Skip to main content

Abstract

Major depressive disorder is a multifactorial disease, with molecular mechanisms not fully understood. A breakthrough could be reached with a panel of diagnostic biomarkers, which could be helpful to stratify patients and guide physicians to a better therapeutic choice, reducing the time between diagnostic and remission. This review brings the most recent works in proteomic biomarkers and highlights several potential proteins that could compose a panel of biomarkers to diagnostic and response to medication. These proteins are related to immune, inflammatory, and coagulatory systems and may also be linked to energy metabolism, oxidative stress, cell communication, and oligodendrogenesis.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bromet E, Andrade LH, Hwang I, Sampson NA, Alonso J, de Girolamo G et al (2011) Cross-national epidemiology of DSM-IV major depressive episode. BMC Med 9:90. https://doi.org/10.1186/1741-7015-9-90

    Article  PubMed  PubMed Central  Google Scholar 

  2. World Health Organization (2017) Depression and other common mental disorders: global health estimates. World Heal Organ. 1–24. doi: CC BY-NC-SA 3.0 IGO

    Google Scholar 

  3. Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman ATF, Penninx BWJH (2013) Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 18:692–699. https://doi.org/10.1038/mp.2012.144

    Article  CAS  PubMed  Google Scholar 

  4. KS A-H (2012) Treatment-resistant depression: therapeutic trends, challenges, and future directions. Patient Prefer Adherence 6:369–388. https://doi.org/10.2147/PPA.S29716

    Article  Google Scholar 

  5. Souery D, Amsterdam J, de Montigny C, Lecrubier Y, Montgomery S, Lipp O et al (1999) Treatment resistant depression: methodological overview and operational criteria. Eur Neuropsychopharmacol 9:83–91

    Article  CAS  PubMed  Google Scholar 

  6. Paris J (2014) The mistreatment of major depressive disorder. Can J Psychiatry 59:148–151. https://doi.org/10.1177/070674371405900306

    Article  PubMed  PubMed Central  Google Scholar 

  7. Belmaker RH, Agam G (2008) Major depressive disorder. N Engl J Med 358:55–68. https://doi.org/10.1056/NEJMra073096

    Article  CAS  PubMed  Google Scholar 

  8. Striegel-Moore R (2011) Developing an evidence-based classification of eating disorders: scientific findings for DSM-5, 1st edn. American Psychiatric Publishing, Washington, DC. ISBN:9780890426661

    Google Scholar 

  9. Stassen HH, Angst J, Delini-Stula A (1997) Delayed onset of action of antidepressant drugs? Survey of recent results. Eur Psychiatry 12:166–176

    Article  CAS  PubMed  Google Scholar 

  10. Fava M, Davidson KG (1996) Definition and epidemiology of treatment-resistant depression. Psychiatr Clin North Am 19:179–200

    Article  CAS  PubMed  Google Scholar 

  11. Duric V, Duman RS (2013) Depression and treatment response: dynamic interplay of signaling pathways and altered neural processes. Cell Mol Life Sci 70:39–53

    Article  CAS  PubMed  Google Scholar 

  12. Post RM (1992) Transduction of psychosocial stress into the neurobiology of recurrent affective disorder. Am J Psychiatry 149:999–1010

    Article  CAS  PubMed  Google Scholar 

  13. Vialou V, Feng J, Robison AJ, Nestler EJ (2013) Epigenetic mechanisms of depression and antidepressant action. Annu Rev Pharmacol Toxicol 53:59–87. https://doi.org/10.1146/annurev-pharmtox-010611-134540

    Article  CAS  PubMed  Google Scholar 

  14. Lopizzo N, Bocchio Chiavetto L, Cattane N, Plazzotta G, Tarazi FI, Pariante CM et al (2015) Gene-environment interaction in major depression: focus on experience-dependent biological systems. Front Psychiatry 6:68. https://doi.org/10.3389/fpsyt.2015.00068

    Article  PubMed  PubMed Central  Google Scholar 

  15. López-Muñoz F, Alamo C (2009) Monoaminergic neurotransmission: the history of the discovery of antidepressants from 1950s until today. Curr Pharm Des 15:1563–1586

    Article  PubMed  Google Scholar 

  16. Schildkraut JJ (1965) The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am J Psychiatry 122:509–522

    Article  CAS  PubMed  Google Scholar 

  17. Paul IA, Skolnick P (2003) Glutamate and depression. Ann N Y Acad Sci 1003:250–272

    Article  CAS  PubMed  Google Scholar 

  18. Sanacora G, Treccani G, Popoli M (2012) Towards a glutamate hypothesis of depression: an emerging frontier of neuropsychopharmacology for mood disorders. Neuropharmacology 62:63–77

    Article  CAS  PubMed  Google Scholar 

  19. Schiepers OJG, Wichers MC, Maes M (2005) Cytokines and major depression. Prog Neuropsychopharmacol Biol Psychiatry 29:201–217

    Article  CAS  PubMed  Google Scholar 

  20. Maes M (1995) Evidence for an immune response in major depression: a review and hypothesis. Prog Neuropsychopharmacol Biol Psychiatry 19:11–38

    Article  CAS  PubMed  Google Scholar 

  21. Tong L, Balazs R, Soiampornkul R, Thangnipon W, Cotman CW (2008) Interleukin-1β impairs brain derived neurotrophic factor-induced signal transduction. Neurobiol Aging 29:1380–1393

    Article  CAS  PubMed  Google Scholar 

  22. Hayley S (2014) The neuroimmune-neuroplasticity interface and brain pathology. Front Cell Neurosci 8:419. https://doi.org/10.3389/fncel.2014.00419

    Article  PubMed  PubMed Central  Google Scholar 

  23. Pittenger C, Duman RS (2008) Stress, depression, and neuroplasticity: a convergence of mechanisms. Neuropsychopharmacology 33:88–109

    Article  CAS  PubMed  Google Scholar 

  24. Rush AJ (2007) The varied clinical presentations of major depressive disorder. J Clin Psychiatry 68(Suppl 8):4–10

    PubMed  Google Scholar 

  25. Ostergaard SD, Jensen SOW, Bech P (2011) The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatr Scand 124:495–496686

    Article  PubMed  Google Scholar 

  26. Wang PS, Insel TR (2010) NIMH-funded pragmatic trials: moving on. Neuropsychopharmacology 35:2489–2490

    Article  PubMed  PubMed Central  Google Scholar 

  27. CONVERGE consortium (2015) Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523:588–591

    Article  PubMed Central  Google Scholar 

  28. Bosker FJ, Hartman CA, Nolte IM, Prins BP, Terpstra P, Posthuma D et al (2011) Poor replication of candidate genes for major depressive disorder using genome-wide association data. Mol Psychiatry 16:516–532

    Article  CAS  PubMed  Google Scholar 

  29. Gottesman II, Gould TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 160:636–645

    Article  PubMed  Google Scholar 

  30. Glahn DC, Knowles EEM, McKay DR, Sprooten E, Raventós H, Blangero J et al (2014) Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. Am J Med Genet B Neuropsychiatr Genet 165B:122–130

    Article  CAS  Google Scholar 

  31. Hasler G, Drevets WC, Manji HK, Charney DS (2004) Discovering endophenotypes for major depression. Neuropsychopharmacology 29:1765–1781

    Article  CAS  PubMed  Google Scholar 

  32. Goldstein BL, Klein DN (2014) A review of selected candidate endophenotypes for depression. Clin Psychol Rev 34:417–427

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lenzenweger MF (2013) Endophenotype, intermediate phenotype, biomarker: definitions, concept comparisons, clarifications. Depress Anxiety 30:185–189

    Article  PubMed  Google Scholar 

  34. Gadad BS, Jha MK, Czysz A, Furman JL, Mayes TL, Emslie MP et al (2018) Peripheral biomarkers of major depression and antidepressant treatment response: current knowledge and future outlooks. J Affect Disord 233:3–14

    Article  CAS  PubMed  Google Scholar 

  35. Biomarkers Definitions Working Group (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89–95

    Article  Google Scholar 

  36. Strawbridge R, Young AH, Cleare AJ (2017) Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr Dis Treat 13:1245–1262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Macaluso M, Preskorn SH (2012) How biomarkers will change psychiatry: from clinical trials to practice. Part I: introduction. J Psychiatr Pract 18:118–121

    PubMed  Google Scholar 

  38. Jentsch MC, Van Buel EM, Bosker FJ, Gladkevich AV, Klein HC, Oude Voshaar RC et al (2015) Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses. Biomark Med 9:277–297

    Article  CAS  PubMed  Google Scholar 

  39. Lim MD, Dickherber A, Compton CC (2011) Before you analyze a human specimen, think quality, variability, and bias. Anal Chem 83:8–13

    Article  CAS  PubMed  Google Scholar 

  40. Elliott P, Peakman TC (2008) The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol 37:234–244

    Article  PubMed  Google Scholar 

  41. Su S-Y, Hogrefe-Phi CE, Asara JM, Turck CW, Golub MS (2016) Peripheral fibroblast metabolic pathway alterations in juvenile rhesus monkeys undergoing long-term fluoxetine administration. Eur Neuropsychopharmacol 26:1110–1118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1:845–867

    Article  CAS  PubMed  Google Scholar 

  43. Urbas L, Brne P, Gabor B, Barut M, Strlic M, Petric TC et al (2009) Depletion of high-abundance proteins from human plasma using a combination of an affinity and pseudo-affinity column. J Chromatogr A 1216:2689–2694

    Article  CAS  PubMed  Google Scholar 

  44. Anderson NL, Leigh Anderson N, Anderson NG (2003) The human plasma proteome: history, character, and diagnostic prospects: fig 3. Mol Cell Proteomics 2:50–50

    Article  CAS  Google Scholar 

  45. Putnam FW (2012) The plasma proteins: structure, function, and genetic control, 2nd edn. Elsevier, New York. ISBN:9780323138086

    Google Scholar 

  46. Jambunathan K, Galande AK (2014) Sample collection in clinical proteomics—proteolytic activity profile of serum and plasma. Proteomics Clin Appl 8:299–307

    Article  CAS  PubMed  Google Scholar 

  47. Martins-de-Souza D, Harris LW, Guest PC, Turck CW, Bahn S (2010) The role of proteomics in depression research. Eur Arch Psychiatry Clin Neurosci 260:499–506

    Article  PubMed  Google Scholar 

  48. Licznerski P, Duman RS (2013) Remodeling of axo-spinous synapses in the pathophysiology and treatment of depression. Neuroscience 251:33–50

    Article  CAS  PubMed  Google Scholar 

  49. Stelzhammer V, Haenisch F, Chan MK, Cooper JD, Steiner J, Steeb H et al (2014) Proteomic changes in serum of first onset, antidepressant drug-naïve major depression patients. Int J Neuropsychopharmacol 17:1599–1608

    Article  CAS  PubMed  Google Scholar 

  50. Lee MY, Kim EY, Kim SH, Cho K-C, Ha K, Kim KP et al (2016) Discovery of serum protein biomarkers in drug-free patients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 69:60–68

    Article  PubMed  Google Scholar 

  51. Kim EY, Lee MY, Kim SH, Ha K, Kim KP, Ahn YM (2017) Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm. Prog Neuropsychopharmacol Biol Psychiatry 76:65–71

    Article  CAS  PubMed  Google Scholar 

  52. Wang Q, Su X, Jiang X, Dong X, Fan Y, Zhang J et al (2016) iTRAQ technology-based identification of human peripheral serum proteins associated with depression. Neuroscience 330:291–325

    Article  CAS  PubMed  Google Scholar 

  53. Haines RJ, Pendleton LC, Eichler DC (2011) Argininosuccinate synthase: at the center of arginine metabolism. Int J Biochem Mol Biol 2:8–23

    CAS  PubMed  Google Scholar 

  54. Wu D, Peng Y, Zhou J, Yang Y-T, Rao C-L, Bai S-J et al (2015) Identification and validation of argininosuccinate synthase as a candidate urinary biomarker for major depressive disorder. Clin Chim Acta 451:142–148

    Article  CAS  Google Scholar 

  55. Grunze H (2011) The clinical side of bipolar disorders. Pharmacopsychiatry 44(Suppl 1):S43–S48.

    Article  PubMed  Google Scholar 

  56. Preece RL, Han SYS, Bahn S (2018) Proteomic approaches to identify blood-based biomarkers for depression and bipolar disorders. Expert Rev Proteomics 15:325–340

    Article  CAS  PubMed  Google Scholar 

  57. Stelzhammer V, Alsaif M, Chan MK, Rahmoune H, Steeb H, Guest PC et al (2015) Distinct proteomic profiles in post-mortem pituitary glands from bipolar disorder and major depressive disorder patients. J Psychiatr Res 60:40–48

    Article  PubMed  Google Scholar 

  58. Fleming CE, Nunes AF, Sousa MM (2009) Transthyretin: more than meets the eye. Prog Neurobiol 89:266–276

    Article  CAS  PubMed  Google Scholar 

  59. Frye MA, Nassan M, Jenkins GD, Kung S, Veldic M, Palmer BA et al (2015) Feasibility of investigating differential proteomic expression in depression: implications for biomarker development in mood disorders. Transl Psychiatry 5:e689. https://doi.org/10.1038/tp.2015.185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ren J, Zhao G, Sun X, Liu H, Jiang P, Chen J et al (2017) Identification of plasma biomarkers for distinguishing bipolar depression from major depressive disorder by iTRAQ-coupled LC-MS/MS and bioinformatics analysis. Psychoneuroendocrinology 86:17–24

    Article  CAS  PubMed  Google Scholar 

  61. Yang Y, Chen J, Liu C, Fang L, Liu Z, Guo J et al (2016) The extrinsic coagulation pathway: a biomarker for suicidal behavior in major depressive disorder. Sci Rep 6:32882. https://doi.org/10.1038/srep32882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Aziz R, Steffens DC (2013) What are the causes of late-life depression? Psychiatr Clin North Am 36:497–516

    Article  PubMed  Google Scholar 

  63. Diniz BS, Lin C-W, Sibille E, Tseng G, Lotrich F, Aizenstein HJ et al (2016) Circulating biosignatures of late-life depression (LLD): towards a comprehensive, data-driven approach to understanding LLD pathophysiology. J Psychiatr Res 82:1–7

    Article  PubMed  Google Scholar 

  64. Diniz BS, Sibille E, Ding Y, Tseng G, Aizenstein HJ, Lotrich F et al (2015) Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Mol Psychiatry 20:594–601

    Article  PubMed  PubMed Central  Google Scholar 

  65. Diniz BS, Reynolds CF 3rd, Sibille E, Lin C-W, Tseng G, Lotrich F et al (2017) Enhanced molecular aging in late-life depression: the senescent-associated secretory phenotype. Am J Geriatr Psychiatry 25:64–72

    Article  PubMed  Google Scholar 

  66. Bot M, Chan MK, Jansen R, Lamers F, Vogelzangs N, Steiner J et al (2015) Serum proteomic profiling of major depressive disorder. Transl Psychiatry 5:e599. https://doi.org/10.1038/tp.2015.88

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lee J, Joo E-J, Lim H-J, Park J-M, Lee KY, Park A et al (2015) Proteomic analysis of serum from patients with major depressive disorder to compare their depressive and remission statuses. Psychiatry Investig 12:249–259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Martins-de-Souza D, Maccarrone G, Ising M, Kloiber S, Lucae S, Holsboer F et al (2014) Plasma fibrinogen: now also an antidepressant response marker? Transl Psychiatry 4:e352. https://doi.org/10.1038/tp.2013.129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Gadad BS, Jha MK, Grannemann BD, Mayes TL, Trivedi MH (2017) Proteomics profiling reveals inflammatory biomarkers of antidepressant treatment response: findings from the CO-MED trial. J Psychiatr Res 94:1–6

    Article  PubMed  PubMed Central  Google Scholar 

  70. Turck CW, Guest PC, Maccarrone G, Ising M, Kloiber S, Lucae S et al (2017) Proteomic differences in blood plasma associated with antidepressant treatment response. Front Mol Neurosci 10:272. https://doi.org/10.3389/fnmol.2017.00272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Martins-de-Souza D, Maccarrone G, Ising M, Kloiber S, Lucae S, Holsboer F et al (2014) Blood mononuclear cell proteome suggests integrin and Ras signaling as critical pathways for antidepressant treatment response. Biol Psychiatry 76:e15–e17. https://doi.org/10.1016/j.biopsych.2014.01.022

    Article  CAS  PubMed  Google Scholar 

  72. Park DI, Štambuk J, Razdorov G, Pučić-Baković M, Martins-de-Souza D, Lauc G et al (2018) Blood plasma/IgG N-glycome biosignatures associated with major depressive disorder symptom severity and the antidepressant response. Sci Rep 8:179. https://doi.org/10.1038/s41598-017-17500-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chen C, Hu Y, Dong X-Z, Zhou X-J, Mu L-H, Liu P (2018) Proteomic analysis of the antidepressant effects of Shen-Zhi-Ling in depressed patients: identification of proteins associated with platelet activation and lipid metabolism. Cell Mol Neurobiol 38:1123–1135

    Article  PubMed  Google Scholar 

  74. Pisoni A, Strawbridge R, Hodsoll J, Powell TR, Breen G, Hatch S et al (2018) Growth factor proteins and treatment-resistant depression: a place on the path to precision. Front Psychiatry 9:386. https://doi.org/10.3389/fpsyt.2018.00386

    Article  PubMed  PubMed Central  Google Scholar 

  75. Ruland T, Chan MK, Stocki P, Grosse L, Rothermundt M, Cooper JD et al (2016) Molecular serum signature of treatment resistant depression. Psychopharmacology 233:3051–3059

    Article  CAS  PubMed  Google Scholar 

  76. Stelzhammer V, Guest PC, Rothermundt M, Sondermann C, Michael N, Schwarz E et al (2013) Electroconvulsive therapy exerts mainly acute molecular changes in serum of major depressive disorder patients. Eur Neuropsychopharmacol 23:1199–1207

    Article  CAS  PubMed  Google Scholar 

  77. Zhao H, Du H, Liu M, Gao S, Li N, Chao Y et al (2018) Integrative proteomics-metabolomics strategy for pathological mechanism of vascular depression mouse model. J Proteome Res 17:656–669

    Article  PubMed  Google Scholar 

  78. Mallei A, Failler M, Corna S, Racagni G, Mathé AA, Popoli M (2014) Synaptoproteomic analysis of a rat gene-environment model of depression reveals involvement of energy metabolism and cellular remodeling pathways. Int J Neuropsychopharmacol 18. https://doi.org/10.1093/ijnp/pyu067

    Article  PubMed  Google Scholar 

  79. Wesseling H, Rahmoune H, Tricklebank M, Guest PC, Bahn S (2014) A targeted multiplexed proteomic investigation identifies ketamine-induced changes in immune markers in rat serum and expression changes in protein kinases/phosphatases in rat brain. J Proteome Res 14:411–421

    Article  PubMed  Google Scholar 

  80. Ge L, Zhu M-M, Yang J-Y, Wang F, Zhang R, Zhang J-H et al (2015) Differential proteomic analysis of the anti-depressive effects of oleamide in a rat chronic mild stress model of depression. Pharmacol Biochem Behav 131:77–86

    Article  CAS  PubMed  Google Scholar 

  81. Palmfeldt J, Henningsen K, Eriksen SA, Müller HK, Wiborg O (2016) Protein biomarkers of susceptibility and resilience to stress in a rat model of depression. Mol Cell Neurosci 74:87–95

    Article  CAS  PubMed  Google Scholar 

  82. Stelzhammer V, Ozcan S, Gottschalk MG, Steeb H, Hodes GE, Guest PC et al (2015) Central and peripheral changes underlying susceptibility and resistance to social defeat stress—a proteomic profiling study. Diagnostics in Neuropsychiatry 1:1–7. https://doi.org/10.1016/j.dineu.2015.08.001

    Article  Google Scholar 

  83. Gottschalk MG, Wesseling H, Guest PC, Bahn S (2014) Proteomic enrichment analysis of psychotic and affective disorders reveals common signatures in presynaptic glutamatergic signaling and energy metabolism. Int J Neuropsychopharmacol 18. https://doi.org/10.1093/ijnp/pyu019

  84. Goodwin GM (2015) The overlap between anxiety, depression, and obsessive-compulsive disorder. Dialogues Clin Neurosci 17:249–260

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors thank FAPESP (Sao Paulo Research Foundation, grants 2013/08711-3, 2017/18242-1 2017/25588-1 and 2018/03422-7) and Serrapilheira Institute (grant number Serra-1709-16349) for funding. We also thank CAPES (Coordination for the Improvement of Higher Education Personnel) for the scholarship 88887.179832/2018-00. The authors thank Prof. Brett Vern Carlson (Technological Institute of Aeronautics (ITA)) for assistance with the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel Martins-de-Souza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Silva-Costa, L.C., Carlson, P.T., Guest, P.C., de Almeida, V., Martins-de-Souza, D. (2019). Proteomic Markers for Depression. In: Guest, P. (eds) Reviews on Biomarker Studies in Psychiatric and Neurodegenerative Disorders. Advances in Experimental Medicine and Biology(), vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-030-05542-4_10

Download citation

Publish with us

Policies and ethics