Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease
Ana ReisAlisa RudnitskayaPajaree ChariyavilaskulNeeraj DhaunVanessa MelvilleJane GoddardDavid J. WebbAndrew R. PittCorinne M. Spickett
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This study compared the molecular lipidomic profile of LDL in patients with nondiabetic advanced renal disease and no evidence of CVD to that of age-matched controls, with the hypothesis that it would reveal proatherogenic lipid alterations. LDL was isolated from 10 normocholesterolemic patients with stage 4/5 renal disease and 10 controls, and lipids were analyzed by accurate mass LC/MS. Top-down lipidomics analysis and manual examination of the data identified 352 lipid species, and automated comparative analysis demonstrated alterations in lipid profile in disease. The total lipid and cholesterol content was unchanged, but levels of triacylglycerides and N-acyltaurines were significantly increased, while phosphatidylcholines, plasmenyl ethanolamines, sulfatides, ceramides, and cholesterol sulfate were significantly decreased in chronic kidney disease (CKD) patients. Chemometric analysis of individual lipid species showed very good discrimination of control and disease sample despite the small cohorts and identified individual unsaturated phospholipids and triglycerides mainly responsible for the discrimination. These findings illustrate the point that although the clinical biochemistry parameters may not appear abnormal, there may be important underlying lipidomic changes that contribute to disease pathology. The lipidomic profile of CKD LDL offers potential for new biomarkers and novel insights into lipid metabolism and cardiovascular risk in this disease. This study compared the molecular lipidomic profile of LDL in patients with nondiabetic advanced renal disease and no evidence of CVD to that of age-matched controls, with the hypothesis that it would reveal proatherogenic lipid alterations. LDL was isolated from 10 normocholesterolemic patients with stage 4/5 renal disease and 10 controls, and lipids were analyzed by accurate mass LC/MS. Top-down lipidomics analysis and manual examination of the data identified 352 lipid species, and automated comparative analysis demonstrated alterations in lipid profile in disease. The total lipid and cholesterol content was unchanged, but levels of triacylglycerides and N-acyltaurines were significantly increased, while phosphatidylcholines, plasmenyl ethanolamines, sulfatides, ceramides, and cholesterol sulfate were significantly decreased in chronic kidney disease (CKD) patients. Chemometric analysis of individual lipid species showed very good discrimination of control and disease sample despite the small cohorts and identified individual unsaturated phospholipids and triglycerides mainly responsible for the discrimination. These findings illustrate the point that although the clinical biochemistry parameters may not appear abnormal, there may be important underlying lipidomic changes that contribute to disease pathology. The lipidomic profile of CKD LDL offers potential for new biomarkers and novel insights into lipid metabolism and cardiovascular risk in this disease. Chronic kidney disease (CKD) is a serious and increasingly common condition (1Meguid El Nahas A. Bello A.K. Chronic kidney disease: the global challenge.Lancet. 2005; 365: 331-340Abstract Full Text Full Text PDF PubMed Scopus (895) Google Scholar). Patients with CKD have a greatly increased risk of CVD, which represents the most common cause of mortality and morbidity in these patients, to the extent that CKD is considered an independent risk factor for CVD (2Sarnak M.J. Levey A.S. Schoolwerth A.C. Coresh J. Culleton B. Hamm L.L. McCullough P.A. Kasiske B.L. Kelepouris E. Klag M.J. et al.Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association councils on kidney in cardiovascular disease, high blood pressure research, clinical cardiology, and epidemiology and prevention.Circulation. 2003; 108: 2154-2169Crossref PubMed Scopus (2881) Google Scholar, 3Schiffrin E.L. Lipman M.L. Mann J.F.E. Chronic kidney disease: effects on the cardiovascular system.Circulation. 2007; 116: 85-97Crossref PubMed Scopus (1195) Google Scholar). In CKD, many conventional risk factors for CVD are prevalent, including hypertension, dyslipidemia, and insulin resistance. Underlying conditions that are typical of CVD also occur, such as heightened inflammatory status, oxidative stress, endothelial dysfunction, and arterial stiffness (3Schiffrin E.L. Lipman M.L. Mann J.F.E. Chronic kidney disease: effects on the cardiovascular system.Circulation. 2007; 116: 85-97Crossref PubMed Scopus (1195) Google Scholar, 4Zoccali C. Traditional and emerging cardiovascular and renal risk factors: an epidemiologic perspective.Kidney Int. 2006; 70: 26-33Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar). Consequently, understanding the factors in CKD that could contribute to increased CVD risk is very important. In CVD there is a clearly established link between dyslipidemia (specifically hypercholesterolemia and hypertriglyceridemia) and atherosclerosis, an underlying pathology of most CVD (5Steinberg D. Hypercholesterolemia and inflammation in atherogenesis: two sides of the same coin.Mol. Nutr. Food Res. 2005; 49: 995-998Crossref PubMed Scopus (83) Google Scholar, 6Shepherd J. Lipids in health and disease.Biochem. Soc. Trans. 2004; 32: 1051-1056Crossref PubMed Scopus (13) Google Scholar). In view of the clear cardio-renal relationship, there has been considerable interest in the possible contribution of hyperlipidemia to CKD-associated CVD (7Keane W.F. Tomassini J.E. Neff D.R. Lipid abnormalities in patients with chronic kidney disease: implications for the pathophysiology of atherosclerosis.J. Atheroscler. Thromb. 2013; 20: 123-133Crossref PubMed Scopus (69) Google Scholar, 8Cheung A.K. Is lipid control necessary in hemodialysis patients?.Clin. J. Am. Soc. Nephrol. 2009; 4: S95-S101Crossref PubMed Scopus (23) Google Scholar). The nature of this lipid imbalance is significantly different to nonrenal-related CVD; in particular, the relationship with cholesterol level is less clear than in the general population and is dependent on the stage of disease (9Kaysen G.A. Lipid and lipoprotein metabolism in chronic kidney disease.J. Ren. Nutr. 2009; 19: 73-77Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 10Vaziri N.D. Norris K. Lipid disorders and their relevance to outcomes in chronic kidney disease.Blood Purif. 2011; 31: 189-196Crossref PubMed Scopus (103) Google Scholar). In some patients, total cholesterol and LDL-cholesterol are not elevated, while patients on hemodialysis may even have reduced cholesterol compared with control subjects (11Lacquaniti A. Bolignano D. Donato V. Bono C. Fazio M.R. Buemi M. Alterations of lipid metabolism in chronic nephropathies: mechanisms, diagnosis and treatment.Kidney Blood Press. Res. 2010; 33: 100-110Crossref PubMed Scopus (30) Google Scholar). It is apparent that CKD involves multiple lipid abnormalities, some of which may contribute to increased CVD risk. However, most studies in lipid abnormalities in CKD have focused on lipoprotein profile or on overall lipid classes such as triglycerides. While in many inflammatory diseases, including preeclampsia (12Romanowicz L. Bankowski E. Sphingolipids of human umbilical cord vein and their alteration in preeclampsia.Mol. 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Cell Biol. 2012; 44: 1839-1846Crossref PubMed Scopus (38) Google Scholar), lipidomic studies have identified characteristic lipid signatures that have potential as diagnostic tools, there have as yet been few attempts at profiling individual lipids in CKD. Evidence for an altered phospholipid profile in CKD (16Jia L. Wang C. Zhao S. Lu X. Xu G. Metabolomic identification of potential phospholipid biomarkers for chronic glomerulonephritis by using high performance liquid chromatography-mass spectrometry.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2007; 860: 134-140Crossref PubMed Scopus (37) Google Scholar) and a decrease of serum sulfatide (ST) levels in patients with end-stage renal failure (ESRF) (17Hu R. Li G. Kamijo Y. Aoyama T. Nakajima T. Inoue T. Node K. Kannagi R. Kyogashima M. Hara A. Serum sulfatides as a novel biomarker for cardiovascular disease in patients with end-stage renal failure.Glycoconj. J. 2007; 24: 565-571Crossref PubMed Scopus (35) Google Scholar) have been reported, but otherwise little is known about molecular changes. Modern lipidomics depends almost entirely on analysis by electrospray MS, as this is able to identify a very wide variety of individual lipid species in several classes. Both shotgun lipidomics, involving direct infusion of the sample into the instrument, and LC/MS are widely used for this purpose (18Han X. Yang K. Gross R.W. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses.Mass Spectrom. Rev. 2012; 31: 134-178Crossref PubMed Scopus (417) Google Scholar). Chromatographic separation provides additional information to facilitate lipid identification, and separation of the lipids reduces interference (19Lam S.M. Shui G.H. Lipidomics as a principal tool for advancing biomedical research.J. Genet. Genomics. 2013; 40: 375-390Crossref PubMed Scopus (87) Google Scholar). Although with lower-resolution instruments MS/MS is necessary to distinguish lipids of similar mass but different formula, modern high-resolution instruments such as orbitraps offer sufficient mass accuracy that isobaric species can be distinguished, thus allowing classification of lipid analytes and identification of the total number of carbons and double bonds in the acyl chains by a top-down approach (20Schwudke D. Schuhmann K. Herzog R. Bornstein S.R. Shevchenko A. Shotgun lipidomics on high resolution mass spectrometers.Cold Spring Harb. Perspect. Biol. 2011; 3: a004614Crossref PubMed Scopus (145) Google Scholar). It has been demonstrated that this untargeted approach, coupled with principle component analysis, can be used without internal standards for comparative analysis of lipidomes, owing to the high dynamic range of the orbitrap (21Schwudke D. Hannich J.T. Surendranath V. Grimard V. Moehring T. Burton L. Kurzchalia T. Shevchenko A. Top-down lipidomic screens by multivariate analysis of high-resolution survey mass spectra.Anal. Chem. 2007; 79: 4083-4093Crossref PubMed Scopus (150) Google Scholar). Similar “semiquantitative” approaches on a triple quadrupole instrument have also been used for comparative lipidomics in CVD (22Stegemann C. Drozdov I. Shalhoub J. Humphries J. Ladroue C. Didangelos A. Baumert M. Allen M. Davies A.H. Monaco C. et al.Comparative lipidomics profiling of human atherosclerotic plaques.Circ. Cardiovasc. Genet. 2011; 4: 232-242Crossref PubMed Scopus (166) Google Scholar, 23Stegemann C. Pechlaner R. Willeit P. Langley S.R. Mangino M. Mayr U. Menni C. Moayyeri A. Santer P. Rungger G. et al.Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study.Circulation. 2014; 129: 1821-1831Crossref PubMed Scopus (340) Google Scholar). However, MS/MS or MSn is still required for confirmation of individual acyl chain length and double bonds. We recently demonstrated that a top-down lipidomics approach using LC/MS on a high resolution instrument (Orbitrap Exactive) was able to identify more than 350 individual lipid species or isomeric lipid clusters in normo-lipidemic LDL (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). The lipids were identified by matching the experimental m/z for the molecular ions to calculated mono-isotopic masses available in lipidomic and metabolic databases. LDL is an important carrier of a wide variety of lipid species within the plasma and reflects systemic changes in lipid metabolism. We hypothesized that the application of this methodology to CKD would identify novel differences in lipid profile at a molecular level between disease and control samples that would enhance understanding of the disease mechanisms and offer potential as diagnostic markers. All chemicals used were of analytical quality and purchased from Sigma-Aldrich (UK) or ThermoFisher (UK) unless stated otherwise. Organic solvents were HPLC-grade and purchased from Fisher Scientific (Loughborough, UK). Male CKD patients (stage 4/5) were recruited from the renal outpatient clinic at the Royal Infirmary of Edinburgh following ethical approval by NHS Lothian Research Ethics Committee and gave informed consent as described previously (25Lilitkarntakul P. Dhaun N. Melville V. Blackwell S. Talwar D.K. Liebman B. Asai T. Pollock J. Goddard J. Webb D.J. Blood pressure and not uraemia is the major determinant of arterial stiffness and endothelial dysfunction in patients with chronic kidney disease and minimal co-morbidity.Atherosclerosis. 2011; 216: 217-225Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). Renal patients were excluded on the basis of renal transplant, dialysis, systemic vasculitis or connective tissue disease, a history of established CVD, peripheral vascular disease, diabetes mellitus, respiratory disease, neurological disease, alcohol abuse, or treatment with an organic nitrate or β-agonist. The causes of kidney disease in patients were autosomal dominant polycystic kidney disease (n = 4), IgA nephropathy (n = 2), reflux nephropathy (n = 3), and neurogenic bladder (n = 1). Smokers and hypercholesterolemic patients were not excluded, but the latter were controlled by statin medication (two individuals in the disease group) and stable on treatment for 3 months prior to inclusion in the study. Subjects refrained from alcohol for at least 24 h, and caffeinated drinks and smoking for at least 12 h before the study. Blood samples were collected in polypropylene tubes containing EDTA (final concentration, 1 mg/ml of blood); plasma was promptly separated by centrifugation (2,500 g, 20 min, 4°C) and stored in 2 ml aliquots at −80°C in the dark. Blood pressure, high sensitivity C-reactive protein, oxidized LDL (OxLDL), and interleukin (IL)-6 were determined as described previously (25Lilitkarntakul P. Dhaun N. Melville V. Blackwell S. Talwar D.K. Liebman B. Asai T. Pollock J. Goddard J. Webb D.J. Blood pressure and not uraemia is the major determinant of arterial stiffness and endothelial dysfunction in patients with chronic kidney disease and minimal co-morbidity.Atherosclerosis. 2011; 216: 217-225Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). Other parameters [plasma glucose, total cholesterol, triglyceride, lipoproteins, creatinine, and glycated hemoglobin (HbA1c)] were determined in the hospital biochemistry laboratory by assays validated to Good Laboratory Practice standard. LDL was isolated from plasma aliquots essentially as described previously (26Jerlich A. Pitt A.R. Schaur R.J. Spickett C.M. Pathways of phospholipid oxidation by HOCl in human LDL detected by LC-MS.Free Radic. Biol. Med. 2000; 28: 673-682Crossref PubMed Scopus (89) Google Scholar). KBr (0.3816 mg) was dissolved in 1 ml of plasma at 4°C and underlaid below 4.1 ml of a deoxygenated EDTA solution, before centrifuging in a Beckman VTi 90 rotor for 2 h at 60,000 rpm to generate a density gradient. LDL formed bands in the density range 1.019–1.060 g/ml. The LDL collected was stored in sterile vials under nitrogen and desalted before determining the cholesterol content using CHOL PAD reagent (Roche Diagnostics), and protein concentration of isolated LDL was determined by the Bradford assay as reported by Yue et al. (27Yue H. Jansen S.A. Strauss K.I. Borenstein M.R. Barbe M.F. Rossi L.J. Murphy E. A liquid chromatography/mass spectrometric method for simultaneous analysis of arachidonic acid and its endogenous eicosanoid metabolites prostaglandins, dihydroxyeicosatrienoic acids, hydroxyeicosatetraenoic acids, and epoxyeicosatrienoic acids in rat brain tissue.J. Pharm. Biomed. Anal. 2007; 43: 1122-1134Crossref PubMed Scopus (83) Google Scholar). Purity of isolated LDL was confirmed by polyacrylamide gel electrophoresis (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar, 28Mahley R.W. Innerarity T.L. Rall Jr, S.C. Weisgraber K.H. Plasma lipoproteins: apolipoprotein structure and function.J. Lipid Res. 1984; 25: 1277-1294Abstract Full Text PDF PubMed Google Scholar). Vitamin E content (α-tocopherol) in LDL was determined by reverse-phase chromatography using spectrophotometric detection as described previously (29Zhao B. Tham S.Y. Lu J. Lai M.H. Lee L.K. Moochhala S.M. Simultaneous determination of vitamins C, E and beta-carotene in human plasma by high-performance liquid chromatography with photodiode-array detection.J. Pharm. Pharm. Sci. 2004; 7: 200-204PubMed Google Scholar). The samples and standards were injected randomly in triplicate and area under the curve was plotted against the calibration curves and used to calculate the concentration of vitamin E in the samples (μg/mg protein). Standards (0.1–10 μg/ml), made up in methanol and extracts redissolved in 100 μl methanol, were analyzed in triplicate by injection of 20 μl. The intraday cross-validation (CV; n = 3) at a concentration of 2.5 μg/ml was 3.1%. Statistical analysis was carried out using an unpaired t-test, with Welch's correction to estimate the P values. The particle size of LDL was assessed by 1% agarose gel electrophoresis in barbital buffer as described previously (30Aldred S. Griffiths H.R. Oxidation of protein in human low-density lipoprotein exposed to peroxyl radicals facilitates uptake by monocytes; protection by antioxidants in vitro.Environ. Toxicol. Pharmacol. 2004; 15: 111-117Crossref PubMed Scopus (10) Google Scholar). Retardation factors were defined as the distance (cm) traveled by sample/distance (cm) traveled by dye front. LDL lipids were extracted from LDL containing 25 μg protein by the Folch method as described recently (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). The lipid extracts were combined into an amber vial (Supelco), dried under a stream of nitrogen filtered with a 0.22 µm mesh (Millipore), and stored at −70°C until further analysis. Mean recovery (%) of phosphatidylcholine (PC; 13:0/13:0) lipid standard in spiked LDL samples by the Folch method was 103.9 ± 8.6. Similar recoveries were achieved with dehydroepiandrosterone sulfate as a representative of more polar lipid classes, and d5-myristic acid (Sigma Aldrich Chemical Co., UK) as a representative of less polar lipids. Lipid extracts were solubilized in 100 µL CHCl3-methanol (1:1, v/v), further diluted in methanol and analyzed by LC/MS essentially as described previously (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). Separation of LDL lipid classes was performed using a Dionex Ultimate 3000 HPLC system (Thermo Scientific, Hemel Hempstead, UK) by injection of 10 μl sample onto a silica gel column (150 mm × 3 mm × 3 µm; HiChrom, Reading, UK) used in hydrophilic interaction chromatography mode (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). Two solvents were used: (A) 20% isopropyl alcohol (IPA) in acetonitrile and (B) 20% IPA in ammonium formate (20 mM). Elution was achieved using the following gradient at 0.3 ml/min: elution at 5% B for 1 min, followed by a rise to 9% B at 5 min, to 15% B at 10 min, to 25% B at 16 min, to 35% B at 23 min, and from 28 to 40 min a decrease to 5% B. Detection of lipids was performed in a Orbitrap Exactive Mass Spectrometer (ThermoFisher Scientific Inc., Bremen, Germany) equipped with polarity switching. The instrument was calibrated according to the manufacturer specifications to give an rms mass error <2 ppm. The following electrospray ionization settings were used: source voltage, ±4.50 kV; capillary voltage, 25 V; capillary temperature, 320°C; sheath gas flow, 50 AU; aux gas flow, 17 AU; sweep gas flow, 0 AU. All LC/MS spectra were recorded in the m/z range 100–1200 at 50,000 resolution (Full Width at Half Maximum at m/z =500). Three microscans were collected per data point with the injection time limited by either an automatic gain control target ion intensity of 106 or a maximum inject time of 250 ms. For certain lipids of interest, MS/MS was carried out on an LTQ Orbitrap instrument (ThermoElectron, Hemel Hempstead, UK) controlled by Xcalibur (version 2.0, Thermo Fisher Corporation) in either positive or negative ion modes as appropriate for the best detection of the parent ion. The capillary voltage was set at 4.5 kV, capillary temperature at 275°C, with sheath gas and sweep gas flow rates set at 30 and 10 AU, respectively. Collision energy was set according to the ion of interest, typically between 25 and 35 (arbitrary units). In the first stage, LC/MS data were analyzed and lipid species identified by manual matching of retention times and accurate mass data to a home-built database and the Human Metabolome project database (HMDB) (31Wishart D.S. Knox C. Guo A.C. Eisner R. Young N. Gautam B. Hau D.D. Psychogios N. Dong E. Bouatra S. et al.HMDB: a knowledgebase for the human metabolome.Nucleic Acids Res. 2009; 37: D603-D610Crossref PubMed Scopus (1503) Google Scholar), with identifications based on ions showing a mass error of <5 ppm (and in most cases <2 ppm) to the monoisotopic mass calculated from the theoretical formula. A total of 352 lipids were identified by this approach. Subsequently, LC/MS data were analyzed by filtering with MZMatch (32Scheltema R.A. Jankevics A. Jansen R.C. Swertz M.A. Breitling R. PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis.Anal. Chem. 2011; 83: 2786-2793Crossref PubMed Scopus (216) Google Scholar) followed by using the XCMS pipeline [XCMS Online version 0.0.83, Scripps Center for Metabolomics, https://xcmsonline.scripps.edu/ (33Tautenhahn R. Patti G.J. Rinehart D. Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data.Anal. Chem. 2012; 84: 5035-5039Crossref PubMed Scopus (836) Google Scholar)] for peak detection, alignment, and isotope annotation as described previously (24Reis A. Rudnitskaya A. Blackburn G.J. Fauzi N.M. Pitt A.R. Spickett C.M. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL.J. Lipid Res. 2013; 54: 1812-1824Abstract Full Text Full Text PDF PubMed Scopus (161) Google Scholar). Ions with intensity <5,000 cps were excluded. Integration of features extracted in different samples corresponds to the reported extracted ion chromatogram areas. Peak intensities for the ions identified from individual lipid classes in the data sets were summed and used to evaluate overall differences in disease versus age-matched control groups. Extracted features were included if they were present in >50% of the samples in each group, within 2.5 ppm from the exact monoisotopic mass, and with <5 s retention time deviation. In order to prevent overestimation of the number of lipid species identified, all lipid species detected in positive and negative ion modes were manually cross-referenced. Overall, 142 and 158 individual lipids were identified in positive and negative ion modes, respectively. Isomeric species are reported as one single ion, for instance PC(16:0/18:1), PC(18:1/16:0), PC(16:1/18:0), PC(18:0/16:1), PC(14:0/20:1), and others are expressed as PC(34:1). The data processing steps and number of features or lipids identified at each stage are summarized in supplementary Fig. 1. The merged data set comprising 300 lipids species (supplementary Fig. 1) was further analyzed using partial least squares discriminant analysis (PLSDA) (34Wold S. Sjöström M. Eriksson L. PLS-regression: a basic tool of chemometrics.Chemom. Intell. Lab. Syst. 2001; 58: 109-130Crossref Scopus (6871) Google Scholar, 35Barker M. Rayens W. Partial least squares for discrimination.J. Chemom. 2003; 17: 166-173Crossref Scopus (2021) Google Scholar). PLSDA calibration models were validated using segmented CV, and optimization of PLSDA models was achieved using the variable importance in projection (VIP) score (36Chi-Hyuck J. Lee S.H. Park H.S. Lee J.H. Use of partial least squares regression for variable selection and quality prediction. In Computers & Industrial Engineering, 2009. CIE 2009. International Conference on Computers &. Industrial Engineering, University of Technology of Troyes. IEEE, New York2009: 1302-1307Google Scholar). A VIP cut-off value of 0.8 was repeatedly applied to eliminate less discriminating variables, with a cut-off of 0.85 for the merged set. The final classification model included 48 species detected in the positive mode and 55 in the negative mode. The statistical significance of the classification PLSDA models was assessed using permutation testing with 1,000 permutations (37Westerhuis J.A. Hoefsloot H.C.J. Smit S. Vis D.J. Smilde A.K. van Velzen E.J.J. van Duijnhoven J.P.M. van Dorsten F.A. Assessment of PLSDA cross validation.Metabolomics. 2008; 4: 81-89Crossref Scopus (1016) Google Scholar). Q2 was used as quality-of-fit criterion for the permutation test (38Westerhuis J.A. Velzen E.J.J. Hoefsloot H.C.J. Smilde A.K. Discriminant Q2 (DQ2) for improved discrimination in PLSDA models.Metabolomics. 2008; 4: 293-296Crossref Scopus (61) Google Scholar). Further details are given in supplementary Methods. Statistical analysis of clinical and biochemical parameters was conducted using nonparametric t-tests (Mann-Whitney) using two-tailed P value calculation, and values with P < 0.05 were considered statistically significant. Baseline measurements of clinical and biochemical parameters for age- and body mass-matched subjects included in this study are summarized in Table 1. Glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease study equation and confirmed all patients as stage 4 or 5 CKD; they also had significantly increased systolic blood pressure. There were no significant differences in levels of glycated hemoglobin and plasma glucose. The inflammatory marker C-reactive protein was significantly elevated, although IL-6 was not. The levels of total plasma cholesterol and LDL were not altered with CKD, and there was no change in OxLDL. In contrast, HDL levels showed a significant decrease, and plasma triglycerides were elevated, as expected for patients with CKD and published previously (25Lilitkarntakul P. Dhaun N. Melville V. Blackwell S. Talwar D.K. Liebman B. Asai T. Pollock J. Goddard J. Webb D.J. Blood pressure and not uraemia is the major determinant of arterial stiffness and endothelial dysfunction in patients with chronic kidney disease and minimal co-morbidity.Atherosclerosis. 2011; 216: 217-225Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). LDL vitamin E content and particle heterogeneity (electrophoretic mobility) were also determined, but there was no statistical difference (Table 1).TABLE 1Clinical biochemistry parameters in plasma for control subjects and CKD patientsClinical ParametersControlsCKDPn1010—Age (years)47 ± 644 ± 30.111BMI (kg/m2)26 ± 229 ± 60.113Smokers/ex-smokers/nonsmokers0/1/92/2/8—Systolic blood pressure (mm Hg)113 ± 12124 ± 100.049Diastolic blood pressure (mm Hg)72 ± 1178 ± 60.103Mean arterial pressure (mm Hg)85 ± 1193 ± 70.065Pulse pressure (mm Hg)42 ± 646 ± 70.189Plasma glucose (mg/dl)5.1 ± 0.54.8 ± 0.40.231HbA1c (% of Hb)5.3 ± 0.405.6 ± 0.500.117Serum creatinine (mg/dl)85 ± 11460 ± 179<0.0001MDRD eGFR (ml/min/1.73 m2)91.2 ± 14.114.8 ± 5.3<0.0001High sensitivity C-reactive protein (µg/ml)1.2 ± 1.54.2 ± 3.50.027IL-6 (pg/ml)9.6 ± 10.57.9 ± 8.70.713Total cholesterol (mg/dl)5.1 ± 0.84.5 ± 0.80.130Triglycerides (mM)1.0 ± 0.31.8 ± 0.70.004HDL (mM)1.4 ± 0.51.0 ± 0.20.020LDL (mM)4.8 ± 0.7 (n = 9)4.2 ± 0.80.091OxLDL (U/l)56 ± 1851 ± 120.475LDL vitamin E (μg/mg protein)2.43 ± 0.5402.39 ± 0.5240.65LDL particle size (nm)0.24 ± 0.020.24 ± 0.030.387Values are given as mean ± SD. Significance (P values) was calculated using a two-tailed Student's t-test, and statistically significant differencKeywords:
Lipid Profile
Abstract Lipidomics is a measurement of a large scale of lipid species to understand roles of their carbon atoms, dual bonds, or isomerism in the lipid molecule. Clinical lipidomics was recently defined “as a new integrative biomedicine to discover the correlation and regulation between a large scale of lipid elements measured and analyzed in liquid biopsies from patients with those patient phenomes and clinical phenotypes”. The first step to translate lipidomics into clinical lipidomics is to settle a number of standard operation procedures and protocols of lipidomics performance and measurement. Clinical lipidomics is the part of clinical trans‐omics which was coined as a new emerging scientific discipline where clinical phenomes are integrated with molecular multiomics. We believe it is the time to translate lipid science and lipidomics into clinical application and to understand the importance of clinical lipidomics as one of the most helpful approaches during the design and decision‐making of therapeutic strategies for individuals. We emphasize here that clinical lipidomics should be merged with clinical phenomes, e.g. patient signs and symptoms, biomedical analyses, pathology, images, and responses to therapies, although it is difficult to integrate and fuse the information of clinical lipidomics with clinical phenomes. It will be a great achievement if we can draw the networks of lipidomic species fused with networks of genes and proteins to describe the molecular mechanisms of the disease in multi‐dimensions.
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Rheumatoid arthritis (RA) is a systemic autoimmune disease dominated by chronic inflammatory lesions of peripheral synovial joints. Growing evidence suggests that abnormal lipid metabolism levels contribute to the progression of RA. Although several metabolomics studies have shown abnormality in the RA lipidome, the relationship between the overall lipid metabolites and RA has not been systematically evaluated. In this study, an untargeted lipidomics method based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the serum and urine lipidomes of adjuvant-induced arthritis rats to study the characteristics of lipid metabolism changes in the rats and search lipid markers for diagnosing RA. By combining with orthogonal partial least squares discriminant analysis, a total of 52 potential lipid markers were identified, mainly involved in sphingolipid metabolism, glycerophospholipid metabolism, sterol lipid metabolism, glycerolipid metabolism and fatty acid metabolism, which provided crucial insight into lipid metabolism disturbances in RA. Further receiver operating characteristic analysis revealed that the areas under the curve of PC(22:4/16:0), PI(18:1/16:0) and LacCer(d18:1/12:0) from serum and 25-hydroxycholesterol from urine were 0.94, 1.00, 1.00 and 1.00, respectively, indicating the high predictive ability of this method for RA. In this study, our results indicated that a combination of serum and urine analysis can provide a more comprehensive and reliable assessment of RA, and a UPLC-MS-based lipidomics strategy is a powerful tool to search for potential lipid markers associated with RA and explore the pathogenesis of RA.
Lipidome
Fatty Acid Metabolism
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Lipidomics, which focuses on the global study of molecular lipids in biological systems, could provide valuable insights about disease mechanisms. In this study, we present a nontargeted lipidomics strategy to determine cellular lipid alterations after scoparone exposure in primary hepatocytes. Lipid metabolic profiles were analyzed by high-performance liquid chromatography coupled with time-of-flight mass spectrometry, and a novel imaging TransOmics tool has been developed for the analysis of high-resolution MS data, including the data pretreatment, visualization, automated identification, deconvolution and quantification of lipid species. Chemometric and statistical analyses of the obtained lipid fingerprints revealed the global lipidomic alterations and tested the therapeutic effects of scoparone. Identification of ten proposed lipids contributed to the better understanding of the effects of scoparone on lipid metabolism in hepatocytes. The most striking finding was that scoparone caused comprehensive lipid changes, as represented by significant changes of the identificated lipids. The levels of identified PG(19:1(9Z)/14:0), PE(17:1(9Z)/0:0), PE(19:1(9Z)/0:0) were found to be upregulated in ethanol-induced group, whereas the levels in scoparone group were downregulated. Lipid metabolism in primary hepatocytes was changed significantly by scoparone treatment. We believe that this novel approach could substantially broaden the applications of high mass resolution mass spectrometry for cellular lipidomics.
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Valproic acid (VPA) is one of the most widely-prescribed antiepileptic drugs, as VPA-induced hepatotoxicity is one of the most severe adverse reaction that can lead to death. The objective of this study was to gain an understanding of dysregulated lipid metabolism in mechanism of hepatotoxicity. Nontargeted lipidomics analysis with LC-Q-TOF/MS was performed to explore differential lipids from the patient serum and L02 cells. Lipidomics data interpretation was augmented by gene expression analyses for the key enzymes in lipid metabolism pathways. From patient serum lipidomics, pronouncedly changed lipid species between abnormal liver function (ALF) patients and normal liver function (NLF) patients were identified. Among these lipid species, LPCs, Cers and SMs were markedly reduced in the ALF group and showed negative relationships with liver injury severity (ALT levels), while significantly increased TAGs with higher summed carbon numbers demonstrated a positive relationship with ALT levels. Regarding lipidomics in hepatic L02 cells, TAG was markedly elevated after VPA exposure, especially in TAGs with more than 53 summed carbons. Besides, gene expression analysis revealed dysregulated lipid metabolism in VPA-treated L02 cells. PPARγ pathway played an important role in VPA-induced lipid disruption through inducing long-chain fatty acid uptake and TAG synthesis, which was also regulated by Akt pathway. Our findings present that VPA-induced lipid metabolism disruption might lead to the lipotoxicity in the liver. This approach is expected to be applicable for other drug-induced toxicity assessments.
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Liver function
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Abstract Lipidomics is a rapidly growing field with numerous examples showing the importance of lipid molecules throughout biology. It has also shed light onto the vast and complex functions performed by many lipids that possess an immense diversity in molecular structures. Mass spectrometry (MS) is the tool of choice for analyzing lipids and has been the key catalyst driving the field forward. However, MS does not yet permit true molecular lipidomics wherein the identification and quantification of lipids having defined molecular structures can be routinely achieved. Here we describe recent advances in MS‐based lipidomics that allow access to higher levels of molecular information in lipidomics experiments. These advances will form a key piece of the puzzle as the field moves towards systems characterization of lipids at the molecular level.
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For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.
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The area of lipidomics is relatively new: although publications on MS-based analysis of cellular lipids started to emerge in the early 1990s, the actual term lipidomics appeared in the literature in 2003. However, in the short space of less than 10 years, lipidomics has emerged as an extremely valuable tool for biosciences 1-4. Large research networks (e.g. LIPID MAPS in USA, LipidomicsNet in Europe, etc.), smaller consortia and specialised laboratories around the world have focused their effort towards standardising experimental protocols and lipid nomenclature, whilst attempting to map the lipidome of model systems, cells, tissues and body fluids, and in this way gain new information into the role of lipid networks in health and disease. At the same time there is an increased awareness of the field with more conferences organising sessions on lipids and lipidomics, and societies such as EuroFedLipid, forming interest groups and divisions. We cannot dispute the fact that lipidomics owes its existence to modern MS. Although other technologies are relevant and continue to offer valuable service to lipid analysis and biology, MS is the primary methodology that allows qualitative and quantitative assessment of multiple lipid species, and can generate the large data sets needed for mapping complex lipid associations and exploration of interactions that characterise biological ‘omics’ approaches. Looking back, it is evident that electrospray ionisation (ESI) has influenced and shaped lipidomics more than any other technique. When coupled to tandem MS (MS/MS), ESI-MS/MS led to the first lipidomic data sets obtained from cellular phospholipids, an approach that is now widely employed by almost all labs working on membrane lipids. The system is applicable to direct infusion or gunshot experiments used to analyse relatively crude lipid extracts without any prior separation or purification, an approach described as global lipidomics. Furthermore, ESI is easily coupled to liquid chromatography (LC) allowing for the development of LC-MS/MS-based targeted approaches. Spectrometers used for such applications include triple quadrupoles (Q3) that are particularly good for quantitative analysis through multiple reaction monitoring, although generally characterised by low mass resolution, and hybrid systems where quadrupoles are coupled to ion traps (Q-Trap) or time-of-flight (Q-TOF) analysers that allow for both quantitative approaches and high mass accuracy. Recently, we have seen an increase in the use of ultrahigh performance liquid chromatography (UPLC) coupled to MS in an attempt to increase the high-through-put efficiency of lipidomic analyses, whilst the availability of spectrometers with high mass accuracy and resolving power, such as the new linear ion trap-Orbitrap instruments, have started to make an impact on the field. Another emerging direction for lipidomics is marked by the appearance of publications on lipid imaging using matrix-assisted laser desorption ionisation (MALDI). MALDI-TOF permits identification of the spatial distribution and localisation of various lipid species in tissue slices, an area that is anticipated to generate exciting findings. Some of the main challenges faced by lipidomics stem out of the nature of its subject matter: lipids are not a uniform class of compounds; unlike genes or proteins, they are not composed of similar units and, because of their structural and chemical diversity, we have no means of predicting neither the type nor the number of lipid species present in any given biological system. Furthermore, there is no single extraction or analytical protocol applicable to all lipid classes, consequently we are restricted in our ability to reliably map the thousands of lipid species present in a particular sample, following one single uniform approach. Currently, this problem is addressed through the development of targeted approaches informed and directed by the properties of individual lipid classes, e.g. sphingolipid or eicosanoid lipidomics. Even in this case, there are issues around the accurate quantitation of new lipid species identified by lipidomics. This need for synthetic lipid standards calls for close collaboration with organic chemists. Global lipidomic approaches can generate an overwhelming number of data points that require computer assisted data analysis and management, an obvious area of interaction with bioinformatics. Recent publications indicate that this is an active area of research that is expected to expand as we move towards integrated approaches including lipidomics, transcriptomics and proteomics, brought together to address systems biology and systems medicine applications. Another important issue is the curation of lipidomic data: over time we have gathered and will continue to generate information on the lipidome of various cells, tissues and clinical samples. This data need to be collected in a coherent manner and made available for meta-analysis, comparative studies and further research. Although some consortia have started to make their data available through dedicated web sites, there is strong need for organised and sustained effort to capture this activity. So, what does the future hold for lipidomics? Where do we go from here? Are we going to see the formation of large research units dedicated to lipidomics in a way similar to what has happened to proteomics and genomics? Are we going to see lipidomic maps of cells and tissues used in systems medicine, biomarker discovery, drug development and personalised treatment? Although it is not easy to make predictions, it is evident that lipidomics is influencing the current thinking in many fields of research and is making a strong impact in biology and medicine. Lipidomics is already part of studies into cardiovascular disease, diabetes, brain disorders, multiple sclerosis, obesity, all characterised by altered lipid biochemistry. New technologies will undoubtedly increase our analytical capabilities, but, I strongly believe that it is the actual application of lipidomics that will generate breakthroughs and innovation in biosciences.
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This chapter contains sections titled: Introduction Hierarchical Categorization of the Analytical Lipid Outputs The Type of Lipid Information Delivers Different Biological Knowledge Untying New Biological Evidences through Molecular Lipidomic Applications Molecular Lipidomics Approaches Clinical Diagnostics Current Roadblocks in Lipidomics Conclusions References
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Nonalcoholic fatty liver disease (NAFLD) is characterized by marked imbalances in lipid storage and metabolism. Because the beneficial health effects of cereal β-glucan (BG) include lowering cholesterol and regulating lipid metabolism, BG may alleviate the imbalances in lipid metabolism observed during NAFLD. The aim of our study was to investigate whether BG from highland barley has an effect on western diet-induced NAFLD in mice. Using lipidomics, we investigated the underlying mechanisms of BG intervention, and identified potential lipid biomarkers. The results reveal that BG (300 mg/kg body weight) significantly alleviated liver steatosis. Lipidomics analysis demonstrated that BG also altered lipid metabolic patterns. We were able to identify 13 differentially regulated lipid species that may be useful as lipid biomarkers. Several genes in the hepatic lipid and cholesterol metabolism pathways were also modulated. These findings provide evidence that BG ameliorates NAFLD by altering liver lipid metabolites and regulating lipid metabolism-related genes.
Steatosis
Lipid Profile
Lipid Metabolism Disorder
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