Motor neuron diseases (MND) are a heterogeneous group of disorders that includes amyotrophic lateral sclerosis (ALS) and result in death of motor neurons. These diseases may produce characteristic perturbations of the metabolome, the collection of small-molecules (metabolites) present in a cell, tissue, or organism. To test this hypothesis, we used high performance liquid chromatography followed by electrochemical detection to profile blood plasma from 28 patients with MND and 30 healthy controls. Of 317 metabolites, 50 were elevated in MNDpatients and more than 70 were decreased (p < 0.05). Among the compounds elevated, 12 were associated with the drug Riluzole. In a subsequent study of 19 subjects with MND who were not taking Riluzole and 33 healthy control subjects, six compounds were significantly elevated in MND, while the number of compounds with decreased concentration was similar to study 1. Our data also revealed a distinctive signature of highly correlated metabolites in a set of four patients, three of whom had lower motor neuron (LMN) disease. In both datasets we were able to separate MND patients from controls using multivariate regression techniques. These results suggest that metabolomic studies can be used to ascertain metabolic signatures of disease in a non-invasive fashion.
This report, the first in a series on diet-dependent changes in the serum metabolome (metabolic serotype), describes validation of the use of high performance liquid chromatography (HPLC) separations coupled with Coulometric array detectors to characterize changes in the metabolome. The long-term aim of these studies is to improve understanding of the effects of significant variation in nutritive status on physiology and on disease processes. Initial studies focus on identifying the effects of dietary (or caloric) restriction on the redox-active components of rat serum. Identification of compounds of interest is being carried out using HPLC separations coupled with coulometric array analysis, an approach allowing simultaneous examination of nearly 1200 serum compounds. The technical and practical issues discussed in this report are related to both analytical validity (HPLC running conditions, computer-automated peak identification, mathematical compensation for chromatographic drift, etc.) and biological variability (individual variability, cohort-cohort variability, outliers). Attention to these issues suggests approximately 250 compounds in serum are sufficiently reliable, both analytically and biologically, for potential use in building mathematical models of serotype.
Our research seeks to identify a scrum profile, or serotype, that reflects the systemic physiologic modifications resultant from dietary restriction (DR), in part such that this knowledge can be applied for biomarker studies. Direct comparison suggests that component-based classification algorithms consistently out-perform distance-based metrics for studies of nutritional modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort differences in the sera metabolome could partially obscure the effects of DR. Further analysis now shows that implementation of component-based approaches (also called projection methods) optimized for class separation and controlled for over-fitting have >97% accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is shown to be robust across cohorts, but differs in males and females (although some metabolites are affected in both). We demonstrate the utility of projection-based methods for both sample and variable diagnostics, including identification of critical metabolites and samples that are atypical with respect to both class and variable models. Inclusion of non-statistically different variables enhances classification models. Variables that contribute to these models are sharply dependent on mathematical processing techniques; some variables that do not contribute under one paradigm arc powerful under alternative mathematical paradigms. In practical terms, this information may find purpose in other endeavors, such as mechanistic studies of DR. Application of these approaches confirms the utility of megavariate data analysis techniques for optimal generation of biomarkers based on nutritional modulation of physiological processes.
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