3 research outputs found
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 Ă— 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 Ă— 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