LIPID MAPS online tools for lipid research

LIPID MAPS Bioinformatics Core, San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA.
Nucleic Acids Research (Impact Factor: 9.11). 08/2007; 35(Web Server issue):W606-12. DOI: 10.1093/nar/gkm324
Source: PubMed


The LIPID MAPS consortium has developed a number of online tools for performing tasks such as drawing lipid structures and
predicting possible structures from mass spectrometry (MS) data. A simple online interface has been developed to enable an
end-user to rapidly generate a variety of lipid chemical structures, along with corresponding systematic names and ontological
information. The structure-drawing tools are available for six categories of lipids: (i) fatty acyls, (ii) glycerolipids,
(iii) glycerophospholipids, (iv) cardiolipins, (v) sphingolipids and (vi) sterols. Within each category, the structure-drawing
tools support the specification of various parameters such as chain lengths at a specific sn position, head groups, double bond positions and stereochemistry to generate a specific lipid structure. The structure-drawing
tools have also been integrated with a second set of online tools which predict possible lipid structures from precursor-ion
and product-ion MS experimental data. The MS prediction tools are available for three categories of lipids: (i) mono/di/triacylglycerols,
(ii) glycerophospholipids and (iii) cardiolipins. The LIPID MAPS online tools are publicly available at

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Available from: Shankar Subramaniam, May 25, 2015
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    • "Most of the phylogenetic and statistical analysis was performed using R packages ''phytools'' (Revell, 2012) and ''phylolm'' (Ho and Ané , 2014). Based on LIPID MAPS Classification System (Fahy et al., 2007), we grouped the lipids as acylglycerols (diacylglycerol [DAG] and TAG), glycerophospholipids (PC, PE, LPC, and LPE), sphingolipids (SM), and sterols (CE). Pathway enrichment statistics were based on hypergeometric distribution and a 5,000-time bootstrap procedure. "
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    ABSTRACT: Biological diversity among mammals is remarkable. Mammalian body weights range seven orders of magnitude and lifespans differ more than 100-fold among species. While genetic, dietary, and pharmacological interventions can be used to modulate these traits in model organisms, it is unknown how they are determined by natural selection. By profiling metabolites in brain, heart, kidney, and liver tissues of 26 mammalian species representing ten taxonomical orders, we report metabolite patterns characteristic of organs, lineages, and species longevity. Our data suggest different rates of metabolite divergence across organs and reveal patterns representing organ-specific functions and lineage-specific physiologies. We identified metabolites that correlated with species lifespan, some of which were previously implicated in longevity control. We also compared the results with metabolite changes in five long-lived mouse models and observed some similar patterns. Overall, this study describes adjustments of the mammalian metabolome according to lifespan, phylogeny, and organ and lineage specialization. Copyright © 2015 Elsevier Inc. All rights reserved.
    Full-text · Article · Aug 2015 · Cell metabolism
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    • "Current structural libraries for glycolipids are limited in size. Partial tandem mass spectra libraries for glycolipids are available from LipidMaps (Dennis et al., 2005; Fahy et al., 2007). Dallas recently published a library of endogenous human milk peptides (Dallas et al., 2013a,b). "

    Full-text · Chapter · Jul 2014
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    • "The AbsoluteIDQ™ p180 assay enables the detection of 186 metabolites including: 40 acylcarnitines (free carnitine – C0 and acylcarnitines – Cx:y), 21 amino acids, 19 biogenic amines, 90 glycerophospholipids including lysophosphatidylcholines (LysoPC a Cx:y) and phosphatidylcholines with acyl (PC aa Cx:y) or ether (PC ae Cx:y) side chain, hexoses (H1) and 15 sphingolipids (SM Cx:y). As previously described [25,26], the nomenclature used for lipid metabolites refers to the Lipid Maps comprehensive classification system [27]. The nomenclature for lipids is as follows: Cx:y, where “x” denotes the number of carbons (C) and “y” represents the number of double bonds. "
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    ABSTRACT: High-throughput screening techniques that analyze the metabolic endpoints of biological processes can identify the contributions of genetic predisposition and environmental factors to the development of common diseases. Studies applying controlled physiological challenges can reveal dysregulation in metabolic responses that may be predictive for or associated with these diseases. However, large-scale epidemiological studies with well controlled physiological challenge conditions, such as extended fasting periods and defined food intake, pose logistic challenges. Culturally and religiously motivated behavioral patterns of life style changes provide a natural setting that can be used to enroll a large number of study volunteers. Here we report a proof of principle study conducted within a Muslim community, showing that a metabolomics study during the Holy Month of Ramadan can provide a unique opportunity to explore the pre-prandial and postprandial response of human metabolism to nutritional challenges. Up to five blood samples were obtained from eleven healthy male volunteers, taken directly before and two hours after consumption of a controlled meal in the evening on days 7 and 26 of Ramadan, and after an over-night fast several weeks after Ramadan. The observed increases in glucose, insulin and lactate levels at the postprandial time point confirm the expected physiological response to food intake. Targeted metabolomics further revealed significant and physiologically plausible responses to food intake by an increase in bile acid and amino acid levels and a decrease in long-chain acyl-carnitine and polyamine levels. A decrease in the concentrations of a number of phospholipids between samples taken on days 7 and 26 of Ramadan shows that the long-term response to extended fasting may differ from the response to short-term fasting. The present study design is scalable to larger populations and may be extended to the study of the metabolic response in defined patient groups such as individuals with type 2 diabetes.
    Full-text · Article · Jun 2014 · Journal of Translational Medicine
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