Analytical Strategies in Metabonomics

Lenz, Eva M.; Wilson, Ian D.

doi:10.1021/pr0605217

Cited by 495 publications

(441 citation statements)

References 128 publications

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“…Both methods enable the comprehensive investigation of metabolic profiles (Dunn et al 2005, Hollywood et al 2006, Lenz & Wilson 2007 and can provide complementary snapshots of the metabolome of body fluids such as plasma, urine or cerebrospinal fluid (Bictash et al 2010).…”

Section: Metabolomics Technologiesmentioning

confidence: 99%

Metabolomics in diabetes research

Friedrich¹

2012

Journal of Endocrinology

124

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Diabetes represents one of the most important global health problems because it is associated with a large economic burden on the health systems of many countries. Whereas the diagnosis and treatment of manifest diabetes have been well investigated, the identification of novel pathways or early biomarkers indicative of metabolic alterations or insulin resistance related to the development of diabetes is still in progress. Over half of the type 2 diabetes patients show manifestations of diabetes-related diseases, which highlight the need for early screening markers of diabetes. During the last decade, the rapidly growing research field of metabolomics has introduced new insights into the pathology of diabetes as well as methods to predict disease onset and has revealed new biomarkers. Recent epidemiological studies first used metabolism to predict incident diabetes and revealed branched-chain and aromatic amino acids including isoleucine, leucine, valine, tyrosine and phenylalanine as highly significant predictors of future diabetes. This review summarises the current findings of metabolic research regarding diabetes in animal models and human investigations.

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Section: Metabolomics Technologiesmentioning

confidence: 99%

Metabolomics in diabetes research

Friedrich¹

2012

Journal of Endocrinology

124

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“…If the correlation value of two mass spectra is lower than the threshold, the pair is classified as negative. After the calculation of the correlation value for every pair of spectra, we can obtain TPR and FPR for a given threshold using the following, (9) where TP (true positive) is the number of positive pairs that were classified as positive, FN (false negative) is the number of positive pairs that were classified as negative, FP (false positive) is the number of negative pairs that were classified as positive, and TN (true negative) is the number of negative pairs that were classified as negative. While changing the threshold value from −1 to 1, we calculated the values of FPRs and TPRs, where each pair of (FPR, TPR) corresponds to a specific threshold value.…”

Section: Analysis Of Data Generated From Standard Metabolite Mixturesmentioning

confidence: 99%

“…Although the number of known metabolites in many organisms is fewer than the number of genes or proteins, development of profiling methods and analytical techniques for metabolite detection and identification is a challenge due to the chemical diversity of metabolites and large concentration ranges. Many analytical techniques have been applied in metabolomics including liquid chromatography (LC), capillary electrophoresis (CE), gas chromatography (GC), and each of these coupled with mass spectrometry (LC-MS, CE-MS and GC-MS) [5][6][7][8][9]. Single dimension methods however, often provide discriminating capability that is too limiting.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm

Huang

Regnier

et al. 2008

Journal of Chromatography A

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We report a novel peak sorting method for the two-dimensional gas chromatography/time-of-flight mass spectrometry (GC×GC/TOF-MS) system. The objective of peak sorting is to recognize peaks from the same metabolite occurring in different samples from thousands of peaks detected in the analytical procedure. The developed algorithm is based on the fact that the chromatographic peaks for a given analyte have similar retention times in all of the chromatograms. Raw instrument data are first processed by ChromaTOF (Leco) software to provide the peak tables. Our algorithm achieves peak sorting by utilizing the first and second dimension retention times in the peak tables and the mass spectra generated during the process of electron impact ionization. The algorithm searches the peak tables for the peaks generated by the same type of metabolite using several search criteria. Our software also includes options to eliminate non-target peaks from the sorting results, e.g., peaks of contaminants. The developed software package has been tested using a mixture of standard metabolites and another mixture of standard metabolites spiked into human serum. Manual validation demonstrates high accuracy of peak sorting with this algorithm.

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“…Although there are many platforms for metabolic profiling (for example, LC-MS, GC-MS, NMR, electrocapillary phoresis-MS), no one technology gives complete coverage of the metabonome. This technique has been used extensively in studies on schizophrenia, [16][17][18][19] Alzheimer's disease, 20 Huntington's disease, 21 Batten disease 22 and human brain tumors 23 (for detailed reviews on metabonomics, see Holmes et al 24 and Lenz and Wilson 25 ). These studies have identified a number of biochemical alterations that may be occurring as part of the pathogenesis of these diseases.…”

Section: Introductionmentioning

confidence: 99%

Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder

et al. 2008

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Bipolar affective disorder is a severe and debilitating psychiatric condition characterized by the alternating mood states of mania and depression. Both the molecular pathophysiology of the disorder and the mechanism of action of the mainstays of its treatment remain largely unknown. Here, 1 H NMR spectroscopy-based metabonomic analysis was performed to identify molecular changes in post-mortem brain tissue (dorsolateral prefrontal cortex) of patients with a history of bipolar disorder. The observed changes were then compared to metabolic alterations identified in rat brain following chronic oral treatment with either lithium or valproate. This is the first study to use 1 H NMR spectroscopy to study post-mortem bipolar human brain tissue, and it is the first to compare changes in disease brain with changes induced in rat brain following mood stabilizer treatment. Several metabolites were found to be concordantly altered in both the animal and human tissues. Glutamate levels were increased in post-mortem bipolar brain, while the glutamate/glutamine ratio was decreased following valproate treatment, and c-aminobutyric acid levels were increased after lithium treatment, suggesting that the balance of excitatory/inhibitory neurotransmission is central to the disorder. Both creatine and myo-inositol were increased in the post-mortem brain but depleted with the medications. Lastly, the level of N-acetyl aspartate, a clinically important metabolic marker of neuronal viability, was found to be unchanged following chronic mood stabilizer treatment. These findings promise to provide new insight into the pathophysiology of bipolar disorder and may be used to direct research into novel therapeutic strategies.

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Analytical Strategies in Metabonomics

Cited by 495 publications

References 128 publications

Metabolomics in diabetes research

Metabolomics in diabetes research

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm

Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder

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