A Generalized Model for Metabolomic Analysis: Application to Dose and Time Dependent Toxicity

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As metabolomic technology expands, validated techniques for analyzing highly dimensional categorical data are becoming increasingly important. This manuscript presents a novel latent vector-based methodology for analyzing complex data sets with multiple groups that include both high and low doses using orthogonal projections to latent structures (OPLS) coupled with hierarchical clustering. This general methodology allows complex experimental designs (e.g., multiple dose and time combinations) to be encoded and directly compared. Further, it allows for the inclusion of low dose samples that do not exhibit a strong enough individual response to be modeled independently. A dose- and time-responsive metabolomic study was completed to evaluate and demonstrate this methodology. Single doses (0.1–100 mg/kg body weight) of α-naphthylisothiocyanate (ANIT), a common model of hepatic cholestasis, were administered orally in corn oil to male Fischer 344 rats. Urine samples were collected pre-dose and daily through day-4 post-dose. Blood samples were collected pre and post-dose to assess indices of clinical toxicity. Urine samples were analyzed by 1H-NMR spectroscopy, and the spectra were adaptively binned to reduce dimensionality. The proposed methodology for NMR-based urinary metabolomics was sensitive enough to detect ANIT-induced effects with respect to both dose and time at doses below the threshold of clinical toxicity. A pattern of ANIT-dependent effects established at the highest dose was seen in the 50 and 20 mg/kg dose groups, an effect not directly identifiable with individual principal component analysis (PCA). Coupling the pattern found by the OPLS algorithm and hierarchical clustering revealed a relationship between the 100, 50 and 20 mg/kg dose groups, suggesting a characteristic effect of ANIT exposure. These studies demonstrate that the use of a metabolomics approach with flexible binning of 1H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal metabolic perturbations caused by exposure and toxicity.



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