Simplivariate models: ideas and first examples

Spectroscopic methods to analyze drug metabolites

Drug metabolites have been monitored with various types of newly developed techniques and/or combination of common analytical methods, which could provide a great deal of information on metabolite profiling. Because it is not easy to analyze whole drug metabolites qualitatively and quantitatively, a single solution of analytical techniques is combined in a multilateral manner to cover the widest range of drug metabolites. Mass-based spectroscopic analysis of drug metabolites has been expanded with the help of other parameter-based methods. The current development of metabolism studies through contemporary pharmaceutical research are reviewed with an overview on conventionally used spectroscopic methods. Several technical approaches for conducting drug metabolic profiling through spectroscopic methods are discussed in depth.

Group-wise ANOVA simultaneous component analysis for designed omics experiments

INTRODUCTION

Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.

OBJECTIVES

We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.

METHODS

GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.

RESULTS

The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.

CONCLUSIONS

GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

The impact of free or standardized lifestyle and urine sampling protocol on metabolome recognition accuracy

Urine contains a clear individual metabolic signature, although embedded within a large daily variability. Given the potential of metabolomics to monitor disease onset from deviations from the "healthy" metabolic state, we have evaluated the effectiveness of a standardized lifestyle in reducing the "metabolic" noise. Urine was collected from 24 (5 men and 19 women) healthy volunteers over a period of 10 days: phase I, days 1-7 in a real-life situation; phase II, days 8-10 in a standardized diet and day 10 plus exercise program. Data on dietary intake and physical activity have been analyzed by a nation-specific software and monitored by published protocols. Urine samples have been analyzed by (1)H NMR followed by multivariate statistics. The individual fingerprint emerged and consolidated with increasing the number of samples and reaches ~100 % cross-validated accuracy for about 40 samples. Diet standardization reduced both the intra-individual and the interindividual variability; the effect was due to a reduction in the dispersion of the concentration values of several metabolites. Under standardized diet, however, the individual phenotype was still clearly visible, indicating that the individual's signature was a strong feature of the metabolome. Consequently, cohort studies designed to investigate the relation of individual metabolic traits and nutrition require multiple samples from each participant even under highly standardized lifestyle conditions in order to exploit the analytical potential of metabolomics. We have established criteria to facilitate design of urine metabolomic studies aimed at monitoring the effects of drugs, lifestyle, dietary supplements, and for accurate determination of signatures of diseases.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study

BACKGROUND

Metabolomics, a global study of metabolites and small molecules, is a novel expanding field. In this pilot study, metabolomics has been applied to serum samples from women with metastatic breast cancer to explore outcomes and response to treatment.

PATIENTS AND METHODS

Pre-treatment and serial on-treatment serum samples were available from an international clinical trial in which 579 women with metastatic breast cancer were randomized to paclitaxel plus either a targeted anti-HER2 treatment (lapatinib) or placebo. Serum metabolomic profiles were obtained using 600 MHz nuclear magnetic resonance spectroscopy. Profiles were compared with time to progression, overall survival and treatment toxicity.

RESULTS

Pre- and on-treatment serum samples were assessed for over 500 patients. Unbiased metabolomic profiles in the biologically unselected overall trial population did not correlate with outcome or toxicity. In a subgroup of patients with HER2-positive disease treated with paclitaxel plus lapatinib, metabolomic profiles from patients in the upper and lower thirds of the dataset showed significant differences for time to progression (N = 22, predictive accuracy = 89.6%) and overall survival (N = 16, predictive accuracy = 78.0%).

CONCLUSIONS

In metastatic breast cancer, metabolomics may play a role in sub selecting patients with HER2 positive disease with greater sensitivity to paclitaxel plus lapatinib.

The cardiovascular risk of healthy individuals studied by NMR metabonomics of plasma samples

The identification and the present wide acceptance of cardiovascular risk factors such as age, sex, hypertension, hyperlipidemia, smoking, obesity, diabetes, and physical inactivity have led to dramatic reductions in cardiovascular morbidity and mortality. However, novel risk predictors present opportunities to identify more patients at risk and to more accurately define the biochemical signature of that risk. In this paper, we present a comprehensive metabonomic analysis of 864 plasma samples from healthy volunteers, through Nuclear Magnetic Resonance (NMR) and multivariate statistical analysis (regression and classification). We have found that subjects that are classified as at high or at low risk using the common clinical markers can also be discriminated using NMR metabonomics. This discrimination is not only due to common markers (such as total cholesterol, triglycerides, LDL, HDL), but also to (p < 0.05 after Bonferroni correction) other metabolites (e.g., 3-hydroxybutyrate, α-ketoglutarate, threonine, dimethylglycine) previously not associated with cardiovascular diseases.

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.

We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.

High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format

Metabolomics is a founding pillar of quantitative biology and a valuable tool for studying metabolism and its regulation. Here we present a workflow for metabolomics in microplate format which affords high-throughput and yet quantitative monitoring of primary metabolism in microorganisms and in particular yeast. First, the most critical step of rapid sampling was adapted to a multiplex format by using fritted 96-well plates for cultivation, which ensure fast sample transfer and permit us to use well-established quenching in cold solvents. Second, extensive optimization of large-volume injection on a GC/TOF instrument provided the sensitivity necessary for robust quantification of 30 primary metabolites in 0.6 mg of yeast biomass. The metabolome profiles of baker's yeast cultivated in fritted well plates or in shake flasks were equivalent. Standard deviations of measured metabolites were between 10% and 50% within one plate. As a proof of principle we compared the metabolome of wild-type Saccharomyces cerevisiae and the single-deletion mutant Delta sdh1, which were clearly distinguishable by a 10-fold increase of the intracellular succinate concentration in the mutant. The described workflow allows the production of large amounts of metabolome samples within a day, is compatible with virtually all liquid extraction protocols, and paves the road to quantitative screens.