Metabolomics identifies metabolite biomarkers associated with acute rejection after heart transplantation in rats

Feng Lin, Yi Ou, Chuan-Zhong Huang, Sheng-Zhe Lin, Yun-Bin Ye, Feng Lin, Yi Ou, Chuan-Zhong Huang, Sheng-Zhe Lin, Yun-Bin Ye

Abstract

The aim of this study was to identify metabolite biomarkers associated with acute rejection after heart transplantation in rats using a LC-MS-based metabolomics approach. A model of heterotopic cardiac xenotransplantation was established in rats, with Wistar rats as donors and SD rats as recipients. Blood and cardiac samples were collected from blank control rats (Group A), rats 5 (Group B) and 7 days (Group C) after heart transplantation, and pretreated rats 5 (Group D) and 7 days (Group E) post-transplantation for pathological and metabolomics analyses. We assessed International Society for Heart and Lung Transplantation (ISHLT) grades 0, 3B, 4, 1 and 1 rejection in groups A to E. There were 15 differential metabolites between groups A and B, 14 differential metabolites between groups A and C, and 10 differential metabolites between groups B and C. In addition, four common differential metabolites, including D-tagatose, choline, C16 sphinganine and D-glutamine, were identified between on days 5 and 7 post-transplantation. Our findings demonstrate that the panel of D-tagatose, choline, C16 sphinganine and D-glutamine exhibits a high sensitivity and specificity for the early diagnosis of acute rejection after heart transplantation, and LC-MS-based metabolomics approach has a potential value for screening post-transplantation biomarkers.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
HE staining of rat myocardium (×100). (A) Normal myocardium; (B) myocardial tissues sampled 5 days after organ transplantation; (C) myocardial tissues sampled 7 days after organ transplantation; (D) rats are intraperitoneally injected with cyclosporine A at a dose of 20 mg/kg one day pre-transplantation and at a dose of 10 mg/kg post-transplantation for 5 days, and then myocardial tissues are sampled; (E) rats are intraperitoneally injected with cyclosporine A at a dose of 20 mg/kg one day pre-transplantation and at a dose of 10 mg/kg post-transplantation for 7 days, and then myocardial tissues are sampled.
Figure 2
Figure 2
The principal component analysis (PCA) score plot of the quality control samples. All quality control (QC) samples are found to cluster in the PCA plot and the error in QC is within two standard deviations (SDs).
Figure 3
Figure 3
The typical total ion chromatograms (TICs) and base peak chromatograms (BPCs). (A) Positive ion; (B) negative ion.
Figure 4
Figure 4
Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) score plots. (A) PCA score plot. The horizontal ordinate indicates the score of samples in the first principle component, and the vertical ordinate indicates the score of samples in the second principle component. R2X1, the interpretable degree of the first principle component; R2X2, the interpretable degree of the second principle component. (B) PLS-DA score plot; (C), permutation tests for the PLS-DA score plot. The horizontal ordinate indicates the score of samples in the first principle component, and the vertical ordinate indicates the score of samples in the second principle component. R2X1, the interpretable degree of the first principle component; R2X2, the interpretable degree of the second principle component.
Figure 5
Figure 5
Hierarchical clustering and KEGG metabolic pathways of differential metabolites. (A) Heat maps of differential metabolites; (B) heat maps of KEGG metabolic pathways. Pathway activity profiling (PAPi) is used to calculate the activity score of each metabolic pathway. The heat maps are based on metabolic process and classified, and the scattergrams of the metabolic pathways are statistical analyses of the KEGG metabolic pathways (P < 0.05 as revealed by ANOVA).

References

    1. Andrew J, Macdonald P. Latest developments in heart transplantation: a review. Clin. Ther. 2015;37:2234–2241. doi: 10.1016/j.clinthera.2015.08.019.
    1. Argenziano M, Michler RE, Rose EA. Cardiac transplantation for endstage heart disease. Heart. Vessels. 1997;7:23–27.
    1. Heroux AL, et al. Heart transplantation as a treatment option for end-stage heart disease in patients older than 65 years of age. J. Heart. Lung. Transplant. 1993;12:573–578.
    1. Toyoda Y, Guy TS, Kashem A. Present status and future perspectives of heart transplantation. Circ. J. 2013;77:1097–1110. doi: 10.1253/circj.CJ-13-0296.
    1. Almenar L, et al. Risk factors affecting survival in heart transplant patients. Transplant. Proc. 2005;37:4011–4013. doi: 10.1016/j.transproceed.2005.09.160.
    1. Bove AA, et al. Factors affecting survival after heart transplantation: comparison of pre- and post-1999 listing protocols. J. Heart. Lung. Transplant. 2006;25:42–47. doi: 10.1016/j.healun.2005.07.004.
    1. Cole E, et al. A pilot study of steroid-free immunosuppression in the prevention of acute rejection in renal allograft recipients. Transplantation. 2001;72:845–850. doi: 10.1097/00007890-200109150-00018.
    1. Bhorade SM, et al. New combination of immunosuppressive therapy decreases acute rejection in lung transplantation. J. Heart. Lung. Transplant. 2003;22:S120. doi: 10.1016/S1053-2498(02)00838-0.
    1. Garcia M, et al. Campath-1H immunosuppressive therapy reduces incidence and intensity of acute rejection in intestinal and multivisceral transplantation. Transplant. Proc. 2004;36:323–324. doi: 10.1016/j.transproceed.2004.01.105.
    1. Billingham ME. Endomyocardial biopsy diagnosis of acute rejection in cardiac allografts. Prog. Cardiovasc. Dis. 1990;33:11–18. doi: 10.1016/0033-0620(90)90036-2.
    1. Cooper LT, et al. The role of endomyocardial biopsy in the management of cardiovascular disease: a scientific statement from the American Heart Association, the American College of Cardiology, and the European Society of Cardiology. Circulation. 2007;116:2216–2233. doi: 10.1161/CIRCULATIONAHA.107.186093.
    1. From AM, Maleszewski JJ, Rihal CS. Current status of endomyocardial biopsy. Mayo. Clin. Proc. 2011;86:1095–1102. doi: 10.4065/mcp.2011.0296.
    1. Winters GL. The challenge of endomyocardial biopsy interpretation in assessing cardiac allograft rejection. Curr. Opin. Cardiol. 1997;12:146–152. doi: 10.1097/00001573-199703000-00009.
    1. Patel JK, Kobashigawa JA. Should we be doing routine biopsy after heart transplantation in a new era of anti-rejection? Curr. Opin. Cardiol. 2006;21:127–131. doi: 10.1097/01.hco.0000210309.71984.30.
    1. Zwang NA, Turka LA. Transplantation immunology in 2013: New approaches to diagnosis of rejection. Nat. Rev. Nephrol. 2014;10:72–74. doi: 10.1038/nrneph.2013.262.
    1. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug. Discov. 2002;1:153–161. doi: 10.1038/nrd728.
    1. Lindon JC, Holmes E, Nicholson JK. Metabonomics and its role in drug development and disease diagnosis. Expert. Rev. Mol. Diagn. 2004;4:189–199. doi: 10.1586/14737159.4.2.189.
    1. Lindon JC, Holmes E, Bollard ME, Stanley EG, Nicholson JK. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers. 2004;9:1–31. doi: 10.1080/13547500410001668379.
    1. Ng JY, Pasikanti KK, Chan ECY. Trend analysis of metabonomics and systematic review of metabonomics-derived cancer marker metabolites. Metabolomics. 2011;7:155–178. doi: 10.1007/s11306-010-0250-7.
    1. Trezzi JP, Vlassis N, Hiller K. The role of metabolomics in the study of cancer biomarkers and in the development of diagnostic tools. Adv. Exp. Med. Biol. 2015;867:41–57. doi: 10.1007/978-94-017-7215-0_4.
    1. Nicholson JK, Lindon JC. Systems biology: Metabonomics. 2008;455:1054–1056.
    1. Stewart S, et al. Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J. Heart. Lung. Transplant. 2005;24:1710–1720. doi: 10.1016/j.healun.2005.03.019.
    1. Zhou B, Xiao JF, Tuli L, Habtom W. LC-MS-based metabolomics. Mol. Biosyst. 2012;8:470–481. doi: 10.1039/C1MB05350G.
    1. Foster JC, Nan B, Shen L, Kaciroti N, Taylor JM. Permutation testing for treatment-covariate interactions and subgroup identification. Stat. Biosci. 2016;8:77–98. doi: 10.1007/s12561-015-9125-9.
    1. Delgado JF, Sánchez V, de la Calzada CS. Acute rejection after heart transplantation. Expert. Opin. Pharmacother. 2006;7:1139–1149. doi: 10.1517/14656566.7.9.1139.
    1. Cohen-Freue GV, et al. Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation. PLoS. Comput. Biol. 2013;9:e1002963. doi: 10.1371/journal.pcbi.1002963.
    1. Schaub S, Wilkins JA, Nickerson P. Proteomics and renal transplantation: searching for novel biomarkers and therapeutic targets. Contrib. Nephrol. 2008;160:65–75. doi: 10.1159/000125934.
    1. Liang SL, Clarke W. Urine proteomic profiling for biomarkers of acute renal transplant rejection. Methods. Mol. Biol. 2010;641:185–191. doi: 10.1007/978-1-60761-711-2_11.
    1. Mas VR, Mueller TF, Archer KJ, Maluf DG. Identifying biomarkers as diagnostic tools in kidney transplantation. Expert. Rev. Mol. Diagn. 2011;11:183–196. doi: 10.1586/erm.10.119.
    1. Anglicheau D, Naesens M, Essig M, Gwinner W, Marquet P. Establishing biomarkers in transplant medicine: a critical review of current approaches. Transplantation. 2016;100:2024–2038. doi: 10.1097/TP.0000000000001321.
    1. Bohra R, et al. Proteomics and metabolomics in renal transplantation-quo vadis? Transpl Int. 2013;26:225–241. doi: 10.1111/tri.12003.
    1. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29:1181–1189. doi: 10.1080/004982599238047.
    1. Wishart DS, et al. HMDB 3.0–The Human Metabolome Database in 2013. Nucleic. Acids. Res. 2013;41:D801–D807. doi: 10.1093/nar/gks1065.
    1. Dieterle F, et al. NMR and MS methods for metabonomics. Methods. Mol. Biol. 2011;691:385–415. doi: 10.1007/978-1-60761-849-2_24.
    1. Waters NJ. The role of metabonomics at the interface between drug metabolism and safety assessment. Curr. Drug. Metab. 2010;11:686–692. doi: 10.2174/138920010794233512.
    1. Nicholson JK, Everett JR, Lindon JC. Longitudinal pharmacometabonomics for predicting patient responses to therapy: drug metabolism, toxicity and efficacy. Expert. Opin. Drug. Metab. Toxicol. 2012;8:135–139. doi: 10.1517/17425255.2012.646987.
    1. Sengupta A, Ghosh S, Sharma S, Sonawat HM. 1H NMR metabonomics indicates continued metabolic changes and sexual dimorphism post-parasite clearance in self-limiting murine malaria model. PLoS One. 2013;8:e66954. doi: 10.1371/journal.pone.0066954.
    1. Zhao L, et al. Identification of key metabolic changes in renal interstitial fibrosis rats using metabonomics and pharmacology. Sci. Rep. 2016;6:27194. doi: 10.1038/srep27194.
    1. Li Q, et al. Metabonomics study of intestinal transplantation using ultrahigh-performance liquid chromatography time-of-flight mass spectrometry. Digestion. 2008;77:122–130. doi: 10.1159/000123842.
    1. Chen J, et al. Metabonomics study of the acute graft rejection in rat renal transplantation using reversed-phase liquid chromatography and hydrophilic interaction chromatography coupled with mass spectrometry. Mol. Biosyst. 2012;8:871–878. doi: 10.1039/c2mb05454j.
    1. Benahmed MA, et al. The assessment of the quality of the graft in an animal model for lung transplantation using the metabolomics 1H high-resolution magic angle spinning NMR spectroscopy. Magn. Reson. Med. 2012;68:1026–1038. doi: 10.1002/mrm.24110.
    1. Serkova NJ, Standiford TJ, Stringer KA. The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses. Am. J. Respir. Crit. Care. Med. 2011;184:647–655. doi: 10.1164/rccm.201103-0474CI.
    1. Serkova NJ, Brown MS. Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers. Bioanalysis. 2012;4:321–341. doi: 10.4155/bio.11.320.
    1. Serkova NJ, et al. Early detection of graft failure using the blood metabolic profile of a liver recipient. Transplantation. 2007;83:517–521. doi: 10.1097/01.tp.0000251649.01148.f8.
    1. Calne R. Challenges of organ transplantation. Transplant. Proc. 2005;37:1979–1983. doi: 10.1016/j.transproceed.2005.03.066.
    1. Perk S, et al. A metabolic index of ischemic injury for perfusion-recovery of cadaveric rat livers. PLoS One. 2011;6:e28518. doi: 10.1371/journal.pone.0028518.
    1. Nath J, et al. Metabolic differences between cold stored and machine perfused porcine kidneys: A 1H NMR based study. Cryobiology. 2017;74:115–120. doi: 10.1016/j.cryobiol.2016.11.006.
    1. Hyndman ME, Mullins JK, Bivalacqua TJ. Metabolomics and bladder cancer. Urol. Oncol. 2011;29:558–561. doi: 10.1016/j.urolonc.2011.05.014.
    1. Bodi V, et al. Metabolomic profile of human myocardial ischemia by nuclear magnetic resonance spectroscopy of peripheral blood serum: a translational study based on transient coronary occlusion models. J. Am. Coll. Cardiol. 2012;59:1629–1641. doi: 10.1016/j.jacc.2011.09.083.
    1. Abbott PP, Lindsey ES, Creech O, Jr., Dewitt CW. A technique for heart transplantation in the rat. Arch. Surg. 1964;89:645–652. doi: 10.1001/archsurg.1964.01320040061009.
    1. Ono K, Lindsey ES. Improved technique of heart transplantation in rats. J. Thorac. Cardiovasc. Surg. 1969;57:225–229.
    1. Ponticelli C. The mechanisms of acute transplant rejection revisited. J. Nephrol. 2012;25:150–158. doi: 10.5301/jn.5000048.
    1. Wang LY, et al. Protective effect of choline on ischemia-reperfusion injury in isolated rat heart. Chin. Pharmacol. Bull. 2007;23:600–603.
    1. Bartke N, Hannun YA. Bioactive sphingolipids: metabolism and function. J. Lipid. Res. 2009;50:91–96. doi: 10.1194/jlr.R800080-JLR200.
    1. Livesey G, Brown JC. D-tagatose is a bulk sweetener with zero energy determined in rats. J. Nutr. 1996;126:1601–1609.
    1. Zhang, L. & Liu, S.Y. Experimental study on F protein-induced immune tolerance in rats undergoing orthotopic liver transplantation. Tianjin: Tianjin Medical University, 2012.
    1. Chen H, et al. Studying the signaling role of 2-oxoglutaric acid using analogs that mimic the ketone and ketal forms of 2-oxoglutaric acid. Chem. Biol. 2006;13:849–56. doi: 10.1016/j.chembiol.2006.06.009.
    1. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic. Acids. Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27.
    1. Hasan RI, White DJ. Modification of Heron’s technique for heterotopic cardiac transplantation in rats. J. Thorac. Cardiovasc. Surg. 1994;107:1168–1169.
    1. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006;78:779–787. doi: 10.1021/ac051437y.
    1. Wang JB, et al. Metabolomic profiling of autoimmune hepatitis: the diagnostic utility of nuclear magnetic resonance spectroscopy. J. Proteome. Res. 2014;13:3792–3801. doi: 10.1021/pr500462f.
    1. Eriksson, L. et al. Multivariate and Megavariate Data Analysis (eds Eriksson, L. et al.). 27–29 (Umetrics Academy, 2006).

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera