Skip to main content
Log in

GC–MS-based metabolic profiling reveals metabolic changes in anaphylaxis animal models

  • Original Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Clinical definition and appropriate management of anaphylaxis is a clinical challenge because there is large variability in presenting clinical signs and symptoms. Monitoring of the metabolic status of anaphylaxis may be helpful in understanding its pathophysiological processes and diagnosis. The purpose of this study was to conduct GC–MS serum metabolic profiling of anaphylaxis animal models and search for potential biomarkers of anaphylaxis. Thirty-six guinea pigs were randomly divided into an ovalbumin group (n = 12), a cattle albumin group (n = 12), and a control group (n = 12). The IgE level in the serum of the guinea pigs was evaluated by use of ELISA kits and the major metabolic changes in serum were detected by gas chromatography–mass spectrometry. Typical clinical symptoms appeared after the animals had been challenged with ovalbumin or cattle albumin. The IgE levels in serum of both model groups were significantly higher than those of the control group. Clustering trend of the three groups based on variables was observed and nine out of 858 metabolomic features were found to be significantly different between control group and model groups. Among the nine features, six features were tentatively identified as metabolites related to energy metabolism and signal transduction in anaphylaxis. In conclusion, GC–MS-based metabolic profiling analysis might be an effective auxiliary tool for investigation of anaphylaxis.

Chromatographic profile of the derivatized serum

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Decker WW, Campbell RL, Manivannan V, Luke A, St Sauver JL, Weaver A, Bellolio MF, Bergstralh EJ, Stead LG, Li JT (2008) The etiology and incidence of anaphylaxis in Rochester, Minnesota: a report from the Rochester Epidemiology Project. J Allergy Clin Immunol 122:1161–1165

    Article  Google Scholar 

  2. Simons FE, Sampson HA (2008) Anaphylaxis epidemic: fact or fiction? J Allergy Clin Immunol 122:1166–1168

    Article  Google Scholar 

  3. Yocum MW, Butterfield JH, Klein JS, Volcheck GW, Schroeder DR, Silverstein MD (1999) Epidemiology of anaphylaxis in Olmsted County: a population-based study. J Allergy Clin Immunol 104:452–456

    Article  CAS  Google Scholar 

  4. Silva IL, Mehr SS, Tey D, Tang ML (2008) Paediatric anaphylaxis: a 5 year retrospective review. Allergy 63:1071–1076

    Article  Google Scholar 

  5. Lieberman P, Camargo CA Jr, Bohlke K, Jick H, Miller RL, Sheikh A, Simons FE (2006) Epidemiology of anaphylaxis: findings of the American College of Allergy, Asthma and Immunology Epidemiology of Anaphylaxis Working Group. Ann Allergy Asthma Immunol 97:596–602

    Article  Google Scholar 

  6. Bjornsson HM, Graffeo CS (2010) Improving diagnostic accuracy of anaphylaxis in the acute care setting. West J Emerg Med 11:456–461

    Google Scholar 

  7. Kemp SF, Lockey RF (2002) Anaphylaxis: a review of causes and mechanisms. J Allergy Clin Immunol 110:341–348

    Article  CAS  Google Scholar 

  8. Ono E, Taniguchi M, Mita H, Fukutomi Y, Higashi N, Miyazaki E, Kumamoto T, Akiyama K (2009) Increased production of cysteinyl leukotrienes and prostaglandin D2 during human anaphylaxis. Clin Exp Allergy 39:72–80

    Article  CAS  Google Scholar 

  9. Watkins SM, German JB (2002) Metabolomics and biochemical profiling in drug discovery and development. Curr Opin Mol Ther 4:224–228

    CAS  Google Scholar 

  10. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26:51–78

    Article  CAS  Google Scholar 

  11. Kenny LC, Dunn WB, Ellis DI, Myers J, Baker PN, Consortium G, Kell DB (2005) Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 1:227–234

    Article  Google Scholar 

  12. Underwood BR, Broadhurst D, Dunn WB, Ellis DI, Michell AW, Vacher C, Mosedale DE, Kell DB, Barker RA, Grainger DJ, Rubinsztein DC (2006) Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles. Brain 129:877–886

    Article  Google Scholar 

  13. Michell AW, Mosedale D, Grainger DJ, Barker RA (2008) Metabolomic analysis of urine and serum in Parkinson’s disease. Metabolomics 4:191–201

    Article  CAS  Google Scholar 

  14. Thysell E, Surowiec I, Hornberg E, Crnalic S, Widmark A, Johansson AI, Stattin P, Bergh A, Moritz T, Antti H, Wikstrom P (2010) Metabolomic characterization of human prostate cancer bone metastases reveals increased levels of cholesterol. PLoS One 5:e14175

    Article  CAS  Google Scholar 

  15. Chen T, Xie G, Wang X, Fan J, Qiu Y, Zheng X, Qi X, Cao Y, Su M, Wang X, Xu LX, Yen Y, Liu P, Jia W (2011) Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol Cell Proteomics 10(M110):004945

    Google Scholar 

  16. Yang J, MacDougall ML, McDowell MT, Xi L, Wei R, Zavadoski WJ, Molloy MP, Baker JD, Kuhn M, Cabrera O, Treadway JL (2011) Polyomic profiling reveals significant hepatic metabolic alterations in glucagon-receptor (GCGR) knockout mice: implications on anti-glucagon therapies for diabetes. BMC Genomics 12:281

    Article  CAS  Google Scholar 

  17. Favretto D, Cosmi E, Ragazzi E, Visentin S, Tucci M, Fais P, Cecchetto G, Zanardo V, Viel G, Ferrara SD (2012) Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem 402:1109–1121

    Article  CAS  Google Scholar 

  18. Dunn WB, Ellis DI (2005) metabolomics: current analytical platforms and methodologies. TrAC Trend Anal Chem 4:285–294

    Google Scholar 

  19. Wu Z, Huang Z, Lehmann R, Zhao C, Xu G (2009) The application of chromatography – mass spectrometry: methods to metabonomics. Chromatographia Supplement 69:S23–S32

    Google Scholar 

  20. Canning BJ (2003) Modeling asthma and COPD in animals: a pointless exercise? Curr Opin Pharmacol 3:244–250

    Article  CAS  Google Scholar 

  21. Sieber M, Hoffmann D, Adler M, Vaidya VS, Clement M, Bonventre JV, Zidek N, Rached E, Amberg A, Callanan JJ, Dekant W, Mally A (2009) Comparative analysis of novel noninvasive renal biomarkers and metabonomic changes in a rat model of gentamicin nephrotoxicity. Toxicol Sci 109:336–349

    Article  CAS  Google Scholar 

  22. Kemp SF, Lockey RF (2011) Pathophysiology and organ damage in anaphylaxis. In: Castells MC (ed) Anaphylaxis and hypersensitivity reactions. Springer Science+Business Media, LLC, New York, pp 33–46

    Chapter  Google Scholar 

  23. Muszbek L, Csaba B (1973) Changes of blood glucose during anaphylaxis in pertussis sensitized rats. Experientia 29:1411–1412

    Article  CAS  Google Scholar 

  24. Reginald H, Garrett CM (2006) Biochemistry, 3rd edn. Cole Publishing, Kentucky

    Google Scholar 

  25. Janardhan A, Chen J, Crawford PA (2011) Altered systemic ketone body metabolism in advanced heart failure. Tex Heart Inst J 38:533–538

    Google Scholar 

  26. Huang YH, Sauer K (2010) Lipid signaling in T-cell development and function. Cold Spring Harb Perspect Biol 2:a002428

    Article  CAS  Google Scholar 

  27. Kuehn HS, Beaven MA, Ma HT, Kim MS, Metcalfe DD, Gilfillan AM (2008) Synergistic activation of phospholipases Cgamma and Cbeta: a novel mechanism for PI3K-independent enhancement of FcepsilonRI-induced mast cell mediator release. Cell Signal 20:625–636

    Article  CAS  Google Scholar 

  28. Leung WH, Tarasenko T, Bolland S (2009) Differential roles for the inositol phosphatase SHIP in the regulation of macrophages and lymphocytes. Immunol Res 43:243–251

    Article  CAS  Google Scholar 

  29. Roongapinun S, Oh SY, Wu F, Panthong A, Zheng T, Zhu Z (2010) Role of SHIP-1 in the adaptive immune responses to aeroallergen in the airway. PLoS One 5:e14174

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge financial support by the Special Projects Research and Innovation Conditions in Hunan Province (no: 2010TT2025) and the Major Projects Plan of Science and Technology in Changsha (no: K0904032-11).

Conflict of interest

The authors declare that there are no conflicts of interest

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hong Tian or Dong-bo Liu.

Additional information

Hu and Wu contributed equally to the article

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hu, X., Wu, Gp., Zhang, Mh. et al. GC–MS-based metabolic profiling reveals metabolic changes in anaphylaxis animal models. Anal Bioanal Chem 404, 887–893 (2012). https://doi.org/10.1007/s00216-012-6129-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-012-6129-x

Keywords

Navigation