Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling

Nuclear Magnetic Resonance Metabonomics

Nuclear magnetic resonance (NMR)-based metabonomics is gaining popularity in drug discovery and development and in academia in a variety of settings, ranging from toxicology, preclinical, and clinical approaches to nutrition research, studies on microorganisms, and research on plants. This chapter focuses on the basic steps in a metabonomics study and emphasizes experience and lessons learned in our lab where we focused on metabonomic analyses of plant extracts, cell lines, and a variety of animal tissues and biofluids. We emphasize that a comprehensive and suitable study design is pivotal for a correct biological interpretation of the results, as well as highly controlled experimental conditions. Sample preparation and NMR protocols are detailed for a wide range of sample types. We discuss alternative data processing strategies and considerations for a general data analysis approach, paying particular attention to the statistical interpretation and validation of the results while also highlighting approaches to avoid possible pitfalls resulting from systematic and random errors. A tutorial written for the R statistical package and other small utilities are available from the authors upon request.

Metabolic profiling technologies for biomarker discovery in biomedicine and drug development

The state-of-the-art of nuclear magnetic resonance spectroscopy, mass spectrometry and statistical tools for the acquisition and evaluation of complex multidimensional spectroscopic data in metabolic profiling is reviewed in this article. The continuous evolution of the sensitivity, precision and throughput has made these technologies powerful and extremely robust tools for application in systems biology, pharmaceutical and diagnostics research. Particular emphasis is also given to the collection and storage of biological samples that are subjected to metabolite profiling. Selected examples from preclinical and clinical applications are paradigmatically shown. These illustrate the power of the profiling technologies for characterizing the metabolic phenotype of healthy, diseased and treated subjects. The complexity of disease and drug treatment is asking for an adequate response by integrated and comprehensive metabolite profiling approaches that allow the discovery of new combinations of metabolic biomarkers.

Effect of PA-MSHA vaccine on plasma phospholipids metabolic profiling and the ratio of Th2/Th1 cells within immune organ of mouse IgA nephropathy

Phospholipids as a class of important constituents in the biomembranes have been paid increasing attention in many fields. IgA nephropathy is now generally known to be the most common form of primary glomerulonephritis in the world. However, phospholipids metabolism in IgA nephropathy was not clear. Until recently, there was no effective treatment available for patients with IgA nephropathy. In this paper, effect of PA-MSHA vaccine on plasma phospholipid metabolic profile of mouse IgA nephropathy was investigated using high performance liquid chromatography/mass spectrometry (HPLC/MS) and principal components analysis (PCA). Female Balb/c mice were divided into four groups: model group, control group, PA-MSHA treatment group and medicine control group (dipyridamole+common threewingnut root). The experimental IgA nephropathy model was established by the immunity combination method of oral BSA and injection of SEB. It was found that combination of LC/MS technology with PCA can be successfully applied to phospholipids profile analysis, clearly classify the model group and normal group, and PA-MSHA treatment group is closer to the normal control group than medicine control group. The result showed that Th(2)/Th(1) (=CD(4)(+)CD(30)(+)/CD(4)(+)CD(30)(-)) of the model group is 20.70+/-3.57, which is significantly higher than that of the control group (1.34+/-0.14) (P<0.001). The Th(2)/Th(1) ratio of the PA-MSHA treatment group and the medicine control group are lower than that of the model group (P<0.01). It is suggested that mouse IgA nephropathy has the phospholipids metabolic abnormality, PA-MSHA vaccine cannot only regulate the abnormal phospholipids metabolism mouse with the IgA nephropathy, but also correct the over unbalance of Th(2)/Th(1) proportion.

The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements

The utility of metabonomics in the evaluation of xenobiotic toxicity has been comprehensively assessed by the Consortium for Metabonomic Toxicology (COMET), formed between five major pharmaceutical companies and Imperial College London, UK. The main objectives were to assess methodologies, to generate a metabonomic database using (1)H nuclear magnetic resonance (NMR) spectroscopy of rodent urine and blood serum and to build a predictive expert system for target organ toxicity. The analytic and biologic variation that might arise through the use of metabonomics was evaluated and a high degree of robustness demonstrated. With the completion of 147 studies, the chief deliverables of a curated database of rodent biofluid NMR spectra and computer-based expert systems for the prediction of kidney or liver toxicity in rat and mouse based on the spectral data have been generated, and delivered to the sponsoring companies. The project, with its relatively modest resources, has met and exceeded all of its targets and was judged a resounding success by the sponsoring companies who are, in many cases, already enhancing and making use of the data in their in-house studies.

Centering, scaling, and transformations: improving the biological information content of metabolomics data

BACKGROUND

Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability.

RESULTS

Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis.

CONCLUSION

Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

Negative impact of noise on the principal component analysis of NMR data

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.

New modes of data partitioning based on PARS peak alignment for improved multivariate biomarker/biopattern detection in 1H-NMR spectroscopic metabolic profiling of urine

This paper addresses the possibility of mathematically partition and process urine ¹H-NMR spectra to enhance the efficiency of the subsequent multivariate data analysis in the context of metabolic profiling of a toxicity study. We show that by processing the NMR data with the peak alignment using reduced set mapping (PARS) algorithm and the use of sparse representation of the data results in the information contained in the original NMR data being preserved with retained resolution but free of the problem of peak shifts. We can now describe a method for differential expression analysis of NMR spectra by using prior knowledge, i.e., the onset of dosing, a partitioning not possible to achieve using raw or bucketed data. In addition we also outline a scheme for soft removal of "biological noise" from the aligned data: exhaustive bio-noise subtraction (EBS). The result is a straightforward protocol for detection of peaks that appear as a consequence of the drug response. In other words, it is possible to elucidate peak origin, either from endogenous substances or from the administered drug/biomarkers. The partition of data originating from the normally regulating metabolome can, furthermore, be analyzed free of the superimposed biological noise. The proposed protocol results in enhanced interpretability of the processed data, i.e., a more refined metabolic trace, simplification of detection of consistent biomarkers, and a simplified search for metabolic end products of the administered drug.

Metabonomic modeling of drug toxicity

Global metabolic profiling (metabonomics/metabolomics) has shown particular promise in the area of toxicology and drug development. In both preclinical screening and mechanistic exploration, metabolic profiling can offer rapid, noninvasive toxicological information that is robust and reproducible, with little or no added technical resources to existing studies in drug metabolism and toxicity. In this review, the study design and analytical technology required for metabonomics are discussed, along with key examples of how fundamental questions in drug development can be addressed. Strategies for metabonomic data analysis in toxicity assessment are detailed in both principle and practice, together with a description of toxicologically relevant metabolic biomarkers. Extended into the assessment of efficacy and toxicity in the clinic, metabonomics may prove crucial in making personalized therapy and pharmacogenomics a reality.

ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

MOTIVATION

Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets.

RESULTS

We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.

Measuring the metabolome: current analytical technologies

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.