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• W2772219889 abstract "Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage. Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage. Lipids are vital metabolites that serve as structural components of cell membranes, signaling mediators, and energy depots. The advent of MS-based lipidomics has expanded our knowledge on lipid alterations in disease and has identified lipid biomarkers of disease states (1.Han X. Lipidomics for studying metabolism.Nat. Rev. Endocrinol. 2016; 12: 668-679Crossref PubMed Scopus (356) Google Scholar). A limitation of current comprehensive shotgun lipidomic techniques is that lipids are identified by class group along with the total number of carbon molecules and double bonds in the aliphatic side chains. This information does not allow for the definitive identification of most lipid species because structural isomers cannot be positively assigned. This limits input primarily to the lipid class level and restricts the ability to map these lipid metabolites to databases such as Kyoto Encyclopedia of Genes and Genomes. Although a typical shotgun lipidomic experiment can characterize hundreds of unique lipid identities, these get condensed to a handful (10–20) when only considering lipid class. Furthermore, individual lipids are under-represented in databases, such as Kyoto Encyclopedia of Genes and Genomes, which are frequently used in systems analysis and pathway mapping, limiting the potential of using multi-omics datasets for understanding disease processes (2.Yetukuri L. Ekroos K. Vidal-Puig A. Oresic M. Informatics and computational strategies for the study of lipids.Mol. Biosyst. 2008; 4: 121-127Crossref PubMed Scopus (166) Google Scholar). To that end, reconstruction of interaction networks among lipid species from their quantitative MS profiles would provide insights into associated biological mechanisms (3.Creixell P. Reimand J. Haider S. Wu G. Shibata T. Vazquez M. Mustonen V. Gonzalez-Perez A. Pearson J. Sander C. et al.Mutation Consequences and Pathway Analysis Working Group of the International Cancer Genome Consortium. Pathway and network analysis of cancer genomes.Nat. Methods. 2015; 12: 615-621Crossref PubMed Scopus (221) Google Scholar). Both type 1 and type 2 diabetes are accompanied by dyslipidemia, which is characterized clinically by elevated plasma triglycerides and cholesterol with an enrichment of LDL cholesterol and decreased HDL cholesterol. Recently, lipidomics has revealed abnormalities in other lipid classes in diabetes along with intra-class variation (4.Floegel A. Stefan N. Yu Z. Muhlenbruch K. Drogan D. Joost H.G. Fritsche A. Haring H.U. Hrabe de Angelis M. Peters A. et al.Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach.Diabetes. 2013; 62: 639-648Crossref PubMed Scopus (683) Google Scholar, 5.Haus J.M. Kashyap S.R. Kasumov T. Zhang R. Kelly K.R. Defronzo R.A. Kirwan J.P. Plasma ceramides are elevated in obese subjects with type 2 diabetes and correlate with the severity of insulin resistance.Diabetes. 2009; 58: 337-343Crossref PubMed Scopus (446) Google Scholar, 6.Rhee E.P. Cheng S. Larson M.G. Walford G.A. Lewis G.D. McCabe E. Yang E. Farrell L. Fox C.S. O'Donnell C.J. et al.Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J. Clin. Invest. 2011; 121: 1402-1411Crossref PubMed Scopus (448) Google Scholar, 7.Ståhlman M. Fagerberg B. Adiels M. Ekroos K. Chapman J.M. Kontush A. Borén J. Dyslipidemia, but not hyperglycemia and insulin resistance, is associated with marked alterations in the HDL lipidome in type 2 diabetic subjects in the DIWA cohort: impact on small HDL particles.Biochim. Biophys. Acta. 2013; 1831: 1609-1617Crossref PubMed Scopus (86) Google Scholar, 8.Ståhlman M. Pham H.T. Adiels M. Mitchell T.W. Blanksby S.J. Fagerberg B. Ekroos K. Borén J. Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apolipoprotein-B-containing lipoproteins from women with type 2 diabetes.Diabetologia. 2012; 55: 1156-1166Crossref PubMed Scopus (75) Google Scholar, 9.Wang-Sattler R. Yu Z. Herder C. Messias A.C. Floegel A. He Y. Heim K. Campillos M. Holzapfel C. Thorand B. et al.Novel biomarkers for pre-diabetes identified by metabolomics.Mol. Syst. Biol. 2012; 8: 615Crossref PubMed Scopus (508) Google Scholar). Expansion of lipid coverage has improved diabetes risk prediction (4.Floegel A. Stefan N. Yu Z. Muhlenbruch K. Drogan D. Joost H.G. Fritsche A. Haring H.U. Hrabe de Angelis M. Peters A. et al.Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach.Diabetes. 2013; 62: 639-648Crossref PubMed Scopus (683) Google Scholar, 6.Rhee E.P. Cheng S. Larson M.G. Walford G.A. Lewis G.D. McCabe E. Yang E. Farrell L. Fox C.S. O'Donnell C.J. et al.Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J. Clin. Invest. 2011; 121: 1402-1411Crossref PubMed Scopus (448) Google Scholar, 9.Wang-Sattler R. Yu Z. Herder C. Messias A.C. Floegel A. He Y. Heim K. Campillos M. Holzapfel C. Thorand B. et al.Novel biomarkers for pre-diabetes identified by metabolomics.Mol. Syst. Biol. 2012; 8: 615Crossref PubMed Scopus (508) Google Scholar) and increased research on the impact of other lipid classes on diabetes and diabetic complications (10.Afshinnia F. Rajendiran T.M. Karnovsky A. Soni T. Wang X. Xie D. Yang W. Shafi T. Weir M.R. He J. et al.Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort.Kidney Int. Rep. 2016; 1: 256-268Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar, 11.Chaurasia B. Summers S.A. Ceramides - lipotoxic inducers of metabolic disorders.Trends Endocrinol. Metab. 2015; 26: 538-550Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar, 12.García-Fontana B. Morales-Santana S. Díaz Navarro C. Rozas-Moreno P. Genilloud O. Vicente Pérez F. Perez Del Palacio J. Muñoz-Torres M. Metabolomic profile related to cardiovascular disease in patients with type 2 diabetes mellitus: a pilot study.Talanta. 2016; 148: 135-143Crossref PubMed Scopus (37) Google Scholar). Three devastating complications of diabetes are diabetic kidney disease (DKD), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (DR), and most diabetic patients develop at least one of these complications in their lifetime. Dyslipidemia is associated with the onset and progression of DKD (10.Afshinnia F. Rajendiran T.M. Karnovsky A. Soni T. Wang X. Xie D. Yang W. Shafi T. Weir M.R. He J. et al.Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort.Kidney Int. Rep. 2016; 1: 256-268Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar, 13.Chen S.C. Tseng C.H. Dyslipidemia, kidney disease, and cardiovascular disease in diabetic patients.Rev. Diabet. Stud. 2013; 10: 88-100Crossref PubMed Scopus (61) Google Scholar, 14.Herman-Edelstein M. Scherzer P. Tobar A. Levi M. Gafter U. Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy.J. Lipid Res. 2014; 55: 561-572Abstract Full Text Full Text PDF PubMed Scopus (311) Google Scholar, 15.Stadler K. Goldberg I.J. Susztak K. The evolving understanding of the contribution of lipid metabolism to diabetic kidney disease.Curr. Diab. Rep. 2015; 15: 40Crossref PubMed Scopus (106) Google Scholar), DPN (16.Hur J. Sullivan K.A. Callaghan B.C. Pop-Busui R. Feldman E.L. Identification of factors associated with sural nerve regeneration and degeneration in diabetic neuropathy.Diabetes Care. 2013; 36: 4043-4049Crossref PubMed Scopus (26) Google Scholar, 17.Wiggin T.D. Sullivan K.A. Pop-Busui R. Amato A. Sima A.A. Feldman E.L. Elevated triglycerides correlate with progression of diabetic neuropathy.Diabetes. 2009; 58: 1634-1640Crossref PubMed Scopus (229) Google Scholar), and potentially DR (18.Chew E.Y. Klein M.L. Ferris 3rd, F.L. Remaley N.A. Murphy R.P. Chantry K. Hoogwerf B.J. Miller D. Association of elevated serum lipid levels with retinal hard exudate in diabetic retinopathy. Early Treatment Diabetic Retinopathy Study (ETDRS) Report 22.Arch. Ophthalmol. 1996; 114: 1079-1084Crossref PubMed Scopus (577) Google Scholar, 19.Ioannidou E. Tseriotis V.S. Tziomalos K. Role of lipid-lowering agents in the management of diabetic retinopathy.World J. Diabetes. 2017; 8: 1-6Crossref PubMed Google Scholar), although how dyslipidemia correlates with tissue lipid metabolism and disease progression is poorly understood. While plasma lipid composition is used to determine biomarkers for progression of DKD, DPN, and DR, it is unknown whether these extrinsic systemic lipid alterations are reflected in the tissues themselves and can potentially be used to provide insight into specific tissue metabolism or if they are relatively nonspecific and reflective of broader changes. DKD, DPN, and DR are classically termed diabetic microvascular complications. Although the metabolic response of these tissues to diabetes has historically been thought to be similar and downstream of altered glucose metabolism (20.Giacco F. Brownlee M. Oxidative stress and diabetic complications.Circ. Res. 2010; 107: 1058-1070Crossref PubMed Scopus (3396) Google Scholar), we recently found that glucose and fatty acid oxidation are tissue specific and dissimilar among the three diabetic end-organ target tissues of kidney, nerve, and retina (21.Sas K.M. Kayampilly P. Byun J. Nair V. Hinder L.M. Hur J. Zhang H. Lin C. Qi N.R. Michailidis G. et al.Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications.JCI Insight. 2016; 1: e86976Crossref PubMed Scopus (142) Google Scholar). Transcriptomic analysis identified several pathways involved in lipid biosynthesis that were enriched in both the diabetic mouse kidney and nerve (21.Sas K.M. Kayampilly P. Byun J. Nair V. Hinder L.M. Hur J. Zhang H. Lin C. Qi N.R. Michailidis G. et al.Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications.JCI Insight. 2016; 1: e86976Crossref PubMed Scopus (142) Google Scholar), although dysregulation of gene expression in the diabetic kidney and nerve was overall discordant (22.Hinder L.M. Park M. Rumora A.E. Hur J. Eichinger F. Pennathur S. Kretzler M. Brosius 3rd, F.C. Feldman E.L. Comparative RNA-Seq transcriptome analyses reveal distinct metabolic pathways in diabetic nerve and kidney disease.J. Cell. Mol. Med. 2017; 21: 2140-2152Crossref PubMed Scopus (36) Google Scholar, 23.Hur J. O'Brien P.D. Nair V. Hinder L.M. McGregor B.A. Jagadish H.V. Kretzler M. Brosius 3rd, F.C. Feldman E.L. Transcriptional networks of murine diabetic peripheral neuropathy and nephropathy: common and distinct gene expression patterns.Diabetologia. 2016; 59: 1297-1306Crossref PubMed Scopus (26) Google Scholar). Transcriptomic data indicates altered lipid synthesis, but there is a lack of information on broader lipid changes derived from synthesis in these diabetic tissues and how any lipid changes relate to mRNA expression. In the current study, we performed lipid profiling on mouse plasma, kidney, nerve, and retina. We used the BKS db/db mouse model of type 2 diabetes because this model develops all three diabetic microvascular complications (DKD, DPN, and DR) and is a representative model of human dyslipidemia (24.Yin W. Carballo-Jane E. McLaren D.G. Mendoza V.H. Gagen K. Geoghagen N.S. McNamara L.A. Gorski J.N. Eiermann G.J. Petrov A. et al.Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia.J. Lipid Res. 2012; 53: 51-65Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar). Using these profiles, we compared the lipidome from plasma to each tissue and compared lipidomes for all three tissues to determine similarities among profiles. We also used correlation analysis to provide precursory information on interaction networks in each tissue and focused on the diabetic kidney for a proof of concept of a lipid-centric approach to multi-omic data integration. These analyses will help determine which classes of lipids in plasma are most useful for analysis of diabetic tissue health and provide a unique approach for linking lipid changes to enzymatic pathways. All HPLC grade reagents were from Sigma-Aldrich (St. Louis, MO). The lipid internal standards were from Avanti Polar Lipids (Alabaster, AL). Male BKS db/+ and db/db mice (BKS.Cg-m+/+ Leprdb/J; Jackson Laboratory, Bar Harbor, ME) were used at 24 weeks of age, corresponding to advanced DKD, DPN, and DR (25.Brosius III, F.C. Alpers C.E. Bottinger E.P. Breyer M.D. Coffman T.M. Gurley S.B. Harris R.C. Kakoki M. Kretzler M. Leiter E.H. et al.Animal Models of Diabetic Complications Consortium. Mouse models of diabetic nephropathy.J. Am. Soc. Nephrol. 2009; 20: 2503-2512Crossref PubMed Scopus (430) Google Scholar, 26.Cheng H.T. Dauch J.R. Hayes J.M. Hong Y. Feldman E.L. Nerve growth factor mediates mechanical allodynia in a mouse model of type 2 diabetes.J. Neuropathol. Exp. Neurol. 2009; 68: 1229-1243Crossref PubMed Scopus (80) Google Scholar, 27.Sharma K. McCue P. Dunn S.R. Diabetic kidney disease in the db/db mouse.Am. J. Physiol. Renal Physiol. 2003; 284: F1138-F1144Crossref PubMed Scopus (371) Google Scholar, 28.Bogdanov P. Corraliza L. Villena J.A. Carvalho A.R. Garcia-Arumi J. Ramos D. Ruberte J. Simo R. Hernandez C. The db/db mouse: a useful model for the study of diabetic retinal neurodegeneration.PLoS One. 2014; 9: e97302Crossref PubMed Scopus (126) Google Scholar). Food and water were provided ad libitum. Mice (n = 10 per group) were fasted 2 h prior to euthanasia. At the time of euthanasia, plasma, liver, kidney cortex, sciatic nerve, and retina were collected, snap-frozen, and stored at −80°C until use. The University of Michigan committee on use and care of animals reviewed and approved all animal protocols for this study. Lipid standards [cholesteryl ester (CE) 17:0, ceramide d18:1/17:0, diacylglycerol (DAG) d5 19:0, lysophosphatidylcholine (LPC) 17:0/0:0, monoacylglycerol (MAG) 17:0, phosphatidic acid (PA) 17:0, phosphatidylcholine (PC) 17:0, phosphatidylethanolamine (PE) 17:0, phosphatidylglycerol (PG) 17:0, phosphatidylinositol (PI) 17:0/20:4, phosphatidylserine (PS) 17:0, SM d18:1/17:0, and triacylglycerol (TAG) 17:0] were prepared at 1 mg/ml in chloroform/methanol/water and stored at −20°C. For analysis, the final concentration of each standard was 100 pmol/—l. To monitor instrument performance, 10 —l of a dried matrix-free mixture of internal standards reconstituted in 100 —l buffer B were analyzed. To monitor the lipid extraction process, a standard pool of plasma samples and a pool comprised of each test sample were analyzed at the beginning and end of each batch, as well as after every 20 samples. Lipids were extracted from plasma (30 —l) or homogenized tissues in a randomized order using a modified Bligh-Dyer method (29.Bligh E.G. Dyer W.J. A rapid method of total lipid extraction and purification.Can. J. Biochem. Physiol. 1959; 37: 911-917Crossref PubMed Scopus (42689) Google Scholar), as previously described (10.Afshinnia F. Rajendiran T.M. Karnovsky A. Soni T. Wang X. Xie D. Yang W. Shafi T. Weir M.R. He J. et al.Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort.Kidney Int. Rep. 2016; 1: 256-268Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). Briefly, extraction was performed using a 2:2:2 vol ratio of water/methanol/dichloromethane at room temperature after spiking internal standards. The organic layer was collected, dried completely under nitrogen, resuspended in 100 —l buffer B (5:10:85 water:acetonitrile:isopropanol containing 10 mM ammonium acetate) and analyzed using LC/MS/MS-based lipidomics. Chromatographic separation was performed on a Shimadzu CTO-20A Nexera X2 UHPLC system (Shimadzu, Kyoto, Japan) with a 1.8 —m particle 50 × 2.1 mm internal diameter Waters Acquity HSS T3 column (Waters, Milford, MA), as previously described (10.Afshinnia F. Rajendiran T.M. Karnovsky A. Soni T. Wang X. Xie D. Yang W. Shafi T. Weir M.R. He J. et al.Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort.Kidney Int. Rep. 2016; 1: 256-268Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). The injection volume was 5 —l for all analyses. The data acquisition of each sample was performed in both positive and negative ionization modes using a TripleTOF 5600 equipped with a Turbo VTM ion source (AB Sciex, Concord, Canada). The mass range of both modes was m/z 50–1,200. Acquisition of MS/MS spectra was controlled by data-dependent acquisition function of the Analyst TF software (AB Sciex) with the application of the following parameters: dynamic background subtraction, charge monitoring to exclude multiply charged ions and isotopes, and dynamic exclusion of former target ions for 9 s. A collision energy spread of 20 V was set whereby the software calculated the collision energy value to be applied as a function of m/z. Mass accuracy was maintained by the use of an automated calibrant delivery system (AB Sciex) interfaced to the second inlet of the DuoSpray source. Calibrations were performed at the start of a workday and whenever ionization polarity was changed. MS data files were processed using MultiQuant 1.1.0.26 (Applied Biosystems/MDS Analytical Technologies, Foster City, CA) (30.Ejsing C.S. Duchoslav E. Sampaio J. Simons K. Bonner R. Thiele C. Ekroos K. Shevchenko A. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning.Anal. Chem. 2006; 78: 6202-6214Crossref PubMed Scopus (329) Google Scholar). Identified lipids were normalized to plasma volume or tissue weight. Quality control samples were used to monitor the overall quality of the lipid extraction and MS analyses (31.Jung H.R. Sylvanne T. Koistinen K.M. Tarasov K. Kauhanen D. Ekroos K. High throughput quantitative molecular lipidomics.Biochim. Biophys. Acta. 2011; 1811: 925-934Crossref PubMed Scopus (134) Google Scholar). The quality control samples were mainly used to remove technical outliers and lipid species that were detected below the lipid class-based lower limit of quantification. After data acquisition, the identified lipids underwent a compound-by-compound review to combine different adducts of the same lipid feature. Missing values were imputed using the k-nearest neighbor method (32.Altman N.S. An introduction to kernel and nearest neighbor nonparametric regression.Am. Stat. 1992; 46: 175-185Crossref Google Scholar). Data were log transformed followed by normalization using the cross-contribution compensating multiple internal standard normalization method (33.Redestig H. Fukushima A. Stenlund H. Moritz T. Arita M. Saito K. Kusano M. Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data.Anal. Chem. 2009; 81: 7974-7980Crossref PubMed Scopus (127) Google Scholar). Data from positive and negative modes were combined and scaled by the standard deviation. For any tissue and all lipids that were detected for this tissue, we used t-tests to test differentials between groups (control vs. diabetic) followed by Benjamini-Hochberg false discovery rate (FDR) correction (34.Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J. R. Stat. Soc. Series B Stat. Methodol. 1995; 57: 289-300Google Scholar) to control the family-wise type I error. Specifically, for each tissue, the t-statistic for lipid i is calculated as where is the pooled standard deviation of the two groups under consideration, and and are the unbiased estimator of the variance for the two groups, respectively. For any specific lipid, the -value is calculated as where is the Student's t-distribution with being the degree of freedom; note that for this data set. Finally, we performed FDR correction on all s for any specific tissue/plasma to get the adjusted p-values, . The null hypothesis that the lipid does not differ at the mean level for the two groups was rejected if. To obtain the correlations for all lipids that are common across all three tissues and plasma, we calculated the Pearson correlation for lipid i and lipid j as where is the th sample of lipid and is the sample mean for lipid i. Further, we only retained the correlations that were statistically significantly different than 0. To do so, we first calculated Fisher's z-score for each, given by which under the null hypothesis, is normally distributed with mean zero and standard deviation, where denotes the sample size. The corresponding -value is given by Moreover, to control the family-wise type I error rate, we adjusted all pairs of the resulting -values using the Benjamini-Hochberg FDR correction, and denote the adjusted -values by. Finally, the statistically significant correlations, are given by where is the family-wise type I error rate under control. In this study, we set. To test whether any prespecified sets of lipids are differentially coexpressed between plasma and any of the tissues under study (e.g., diabetic plasma vs. diabetic retina), we propose the following procedure, which provides adequate power in detecting the differentially coexpressed sets, while simultaneously keeping the FDR controlled at a prespecified level (e.g., 0.1). The proposed test is in the spirit of pathway enrichment tests, such as Gene Set Enrichment Analysis (35.Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. USA. 2005; 102: 15545-15550Crossref PubMed Scopus (26549) Google Scholar), and its variants that look for persistent activity in the quantities under study (e.g., correlation differentials in our setting). Specifically, the testing procedure is carried out in three steps: Test for individual pairs of lipids between tissues. For each pair of lipids indexed by, we test whether they are differentially coexpressed between plasma and the tissue under consideration; that is, the null hypothesis is given by versus the alternative, where represents the correlation between lipid and lipid for tissue. The test statistic is given by where and respectively denote the sample size for tissues and. The -value is given by where is distributed as a standard normal random variable. Based on a thresholding level, which controls the type I error of this individual test, the decision of the test is given by that is, we reject if the -value is smaller than the threshold. In this study, we set. Calculate -values for each lipid set. For any prespecified set, we test whether at the set level, it is differentially coexpressed between tissues and. The test statistic is defined as which counts the total number of pairs of lipids for which the null hypothesis is rejected based on the individual test in Step 1. Under the null hypothesis that set is commonly coexpressed between tissues and follows a binomial distribution with size and success probability, that is, where is the total number of correlations and is the number of lipids in set. The binomial distribution can be approximated by a normal distribution when is sufficiently large. The corresponding -value is then given by Control FDR across lipid sets. Next, for all lipid sets under consideration, we calculate their-values. Finally, lipid sets whose adjusted p-values are smaller than the thresholding level (e.g., 0.1) are declared as differentially coexpressed sets. The lipidomic data have been deposited in the Metabolomics Workbench Data Repository (www.metabolomicsworkbench.org). Raw and processed microarray data are available in the National Center for Biotechnology Information Gene Expression Omnibus repository (http://www.ncbi.nlm.nih.gov/geo), accession number GSE86300 (21.Sas K.M. Kayampilly P. Byun J. Nair V. Hinder L.M. Hur J. Zhang H. Lin C. Qi N.R. Michailidis G. et al.Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications.JCI Insight. 2016; 1: e86976Crossref PubMed Scopus (142) Google Scholar). Shotgun lipidomics was used to determine differences in complex lipids present in plasma, kidney cortex, sciatic nerve, and retina from db/db type 2 diabetic mice and db/+ normoglycemic littermate controls. Over 500 unique lipid features were detected in each sample matrix, with 364 lipid features present in plasma and all three tissues (Fig. 1A). The number of lipids that were significantly altered (P < 0.01) between control and diabetic mice in each sample type varied, with 61 (11.7%) in retina, 155 (24.4%) in kidney, 133 (25.9%) in plasma, and 258 (49.7%) in nerve (Fig. 1B, supplemental Tables S1–S4). Of the 364 unique lipid features present in every sample matrix, only 15 were significantly different between control and diabetic mice in all three complication-prone tissues and only five were significantly changed in plasma, kidney, nerve, and retina (Fig. 1B, Table 1). Even in these shared features, however, the direction of the change with diabetes was often inconsistent. Only plasma and nerve showed the same direction of change in diabetes among all five shared features (Table 1).TABLE 1Significantly different lipid features in diabetic plasma, kidney, nerve and retinaLipid NamePlasmaKidneyNerveRetinaPMean Change (db/db)PMean Change (db/db)PMean Change (db/db)PMean Change (db/db)Significant in plasma, kidney, nerve, and retinaDAG 34:20.00841.26250.00701.2870<0.00011.93510.0065−1.3539DAG 38:50.00891.25400.00031.5501<0.00011.63580.0005−1.5475LPC 18:10.0092−1.2484<0.00011.6997<0.0001−1.65100.0001−1.6771PC 36:40.00011.59370.0009−1.4768<0.00011.81680.00041.5771SM 36:2<0.00011.7578<0.00011.7121<0.00011.64270.00011.6602Significant in kidney, nerve and retinaDAG 36:10.03341.08500.00221.4065<0.00011.83050.0002−1.6232DAG 40:50.05371.00440.00291.38120.00021.49520.0031−1.4129DAG 40:70.01331.20750.00201.4147<0.00011.71800.0022−1.4473DAG 44:12NDND0.00341.3650<0.00011.75000.0005−1.5430LPC 18:20.29070.6101<0.00011.64730.00031.48850.0065−1.3511PC 18:00.1757−0.75460.0001−1.59310.00451.26030.0005−1.5423PE 34:20.05690.99260.00331.3680<0.00011.77490.0005−1.5451PE 40:9NDND0.00011.6079<0.00011.5950<0.00011.7529Plasmenyl-PE 36:40.4021−0.4952<0.00011.8557<0.00011.90670.00021.6183PS 36:4NDND<0.00011.6834<0.00011.59010.00241.4383Values are given as mean difference in 24-week-old db/db versus db/+. P < 0.01 based on a two-sample t-test with FDR correction, n = 10 per group. ND, not detected." @default.
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• W2772219889 date "2018-02-01" @default.
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• W2772219889 title "Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model" @default.
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