has been exploited further to treble this number to >4000 raw peaks, which equates to 1800 metabolite peaks [47,48]. Comparable liquid-phase chromatographies based on reverse-phase UPLC-MS [49,50] can measure thousands of peaks, and a new version of normal-phase UPLC-MS using HILIC [51,52] columns provides complementary measurements. Although what one measures can be influenced strongly by the means of extraction [53], how do these numbers compare with the known or inferred size of the human metabolome?…”

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

265

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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High Resolution “Ultra Performance” Liquid Chromatography Coupled to oa-TOF Mass Spectrometry as a Tool for Differential Metabolic Pathway Profiling in Functional Genomic Studies

Cited by 403 publications

References 18 publications

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Ultra‐performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research

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

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