Chemometrics in Metabonomics

Trygg, Johan; Holmes, Elaine; Lundstedt, Torbjörn

doi:10.1021/pr060594q

Cited by 1,164 publications

(979 citation statements)

References 80 publications

Supporting

Mentioning

951

Contrasting

Unclassified

Order By: Relevance

“…Our initial way of searching for relationships between the predose and postdose data was by means of the PLS (Projection to Latent Structure)-based pattern recognition methods that are typically used in metabonomic studies (22) and which had proved highly effective in our earlier animal-based work (9). However, in the present case, the PLS-based approach was relatively unproductive (SI Text) and, subsequently, we made a detailed visual comparison of creatinine-normalized predose spectra for subjects at the 2 ends of the S/G ratio distribution (25 subjects at each end).…”

Section: Resultsmentioning

confidence: 99%

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

Clayton

Baker

Lindon

et al. 2009

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

697

573

View full text Add to dashboard Cite

We provide a demonstration in humans of the principle of pharmacometabonomics by showing a clear connection between an individual's metabolic phenotype, in the form of a predose urinary metabolite profile, and the metabolic fate of a standard dose of the widely used analgesic acetaminophen. Predose and postdose urinary metabolite profiles were determined by 1 H NMR spectroscopy. The predose spectra were statistically analyzed in relation to drug metabolite excretion to detect predose biomarkers of drug fate and a human-gut microbiome cometabolite predictor was identified. Thus, we found that individuals having high predose urinary levels of p-cresol sulfate had low postdose urinary ratios of acetaminophen sulfate to acetaminophen glucuronide. We conclude that, in individuals with high bacterially mediated p-cresol generation, competitive O-sulfonation of p-cresol reduces the effective systemic capacity to sulfonate acetaminophen. Given that acetaminophen is such a widely used and seemingly well-understood drug, this finding provides a clear demonstration of the immense potential and power of the pharmacometabonomic approach. However, we expect many other sulfonation reactions to be similarly affected by competition with p-cresol and our finding also has important implications for certain diseases as well as for the variable responses induced by many different drugs and xenobiotics. We propose that assessing the effects of microbiome activity should be an integral part of pharmaceutical development and of personalized health care. Furthermore, we envisage that gut bacterial populations might be deliberately manipulated to improve drug efficacy and to reduce adverse drug reactions.T he effects of drug treatments can vary greatly between different individuals, and pharmacogenomics has been widely advocated as a potential means of personalizing human drug treatments to increase drug efficacy and to decrease adverse reactions (1-6). However, environmental factors (such as nutritional status, gut bacterial activities, age, disease, and other drug use) are also important determinants of individual metabolic phenotypes, which modulate drug metabolism, efficacy, and toxicity. Such environmental complications, which may also alter gene expression, will tend to limit the usefulness of predictions of drug-induced responses that are based only on genomic differences (7,8). For instance, for many classes of compound, enzyme induction state, which is environmentally determined, influences drug metabolism and toxicity and this is not captured in genomic data. Recognizing this important limitation of pharmacogenomics, a different approach to personalized drug treatment has recently been proposed wherein predose metabolite profiling would instead be used to predict a subject's responses to potential drug interventions (9). This ''pharmacometabonomic'' approach has a number of major advantages, which include the ready availability and relative ease of analysis of biofluids, such as urine and blood plasma, as well as the fact that ...

show abstract

Section: Resultsmentioning

confidence: 99%

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

Clayton

Baker

Lindon

et al. 2009

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

697

573

View full text Add to dashboard Cite

show abstract

“…The large data sets are analyzed by multivariate techniques, such as principal component analysis and orthogonal partial least squares discriminant analysis. Both methods are based on projection methods with the underlying assumption that the system is affected by a limited number of variables (31). Principal component analysis is essential for the visualization and subsequent removal of outliers that may prejudice further analysis.…”

Section: Analytical Platforms and Multivariate Data Analysismentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

View full text Add to dashboard Cite

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

show abstract

“…The z-score is expressed as: z=(x−m)/σ, where x represents a raw score to be standardized, and m and σ represent the mean and standard deviation (SD) of the reference population, respectively. Principal component analysis (PCA), a data visualization method, was used to differentiate the spectroscopic serum metabolite profiles of the three groups (Trygg et al, 2007). Kruskal-Wallis tests were used for three-way comparisons among all three groups.…”

Section: Data Processing and Pattern Recognitionmentioning

confidence: 99%

Response to weaning and dietary l-glutamine supplementation: metabolomic analysis in piglets by gas chromatography/mass spectrometry

Xiao

Hong

et al. 2012

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

A novel metabolomic method based on gas chromatography/mass spectrometry (GC-MS) was applied to determine the metabolites in the serum of piglets in response to weaning and dietary L-glutamine (Gln) supplementation. Thirty-six 21-d-old piglets were randomly assigned into three groups. One group continued to suckle from the sows (suckling group), whereas the other two groups were weaned and their diets were supplemented with 1% (w/w) Gln or isonitrogenous L-alanine, respectively, representing Gln group or control group. Serum samples were collected to characterize metabolites after a 7-d treatment. Results showed that twenty metabolites were down-regulated significantly (P<0.05) in control piglets compared with suckling ones. These data demonstrated that early weaning causes a wide range of metabolic changes across arginine and proline metabolism, aminosugar and nucleotide metabolism, galactose metabolism, glycerophospholipid metabolism, biosynthesis of unsaturated fatty acid, and fatty acid metabolism. Dietary Gln supplementation increased the levels of creatinine, D-xylose, 2-hydroxybutyric acid, palmitelaidic acid, and α-L-galactofuranose (P<0.05) in early weaned piglets, and were involved in the arginine and proline metabolism, carbohydrate metabolism, and fatty acid metabolism. A leave-one-out cross-validation of random forest analysis indicated that creatinine was the most important metabolite among the three groups. Notably, the concentration of creatinine in control piglets was decreased (P=0.00001) compared to the suckling piglets, and increased (P=0.0003) in Gln-supplemented piglets. A correlation network for weaned and suckling piglets revealed that early weaning changed the metabolic pathways, leading to the abnormality of carbohydrate metabolism, amino acid metabolism, and lipid metabolism, which could be partially improved by dietary Gln supplementation. These findings provide fresh insight into the complex metabolic changes in response to early weaning and dietary Gln supplementation in piglets.

show abstract

Chemometrics in Metabonomics

Cited by 1,164 publications

References 80 publications

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

Metabolomics: Applications and Promise in Mycobacterial Disease

Response to weaning and dietary l-glutamine supplementation: metabolomic analysis in piglets by gas chromatography/mass spectrometry

Contact Info

Product

Resources

About