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Neuroscience Bulletin

, Volume 34, Issue 4, pp 626–638 | Cite as

Comprehensive Proteomic Profiling of Patients’ Tears Identifies Potential Biomarkers for the Traumatic Vegetative State

  • Qilin Tang
  • Chao Zhang
  • Xiang Wu
  • Wenbin Duan
  • Weiji Weng
  • Junfeng Feng
  • Qing Mao
  • Shubin Chen
  • Jiyao Jiang
  • Guoyi GaoEmail author
Original Article

Abstract

The vegetative state is a complex condition with unclear mechanisms and limited diagnostic, prognostic, and therapeutic methods. In this study, we aimed to explore the proteomic profile of tears from patients in a traumatic vegetative state and identify potential diagnostic markers using tears—a body fluid that can be collected non-invasively. Using iTRAQ quantitative proteomic technology, in the discovery phase, tear samples collected from 16 patients in a traumatic vegetative state and 16 normal individuals were analyzed. Among 1080 identified tear proteins, 57 were upregulated and 15 were downregulated in the patients compared to the controls. Bioinformatics analysis revealed that the differentially-expressed proteins were mainly involved in the wound response and immune response signaling pathways. Furthermore, we verified the levels of 7 differentially-expressed proteins in tears from 50 traumatic vegetative state patients and 50 normal controls (including the samples used in the discovery phase) using ELISA. The results showed that this 7-protein panel had a high discrimination ability for traumatic vegetative state (area under the curve = 0.999). In summary, the altered tear proteomic profile identified in this study provides a basis for potential tear protein markers for diagnosis and prognosis of the traumatic vegetative state and also provides novel insights into the mechanisms of traumatic vegetative state.

Keywords

Trauma Vegetative state Tears Proteomics iTRAQ 

Notes

Acknowledgements

The work was supported by the National Natural Science Foundation of China (81671198), the Grant of Shanghai Municipal Education Commission-Gaofeng Clinical Medicine (20152212), and the Shanghai Shenkang Clinical Research Plan of the Shanghai Hospital Development Center (16CR3011A).

Supplementary material

12264_2018_259_MOESM1_ESM.pdf (382 kb)
Supplementary material 1 (PDF 382 kb)

References

  1. 1.
    van Erp WS, Lavrijsen JC, Vos PE, Bor H, Laureys S, Koopmans RT. The vegetative state: prevalence, misdiagnosis, and treatment limitations. J Am Med Dir Assoc 2015, 16: 85.e9–85.e14.CrossRefGoogle Scholar
  2. 2.
    Monti MM, Laureys S, Owen AM. The vegetative state. BMJ 2010, 341: c3765.CrossRefPubMedGoogle Scholar
  3. 3.
    Hazell AS. The vegetative state and stem cells: therapeutic considerations. Front Neurol 2016, 7: 118.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Marino S, Bramanti P. Neurofunctional imaging in differential diagnosis and evaluation of outcome in vegetative and minimally conscious state. Funct Neurol 2009, 24: 185–188.PubMedGoogle Scholar
  5. 5.
    Owen AM. Coleman MR. Functional neuroimaging of the vegetative state. Nat Rev Neurosci 2008, 9: 235–243.CrossRefPubMedGoogle Scholar
  6. 6.
    Boly M, Tshibanda L, Vanhaudenhuyse A, Noirhomme Q, Schnakers C, Ledoux D, et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 2009, 30: 2393–2400.CrossRefPubMedGoogle Scholar
  7. 7.
    Coleman MR, Davis MH, Rodd JM, Robson T, Ali A, Owen AM, et al. Towards the routine use of brain imaging to aid the clinical diagnosis of disorders of consciousness. Brain 2009, 132: 2541–2552.CrossRefPubMedGoogle Scholar
  8. 8.
    Schnakers C, Perrin F, Schabus M, Majerus S, Ledoux D, Damas P, et al. Voluntary brain processing in disorders of consciousness. Neurology 2008, 71: 1614–1620.CrossRefPubMedGoogle Scholar
  9. 9.
    Monti MM, Coleman MR, Owen AM. Executive functions in the absence of behavior: functional imaging of the minimally conscious state. Prog Brain Res 2009, 177: 249–260.CrossRefPubMedGoogle Scholar
  10. 10.
    Monti MM. Cognition in the vegetative state. Annu Rev Clin Psychol 2012, 8: 431–454.CrossRefPubMedGoogle Scholar
  11. 11.
    Guler A, Kumral E, Sirin TC, Sirin H, Kitis O. Magnetic resonance imaging characteristics of persistent vegetative state due to prolonged hypoglycemia. J Clin Diagn Res 2015, 9: TD01–TD02.PubMedPubMedCentralGoogle Scholar
  12. 12.
    Fernández-Espejo D, Norton L, Owen AM. The clinical utility of fMRI for identifying covert awareness in the vegetative state: a comparison of sensitivity between 3T and 1.5T. PLoS One 2014, 9: e95082.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Shen S, Loo RR, Wanner IB, Loo JA. Addressing the needs of traumatic brain injury with clinical proteomics. Clin Proteomics 2014, 11: 11.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Fraser DD, Close TE, Rose KL, Ward R, Mehl M, Farrell C, et al. Severe traumatic brain injury in children elevates glial fibrillary acidic protein in cerebrospinal fluid and serum. Pediatr Crit Care Med 2011, 12: 319–324.CrossRefPubMedGoogle Scholar
  15. 15.
    Liliang PC, Liang CL, Weng HC, Lu K, Wang KW, Chen HJ, et al. Tau proteins in serum predict outcome after severe traumatic brain injury. J Surg Res 2010, 160: 302–307.CrossRefPubMedGoogle Scholar
  16. 16.
    Zhou L, Beuerman RW. Tear analysis in ocular surface diseases. Prog Retin Eye Res 2012, 31: 527–550.CrossRefPubMedGoogle Scholar
  17. 17.
    Zhou L, Zhao SZ, Koh SK, Chen L, Vaz C, Tanavde V, et al. In-depth analysis of the human tear proteome. J Proteomics 2012, 75: 3877–3885.CrossRefPubMedGoogle Scholar
  18. 18.
    Von Thun Und Hohenstein-Blaul N, Funke S, Grus FH. Tears as a source of biomarkers for ocular and systemic diseases. Exp Eye Res 2013, 117: 126–137.CrossRefGoogle Scholar
  19. 19.
    Chen W, Cao H, Lin J, Olsen N, Zheng SG. Biomarkers for primary Sjögren’s syndrome, genomics. Proteomics Bioinf 2015, 13: 219–223.Google Scholar
  20. 20.
    Zhou L, Beuerman RW, Chan CM, Zhao SZ, Li XR, Yang H, et al. Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics. J Proteome Res 2009, 8: 4889–4905.CrossRefPubMedGoogle Scholar
  21. 21.
    Böhm D, Keller K, Pieter J, Boehm N, Wolters D, Siggelkow W, et al. Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach. Oncol Rep 2012, 28: 429–438.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Dartt DA. Interaction of EGF family growth factors and neurotransmitters in regulating lacrimal gland secretion. Exp Eye Res 2004, 78: 337–345.CrossRefPubMedGoogle Scholar
  23. 23.
    Denisin AK, Karns K, Herr AE. Post-collection processing of Schirmer strip-collected human tear fluid impacts protein content. Analyst 2012, 137: 5088–5096.CrossRefPubMedGoogle Scholar
  24. 24.
    Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, et al. A systems biology approach for pathway level analysis. Genome Res 2007, 17: 1537–1545.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4: 44–57.CrossRefGoogle Scholar
  26. 26.
    Guo S, Dipietro LA. Factors affecting wound healing. J Dent Res 2010, 89: 219–229.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Guo Y, Lin C, Xu P, Wu S, Fu, X, Xia W, et al. AGEs Induced autophagy impairs cutaneous wound healing via stimulating macrophage polarization to M1 in diabetes. Sci Rep 2016, 6: 36416.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Jones SG, Edwards R, Thomas DW. Inflammation and wound healing: the role of bacteria in the immuno-regulation of wound healing. Int J Low Extrem Wounds 2014, 3: 201–208.CrossRefGoogle Scholar
  29. 29.
    Thomas DR, Burkemper NM. Aging skin and wound healing. Clin Geriatr Med. 2013, 29: xi–xx.CrossRefPubMedGoogle Scholar
  30. 30.
    Chen J, Chen Y, Yang Z, You B, Ruan YC, Peng Y. Epidermal CFTR suppresses MAPK/NF-κB to promote cutaneous wound healing. Cell Physiol Biochem 2016, 39: 2262–2274.CrossRefPubMedGoogle Scholar
  31. 31.
    Grada A, Falanga V. Novel stem cell therapies for applications to wound healing and tissue repair. Surg Technol Int 2016, XXIX: 29–37.Google Scholar
  32. 32.
    Li AL, Zhang JD, Xie W, Strong JA, Zhang JM. Inflammatory changes in paravertebral sympathetic ganglia in two rat pain models. Neurosci Bull 2018, 34: 85–97.CrossRefPubMedGoogle Scholar
  33. 33.
    Li D, Zhang L, Huang X, Liu L, He Y, Xu L, et al. WIP1 phosphatase plays a critical neuroprotective role in brain injury induced by high-altitude hypoxic inflammation. Neuroci Bull 2017, 33: 292–298.CrossRefGoogle Scholar
  34. 34.
    Rathor MY, Rani MFA, Shahrin TCA, Hashim HZ. Persistent vegetative state after traumatic brain injury: a case report and review of the literature. Bangladesh J Med Sci 2014, 13: 19159.CrossRefGoogle Scholar
  35. 35.
    Zhou L, Beuerman RW. The power of tears: how tear proteomics research could revolutionize the clinic (editorial). Exp Rev Proteomics 2017, 14: 189–191.CrossRefGoogle Scholar

Copyright information

© Shanghai Institutes for Biological Sciences, CAS and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Qilin Tang
    • 1
    • 2
  • Chao Zhang
    • 1
    • 2
  • Xiang Wu
    • 1
    • 2
  • Wenbin Duan
    • 3
  • Weiji Weng
    • 1
    • 2
  • Junfeng Feng
    • 1
    • 2
  • Qing Mao
    • 1
    • 2
  • Shubin Chen
    • 4
  • Jiyao Jiang
    • 1
    • 2
  • Guoyi Gao
    • 1
    • 2
    Email author
  1. 1.Department of Neurosurgery, Ren Ji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Head Trauma InstituteShanghaiChina
  3. 3.Department of NeurosurgeryBaoshan People’s HospitalBaoshanChina
  4. 4.Department of NeurologyAnda HospitalShanghaiChina

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