Compound Identification Using Partial and Semipartial Correlations for Gas Chromatography–Mass Spectrometry Data

Compound identification is a key component of data analysis in the applications of gas chromatography–mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and <i>m</i>/<i>z</i> value, respectively

Effects of Dietary Different Doses of Copper and High Fructose Feeding on Rat Fecal Metabolome

The gut microbiota plays a critical role in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). Increased fructose consumption and inadequate copper intake are two critical risk factors in the development of NAFLD. To gain insight into the role of gut microbiota, fecal metabolites, obtained from rats exposed to different dietary levels of copper with and without high fructose intake for 4 weeks, were analyzed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOF MS). In parallel, liver tissues were assessed by histology and triglyceride assay. Our data showed that high fructose feeding led to obvious hepatic steatosis in both marginal copper deficient rats and copper supplementation rats. Among the 38 metabolites detected with significant abundance alteration between groups, short chain fatty acids were markedly decreased with excessive fructose intake irrespective of copper levels. C15:0 and C17:0 long chain fatty acids, produced only by bacteria, were increased by either high copper level or high fructose intake. In addition, increased fecal urea and malic acid paralleled the increased hepatic fat accumulation. Collectively, GC × GC-TOF MS analysis of rat fecal samples revealed distinct fecal metabolome profiles associated with the dietary high fructose and copper level, with some metabolites possibly serving as potential noninvasive biomarkers of fructose induced-NAFLD

Metabolomic Analysis of the Effects of Chronic Arsenic Exposure in a Mouse Model of Diet-Induced Fatty Liver Disease

Arsenic is a widely distributed environmental component that is associated with a variety of cancer and non-cancer adverse health effects. Additional lifestyle factors, such as diet, contribute to the manifestation of disease. Recently, arsenic was found to increase inflammation and liver injury in a dietary model of fatty liver disease. The purpose of the present study was to investigate potential mechanisms of this diet–environment interaction via a high-throughput metabolomics approach. GC×GC–TOF MS was used to identify metabolites that were significantly increased or decreased in the livers of mice fed a Western diet (a diet high in fat and cholesterol) and co-exposed to arsenic-contaminated drinking water. The results showed that there are distinct hepatic metabolomic profiles associated with eating a high fat diet, drinking arsenic-contaminated water, and the combination of the two. Among the metabolites that were decreased when arsenic exposure was combined with a high fat diet were short-chain and medium-chain fatty acid metabolites and the anti-inflammatory amino acid, glycine. These results are consistent with the observed increase in inflammation and cell death in the livers of these mice and point to potentially novel mechanisms by which these metabolic pathways could be altered by arsenic in the context of diet-induced fatty liver disease