Search

Coral growth anomalies (GAs) are tumor-like protrusions that are detrimental to coral health, affecting both the coral skeleton and soft tissues. These lesions are increasingly found throughout the tropics and are commonly associated with high human population density, yet little is known about the molecular pathology of the disease. Here, we investigate the metabolic impacts of GAs through 1H nuclear magnetic resonance (NMR) metabolomics in Porites compressa tissues from a site of high disease prevalence (Coconut Island, Hawaii). We putatively identified 18 metabolites (8.1% of total annotated features) through complementary 1H and 1H–13C heteronuclear single quantum correlation NMR data that increase confidence in pathway analyses and may bolster future coral metabolite annotation efforts. Extract yield was elevated in both GA and unaffected (normal tissue from a diseased colony) compared to reference (normal tissue from GA-free colony) samples, potentially indicating elevated metabolic activity in GA-impacted colonies. Relatively high variation in metabolomic profiles among coral samples of the same treatment (i.e., inter-colony variation) confounded data interpretation, however, analyses of paired GA and unaffected samples identified 73 features that differed between these respective metabolome types. These features were largely annotated as unknowns, but 1-methylnicotinamide and trigonelline were found to be elevated in GA samples, while betaine, glycine, and histamine were lower in GA samples. Pathway analyses indicate decreased choline oxidation in GA samples, making this a pathway of interest for future targeted studies. Collectively, our results provide unique insights into GA pathophysiology by showing these lesions alter both the absolute and relative metabolism of affected colonies and by identifying features (metabolites and unknowns) and metabolic pathways of interest in GA pathophysiology going forward. [ABSTRACT FROM AUTHOR]

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 H-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 H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal metabolic perturbations caused by exposure and toxicity. [ABSTRACT FROM AUTHOR]

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm's ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification. [ABSTRACT FROM AUTHOR]