| 1. |
- Clayton, T. Andrew, et al.
(author)
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Pharmaco-metabonomic phenotyping and personalized drug treatment.
- 2006
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In: Nature. - 1476-4687. ; 440:7087, s. 1073-7
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Journal article (peer-reviewed)abstract
- There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.
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| 2. |
- Nicholson, George, et al.
(author)
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A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection
- 2011
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In: PLoS genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 7:9, s. e1002270-
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Journal article (peer-reviewed)abstract
- We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11<p<2.8×10−23). Three of these—trimethylamine, 3-amino-isobutyrate, and an N-acetylated compound—were measured in urine. The other—dimethylamine—was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%–64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from the corresponding mQTL effects.
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| 3. |
- Antti, Henrik, et al.
(author)
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Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects
- 2004
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 139-49
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Journal article (peer-reviewed)abstract
- Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. 1H NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) 1H NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose×time.
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| 4. |
- Azmi, Jahanara, et al.
(author)
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Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data : Nicholson Jeremy K., Holmes Elaine
- 2002
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In: Analyst. - : Royal Society of Chemistry (RSC). ; 127, s. 271-6
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Journal article (peer-reviewed)abstract
- Multivariate statistical batch processing (BP) analysis of 1H NMR urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin, -naphthylisothiocyanate (ANIT). ANIT (100 mg kg-1) was administered orally to rats (n = 5) and urine samples were collected from dosed and matching control rats at time-points up to 168 h post-dose. Urine samples were measured via1H NMR spectroscopy and partial least squares (PLS) based batch processing analysis was used to interpret the spectral data, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile over the 7 day study period was established, together with model confidence limits (±3 standard deviation), for the control group. Samples obtained from ANIT treated animals were evaluated using the control model. Time-dependent deviations from the control model were evident in all ANIT treated animals consisting of glycosuria, bile aciduria, an initial decrease in taurine levels followed by taurinuria and a reduction of tricarboxylic acid cycle intermediate excretion. BP provided an efficient means of visualising the biochemical response to ANIT in terms of both inter-animal variation and net variation in metabolite excretion profiles. BP also allowed multivariate statistical limits for normality to be established and provided a template for defining the sequence of time-dependent metabolic consequences of toxicity in NMR based metabonomic studies.
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| 5. |
- Blaise, Benjamin J., et al.
(author)
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Statistical analysis in metabolic phenotyping
- 2021
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In: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
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Research review (peer-reviewed)abstract
- Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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| 6. |
- Bollard, Mary E, et al.
(author)
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Comparative metabonomics of differential hydrazine toxicity in the rat and mouse
- 2005
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In: Toxicology and Applied Pharmacology. - : Elsevier BV. - 0041-008X. ; 204:2, s. 135-51
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Journal article (peer-reviewed)abstract
- Interspecies variation between rats and mice has been studied for hydrazine toxicity using a novel metabonomics approach. Hydrazine hydrochloride was administered to male Sprague–Dawley rats (30 mg/kg, n = 10 and 90 mg/kg, n = 10) and male B6C3F mice (100 mg/kg, n = 8 and 250 mg/kg, n = 8) by oral gavage. In each species, the high dose was selected to produce the major histopathologic effect, hepatocellular lipid accumulation. Urine samples were collected at sequential time points up to 168 h post dose and analyzed by 1H NMR spectroscopy. The metabolites of hydrazine, namely diacetyl hydrazine and 1,4,5,6-tetrahydro-6-oxo-3-pyridazine carboxylic acid (THOPC), were detected in both the rat and mouse urine samples. Monoacetyl hydrazine was detected only in urine samples from the rat and its absence in the urine of the mouse was attributed to a higher activity of N-acetyl transferases in the mouse compared with the rat. Differential metabolic effects observed between the two species included elevated urinary β-alanine, 3-d-hydroxybutyrate, citrulline, N-acetylcitrulline, and reduced trimethylamine-N-oxide excretion unique to the rat. Metabolic principal component (PC) trajectories highlighted the greater degree of toxic response in the rat. A data scaling method, scaled to maximum aligned and reduced trajectories (SMART) analysis, was used to remove the differences between the metabolic starting positions of the rat and mouse and varying magnitudes of effect, to facilitate comparison of the response geometries between the rat and mouse. Mice followed “biphasic” open PC trajectories, with incomplete recovery 7 days after dosing, whereas rats followed closed “hairpin” time profiles, indicating functional reversibility. The greater magnitude of metabolic effects observed in the rat was supported by the more pronounced effect on liver pathology in the rat when compared with the mouse.
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| 7. |
- Brindle, Joanne T, et al.
(author)
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Rapid and Nonivasive Diagnosis of the Presence and Severity of Coronary Heart Disease Using 1H-NMR-Based Metabonomics
- 2002
-
In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 8, s. 1439-45
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Journal article (peer-reviewed)abstract
- Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.
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| 8. |
- Bylesjö, Max, et al.
(author)
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K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space
- 2008
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In: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9, s. 1-7
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Journal article (peer-reviewed)abstract
- Background: Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.Results: We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.Conclusion: The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
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| 9. |
- Bylesjö, Max, et al.
(author)
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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
- 2006
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 341-351
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Journal article (peer-reviewed)abstract
- The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.
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| 10. |
- Cloarec, Olivier, et al.
(author)
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Evaluation of the Orthogonal Projection on Latent Structure Model Limitations Caused by Chemical Shift Variability and Improved Visualization of Biomarker Changes in 1H NMR Spectroscopic Metabonomic Studies
- 2005
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In: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 77:2, s. 517-26
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Journal article (peer-reviewed)abstract
- In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.
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| 11. |
- Domingo-Almenara, Xavier, et al.
(author)
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CMS-MRM and METLIN-MRM : a cloud library and public resource for targeted analysis of small molecules
- 2018
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In: Nature Methods. - : Nature Publishing Group. - 1548-7091 .- 1548-7105. ; 15:9, s. 681-
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Journal article (peer-reviewed)abstract
- We report XCMS-MRM and METLIN-MRM (http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.
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| 12. |
- Jonsson, Pär, et al.
(author)
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Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets
- 2005
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In: The Analyst. - : Royal Society of Chemistry (RSC). - 0003-2654 .- 1364-5528. ; 130:5, s. 701-707
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Journal article (pop. science, debate, etc.)abstract
- LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.
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| 13. |
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| 14. |
- Loo, Ruey Leng, et al.
(author)
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Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)
- 2020
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:21, s. 5229-5236
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Journal article (peer-reviewed)abstract
- Motivation: Large-scale population omics data can provide insight into associations between gene-environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets.Results: Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets.
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| 15. |
- Rantalainen, Mattias, et al.
(author)
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Kernel-based orthogonal projections to latent structures (K-OPLS)
- 2007
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:7-9, s. 379-385
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Journal article (peer-reviewed)abstract
- The orthogonal projections to latent structures (OPLS) method has been successfully applied in various chemical and biological systems for modeling and interpretation of linear relationships between a descriptor matrix and response matrix. A kernel-based reformulation of the original OPLS algorithm is presented where the kernel Gram matrix is utilized as a replacement for the descriptor matrix. This enables usage of the kernel trick to efficiently transform the data into a higher-dimensional feature space where predictive and response-orthogonal components are calculated. This strategy has the capacity to improve predictive performance considerably in situations where strong non-linear relationships exist between descriptor and response variables while retaining the OPLS model framework. We put particular focus on describing properties of the rearranged algorithm in relation to the original OPLS algorithm. Four separate problems, two simulated and two real spectroscopic data sets, are employed to illustrate how the algorithm enables separate modeling of predictive and response-orthogonal variation in the feature space. This separation can be highly beneficial for model interpretation purposes while providing a flexible framework for supervised regression.
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| 16. |
- Rantalainen, Mattias, et al.
(author)
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Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
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In: BMC Bioinformatics. - : EMBO. - 1471-2105. ; 9, s. 105-
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Journal article (peer-reviewed)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
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| 17. |
- Rantalainen, Mattias, et al.
(author)
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Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice
- 2006
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 10, s. 2642-55
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Journal article (peer-reviewed)abstract
- A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies
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