, 12:100 | Cite as

Serum metabolomic markers for traumatic brain injury: a mouse model

  • Ray O. Bahado-Singh
  • Stewart F. Graham
  • BeomSoo Han
  • Onur Turkoglu
  • James Ziadeh
  • Rupasri Mandal
  • Anil Er
  • David S. Wishart
  • Philip L. Stahel
Original Article



Traumatic brain injury (TBI) is physical injury to brain tissue that temporarily or permanently impairs brain function.


Evaluate the use of metabolomics for the development of biomarkers of TBI for the diagnosis and timing of injury onset.


A validated model of closed injury TBI was employed using 10 TBI mice and 8 sham operated controls. Quantitative LC–MS/MS metabolomic analysis was performed on the serum.


Thirty-six (24.0 %) of 150 metabolites were altered with TBI. Principal component analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) analyses revealed clear segregation between TBI versus control sera. The combination of methionine sulfoxide and the lipid PC aa C34:4 accurately diagnosed TBI, AUC (95 % CI) 0.85 (0.644–1.0). A combination of metabolite markers were highly accurate in distinguishing early (4 h post TBI) from late (24 h) TBI: AUC (95 % CI) 1.0 (1.0–1.0). Spermidine, which is known to have an antioxidant effect and which is known to be metabolically disrupted in TBI, was the most discriminating biomarker based on the variable importance ranking in projection (VIP) plot. Several important metabolic pathways were found to be disrupted including: pathways for arginine, proline, glutathione, cysteine, and sphingolipid metabolism.


Using serum metabolomic analysis we were able to identify novel putative serum biomarkers of TBI. They were accurate for detecting and determining the timing of TBI. In addition, pathway analysis provided important insights into the biochemical mechanisms of brain injury. Potential clinical implications for diagnosis, timing, and monitoring brain injury are discussed.


Traumatic brain injury Metabolomics Serum Biomarkers Mouse model 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the Institutional Animal Care and Use Committee of the University of Colorado (IACUG), protocol number B-79612(01)1E.

Supplementary material

11306_2016_1044_MOESM1_ESM.pdf (77 kb)
Supplementary Fig. 1. (a) 2D Principal Component analysis (PCA): TBI versus sham operated control mice (serum); (b) 3D Principal Component analysis (PCA): TBI versus sham operated control mice (serum)
11306_2016_1044_MOESM2_ESM.pdf (153 kb)
Supplementary Fig. 2 Partial Least Square Discriminant analysis (PLS-DA) – TBI versus sham operated control mice (serum); (b) Predicted result using cross-validation dataset
11306_2016_1044_MOESM3_ESM.pdf (18 kb)
Supplementary Fig. 3 ROC curve for distinguishing early from late TBI (diagnosis of timing of TBI): serum data. AUC: 1.0 (CI: 1.0–1.0), six metabolites were used in the PLS-DA model to include: methionine sulfoxide, C3, SM C18:0, SM C18:1, methionine and proline
11306_2016_1044_MOESM4_ESM.pdf (180 kb)
Supplementary Table 1 Metabolite concentrations changes over time in serum


  1. Altura, B. M., Gebrewold, A., Zheng, T., & Altura, B. T. (2002). Sphingomyelinase and ceramide analogs induce vasoconstriction and leukocyte-endothelial interactions in cerebral venules in the intact rat brain: insight into mechanisms and possible relation to brain injury and stroke. Brain Research Bulletin, 58, 271–278.CrossRefPubMedGoogle Scholar
  2. Bahado-Singh, (2014). Metabolomic prediction of fetal congenital heart defect in the first trimester. American Journal of Obstetrics Gynecology, 211, 240-e1. doi: 10.1016/j.ajog.2014.03.056.CrossRefGoogle Scholar
  3. Bahado-Singh, R. O., Graham, S. F., Beauchamp, K., Beauchamp, T. C., Han, B., Stahel, P. F., et al. (2016). Identification of candidate biomarkers of brain damage in a mouse model of closed head injury: a metabolomic pilot study. Metabolomics, 12, 1–13. doi: 10.1007/s11306-016-0957-1.CrossRefGoogle Scholar
  4. Bass, C. R., Panzer, M. B., Rafaels, K. A., Wood, G., Shridharani, J., & Capehart, B. (2012). Brain injuries from blast. Annals of Biomedical Engineering, 40, 185–202. doi: 10.1007/s10439-011-0424-0.CrossRefPubMedGoogle Scholar
  5. Boswell, J. E., McErlean, M., & Verdile, V. P. (2002). Prevalence of traumatic brain injury in an ED population. American Journal of Emergency Medicine, 20, 177–180.CrossRefPubMedGoogle Scholar
  6. Chapman, J. C., & Diaz-Arrastia, R. (2014). Military traumatic brain injury: a review. Alzheimers Dement, 10, S97–S104. doi: 10.1016/j.jalz.2014.04.012.CrossRefPubMedGoogle Scholar
  7. Corso, P., Finkelstein, E., Miller, T., Fiebelkorn, I., & Zaloshnja, E. (2006). Incidence and lifetime costs of injuries in the United States. Injury Prevention, 12, 212–218. doi: 10.1136/ip.2005.010983.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Farooqui, A. A., Horrocks, L. A., & Farooqui, T. (2007). Interactions between neural membrane glycerophospholipid and sphingolipid mediators: a recipe for neural cell survival or suicide. Journal of Neuroscience Research, 85, 1834–1850. doi: 10.1002/jnr.21268.CrossRefPubMedGoogle Scholar
  9. Faul, M., Xu, L., Wald M. M. (2006). Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002–2006. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control Google Scholar
  10. Flierl, M. A., Stahel, P. F., Beauchamp, K. M., Morgan, S. J., Smith, W. R., & Shohami, E. (2009). Mouse closed head injury model induced by a weight-drop device. Nature Protocols, 4, 1328–1337. doi: 10.1038/nprot.2009.148.CrossRefPubMedGoogle Scholar
  11. Graham, S. F., Chevallier, O. P., Kumar, P., Turkoglu, O., & Bahado-Singh, R. O. (2016). High Resolution Metabolomic Analysis of ASD human brain uncovers novel biomarkers of disease. Metabolomics. doi: 10.1007/s11306-016-0986-9.Google Scholar
  12. Graham, S. F., Chevallier, O. P., Roberts, D., Holscher, C., Elliott, C. T., & Green, B. D. (2013a). Investigation of the human brain metabolome to identify potential markers for early diagnosis and therapeutic targets of Alzheimer’s disease. Analytical Chemistry, 85, 1803–1811. doi: 10.1021/ac303163f.CrossRefPubMedGoogle Scholar
  13. Graham, S., Holscher, C., & Green, B. (2014). Metabolic signatures of human Alzheimer’s disease (AD): 1H NMR analysis of the polar metabolome of post-mortem brain tissue. Metabolomics, 10, 744–753. doi: 10.1007/s11306-013-0610-1.CrossRefGoogle Scholar
  14. Graham, S., Holscher, C., McClean, P., Elliott, C., & Green, B. (2013b). 1H NMR metabolomics investigation of an Alzheimer’s disease (AD) mouse model pinpoints important biochemical disturbances in brain and plasma. Metabolomics, 9, 974–983. doi: 10.1007/s11306-013-0516-y.CrossRefGoogle Scholar
  15. Hall, E. D., Andrus, P. K., & Yonkers, P. A. (1993). Brain hydroxyl radical generation in acute experimental head injury. Journal of Neurochemistry, 60, 588–594.CrossRefPubMedGoogle Scholar
  16. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer-Verlag New York: New York.CrossRefGoogle Scholar
  17. Jove, M., Portero-Otin, M., Naudi, A., Ferrer, I., & Pamplona, R. (2014). Metabolomics of human brain aging and age-related neurodegenerative diseases. Journal of Neuropathology and Experimental Neurology, 73, 640–657. doi: 10.1097/nen.0000000000000091.CrossRefPubMedGoogle Scholar
  18. Langlois, J. A., W. Rutland-Brown, K. E. Thomas (2004). Traumatic Brain Injury in the United States: Emergency Department visits, hospitalizations, and deaths. Atlanta, Ga: Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Injury Prevention and Control,Google Scholar
  19. Mondello, S., & Hayes, R. L. (2015). Biomarkers. In G. Jordan & M. S. Andres (Eds.), Handbook of clinical neurology (pp. 245–265). Amsterdam: Elsevier.Google Scholar
  20. Neher, M. D., Keene, C. N., Rich, M. C., Moore, H. B., & Stahel, P. F. (2014). Serum biomarkers for traumatic brain injury. Southern Medical Journal, 107, 248–255. doi: 10.1097/smj.0000000000000086.CrossRefPubMedGoogle Scholar
  21. Omalu, B. I., DeKosky, S. T., Minster, R. L., Kamboh, M. L., Hamilton, R. L., & Wecht, C. H. (2005). Chronic traumatic encephalopathy in a National Football League player. Neurosurgery, 57, 128–134.CrossRefPubMedGoogle Scholar
  22. Pan, X., et al. (2016). Alzheimer’s disease-like pathology has transient effects on the brain and blood metabolome. Neurobiology of Aging, 38, 151–163. doi: 10.1016/j.neurobiolaging.2015.11.014.CrossRefPubMedGoogle Scholar
  23. Park, Y. M., Han, S. H., Seo, S. K., Park, K. A., Lee, W. T., & Lee, J. E. (2015). Restorative benefits of transplanting human mesenchymal stromal cells overexpressing arginine decarboxylase genes after spinal cord injury. Cytotherapy, 17, 25–37. doi: 10.1016/j.jcyt.2014.08.006.CrossRefPubMedGoogle Scholar
  24. Psychogios, N., et al. (2011). The human serum metabolome. PLoS ONE, 6, e16957. doi: 10.1371/journal.pone.0016957.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Reed, T. T., Owen, J., Pierce, W. M., Sebastian, A., Sullivan, P. G., & Butterfield, D. A. (2009). Proteomic identification of nitrated brain proteins in traumatic brain-injured rats treated postinjury with gamma-glutamylcysteine ethyl ester: insights into the role of elevation of glutathione as a potential therapeutic strategy for traumatic brain injury. Journal of Neuroscience Research, 87, 408–417. doi: 10.1002/jnr.21872.CrossRefPubMedGoogle Scholar
  26. Stein, T. D., Alvarez, V. E., & McKee, A. C. (2014). Chronic traumatic encephalopathy: a spectrum of neuropathological changes following repetitive brain trauma in athletes and military personnel. Alzheimer’s Research & Therapy, 6, 4. doi: 10.1186/alzrt234.CrossRefGoogle Scholar
  27. Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58, 267–288. doi: 10.2307/2346178.Google Scholar
  28. Wang, H. C., et al. (2015). The role of serial oxidative stress levels in acute traumatic brain injury and as predictors of outcome. World Neurosurgery,. doi: 10.1016/j.wneu.2015.10.010.Google Scholar
  29. Wishart, D. S. (2010). Computational approaches to metabolomics. Methods in Molecular Biology, 593, 283–313. doi: 10.1007/978-1-60327-194-3_14.CrossRefPubMedGoogle Scholar
  30. Wu, G., Fang, Y. Z., Yang, S., Lupton, J. R., & Turner, N. D. (2004). Glutathione metabolism and its implications for health. Journal of Nutrition, 134, 489–492.PubMedGoogle Scholar
  31. Xia, J., Broadhurst, D. I., Wilson, M., & Wishart, D. S. (2013). Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics, 9, 280–299. doi: 10.1007/s11306-012-0482-9.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40, W127–W133. doi: 10.1093/nar/gks374.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Research, 37, W652–W660. doi: 10.1093/nar/gkp356.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257. doi: 10.1093/nar/gkv380.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Zahedi, K., Huttinger, F., Morrison, R., Murray-Stewart, T., Casero, R. A., & Strauss, K. I. (2010). Polyamine catabolism is enhanced after traumatic brain injury. Journal of Neurotrauma, 27, 515–525. doi: 10.1089/neu.2009.1097.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Zaloshnja, E., Miller, T., Langlois, J. A., & Selassie, A. W. (2008). Prevalence of long-term disability from traumatic brain injury in the civilian population of the United States, 2005. The Journal of head trauma rehabilitation, 23, 394–400. doi: 10.1097/ Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ray O. Bahado-Singh
    • 1
  • Stewart F. Graham
    • 2
  • BeomSoo Han
    • 3
  • Onur Turkoglu
    • 2
  • James Ziadeh
    • 4
  • Rupasri Mandal
    • 3
  • Anil Er
    • 5
  • David S. Wishart
    • 3
  • Philip L. Stahel
    • 6
    • 7
  1. 1.Department of Obstetrics and GynecologyOakland University William Beaumont School of MedicineRoyal OakUSA
  2. 2.Department of Obstetrics and GynecologyBeaumont HealthRoyal OakUSA
  3. 3.Department of Biological and Computing SciencesUniversity of AlbertaEdmontonCanada
  4. 4.Department of Emergency MedicineOakland University William Beaumont School of MedicineRoyal OakUSA
  5. 5.Department of Pediatric Emergency Medicine, School of MedicineDokuz Eylul UniversityBalcovaTurkey
  6. 6.Department of Neurosurgery, Denver Health Medical Center, School of MedicineUniversity of ColoradoDenverUSA
  7. 7.Department of Orthopedics, Denver Health Medical Center School of MedicineUniversity of ColoradoDenverUSA

Personalised recommendations