Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC x GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 minutes of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 minutes of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre-to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC × GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment.

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The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Δ mutant yeast strain (S) grown in high glucose medium (R) and in low glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) × (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive twodimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) was used with discovery-based data mining algorithms1, 2 to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis (PARAFAC) to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 h or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.

INTRODUCTION The fetal-to-neonatal transition is one of the most complex processes in biological existence; much is unknown about this transition on the molecular and biochemical level. Based on growing metabolomics literature, we hypothesize that metabolomic analysis will reveal the key biochemical intermediates that change during the birth transition. RESULTS Using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS), we identified 100 metabolites that changed during this transition. Of these 100 metabolites, 23 demonstrated significant change during the first 72 h. Of note, four intermediates of the tricarboxylic acid (TCA) cycle were identified (α-ketoglutaric acid, fumaric acid, malic acid, and succinyl-CoA), demonstrating a consistent rate of rise during the study. This may signify the transition of the neonate from a hypoxic in utero environment to an oxygen-rich environment. Important signaling molecules were also identified, including myo-inositol and glutamic acid. DISCUSSION GC × GC–TOFMS was able to identify important metabolites associated with metabolism and signaling. These data can be used as a baseline for normal birth transition, which may aid in future perinatal research investigations. METHODS Late-preterm Macaca nemestrina were delivered by hysterotomy, with plasma drawn from the cord blood and after birth at eight additional time points to 72 h of age.

A method to analyze volatile compounds from cacao beans has been developed and evaluated. The method utilizes solid phase micro extraction (SPME) sampling followed by comprehensive 2-D (GC x GC) coupled with TOFMS. For the SPME procedure, a polydimethyl siloxane/divinyl benzene (PDMS/DVB) fiber was implemented. Cacao beans from four geographical origins were studied under two storage conditions, either dry or high moisture. A given cacao bean sample was sealed in a SPME vial and heated for 15 min. Extraction temperatures of 45, 60, 80, and 100 degrees C were analyzed and an optimal extraction temperature of 60 degrees C was determined. Many peaks were found to change as a function of storage conditions with Fisher Ratio analysis. Four representative compounds were identified and quantified (on a relative basis): acetic acid, nonanal, tetramethyl pyrazine, and trimethyl pyrazine. Acetic acid and nonanal were elevated in samples without evident mold on the bean surface, while the two pyrazines were elevated when mold was evident on the bean surface. The results for these comparisons, indicate that metabolism at the bean surface plays a role in the concentration of analytes, and can be readily determined using this analytical technology and methodology.