Exploring the boundaries of additivity: mixtures of NADH: quinone oxidoreductase inhibitors.

The activity of mitochondrial complex I of the electron transport chain (ETC) is known to be affected by an extraordinarily large number of diverse xenobiotics, and dysfunction at complex I has been associated with a variety of disparate human diseases, including those with potentially environmentally relevant etiologies. However, the risks associated with mixtures of complex I inhibitors have not been fully explored, and this warrants further examination of potentially greater than additive effects that could lead to toxicity. A potential complication for the prediction of mixture effects arises because mammalian mitochondrial complex I has been shown to exist in two distinct dynamic conformations based upon substrate availability. In this study, we tested the accepted models of additivity as applied to mixtures of rotenone, deguelin, and pyridaben, with and without substrate limitation. These compounds represent both natural and synthetic inhibitors of complex I of the ETC, and experimental evidence to date indicates that these inhibitors share a common binding domain with partially overlapping binding sites. Therefore, we hypothesized that prediction of their mixtures effects would follow dose addition. Using human hepatocytes, we analyzed the effects of these mixtures at doses between 0.001 and 100 µM on overall cellular viability. Analysis of the dose-response curves resulting from challenge with all possible binary and ternary mixtures revealed that the appropriate model was not clear. All of the mixtures tested were found to be in agreement with response addition, but only rotenone plus deguelin and the ternary mixture followed dose addition. To determine if conformational regulation via substrate limitation could improve model selection and our predictions, we tested the models of additivity for the binary and ternary mixtures of inhibitors when coexposed with 2-deoxy-d-glucose (2-DG), which limits NADH via upstream inhibition of glycolysis. Coexposure of inhibitors with 2-DG did facilitate model selection: Rotenone plus pyridaben and the ternary mixture were in sole agreement with dose addition, while deguelin plus pyridaben was in sole agreement with response addition. The only ambiguous result was the agreement of both models with the mixture of rotenone plus deguelin with 2-DG, which may be explained by deguelin's well-known affinity for protein kinase B (Akt) in addition to complex I. Thus, our findings indicate that predictive models for mixtures of mitochondrial complex I inhibitors appear to be compound specific, and our research highlights the need to control for dynamic conformational changes to improve our mechanistic understanding of additivity with these inhibitors.

Bayesian model selection for mining mass spectrometry data.

A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to microorganism detection from mass spectrometry data.