Advertisement

A proteomic signature associated to atypical antipsychotic response in schizophrenia patients: a pilot study

  • Daniel Martins-de-SouzaEmail author
  • Paul C. Guest
  • Johann Steiner
Short Communication

Abstract

A major hurdle faced by most schizophrenia patients is the poor efficacy of current antipsychotic medications. This stems from a poor understanding of the underlying pathophysiology and the lack of biomarkers for the prediction of a positive medication response. By employing state-of-the-art proteomic analysis of blood plasma from 58 patients who were either drug-naive or drug-free at the time of sample collection, we identified potential biomarkers that were predictive of a positive response after 6 weeks of treatment with antipsychotics. Complement and coagulation cascades were the most over-represented biological pathways among these proteins, consistent with the importance of these processes in schizophrenia. Although preliminary, these findings are novel and may drive future efforts in the development of predictive tests for medication efficacy and thereby have a positive influence on disease outcome.

Keywords

Biomarkers Drug response Atypical antipsychotics Proteins Proteome 

Notes

Acknowledgements

The Authors thank FAPESP (São Paulo Research Foundation—Grants 2013/08711-3 and 2017/25588-1), CNPq (The Brazilian National Council for Scientific and Technological Development, Grant 302453/2017-2), and Serrapilheira Institute (Grant number Serra-1709-16349).

Compliance with ethical standards

Conflict of interest

We declare no conflict of interest.

Supplementary material

406_2019_1002_MOESM1_ESM.xls (66 kb)
Supplementary material 1 (XLS 66 KB)

References

  1. 1.
    Tandon R (2010) Schizophrenia, “just the facts” 5. Treatment and prevention. Past, present, and future. Schizophr Res 122:1–23.  https://doi.org/10.1016/j.schres.2010.05.025 CrossRefGoogle Scholar
  2. 2.
    Tandon R (2011) Antipsychotics in the treatment of schizophrenia: an overview. J Clin Psychiatry 72(Suppl 1):4–8.  https://doi.org/10.4088/JCP.10075su1.01 CrossRefGoogle Scholar
  3. 3.
    Martins-de-Souza D, Solari FA, Guest PC et al (2015) Biological pathways modulated by antipsychotics in the blood plasma of schizophrenia patients and their association to a clinical response. NPJ Schizophr 1:15050.  https://doi.org/10.1038/npjschz.2015.50 CrossRefGoogle Scholar
  4. 4.
    Sabherwal S, English JA, Föcking M, Cagney G, Cotter DR (2016) Blood biomarker discovery in drug-free schizophrenia: the contribution of proteomics and multiplex immunoassays. Expert Rev Proteomics 13:1141–1155CrossRefGoogle Scholar
  5. 5.
    Bai ZL, Li XS, Chen GY et al (2018) Serum oxidative stress marker levels in unmedicated and medicated patients with schizophrenia. J Mol Neurosci 66:428–436CrossRefGoogle Scholar
  6. 6.
    Schwarz E, Guest PC, Steiner J, Bogerts B, Bahn S (2012) Identification of blood-based molecular signatures for prediction of response and relapse in schizophrenia patients. Transl Psychiatry 2:e82.  https://doi.org/10.1038/tp.2012.3 CrossRefGoogle Scholar
  7. 7.
    Suvisaari J, Mantere O, Keinänen J et al (2018) Is it possible to predict the future in first-episode. Psychosis? Front Psychiatry 9:580.  https://doi.org/10.3389/fpsyt.2018.00580 CrossRefGoogle Scholar
  8. 8.
    Martinuzzi E, Barbosa S, Daoudlarian D et al (2019) Stratification and prediction of remission in first-episode psychosis patients: the OPTiMiSE cohort study. Transl Psychiatry 9(1):20.  https://doi.org/10.1038/s41398-018-0366-5 CrossRefGoogle Scholar
  9. 9.
    Aquino A, Alexandrino GL, Guest PC et al (2018) Blood-based lipidomics approach to evaluate biomarkers associated with response to olanzapine, risperidone, and quetiapine treatment in schizophrenia patients. Front Psychiatry 9:209.  https://doi.org/10.3389/fpsyt.2018.00209 CrossRefGoogle Scholar
  10. 10.
    Garcia S, Silva-Costa LC, Reis-de-Oliveira G et al (2017) Identifying biomarker candidates in the blood plasma or serum proteome. In: Guest PC (ed) Proteomic methods in neuropsychiatric research. Springer, Cham, pp 193–203CrossRefGoogle Scholar
  11. 11.
    Jaros JA, Martins de Souza D, Rahmoune H et al (2012) Protein phosphorylation patterns in serum from schizophrenia patients and healthy controls. J Proteomics 76:43–55.  https://doi.org/10.1016/j.jprot.2012.05.027 CrossRefGoogle Scholar
  12. 12.
    Sekar A, Bialas AR, de Rivera H et al (2016) Schizophrenia risk from complex variation of complement component 4. Nature 530:177–183.  https://doi.org/10.1038/nature16549 CrossRefGoogle Scholar
  13. 13.
    Presumey J, Bialas AR, Carroll MC (2017) Complement system in neural synapse elimination in development and disease. Elsevier, Amsterdam pp 53–79Google Scholar
  14. 14.
    Sellgren CM, Sheridan SD, Gracias J et al (2016) Patient-specific models of microglia-mediated engulfment of synapses and neural progenitors. Mol Psychiatry 22:170–177.  https://doi.org/10.1038/mp.2016.220 CrossRefGoogle Scholar
  15. 15.
    English JA, Lopez LM, O’Gorman A et al (2018) Blood-based protein changes in childhood are associated with increased risk for later psychotic disorder: evidence from a nested case-control study of the ALSPAC longitudinal birth cohort. Schizophr Bull 44(2):297–306.  https://doi.org/10.1093/schbul/sbx075 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of BiologyUniversity of Campinas (UNICAMP)CampinasBrazil
  2. 2.Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São PauloBrazil
  3. 3.UNICAMP Neurobiology CenterCampinasBrazil
  4. 4.Laboratory of Translational PsychiatryUniversity of MagdeburgMagdeburgGermany
  5. 5.Department of Psychiatry and PsychotherapyUniversity of MagdeburgMagdeburgGermany
  6. 6.Center for Behavioral Brain Sciences (CBBS)MagdeburgGermany

Personalised recommendations