Simulation of triacylglycerol ion profiles: Bioinformatics for interpretation of triacylglycerol biosyntheses.
ABSTRACT Although the synthesis pathways of intracellular triacylglycerol (TAG) species have been well elucidated, assessment of the contribution of an individual pathway to TAG pools in different mammalian organs, particularly under pathophysiological conditions, is difficult, although not impossible. Herein, we developed and validated a novel bioinformatic approach to assess the differential contributions of the known pathways to TAG pools through simulation of TAG ion profiles determined by shotgun lipidomics. This powerful approach was applied to determine such contributions in mouse heart, liver, and skeletal muscle and to examine the changes of these pathways in mouse liver induced after treatment with a high-fat diet. It was clearly demonstrated that assessment of the altered TAG biosynthesis pathways under pathophysiological conditions can be readily achieved through simulation of lipidomics data. Collectively, this new development should greatly facilitate our understanding of the biochemical mechanisms underpinning TAG accumulation at the states of obesity and lipotoxicity.
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ABSTRACT: Soybean seeds are an important source of vegetable oil and biomaterials. The content of individual triacylglycerol species (TAG) in soybean seeds is difficult to quantify in an accurate and rapid way. The present study establishes an approach to quantify TAG species in soybean seeds utilizing an electrospray ionization tandem mass spectrometry with multiple neutral loss scans. Ten neutral loss scans were performed to detect the fatty acyl chains of TAG, including palmitic (P, 16:0), linolenic (Ln, 18:3), linoleic (L, 18:2), oleic (O, 18:1), stearic (S, 18:0), eicosadienoic (20:2), gadoleic (20:1), arachidic (20:0), erucic (22:1), and behenic (22:0). The abundance of ten fatty acyl chains at 46 TAG masses (mass-to-charge ratio, m/z) were determined after isotopic deconvolution and correction by adjustment factors at each TAG mass. The direct sample infusion and multiple internal standards correction allowed a rapid and accurate quantification of TAG species. Ninety-three TAG species were resolved and their levels were determined. The most abundant TAG species were LLL, OLL, LLLn, PLL, OLLn, OOL, POL, and SLL. Many new species were detected and quantified. This shotgun lipidomics approach should facilitate the study of TAG metabolism and genetic breeding of soybean seeds for desirable TAG content and composition.Scientific reports. 01/2014; 4:6581.
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ABSTRACT: Plant triacylglycerols (TAGs), or vegetable oils, provide approximately 25% of dietary calories to humans and are becoming an increasingly important source of renewable bioenergy and industrial feedstocks. TAGs are assembled, by multiple enzymes in the endoplasmic reticulum, from building blocks that include an invariable glycerol backbone and variable fatty acyl chains. It remains a challenge to elucidate the mechanism of synthesis of hundreds of different TAG species in planta. One reason is the lack of an efficient analytical approach quantifying individual molecular species. Here we report a rapid and quantitative TAG profiling approach for Arabidopsis seeds based on electrospray ionization tandem mass spectrometry with direct infusion and multiple neutral loss scans. The levels of 93 TAG molecular species, identified by their acyl components, were determined in Arabidopsis seeds. Quantitative TAG pattern analyses revealed that the TAG assembly machinery preferentially produces TAGs with one elongated fatty acid. The importance of the selectivity in oil synthesis was consistent with an observation that an Arabidopsis mutant overexpressing a patatin-like phospholipase had enhanced seed oil content with elongated fatty acids. This quantitative TAG profiling approach should facilitate investigations aimed at understanding the biochemical mechanisms of TAG metabolism in plants. This article is protected by copyright. All rights reserved.The Plant Journal 10/2013; · 6.82 Impact Factor
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ABSTRACT: Lipids are a diverse group of metabolites that have many key biological functions, acting as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Due to their importance lipids are under tight homeostatic control and exhibit spatial and dynamic complexity at multiple levels. It is thus not surprising that altered lipid metabolism plays important roles in the pathogenesis of most of the common diseases. Lipidomics emerged as a discipline which is dedicated to global study of lipidomes, including pathways and networks of lipids in biological systems. When studying the lipidomes at a systems level, one of the key challenges is how to address the lipid functionality at many physiological levels, from metabolic and signaling pathways to spatial systems such as cellular membranes and lipoprotein particles. Besides the better analytical techniques to study lipids, computational techniques have started to emerge which enable modeling of lipidomes in their spatial and dynamic context. Together, the recent methodological advances in lipidomics have a potential to open novel avenues for predictive and preventive medicine. This review focuses on progress in systems approaches to study lipids in health and disease, with specific emphasis on clinical applications.Progress in Lipid Research 07/2014; · 12.96 Impact Factor