The mitochondrial respiratory chain is a powerful source of reactive oxygen species (ROS), considered as the pathogenic agent of many diseases and of aging. We have investigated the role of Complex I in superoxide radical production and found by combined use of specific inhibitors of Complex I that the one-electron donor in the Complex to oxygen is a redox center located prior to the sites where three different types of coenzyme Q (CoQ) competitors bind, to be identified with an Fe-S cluster, most probably N2, or possibly an ubisemiquinone intermediate insensitive to all the above inhibitors. Short-chain coenzyme Q analogues enhance superoxide formation, presumably by mediating electron transfer from N2 to oxygen. The clinically used CoQ analogue idebenone is particularly effective, raising doubts about its safety as a drug. The mitochondrial theory of aging considers somatic mutations of mitochondrial DNA induced by ROS as the primary cause of energy decline; in rat liver mitochondria, Complex I appears to be most affected by aging and to become strongly rate limiting for electron transfer. Mitochondrial energetics is also deranged in human platelets upon aging, as demonstrated by the decreased Pasteur effect (enhancement of lactate production by respiratory inhibitors). Cells counteract oxidative stress by antioxidants: CoQ is the only lipophilic antioxidant to be biosynthesized. Exogenous CoQ, however, protects cells from oxidative stress by conversion into its reduced antioxidant form by cellular reductases. The plasma membrane oxidoreductase and DT-diaphorase are two such systems: likewise, they are overexpressed under oxidative stress conditions.

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.

Metabolic serotypes sensitive to caloric intake may enable sera metabolomic profiles to validate epidemiological parameters and predict disease risk in humans. This long-range goal is complicated by the lack of known state markers and the requirement for simultaneous monitoring of multiple small changes. Therefore, analytical precision for appropriate high data density studies using HPLC separations coupled with coulometric array detectors was evaluated over a two month period in pooled rat sera samples (previously collected and stored at )80°C), and in authentic biochemical standards. In sera, mean coefficients of variation (CV) of retention time and ratio accuracy within the established metabolic serotype varied within ±1% and ±3%, respectively. In sets of purified standards, the same parameters fluctuated, correspondently, in ranges of ±0.1% and ±1%. Median CV of the metabolite concentrations were 13% in standards and 11-19% in sera, and varied non-monotonically with the analytical system status and experimental design. These parameters were shown to be sufficiently controlled so as not to dominate intra-group biological variability in serum metabolomics studies. Continuation of experimental runs across an analytical breakpoint (column replacement) was associated with disproportionate changes in metabolite concentrations, independent of maintained analytical precision. These changes were sufficient to shift overall profile localization in megavariate projection analyses. We developed a mathematical approach to normalize this break and use partial least squares projection to latent structure discriminant analysis to confirm validity of this normalization approach. This generally applicable mathematical correction helps enable longer term high data density studies by removing a critical source of systemic variation.

Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for physiological status (e.g., relative risk of developing age-related diseases such as cancer). Megavariate analysis (unsupervised hierarchical cluster analysis [HCA]; principal components analysis [PCA]) of serum metabolites reproducibly distinguish DR from ad libitum fed rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets of serum metabolites contain sufficient information to construct mathematical models of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models were built with single cohorts, combined cohorts or mixed samples from previously studied cohorts as training sets. Both algorithms over-fit models based on single cohort training sets. KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of k could not be accurately set in advance). SIMCA models had 100% accuracy within all training sets, 89 % accuracy in test sets, did not appear to over-fit mixed cohort training sets, and did not require post-hoc modeling adjustments. These data indicate that (i) previously defined metabolites are robust enough to construct classification models (expert systems) with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv) subtle inter-cohort variability may be a critical issue for high data density biomarker studies that lack state markers.