Metabolomics and the challenges ahead

MS-Based Metabolite Profiling of Aboveground and Root Components of Zingiber mioga and Officinale

Zingiber species are members of the Zingiberaceae family, and are widely used for medicinal and food purposes. In this study aboveground and root parts of Zingiber mioga and Zingiber officinale were subjected to metabolite profiling by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) in order to characterize them by species and parts and also to measure bioactivities. Both primary and secondary metabolites showed clear discrimination in the PCA score plot and PLS-DA by species and parts. Tetrahydrocurcumin, diarylheptanoid, 8-gingerol, and 8-paradol were discriminating metabolites between Z. mioga and Z. officinale that were present in different quantities. Eleven flavonoids, six amino acids, six organic acids, four fatty acids, and gingerenone A were higher in the aboveground parts than the root parts. Antioxidant activities were measured and were highest in the root part of Z. officinale. The relatively high contents of tetrahydrocurcumin, diarylheptanoid, and galanganol C in the root part of Z. officinale showed highly positive correlation with bioactivities based on correlation assay. On the basis of these results, we can suggest different usages of structurally different parts of Zingiber species as food plants.

Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

Role of metabolomic analysis of biomarkers in the management of male infertility

Metabolomics is the systematic study of metabolites as small-molecule biomarkers that represent the functional phenotype in a cell, tissue or organism. Detection of crucial disturbances in the concentration of metabolites by metabolomic profiling of key biomarkers can be beneficial in the management of various medical conditions, including male-factor infertility. Recent studies have demonstrated the potential role of this rapid, noninvasive analysis in the investigation of infertile men. Differences in the concentration of oxidative stress biomarkers (-CH, -NH, -OH and ROH) have been found to be uniquely associated with semen plasma of healthy men compared with patients with idiopathic infertility, varicocele and vasectomy reversal. Furthermore, NMR spectra have shown significant differences in citrate, lactate, glycerylphosphorylcholine and glycerylphosphorylethanolamine among semen samples of men with spermatogenesis failure, obstructive azoospermia, oligoasthenoteratozoospermia and healthy donors. Evidence has also shown the value of (31)P-magnetic resonance spectroscopy in differentiating patients with testicular failure and ductal obstruction by utilizing phosphomonoester and beta-adenosine triphosphate as biomarkers. In addition, metabolomics has shown promise in assisted reproductive techniques. Recent studies involving spectroscopic measurements of follicular fluid and embryo culture media have revealed an association between biomarkers of oxidative stress and pregnancy outcome of oocytes and embryos.

Comparing metabolomic and pathologic biomarkers alone and in combination for discriminating Alzheimer's disease from normal cognitive aging

Background

A critical and as-yet unmet need in Alzheimer disease (AD) research is the development of novel markers that can identify individuals at risk for cognitive decline due to AD. This would aid intervention trials designed to slow the progression of AD by increasing diagnostic certainty, and provide new pathophysiologic clues and potential drug targets.

Results

We used two metabolomics platforms (gas chromatography-time of flight mass spectrometry [GC-TOF] and liquid chromatography LC-ECA array [LC-ECA]) to measure a number of metabolites in cerebrospinal fluid (CSF) from patients with AD dementia and from cognitively normal controls. We used stepwise logistic regression models with cross-validation to assess the ability of metabolite markers to discriminate between clinically diagnosed AD participants and cognitively normal controls and we compared these data with traditional CSF Luminex immunoassay amyloid-β and tau biomarkers. Aβ and tau biomarkers had high accuracy to discriminate cases and controls (testing area under the curve: 0.92). The accuracy of GC-TOF metabolites and LC-ECA metabolites by themselves to discriminate clinical AD participants from controls was high (testing area under the curve: 0.70 and 0.96, respectively).

Conclusions

Our study identified several CSF small-molecule metabolites that discriminated especially well between clinically diagnosed AD and control groups. They appear to be suitable for further confirmatory and validation studies, and show the potential to provide predictive performance for AD.

Pharmacogenomics: a systems approach

Pharmacogenetics and pharmacogenomics involve the study of the role of inheritance in individual variation in drug response, a phenotype that varies from potentially life-threatening adverse drug reactions to equally serious lack of therapeutic efficacy. Pharmacogenetics-pharmacogenomics represents a major component of the movement to 'individualized medicine'. Pharmacogenetic studies originally focused on monogenic traits, often involving genetic variation in drug metabolism. However, contemporary studies increasingly involve entire 'pathways' that include both pharmacokinetics (PKs)--factors that influence the concentration of a drug reaching its target(s)--and pharmacodynamics (PDs), factors associated with the drug target(s), as well as genome-wide approaches. The convergence of advances in pharmacogenetics with rapid developments in human genomics has resulted in the evolution of pharmacogenetics into pharmacogenomics. At the same time, studies of drug response are expanding beyond genomics to encompass pharmacotranscriptomics and pharmacometabolomics to become a systems-based discipline. This discipline is also increasingly moving across the 'translational interface' into the clinic and is being incorporated into the drug development process and governmental regulation of that process. The article will provide an overview of the development of pharmacogenetics-pharmacogenomics, the scientific advances that have contributed to the continuing evolution of this discipline, the incorporation of transcriptomic and metabolomic data into attempts to understand and predict variation in drug response phenotypes as well as challenges associated with the 'translation' of this important aspect of biomedical science into the clinic.

Metabolomics: a global biochemical approach to drug response and disease

Metabolomics is the study of metabolism at the global level. This rapidly developing new discipline has important potential implications for pharmacologic science. The concept that metabolic state is representative of the overall physiologic status of the organism lies at the heart of metabolomics. Metabolomic studies capture global biochemical events by assaying thousands of small molecules in cells, tissues, organs, or biological fluids-followed by the application of informatic techniques to define metabolomic signatures. Metabolomic studies can lead to enhanced understanding of disease mechanisms and to new diagnostic markers as well as enhanced understanding of mechanisms for drug or xenobiotic effect and increased ability to predict individual variation in drug response phenotypes (pharmacometabolomics). This review outlines the conceptual basis for metabolomics as well as analytical and informatic techniques used to study the metabolome and to define metabolomic signatures. It also highlights potential metabolomic applications to pharmacology and clinical pharmacology.