Molecular Neurobiology

, Volume 56, Issue 4, pp 2908–2921 | Cite as

Chronic Dysregulation of Cortical and Subcortical Metabolism After Experimental Traumatic Brain Injury

  • Jennifer L. McGuireEmail author
  • Erica A. K. DePasquale
  • Miki Watanabe
  • Fatima Anwar
  • Laura B. Ngwenya
  • Gowtham Atluri
  • Lindsey E. Romick-Rosendale
  • Robert E. McCullumsmith
  • Nathan K. Evanson


Traumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide. Although chronic disability is common after TBI, effective treatments remain elusive and chronic TBI pathophysiology is not well understood. Early after TBI, brain metabolism is disrupted due to unregulated ion release, mitochondrial damage, and interruption of molecular trafficking. This metabolic disruption causes at least part of the TBI pathology. However, it is not clear how persistent or pervasive metabolic injury is at later stages of injury. Using untargeted 1H-NMR metabolomics, we examined ex vivo hippocampus, striatum, thalamus, frontal cortex, and brainstem tissue in a rat lateral fluid percussion model of chronic brain injury. We found altered tissue concentrations of metabolites in the hippocampus and thalamus consistent with dysregulation of energy metabolism and excitatory neurotransmission. Furthermore, differential correlation analysis provided additional evidence of metabolic dysregulation, most notably in brainstem and frontal cortex, suggesting that metabolic consequences of injury are persistent and widespread. Interestingly, the patterns of network changes were region-specific. The individual metabolic signatures after injury in different structures of the brain at rest may reflect different compensatory mechanisms engaged to meet variable metabolic demands across brain regions.


NMR metabolomics Chronic TBI Lateral fluid percussion Network structure Correlations-based analysis 


Funding Information

These studies received financial support from Cincinnati Children’s Hospital Medical Center as a Shared Facilities Discovery Award to NKE and JLM.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

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  1. 1.
    Taylor CA et al (2017) Traumatic brain injury–related emergency department visits, hospitalizations, and deaths — United States, 2007 and 2013. MMWR Surveill Summ 66(SS-9):1–16PubMedPubMedCentralGoogle Scholar
  2. 2.
    Faul M, Coronado V (2015) Chapter 1 - Epidemiology of traumatic brain injury. In: Grafman J, Salazar AM (eds) Handbook of Clinical Neurology. Elsevier, New York, pp. 3–13Google Scholar
  3. 3.
    Finkelstein E, Corso PS, Miller TR (2006) The incidence and economic burden of injuries in the United States. Oxford University Press, Oxford xiii, 187 pGoogle Scholar
  4. 4.
    Whitnall L, McMillan T, Murray GD, Teasdale GM (2006) Disability in young people and adults after head injury: 5-7 year follow up of a prospective cohort study. J Neurol Neurosurg Psychiatry 77(5):640–645PubMedPubMedCentralGoogle Scholar
  5. 5.
    Zaloshnja E, Miller T, Langlois JA, Selassie AW (2008) Prevalence of long-term disability from traumatic brain injury in the civilian population of the United States, 2005. J Head Trauma Rehabil 23(6):394–400PubMedGoogle Scholar
  6. 6.
    Yoshino A, Hovda DA, Kawamata T, Katayama Y, Becker DP (1991) Dynamic changes in local cerebral glucose utilization following cerebral conclusion in rats: evidence of a hyper- and subsequent hypometabolic state. Brain Res 561(1):106–119PubMedGoogle Scholar
  7. 7.
    Faden AI et al (1989) The role of excitatory amino acids and NMDA receptors in traumatic brain injury. Science 244(4906):798–800PubMedGoogle Scholar
  8. 8.
    Barkhoudarian G, Hovda DA, Giza CC (2016) The molecular pathophysiology of concussive brain injury - an update. Phys Med Rehabil Clin N Am 27(2):373–393PubMedGoogle Scholar
  9. 9.
    Hovda DA (1996) Metabolic dysfunction. In: Narayan RK, Wilberger JE, Povlishock JT (eds) Neurotrauma. McGraw-Hill, New York, pp. 1459–1478Google Scholar
  10. 10.
    Garnett MR et al (2000) Early proton magnetic resonance spectroscopy in normal-appearing brain correlates with outcome in patients following traumatic brain injury. Brain 123(Pt 10):2046–2054PubMedGoogle Scholar
  11. 11.
    Marino S et al (2007) Acute metabolic brain changes following traumatic brain injury and their relevance to clinical severity and outcome. J Neurol Neurosurg Psychiatry 78(5):501–507PubMedGoogle Scholar
  12. 12.
    Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145PubMedGoogle Scholar
  13. 13.
    Shetty PK, Galeffi F, Turner DA (2012) Cellular links between neuronal activity and energy homeostasis. Front Pharmacol 3:43PubMedPubMedCentralGoogle Scholar
  14. 14.
    Sobieski C, Fitzpatrick MJ, Mennerick SJ (2017) Differential presynaptic ATP supply for basal and high-demand transmission. J Neurosci 37(7):1888–1899PubMedPubMedCentralGoogle Scholar
  15. 15.
    Stender J, Mortensen KN, Thibaut A, Darkner S, Laureys S, Gjedde A, Kupers R (2016) The minimal energetic requirement of sustained awareness after brain injury. Curr Biol 26(11):1494–1499PubMedGoogle Scholar
  16. 16.
    Patel AB, de Graaf RA, Mason GF, Kanamatsu T, Rothman DL, Shulman RG, Behar KL (2004) Glutamatergic neurotransmission and neuronal glucose oxidation are coupled during intense neuronal activation. J Cereb Blood Flow Metab 24(9):972–985PubMedGoogle Scholar
  17. 17.
    Ragan DK, McKinstry R, Benzinger T, Leonard JR, Pineda JA (2013) Alterations in cerebral oxygen metabolism after traumatic brain injury in children. J Cereb Blood Flow Metab 33(1):48–52PubMedGoogle Scholar
  18. 18.
    Kato T, Nakayama N, Yasokawa Y, Okumura A, Shinoda J, Iwama T (2007) Statistical image analysis of cerebral glucose metabolism in patients with cognitive impairment following diffuse traumatic brain injury. J Neurotrauma 24(6):919–926PubMedGoogle Scholar
  19. 19.
    Ito K et al (2016) Differences in brain metabolic impairment between chronic mild/moderate TBI patients with and without visible brain lesions based on MRI. Biomed Res Int 2016:3794029PubMedPubMedCentralGoogle Scholar
  20. 20.
    Fridman EA, Beattie BJ, Broft A, Laureys S, Schiff ND (2014) Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain. Proc Natl Acad Sci U S A 111(17):6473–6478PubMedPubMedCentralGoogle Scholar
  21. 21.
    Sibson NR, Dhankhar A, Mason GF, Rothman DL, Behar KL, Shulman RG (1998) Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Proc Natl Acad Sci 95(1):316–321PubMedGoogle Scholar
  22. 22.
    Hall CN, Klein-Flugge MC, Howarth C, Attwell D (2012) Oxidative phosphorylation, not glycolysis, powers presynaptic and postsynaptic mechanisms underlying brain information processing. J Neurosci 32(26):8940–8951PubMedPubMedCentralGoogle Scholar
  23. 23.
    Kohl AD, Wylie GR, Genova HM, Hillary FG, DeLuca J (2009) The neural correlates of cognitive fatigue in traumatic brain injury using functional MRI. Brain Inj 23(5):420–432PubMedGoogle Scholar
  24. 24.
    Patti GJ, Yanes O, Siuzdak G (2012) Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4):263–269PubMedPubMedCentralGoogle Scholar
  25. 25.
    Viant MR, Lyeth BG, Miller MG, Berman RF (2005) An NMR metabolomic investigation of early metabolic disturbances following traumatic brain injury in a mammalian model. NMR Biomed 18(8):507–516PubMedGoogle Scholar
  26. 26.
    Casey PA, McKenna MC, Fiskum G, Saraswati M, Robertson CL (2008) Early and sustained alterations in cerebral metabolism after traumatic brain injury in immature rats. J Neurotrauma 25(6):603–614PubMedPubMedCentralGoogle Scholar
  27. 27.
    Harris JL, Yeh HW, Choi IY, Lee P, Berman NE, Swerdlow RH, Craciunas SC, Brooks WM (2012) Altered neurochemical profile after traumatic brain injury: (1)H-MRS biomarkers of pathological mechanisms. J Cereb Blood Flow Metab 32(12):2122–2134PubMedPubMedCentralGoogle Scholar
  28. 28.
    Bartnik-Olson BL, Oyoyo U, Hovda DA, Sutton RL (2010) Astrocyte oxidative metabolism and metabolite trafficking after fluid percussion brain injury in adult rats. J Neurotrauma 27(12):2191–2202PubMedPubMedCentralGoogle Scholar
  29. 29.
    Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40(1):387–426PubMedGoogle Scholar
  30. 30.
    Christensen CD, Hofmeyr J-HS, Rohwer JM (2015) Tracing regulatory routes in metabolism using generalised supply-demand analysis. BMC Syst Biol 9(1):89PubMedPubMedCentralGoogle Scholar
  31. 31.
    Morgenthal K, Weckwerth W, Steuer R (2006) Metabolomic networks in plants: transitions from pattern recognition to biological interpretation. Biosystems 83(2–3):108–117PubMedGoogle Scholar
  32. 32.
    Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M et al (2007) Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316(5824):593–597PubMedGoogle Scholar
  33. 33.
    D'Haeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8):707–726PubMedGoogle Scholar
  34. 34.
    Steuer R (2006) Review: on the analysis and interpretation of correlations in metabolomic data. Brief Bioinform 7(2):151–158PubMedGoogle Scholar
  35. 35.
    Steuer R, Kurths J, Fiehn O, Weckwerth W (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19(8):1019–1026PubMedGoogle Scholar
  36. 36.
    Camacho D, De La Fuente A, Mendes P (2005) The origin of correlations in metabolomics data. Metabolomics 1(1):53–63Google Scholar
  37. 37.
    Weckwerth W (2003) Metabolomics in systems biology. Annu Rev Plant Biol 54:669–689PubMedGoogle Scholar
  38. 38.
    Kose F, Weckwerth W, Linke T, Fiehn O (2001) Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics 17(12):1198–1208PubMedGoogle Scholar
  39. 39.
    Jaeger C, Glaab E, Michelucci A, Binz TM, Koeglsberger S, Garcia P, Trezzi JP, Ghelfi J et al (2015) The mouse brain metabolome. Am J Pathol 185(6):1699–1712PubMedGoogle Scholar
  40. 40.
    Dorsett CR, McGuire JL, Niedzielko TL, DePasquale EAK, Meller J, Floyd CL, McCullumsmith RE (2017) Traumatic brain injury induces alterations in cortical glutamate uptake without a reduction in glutamate transporter-1 protein expression. J Neurotrauma 34(1):220–234PubMedPubMedCentralGoogle Scholar
  41. 41.
    Mayeux J, Katz P, Edwards S, Middleton JW, Molina PE (2017) Inhibition of endocannabinoid degradation improves outcomes from mild traumatic brain injury: a mechanistic role for synaptic hyperexcitability. J Neurotrauma 34(2):436–443PubMedPubMedCentralGoogle Scholar
  42. 42.
    Wu H, Southam AD, Hines A, Viant MR (2008) High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. Anal Biochem 372(2):204–212PubMedGoogle Scholar
  43. 43.
    Watanabe M, Meyer KA, Jackson TM, Schock TB, Johnson WE, Bearden DW (2015) Application of NMR-based metabolomics for environmental assessment in the Great Lakes using zebra mussel (Dreissena polymorpha). Metabolomics 11(5):1302–1315PubMedPubMedCentralGoogle Scholar
  44. 44.
    Wishart DS et al (2013) HMDB 3.0--the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807PubMedGoogle Scholar
  45. 45.
    Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37(Database issue):D603–D610PubMedGoogle Scholar
  46. 46.
    Parsons HM, Ekman DR, Collette TW, Viant MR (2009) Spectral relative standard deviation: a practical benchmark in metabolomics. Analyst 134(3):478–485PubMedGoogle Scholar
  47. 47.
    Wei T, Simko V (2017) R package “corrplot”: visualization of a correlation matrix (version 0.84). Available from
  48. 48.
    Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244Google Scholar
  49. 49.
    Fukushima A (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518(1):209–214PubMedGoogle Scholar
  50. 50.
    Pellerin L, Magistretti PJ (1994) Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc Natl Acad Sci U S A 91(22):10625–10629PubMedPubMedCentralGoogle Scholar
  51. 51.
    Joncquel-Chevalier Curt M, Voicu PM, Fontaine M, Dessein AF, Porchet N, Mention-Mulliez K, Dobbelaere D, Soto-Ares G et al (2015) Creatine biosynthesis and transport in health and disease. Biochimie 119:146–165PubMedGoogle Scholar
  52. 52.
    Mader I, Rauer S, Gall P, Klose U (2008) (1)H MR spectroscopy of inflammation, infection and ischemia of the brain. Eur J Radiol 67(2):250–257PubMedGoogle Scholar
  53. 53.
    Schurr A, Miller JJ, Payne RS, Rigor BM (1999) An increase in lactate output by brain tissue serves to meet the energy needs of glutamate-activated neurons. J Neurosci 19(1):34–39PubMedGoogle Scholar
  54. 54.
    Palmada M, Centelles JJ (1998) Excitatory amino acid neurotransmission. Pathways for metabolism, storage and reuptake of glutamate in brain. Front Biosci 3:d701–d718PubMedGoogle Scholar
  55. 55.
    El Idrissi A, Trenkner E (2004) Taurine as a modulator of excitatory and inhibitory neurotransmission. Neurochem Res 29(1):189–197PubMedGoogle Scholar
  56. 56.
    Sonnay S, Duarte JMN, Just N, Gruetter R (2016) Compartmentalised energy metabolism supporting glutamatergic neurotransmission in response to increased activity in the rat cerebral cortex: a 13C MRS study in vivo at 14.1 T. J Cereb Blood Flow Metab 36(5):928–940PubMedPubMedCentralGoogle Scholar
  57. 57.
    Hertz L, Chen Y (2017) Integration between glycolysis and glutamate-glutamine cycle flux may explain preferential glycolytic increase during brain activation, requiring glutamate. Front Integr Neurosci 11:18PubMedPubMedCentralGoogle Scholar
  58. 58.
    Katsu-Jimenez Y, Alves RMP, Gimenez-Cassina A (2017) Food for thought: impact of metabolism on neuronal excitability. Exp Cell Res 360(1):41–46PubMedGoogle Scholar
  59. 59.
    Suarez LM et al (2014) Cooperation of taurine uptake and dopamine D1 receptor activation facilitates the induction of protein synthesis-dependent late LTP. Neuropharmacology 79:101–111PubMedGoogle Scholar
  60. 60.
    del Olmo N, Suarez LM, Orensanz LM, Suarez F, Bustamante J, Duarte JM, del Rio RM, Solis JM (2004) Role of taurine uptake on the induction of long-term synaptic potentiation. Eur J Neurosci 19(7):1875–1886PubMedGoogle Scholar
  61. 61.
    Suzuki A, Stern SA, Bozdagi O, Huntley GW, Walker RH, Magistretti PJ, Alberini CM (2011) Astrocyte-neuron lactate transport is required for long-term memory formation. Cell 144(5):810–823PubMedPubMedCentralGoogle Scholar
  62. 62.
    Tani H, Dulla CG, Farzampour Z, Taylor-Weiner A, Huguenard JR, Reimer RJ (2014) A local glutamate-glutamine cycle sustains synaptic excitatory transmitter release. Neuron 81(4):888–900PubMedPubMedCentralGoogle Scholar
  63. 63.
    Bryant AS, Li B, Beenhakker MP, Huguenard JR (2009) Maintenance of thalamic epileptiform activity depends on the astrocytic glutamate-glutamine cycle. J Neurophysiol 102(5):2880–2888PubMedPubMedCentralGoogle Scholar
  64. 64.
    Ma Z, Wang SJ, Li CF, Ma XX, Gu T (2008) Increased metabolite concentration in migraine rat model by proton MR spectroscopy in vivo and ex vivo. Neurol Sci 29(5):337–342PubMedGoogle Scholar
  65. 65.
    Gu T, Ma XX, Xu YH, Xiu JJ, Li CF (2008) Metabolite concentration ratios in thalami of patients with migraine and trigeminal neuralgia measured with 1H-MRS. Neurol Res 30(3):229–233PubMedGoogle Scholar
  66. 66.
    Moffett JR et al (2007) N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog Neurobiol 81(2):89–131PubMedPubMedCentralGoogle Scholar
  67. 67.
    Moffett JR et al (2013) N-Acetylaspartate reductions in brain injury: impact on post-injury neuroenergetics, lipid synthesis, and protein acetylation. Front Neuroenerg 5:11Google Scholar
  68. 68.
    Ballevre O, Buchan V, Rees WD, Fuller MF, Garlick PJ (1991) Sarcosine kinetics in pigs by infusion of [1-14C]sarcosine: use for refining estimates of glycine and threonine kinetics. Am J Physiol Endocrinol Metab 260(4):E662–E668Google Scholar
  69. 69.
    Somersalo E, Calvetti D (2013) Quantitative in silico analysis of neurotransmitter pathways under steady state conditions. Front Endocrinol 4:137Google Scholar
  70. 70.
    Ipata PL, Balestri F, Camici M, Tozzi MG (2011) Molecular mechanisms of nucleoside recycling in the brain. Int J Biochem Cell Biol 43(1):140–145PubMedGoogle Scholar
  71. 71.
    Guha SK, Rose ZB (1982) Brain glucose bisphosphatase requires inosine monophosphate. J Biol Chem 257(12):6634–6637PubMedGoogle Scholar
  72. 72.
    von Schwarzenfeld I, Fischer HD, Oelszner W (1975) Interaction between cellular metabolism and acetylcholine turnover of rat brain cortex slices in the presence of arecoline. Acta Biol Med Ger 34(9):1525–1528Google Scholar
  73. 73.
    Ksiezak HJ, Gibson GE (1981) Acetylcholine synthesis and CO2 production from variously labeled glucose in rat brain slices and synaptosomes. J Neurochem 37(1):88–94PubMedGoogle Scholar
  74. 74.
    Dolezal V, Tucek S (1981) Utilization of citrate, acetylcarnitine, acetate, pyruvate and glucose for the synthesis of acetylcholine in rat brain slices. J Neurochem 36(4):1323–1330PubMedGoogle Scholar
  75. 75.
    Beani L, Bianchi C, Siniscalchi A, Tanganelli S (1983) Glycine-induced changes in acetylcholine release from guinea-pig brain slices. Br J Pharmacol 79(2):623–628PubMedPubMedCentralGoogle Scholar
  76. 76.
    Granger AJ, Wallace ML, Sabatini BL (2017) Multi-transmitter neurons in the mammalian central nervous system. Curr Opin Neurobiol 45:85–91PubMedPubMedCentralGoogle Scholar
  77. 77.
    McKenna MC, Waagepetersen HS, Schousboe A, Sonnewald U (2006) Neuronal and astrocytic shuttle mechanisms for cytosolic-mitochondrial transfer of reducing equivalents: current evidence and pharmacological tools. Biochem Pharmacol 71(4):399–407PubMedGoogle Scholar
  78. 78.
    Cheeseman AJ, Clark JB (1988) Influence of the malate-aspartate shuttle on oxidative metabolism in synaptosomes. J Neurochem 50(5):1559–1565PubMedGoogle Scholar
  79. 79.
    Roy A, Bernier RA, Wang J, Benson M, French JJ, Good DC, Hillary FG (2017) The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury. PLoS One 12(4):e0170541PubMedPubMedCentralGoogle Scholar
  80. 80.
    Aiello GL, Bach-y-Rita P (2000) The cost of an action potential. J Neurosci Methods 103(2):145–149PubMedGoogle Scholar
  81. 81.
    Rothman DL, de Feyter HM, Graaf RA, Mason GF, Behar KL (2011) 13C MRS studies of neuroenergetics and neurotransmitter cycling in humans. NMR Biomed 24(8):943–957PubMedPubMedCentralGoogle Scholar
  82. 82.
    Bartnik-Olson BL et al (2013) Insights into the metabolic response to traumatic brain injury as revealed by (13)C NMR spectroscopy. Front Neuroenerg 5:8Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jennifer L. McGuire
    • 1
    Email author
  • Erica A. K. DePasquale
    • 2
  • Miki Watanabe
    • 3
  • Fatima Anwar
    • 1
  • Laura B. Ngwenya
    • 1
    • 4
  • Gowtham Atluri
    • 2
    • 5
  • Lindsey E. Romick-Rosendale
    • 3
  • Robert E. McCullumsmith
    • 6
  • Nathan K. Evanson
    • 7
    • 8
  1. 1.Department of NeurosurgeryUniversity of CincinnatiCincinnatiUSA
  2. 2.Graduate Program in Biomedical InformaticsUniversity of CincinnatiCincinnatiUSA
  3. 3.Division of PathologyCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  4. 4.Department of Neurology and Rehabilitation MedicineUniversity of CincinnatiCincinnatiUSA
  5. 5.Department of Electrical Engineering and Computer ScienceUniversity of CincinnatiCincinnatiUSA
  6. 6.Department of Psychiatry and Behavioral NeuroscienceUniversity of CincinnatiCincinnatiUSA
  7. 7.Department of PediatricsUniversity of CincinnatiCincinnatiUSA
  8. 8.Division of Pediatric Rehabilitation MedicineCincinnati Children’s Hospital Medical CenterCincinnatiUSA

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