Metabolomics: a global biochemical approach to the study of central nervous system diseases

Probing the metabolic aberrations underlying mutant huntingtin toxicity in yeast and assessing their degree of preservation in humans and mice

Metabolomics is a powerful multiparameter tool for evaluating phenotypic traits associated with disease processes. We have used (1)H NMR metabolome profiling to characterize metabolic aberrations in a yeast model of Huntington's disease that are attributable to the mutant huntingtin protein's gain-of-toxic-function effects. A group of 11 metabolites (alanine, acetate, galactose, glutamine, glycerol, histidine, proline, succinate, threonine, trehalose, and valine) exhibited significant concentration changes in yeast expressing the N-terminal fragment of a mutant human huntingtin gene. Correspondence analysis was used to compare results from our yeast model to data reported from transgenic mice expressing a mutant huntingtin gene fragment and Huntington's disease patients. This technique enabled us to identify a variety of both model-specific (pertaining to a single species) and conserved (observed in multiple species) biomarkers related to mutant huntingtin's toxicity. Among the 59 metabolites identified, four compounds (alanine, glutamine, glycerol, and valine) changed significantly in concentration in all three Huntington's disease systems. We propose that alanine, glutamine, glycerol, and valine should be considered as promising biomarkers for evaluating new Huntington's disease therapies, as well as for providing unique insight into the mechanisms associated with mutant huntingtin toxicity.

Alterations in tryptophan and purine metabolism in cocaine addiction: a metabolomic study

BACKGROUND

Mapping metabolic "signatures" can provide new insights into addictive mechanisms and potentially identify biomarkers and therapeutic targets.

OBJECTIVE

We examined the differences in metabolites related to the tyrosine, tryptophan, purine, and oxidative stress pathways between cocaine-dependent subjects and healthy controls. Several of these metabolites serve as biological indices underlying the mechanisms of reinforcement, toxicity, and oxidative stress.

METHODS

Metabolomic analysis was performed in 18 DSM-IV-diagnosed cocaine-dependent individuals with at least 2 weeks of abstinence and ten drug-free controls. Plasma concentrations of 37 known metabolites were analyzed and compared using a liquid chromatography electrochemical array platform. Multivariate analyses were used to study the relationship between severity of drug use [Addiction Severity Index (ASI) scores] and biological measures.

RESULTS

Cocaine subjects showed significantly higher levels of n-methylserotonin (p < 0.0017) and guanine (p < 0.0031) and lower concentrations of hypoxanthine (p < 0.0002), anthranilate (p < 0.0024), and xanthine (p < 0.012), compared to controls. Multivariate analyses showed that a combination of n-methylserotonin and xanthine contributed to 73% of the variance in predicting the ASI scores (p < 0.0001). Logistic regression showed that a model combining n-methylserotonin, xanthine, xanthosine, and guanine differentiated cocaine and control groups with no overlap.

CONCLUSIONS

Alterations in the methylation processes in the serotonin pathways and purine metabolism seem to be associated with chronic exposure to cocaine. Given the preliminary nature and cross-sectional design of the study, the findings need to be confirmed in larger samples of cocaine-dependent subjects, preferably in a longitudinal design.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Challenges in applying chemometrics to LC–MS-based global metabolite profile data

Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism’s environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.

Molecular genetic analysis of Down syndrome

Down syndrome (DS) is caused by trisomy of all or part of human chromosome 21 (HSA21) and is the most common genetic cause of significant intellectual disability. In addition to intellectual disability, many other health problems, such as congenital heart disease, Alzheimer's disease, leukemia, hypotonia, motor disorders, and various physical anomalies occur at an elevated frequency in people with DS. On the other hand, people with DS seem to be at a decreased risk of certain cancers and perhaps of atherosclerosis. There is wide variability in the phenotypes associated with DS. Although ultimately the phenotypes of DS must be due to trisomy of HSA21, the genetic mechanisms by which the phenotypes arise are not understood. The recent recognition that there are many genetically active elements that do not encode proteins makes the situation more complex. Additional complexity may exist due to possible epigenetic changes that may act differently in DS. Numerous mouse models with features reminiscent of those seen in individuals with DS have been produced and studied in some depth, and these have added considerable insight into possible genetic mechanisms behind some of the phenotypes. These mouse models allow experimental approaches, including attempts at therapy, that are not possible in humans. Progress in understanding the genetic mechanisms by which trisomy of HSA21 leads to DS is the subject of this review.