A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological States.

Multicellular organisms maintain the stability of their internal environment using metabolic and physiological regulatory mechanisms that are disrupted during disease. The loss of homeostatic control results in a complex set of disordered states that may lead to metabolic network failure and irreversible system damage. We have applied a new statistical entropy-based approach to quantify temporal systemic disorder (divergence of metabolic responses) in experimental patho-physiological states, via NMR-spectroscopy generated metabolic profiles of urine. A recovery (R-) potential metric has also been developed to evaluate the relative extent to which defined metabolic processes are perturbed in the context of a global system in terms of multiple changes in concentrations of biofluid components accompanying the disrupted functional activity. This approach is sensitive to physiological as well as pathological interventions. We show that global disruptions of metabolic processes, lesion reversibility, and disorder in metabolic responses to a stressor can be visualized via metabolic entropy metrics, giving insights into biological robustness and thus providing a new tool for assessing deviation from homeostatic regulation.

Recursive segment-wise peak alignment of biological (1)h NMR spectra for improved metabolic biomarker recovery.

Chemical shift variation in small-molecule (1)H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. We present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple (1)H NMR spectra used in metabonomic studies. The method refines a segmentation of reference and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. We also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 (1)H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. We demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatographic profiles and MS data.

1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies.

Statistical HeterospectroscopY (SHY) is a statistical strategy for the coanalysis of multiple spectroscopic data sets acquired in parallel on the same samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecular fingerprints measured by multiple spectroscopic techniques across cohorts of samples. Here, the method is applied to 600-MHz (1)H NMR and UPLC-TOF-MS (E) data obtained from human urine samples ( n = 86) from a subset of an epidemiological population unselected for any relevant phenotype or disease factor. We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/ z data from MS, together with fragment analysis from MS (E) scans, leads not only to the detection of numerous endogenous urinary metabolites but also the identification of drug metabolites that are part of the latent use of drugs by the population. We show previously unreported positive mode ions of ibuprofen metabolites with their NMR correlates and suggest the detection of new metabolites of disopyramide in the population samples. This approach is of great potential value in the description of population xenometabolomes and in population pharmacology studies, and indeed for drug metabolism studies in general.

Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes.

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.

Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods.

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.

Metabonomics in diabetes research.

Metabonomics has been defined as "quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification" and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.

Statistical search space reduction and two-dimensional data display approaches for UPLC-MS in biomarker discovery and pathway analysis.

A new analytical strategy for biomarker recovery from directly coupled ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC Tof MS) data on biofluids is presented and exemplified using a study on hydrazine-induced liver toxicity. A key step in the strategy involves a novel procedure for reducing the spectroscopic search space by differential analysis of cohorts of normal and pathological samples using an orthogonal projection to latent structures discriminant analysis (O-PLS-DA). This efficiently sorts principal discriminators of toxicity from the background of thousands of metabolic features commonly observed in data sets generated by UPLC-MS analysis of biological fluids and is thus a powerful tool for biomarker discovery.

Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies.

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.