Simulation of triacylglycerol ion profiles: bioinformatics for interpretation of triacylglycerol biosynthesis

Abstract

study the biochemical mechanism(s) underpinning lipotoxicity ( 4-9 ).The demand for systematic identifi cation and quantification of global cellular lipids for the aforementioned purpose has greatly facilitated the rapid spread of lipidomics research in recent years (10)(11)(12). Although extensive utilization of bioinformatics and computational algorithms for accurate identifi cation and quantifi cation of individual lipid molecular species and classes ( 13-17 ) is still a key node in lipidomics , the greatest challenge of bioinformatics for lipidomics research currently is the interpretation of alterations in biological functions resulting in adaptive or pathological changes in lipid metabolism and/or homeostasis ( 18 ).One of the most powerful platforms in lipidomics practice is multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) ( 15,19 ). At its current stage of development, MDMS-SL enables us to analyze over 30 lipid classes and hundreds to thousands of individual lipid species, collectively representing more than 95% of the total content of a cellular lipidome directly from the lipid extracts of limited biological source materials ( 15,19 ). However, it has remained a challenge to utilize the vast datasets associated with the MDMS-SL platform, e.g., to develop a bioinformatic approach for the interpretation of lipidomic networks of TAG biosynthesis and thus reveal their association with lipotoxicity under pathophysiological conditions. The development of such an approach would signifi cantly advance the practical use of the platform for investigating the association of altered TAG mass with any (patho)physiological changes.Recently, we have successfully developed dynamic simulation approaches for understanding the remodeling 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 diffi cult, 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 profi les 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. Triglycerides are an important class of lipids that largely exist as triacylglycerol (TAG) species, although a small amount of ether-linked triglyceride species are also present in biological systems ( 1 ). The main biological role of TAG species is to serve as energy storage depots. As metabolic syndrome becomes increasingly prevalent, the excess levels of TAG ma...

MondoA coordinately regulates skeletal myocyte lipid homeostasis and insulin signaling

“…It is possible, therefore, that glycerolphosphate biosynthetic pathway that involves dephosphorylation of phosphatidic acid, reacylation of monoacylglycerol, and, to a lesser extent, hydrolysis of phosphatidyl inositol. To explore the effects of SBI-477 on specific TAG biosynthesis pathways, a bioinformatics lipidomics modeling approach was used as previously described (18). This approach uses the individual TAG ion profile, as determined by mass spectrometry, to predict the relative contribution of each pathway to the overall cellular TAG pool.…”

Lipidomic analysis reveals significant lipogenesis and accumulation of lipotoxic components in ob/ob mouse organs

“…The DAG species are mainly produced through dephosphorylation of PA, reacylation of MAG, and, to a much less degree, through hydrolysis of phospholipids such as PI. We recently successfully developed a dynamic simulation approach for understanding the remodeling processes of TAG based on these known biosynthesis pathways of TAG and MDMS-SL-derived data [46]. The parameters K1, K2, and K3, which are the probabilities of individual DAG pools being reacylated to TAG, were assigned to the contributions of the PA, MAG, and PI pathways, respectively.…”

“…Identification and quantification of all of the reported lipid molecular species, including NEFA, DAG, TAG, Cer, LPC, LPE, PC, PE, PS, PI, PA, SM [36,39,43] were performed using an in-house automated software program following the principles for quantification by MS as previously described in details with different MS/MS scans specific to each class of lipids [44,45]. FA chains of lipids were identified and quantified by neutral loss scans or precursor ion scans of corresponding acyl chains and calculated using the same in-house software program [36,39,44,46,47]. Quantification of individual lipid species was conducted in comparison to the selected internal standards of the classes.…”

“…Comprehensive cellular lipidomes of mouse organs were determined by MDMSSL as described in MATERIALS AND METHODS. The contributions of different triacylglycerol (TAG) biosynthesis pathways to TAG pools in mouse liver (Panel A) and thigh skeletal muscle (Panel B) of ob/ ob mice (solid bars) and their controls (open bars) at 10, 12, and 16 weeks of age were simulated using lipidomics data as previously described in detail [46]. These pathways represent the diacylglycerol generation, including phosphatidic acid dephosphorylation (K1), monoacylglycerol reacylation (K2), and hydrolysis of phospholipid head groups (K3).…”

Improved method for quantitative analysis of methylated phosphatidylethanolamine species and its application for analysis of diabetic-mouse liver samples

“…An advantage of the Thermo TSQ Vantage triple quadrupole instrument is capable of performing multiple NLS [28] by “event” scans. Mass spectra of NLS59.1, 62.1, 65.1, and 68.1 were sequentially and alternatively acquired at the rate of 1 scan/sec for a total of 16 min.…”