జీవక్రియ: ఓపెన్ యాక్సెస్

Lipidomic profiling can link genetic factors and exposures to risks of chronic diseases. Using untargeted liquid chromatography-Fourier Transform mass spectrometry (LC-FTMS), we explored differences in 3,579 lipidomic features in human plasma from 158 non-fasting subjects, pooled by race, gender and smoking status. Significant associations with race (23 features), smoking status (9 features) and gender (2 features) were detected with analysis of variance (ANOVA)-based permutation tests. Identities of several features were confirmed as plasmalogens (vinyl-ether phospholipids) that were present at 2-fold greater concentrations in black subjects. Other putative features, based on accurate masses, were more abundant in white subjects, namely, dihomo-γ-linolenoyl ethanolamide (DGLEA), an endogenous endocannabinoid receptor agonist and a glycerophosphocholine [PC(16:0/18:1)]. After adjustment for race, multivariable linear regression models showed that gender was significantly associated with levels of plasmalogens and DGLEA and that consumption of animal fat was marginally associated with concentrations of plasmalogens. Interestingly, BMI did not explain additional variability in any race-adjusted model. Since plasmalogens are antioxidants that are generally regarded as health-promoting and DGLEA is an agonist of the cannabinoid receptor, our findings that these molecules differ substantially between black and white Americans and between men and women, could have health implications. The concentration of cotinine was greatly elevated in smoking subjects and 6 features with m/z values suggestive of phospholipids or sphingomyelins were present at significantly lower concentrations in smokers.

Background: Metabolomics is an emerging ‘omics’ science that has demonstrated its fast gaining importance as a powerful profiling tool for determining an individual’s response to a foreign stimulus such as a drug, toxin, or environmental change; or as an indicator of disease progression. Such small molecule profiles can used as biological markers of disease, and provide an indicator of drug efficacy or toxicity.Several studies have demonstrated that the results of any single ‘omics’ analysis may not be sufficient to decode extremely complex biological mechanisms. Methods: We have developed a translational research workflow to enable researchers to perform cutting-edge integrative analysis of metabolomics data with transcriptomics (gene expression) data using knowledge-driven networks. This network based view of interconnected functional partners can provide valuable new insight about the underlying biochemical processes and pathways associated with the phenotype of interest. To enhance and simplify metabolomics annotation we built a fully cross-referenced database called MetPlus DB, which integrates data from the three most comprehensive metabolite databases tailored largely towards mammalian metabolomics: HMDB, HUAMNCYC, and LIPID MAPS.Cross-referencing information is provided for linking to several other mainstream cheminformatics/bioinformatics repositories including KEGG, METLIN, ChEBI, FooDB, Pubchem, and Chemspider to provide unambiguous knowledge on clinically and physiologically relevant metabolites. We have made the integrated database available freely to the research community (https://github.com/ICBI/MetPlus-DB). Results: To demonstrate the usefulness and strength of our methodology, we have tested it on a multi-omics profiling dataset from NCI-60 breast cancer cell lines to explore the biological dynamics of breast cancer. Conclusions: Our results demonstrate a streamlined approach for the comprehensive annotation of metabolites using MetPlus DB, and the subsequent integration of metabolomics and transcriptomics data to explore potentially relevant biological interactions and candidate biomarkers associated with disease phenotype.

Metabolism is characterized by the functional pathways and fluxes connecting substrates, intermediates and products of a given organism. Pathways in carbon metabolism can be identified by incorporating 13C-labelled tracers. In the present model study, we have evaluated potential benefits from a single 13CO2 pulse-chase experiment with the Caucasian endemic plant Thymus transcaucasicus for the analysis of terpene composition and biosynthesis. The study design was conducive of low 13C-enrichments (< 1%) in terpenes that were detected at enhanced NMRsensitivities without hampering GC-MS-based methods for terpene profiling. From the specific 13C-labelling patterns, pathways of terpene biosynthesis could be gleaned as exemplified for the mono-terpenethymol which is made via the non-mevalonate route under the physiological in vivo conditions of the 13CO2 experiment.