Inaccurate quantitation of palmitate in metabolomics and isotope tracer studies due to plastics
Tóm tắt
Palmitate, the typical end product released from fatty acid synthase, is of interest to many researchers performing metabolomics. Although palmitate can be readily detected by using mass spectrometry, many metabolomic platforms involve the use of plastic consumables that introduce a competing background signal of palmitate. The goal of this study was to quantify palmitate contamination in metabolomics and isotope tracer studies and to examine the reliability of approaches for reducing error. We measured the quantitative error introduced by palmitate contamination from 4 vendors of plastic consumables used in combination with several different extraction solvents. The background palmitate signal was as much as sixfold higher than the biological palmitate signal from 4 million 3T3-L1 cells. Importantly, the palmitate contamination signal was highly variable between plastic consumables (even within the same lot) and therefore could not be accurately removed by subtracting the background as measured from a blank. In addition to affecting relative and absolute quantitation, the palmitate background signal from disposable plastics also led to the underestimation of labeled palmitate in isotope tracer experiments. When measuring palmitate standard solutions, the best results were obtained when glass vials and glass pipettes were used. However, much of the palmitate background signal could be eliminated by pre-rinsing plastic vials and plastic pipette tips with methanol prior to sample introduction. For isotope tracer studies, error could also be minimized by estimating palmitate enrichment from palmitoylcarnitine, which does not have a competing contamination signal from plastic consumables.
Tài liệu tham khảo
Glatz, J. F., Luiken, J. J., & Bonen, A. (2010). Membrane fatty acid transporters as regulators of lipid metabolism: implications for metabolic disease. Physiological Reviews, 90, 367–417. doi:10.1152/physrev.00003.2009.
Gross, R. W., & Han, X. (2011). Lipidomics at the interface of structure and function in systems biology. Chemistry & Biology, 18, 284–291. doi:10.1016/j.chembiol.2011.01.014.
Ivanisevic, J., et al. (2013). Toward ‘omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Analytical Chemistry, 85, 6876–6884. doi:10.1021/ac401140h.
Jiang, L., et al. (2016). Reductive carboxylation supports redox homeostasis during anchorage-independent growth. Nature, 532, 255–258. doi:10.1038/nature17393.
Kamphorst, J. J., Chung, M. K., Fan, J., & Rabinowitz, J. D. (2014). Quantitative analysis of acetyl-CoA production in hypoxic cancer cells reveals substantial contribution from acetate. Cancer Metab, 2, 23. doi:10.1186/2049-3002-2-23.
Kurczy, M. E., Ivanisevic, J., Johnson, C. H., Uritboonthai, W., Hoang, L., Fang, M. et al. (2015). Determining conserved metabolic biomarkers from a million database queries. Bioinformatics, 31, 3721–3724. doi:10.1093/bioinformatics/btv475.
Lee, T. W., Tumanov, S., Villas-Boas, S. G., Montgomery, J. M., & Birch, N. P. (2015). Chemicals eluting from disposable plastic syringes and syringe filters alter neurite growth, axogenesis and the microtubule cytoskeleton in cultured hippocampal neurons. Journal of Neurochemistry, 133(1), 53–65.
Li, Q., Deng, S., Ibarra, R. A., Anderson, V. E., Brunengraber, H., & Zhang, G. F. (2015). Multiple mass isotopomer tracing of acetyl-CoA metabolism in Langendorff-perfused rat hearts: channeling of acetyl-CoA from pyruvate dehydrogenase to carnitine acetyltransferase. Journal of Biological Chemistry, 290, 8121–8132. doi:10.1074/jbc.M114.631549.
Mahieu, N. G., Spalding, J., & Patti, G. J. (2015). Warpgroup: increased Precision of metabolomic data processing by consensus integration bound analysis. Bioinformatics. doi:10.1093/bioinformatics/btv564.
Metallo, C. M., et al. (2012). Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature, 481, 380–384. doi:10.1038/nature10602.
Millard, P., Letisse, F., Sokol, S., & Portais, J. C. (2012). IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics, 28, 1294–1296. doi:10.1093/bioinformatics/bts127.
Nikolskiy, I., Mahieu, N. G., Chen, Y. J., Tautenhahn, R., & Patti, G. J. (2013). An untargeted metabolomic workflow to improve structural characterization of metabolites. Analytical Chemistry, 85, 7713–7719. doi:10.1021/ac400751j.
Olivieri, A., Degenhardt, O. S., McDonald, G. R., Narang, D., Paulsen, I. M., Kozuska, J. L., & Holt, A. (2012). On the disruption of biochemical and biological assays by chemicals leaching from disposable laboratory plasticware. Canadian Journal of Physiology and Pharmacology, 90, 697–703. doi:10.1139/y2012-049.
Stillwell, W. (2013). Chapter 4—membrane lipids: fatty acids an introduction to biological membranes (pp. 43–56). San Diego: Elsevier.
Tredwell, G. D., & Keun, H. C. (2015). convISA: a simple, convoluted method for isotopomer spectral analysis of fatty acids and cholesterol. Metabolic Engineering, 32, 125–132. doi:10.1016/j.ymben.2015.09.008.
Tumanov, S., Bulusu, V., & Kamphorst, J. J. (2015). Analysis of fatty acid metabolism using stable isotope tracers and mass spectrometry. Methods in Enzymology, 561, 197–217. doi:10.1016/bs.mie.2015.05.017.
Yao, C. H., Fowle-Grider, R., Mahieu, N. G., Liu, G. Y., Chen, Y. J., Wang, R. et al. (2016). Exogenous fatty acids are the preferred source of membrane lipids in proliferating fibroblasts. Cell Chemical Biology, 23, 483–493. doi:10.1016/j.chembiol.2016.03.007.