Abstract
Lipopolysaccharide (LPS) is associated with the clinical severity of sepsis and other fatal infectious diseases. Increasing evidence suggests that there is significant genetic variability in LPS responses. However, little can be inferred on the disease-causing mechanisms only by associating genotypes with clinical outcomes. An integrated liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry metabonomics analysis was employed to understand inter-animal variability in toxic response to LPS. The pharmacometabonomic analysis of predose serum metabolic profiles of the survival and non-survival rats indicates that the inter-subject difference is mainly associated with lipid metabolism. Based on least absolute shrinkage and selection operator logistic regression and receiver operator characteristic analysis, sphingosine, sphinganine, palmitic acid, oleic acid and cholesterol were selected as the best subset for LPS toxicity prediction. Thus, the pharmacometabonomic approach using pretreatment metabolite profiles may provide some heuristic guidance for the prevention of LPS-induced diseases on an individual level.
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Altmaier, E., Ramsay, S. L., Graber, A., Mewes, H.-W., Weinberger, K. M., & Suhre, K. (2008). Bioinformatics analysis of targeted metabolomics—Uncovering old and new tales of diabetic mice under medication. Endocrinology, 149(7), 3478–3489. doi:10.1210/en.2007-1747.
Altman, T., Travers, M., Kothari, A., Caspi, R., & Karp, P. D. (2013). A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics, 14(1), 112. doi:10.1186/1471-2105-14-112.
Anderson, S. T., Commins, S., Moynagh, P. N., & Coogan, A. N. (2015). Lipopolysaccharide-induced sepsis induces long-lasting affective changes in the mouse. Brain, Behavior, and Immunity, 43, 98–109. doi:10.1016/j.bbi.2014.07.007.
Angus, D. C., & Van Der Poll, T. (2013). Severe sepsis and septic shock. The New England Journal of Medicine, 369(9), 840–851. doi:10.1056/NEJMra1208623.
Arbour, N. C., Lorenz, E., Schutte, B. C., Zabner, J., Kline, J. N., Jones, M., et al. (2000). TLR4 mutations are associated with endotoxin hyporesponsiveness in humans. Nature Genetics, 25(2), 187–191. doi:10.1038/76048.
Attie, A. D., Krauss, R. M., Gray-Keller, M. P., et al. (2002). Relationship between stearoyl-CoA desaturase activity and plasma triglycerides in human and mouse hypertriglyceridemia. Journal of Lipid Research, 43(11), 1899–1907. doi:10.1194/jlr.M200189-JLR200.
Balija, T. M., & Lowry, S. F. (2011). Lipopolysaccharide and sepsis-associated myocardial dysfunction. Current Opinion in Infectious Diseases, 24(3), 248–253. doi:10.1097/QCO.0b013e32834536ce.
Barber, S. A., Perera, P. Y., & Voge, S. N. (1995). Defective ceramide response in C3H/HeJ (Lpsd) macrophages. Journal of Immunology, 155(5), 2303–2305.
Beutler, B., & Rietschel, E. T. (2003). Innate immune sensing and its roots: The story of endotoxin. Nature Reviews Immunology, 3(2), 169–176. doi:10.1038/nri1004.
Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., et al. (2006). Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Analytical Chemistry, 78(2), 567–574. doi:10.1021/ac051495j.
Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2(4), 171–196. doi:10.1007/s11306-006-0037-z.
Chaudhuri, A., Wilson, N. S., Yang, B., et al. (2013). Host genetic background impacts modulation of the TLR4 pathway by RON in tissue-associated macrophages. Immunology and Cell Biology, 91(7), 451–460. doi:10.1038/icb.2013.27.
Clark, S. R., Ma, A. C., Tavener, S. A., McDonald, B., Goodarzi, Z., Kelly, M. M., et al. (2007). Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood. Nature Medicine, 13(4), 463–469. doi:10.1038/nm1565.
Clayton, T. A., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., et al. (2006). Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 440(7087), 1073–1077. doi:10.1038/nature04648.
Cognasse, F., Hamzeh, H., Chavarin, P., Acquart, S., Genin, C., & Garraud, O. (2005). Evidence of Toll-like receptor molecules on human platelets. Immunology and Cell Biology, 83(2), 196–198. doi:10.1111/j.1440-1711.2005.01314.x.
Cohen, J. (2002). The immunopathogenesis of sepsis. Nature, 420(6917), 885–891. doi:10.1038/nature01326.
Dellinger, R. P., Levy, M. M., Rhodes, A., et al. (2013). Surviving sepsis campaign: International guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Medicine, 39(2), 165–228. doi:10.1007/s00134-012-2769-8.
Dunn, W. B., Wilson, I. D., Nicholls, A. W., & Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18), 2249–2264. doi:10.4155/bio.12.204.
Eriksson, L., Trygg, J., & Wold, S. (2008). CV-ANOVA for significance testing of PLS and OPLS® models. Journal of Chemometrics, 22(11–12), 594–600. doi:10.1002/cem.1187.
Fischer, H., Ellström, P., Ekström, K., Gustafsson, L., Gustafsson, M., & Svanborg, C. (2007). Ceramide as a TLR4 agonist; a putative signalling intermediate between sphingolipid receptors for microbial ligands and TLR4. Cellular Microbiology, 9(5), 1239–1251. doi:10.1111/j.1462-5822.2006.00867.x.
Fletcher, J. R., & Ramwell, P. W. (1980). The effects of prostacyclin (PGI2) on endotoxin shock and endotoxin-induced platelet aggregation in dogs. Circulatory Shock, 7(3), 299–308.
Fyrst, H., & Saba, J. D. (2010). An update on sphingosine-1-phosphate and other sphingolipid mediators. Nature Chemical Biology, 6(7), 489–497. doi:10.1038/nchembio.392.
Gao, Y., Lu, Y., Huang, S., et al. (2014). Identifying early urinary metabolic changes with long-term environmental exposure to cadmium by mass-spectrometry-based metabolomics. Environmental Science and Technology, 48(11), 6409–6418. doi:10.1021/es500750w.
Gauster, M., Rechberger, G., Sovic, A., Hörl, G., Steyrer, E., Sattler, W., & Frank, S. (2005). Endothelial lipase releases saturated and unsaturated fatty acids of high density lipoprotein phosphatidylcholine. Journal of Lipid Research, 46(7), 1517–1525. doi:10.1194/jlr.M500054-JLR200.
Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., et al. (2008). Genetics meets metabolomics: A genome-wide association study of metabolite profiles in human serum. PLoS Genetics, 4(11), e1000282. doi:10.1371/journal.pgen.1000282.
Gika, H. G., Theodoridis, G. A., Earll, M., & Wilson, I. D. (2012). A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis, 4(18), 2239–2247. doi:10.4155/bio.12.212.
Grin’kina, N. M., Karnabi, E. E., Damania, D., Wadgaonkar, S., Muslimov, I. A., & Wadgaonkar, R. (2012). Sphingosine kinase 1 deficiency exacerbates LPS-induced neuroinflammation. PLoS ONE, 7(5), e36475. doi:10.1371/journal.pone.0036475.
Guţiu, I. A., Andrieş, A., Mircioiu, C., Rădulescu, F., Georgescu, A.-M., & Cioacă, D. (2010). Pharmacometabonomics, pharmacogenomics and personalized medicine. Romanian Journal of Internal Medicine, 48(2), 187–191.
Hankins, J. L., Fox, T. E., Barth, B. M., Unrath, K. A., & Kester, M. (2011). Exogenous ceramide-1-phosphate reduces lipopolysaccharide (LPS)-mediated cytokine expression. The Journal of Biological Chemistry, 286(52), 44357–44366. doi:10.1074/jbc.M111.264010.
Hannun, Y. A., & Obeid, L. M. (2008). Principles of bioactive lipid signalling: Lessons from sphingolipids. Nature Reviews Molecular Cell Biology, 9(2), 139–150. doi:10.1038/nrm2329.
Hla, T., & Dannenberg, A. J. (2012). Sphingolipid signaling in metabolic disorders. Cell Metabolism, 16(4), 420–434. doi:10.1016/j.cmet.2012.06.017.
Izquierdo, M. S., Scolamacchia, M., Betancor, M., Roo, J., Caballero, M. J., Terova, G., & Witten, P. E. (2013). Effects of dietary DHA and α-tocopherol on bone development, early mineralisation and oxidative stress in Sparus aurata (Linnaeus, 1758) larvae. The British Journal of Nutrition, 109(10), 1796–1805. doi:10.1017/S0007114512003935.
Johnson, J. A., Griswold, J. A., & Muakkassa, F. F. (1993). Essential fatty acids influence survival in sepsis. The Journal of Trauma, 35(1), 128–131.
Jung, Y., Lee, J., Kwon, J., Lee, K.-S., Ryu, D. H., & Hwang, G.-S. (2010). Discrimination of the geographical origin of beef by (1)H NMR-based metabolomics. Journal of Agricultural and Food Chemistry, 58(19), 10458–10466. doi:10.1021/jf102194t.
Kadotani, A., Tsuchiya, Y., Hatakeyama, H., Katagiri, H., & Kanzaki, M. (2009). Different impacts of saturated and unsaturated free fatty acids on COX-2 expression in C(2)C(12) myotubes. American Journal of Physiology: Endocrinology and Metabolism, 297(6), E1291–E1303. doi:10.1152/ajpendo.00293.2009.
Lingwood, D., & Simons, K. (2010). Lipid rafts as a membrane-organizing principle. Science, 327(5961), 46–50. doi:10.1126/science.1174621.
Liu, X., Miyazaki, M., Flowers, M. T., Sampath, H., Zhao, M., Chu, K., et al. (2010). Loss of Stearoyl-CoA desaturase-1 attenuates adipocyte inflammation: Effects of adipocyte-derived oleate. Arteriosclerosis, Thrombosis, and Vascular Biology, 30(1), 31–38. doi:10.1161/ATVBAHA.109.195636.
Maceyka, M., & Spiegel, S. (2014). Sphingolipid metabolites in inflammatory disease. Nature, 510(7503), 58–67. doi:10.1038/nature13475.
Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008). Analysis of metabolomic data using support vector machines. Analytical Chemistry, 80(19), 7562–7570. doi:10.1021/ac800954c.
Makris, G. C., Geroulakos, G., Makris, M. C., Mikhailidis, D. P., & Falagas, M. E. (2010). The pleiotropic effects of statins and omega-3 fatty acids against sepsis: A new perspective. Expert Opinion on Investigational Drugs, 19(7), 809–814. doi:10.1517/13543784.2010.490830.
Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: B, 72(4), 417–473. doi:10.1111/j.1467-9868.2010.00740.x.
Melendez, A. J. (2008). Sphingosine kinase signalling in immune cells: Potential as novel therapeutic targets. Biochimica et Biophysica Acta, 1784(1), 66–75. doi:10.1016/j.bbapap.2007.07.013.
Merrill, A. H. (2002). De novo sphingolipid biosynthesis: A necessary, but dangerous, pathway. The Journal of Biological Chemistry, 277(29), 25843–25846. doi:10.1074/jbc.R200009200.
Miller, S. I., Ernst, R. K., & Bader, M. W. (2005). LPS, TLR4 and infectious disease diversity. Nature Reviews Microbiology, 3(1), 36–46. doi:10.1038/nrmicro1068.
Naz, S., Vallejo, M., García, A., & Barbas, C. (2014). Method validation strategies involved in non-targeted metabolomics. Journal of Chromatography A, 1353, 99–105. doi:10.1016/j.chroma.2014.04.071.
Noreen, M., Shah, M. A. A., Mall, S. M., et al. (2012). TLR4 polymorphisms and disease susceptibility. Inflammation Research, 61(3), 177–188. doi:10.1007/s00011-011-0427-1.
Qureshi, S. T., Larivière, L., Leveque, G., Clermont, S., Moore, K. J., Gros, P., & Malo, D. (1999). Endotoxin-tolerant mice have mutations in toll-like receptor 4(Tlr4). The Journal of Experimental Medicine, 189(4), 615–625. doi:10.1084/jem.189.4.615.
Redl, H., Bahrami, S., Schlag, G., & Traber, D. L. (1993). Clinical detection of LPS and animal models of endotoxemia. Immunobiology, 187(3–5), 330–345.
Rocke, D. M., & Durbin, B. (2003). Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics, 19(8), 966–972. doi:10.1093/bioinformatics/btg107.
Sampath, H., & Ntambi, J. M. (2005). The fate and intermediary metabolism of stearic acid. Lipids, 40(12), 1187–1191. doi:10.1007/s11745-005-1484-z.
Sasaki, T., Kanke, Y., Nagahashi, M., Toyokawa, M., Matsuda, M., Shimizu, J., et al. (2000). Dietary docosahexaenoic acid can alter the surface expression of CD4 and CD8 on T cells in peripheral blood. Journal of Agricultural and Food Chemistry, 48(4), 1047–1049. doi:10.1021/jf990358i.
Semple, J. W., & Freedman, J. (2010). Platelets and innate immunity. Cellular and Molecular Life Sciences, 67(4), 499–511. doi:10.1007/s00018-009-0205-1.
Slupsky, C. M., Steed, H., Wells, T. H., et al. (2010). Urine metabolite analysis offers potential early diagnosis of ovarian and breast cancers. Clinical Cancer Research, 16(23), 5835–5841. doi:10.1158/1078-0432.CCR-10-1434.
Tani, M., Sano, T., Ito, M., & Igarashi, Y. (2005). Mechanisms of sphingosine and sphingosine 1-phosphate generation in human platelets. Journal of Lipid Research, 46(11), 2458–2467. doi:10.1194/jlr.M500268-JLR200.
Tapp, H. S., & Kemsley, E. K. (2009). Notes on the practical utility of OPLS. Trends in Analytical Chemistry, 28(11), 1322–1327. doi:10.1016/j.trac.2009.08.006.
Thiéblemont, N., & Wright, S. D. (1997). Mice genetically hyporesponsive to lipopolysaccharide (LPS) exhibit a defect in endocytic uptake of LPS and ceramide. The Journal of Experimental Medicine, 185(12), 2095–2100. doi:10.1084/jem.185.12.2095.
Triantafilou, M., Miyake, K., Golenbock, D. T., & Triantafilou, K. (2002). Mediators of innate immune recognition of bacteria concentrate in lipid rafts and facilitate lipopolysaccharide-induced cell activation. Journal of Cell Science, 115, 2603–2611.
Triba, M. N., Le Moyec, L., Amathieu, R., et al. (2015). PLS/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Molecular BioSystems, 11(1), 13–19. doi:10.1039/c4mb00414k.
Wheelock, Å. M., & Wheelock, C. E. (2013). Trials and tribulations of ‘omics data analysis: Assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. Molecular BioSystems, 9(11), 2589–2596. doi:10.1039/c3mb70194h.
Wiklund, S., Johansson, E., Sjöström, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., et al. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry, 80(1), 115–122. doi:10.1021/ac0713510.
Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., et al. (2009). HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research, 37(Database issue), D603–D610. doi:10.1093/nar/gkn810.
Xia, J., Broadhurst, D. I., Wilson, M., & Wishart, D. S. (2013). Translational biomarker discovery in clinical metabolomics: An introductory tutorial. Metabolomics, 9(2), 280–299. doi:10.1007/s11306-012-0482-9.
Zhai, G., Wang-Sattler, R., Hart, D. J., Arden, N. K., Hakim, A. J., Illig, T., & Spector, T. D. (2010). Serum branched-chain amino acid to histidine ratio: A novel metabolomic biomarker of knee osteoarthritis. Annals of the Rheumatic Diseases, 69(6), 1227–1231. doi:10.1136/ard.2009.120857.
Ziebuhr, W., Xiao, K., Coulibaly, B., Schwarz, R., & Dandekar, T. (2004). Pharmacogenomic strategies against resistance development in microbial infections. Pharmacogenomics, 5(4), 361–379. doi:10.1517/14622416.5.4.361.
Zipfel, C. (2015). A new receptor for LPS. Nature Immunology, 16(4), 340–341. doi:10.1038/ni.3127.
Acknowledgments
This study was financially supported by the NSFC (Nos. 81274108, 81302733, 81430082), the Research Project of Chinese Ministry of Education (No. 113036A), the Program for Jiangsu Province Innovative Research Team, the Program for New Century Excellent Talents in University (No. NCET-13-1036), the Fundamental Research Funds for the Central Universities (Nos. JKZD2013004, YD2014SK0002) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Dai, D., Tian, Y., Guo, H. et al. A pharmacometabonomic approach using predose serum metabolite profiles reveals differences in lipid metabolism in survival and non-survival rats treated with lipopolysaccharide. Metabolomics 12, 2 (2016). https://doi.org/10.1007/s11306-015-0892-6
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DOI: https://doi.org/10.1007/s11306-015-0892-6