Dataset Information

(13)C NMR Metabolomics: INADEQUATE Network Analysis.

ABSTRACT: The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.

SUBMITTER: Clendinen CS

PROVIDER: S-EPMC4537055 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

(13)C NMR Metabolomics: INADEQUATE Network Analysis.

Clendinen Chaevien S CS Pasquel Christian C Ajredini Ramadan R Edison Arthur S AS

Analytical chemistry 20150514 11

The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis ...[more]

PMID: 25932900

Similar Datasets

Project description:Blood lipids have served as key biomarkers for cardiovascular disease (CVD) risk, yet emerging evidence indicates metabolite profiling might reveal a larger repertoire of small molecules that reflect altered metabolism, and which may be associated with early disease risk. Inadequate micronutrient status may also drive or exacerbate CVD risk factors that emerge during childhood. This study aimed to understand relationships between serum lipid levels, the plasma metabolome, and micronutrient status in 38 children (10 ± 0.8 years) at risk for CVD. Serum lipid levels were measured via autoanalyzer and average daily micronutrient intakes were calculated from 3-day food logs. Plasma metabolites were extracted using 80% methanol and analyzed via ultra-high-performance liquid chromatography-tandem mass spectrometry. Spearman's rank-order coefficients (rs) were computed for correlations between the following serum lipids [total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides (TG)], 805 plasma metabolites, and 17 essential micronutrients. Serum lipid levels in the children ranged from 128-255 mg/dL for total cholesterol, 67-198 mg/dL for LDL, 31-58 mg/dL for HDL, and 46-197 mg/dL for TG. The majority of children (71 to 100%) had levels lower than the Recommended Daily Allowance for vitamin E, calcium, magnesium, folate, vitamin D, and potassium. For sodium, 76% of children had levels above the Upper Limit of intake. Approximately 30% of the plasma metabolome (235 metabolites) were significantly correlated with serum lipids. As expected, plasma cholesterol was positively correlated with serum total cholesterol (rs = 0.6654; p<0.0001). Additionally, 27 plasma metabolites were strongly correlated with serum TG (rs ≥0.60; p≤0.0001), including alanine and diacylglycerols, which have previously been associated with cardiometabolic and atherosclerotic risk in adults and experimental animals. Plasma metabolite profiling alongside known modifiable risk factors for children merit continued investigation in epidemiological studies to assist with early CVD detection, mitigation, and prevention via lifestyle-based interventions.

Dataset Information

(13)C NMR Metabolomics: INADEQUATE Network Analysis.

Publications

(13)C NMR Metabolomics: INADEQUATE Network Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets