OBJECTIVES: This study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode. METHODS: In this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID). RESULTS: Five distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder's trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms. CONCLUSION: This research points to a complex interplay between the plasma proteome and MDD's clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools. LIMITATIONS: The study's limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.

Department of Pathological Physiology Faculty of Medicine Masaryk University Brno Czechia

Department of Physical Activities and Health Sciences Faculty of Sport Science Masaryk University Brno Czechia

Department of Psychiatry Faculty of Medicine Masaryk University and University Hospital Brno Brno Czechia

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GBD 2017 Disease and Injury Incidence and Prevalence Collaborators . Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. (2018) 392:1789–858. doi: 10.1016/S0140-6736(18)32279-7 PubMed DOI PMC

Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. (2013) 34:119–38. doi: 10.1146/annurev-publhealth-031912-114409 PubMed DOI PMC

Haapakoski R, Ebmeier KP, Alenius H, Kivimäki M. Innate and adaptive immunity in the development of depression: An update on current knowledge and technological advances. Prog Neuropsychopharmacol Biol Psychiatry. (2016) 66:63–72. doi: 10.1016/j.pnpbp.2015.11.012 PubMed DOI PMC

Sowa-Kućma M, Styczeń K, Siwek M, Misztak P, Nowak RJ, Dudek D, et al. . Lipid peroxidation and immune biomarkers are associated with major depression and its phenotypes, including treatment-resistant depression and melancholia. Neurotox Res. (2018) 33:448–60. doi: 10.1007/s12640-017-9835-5 PubMed DOI PMC

Anderson G. Linking the biological underpinnings of depression: Role of mitochondria interactions with melatonin, inflammation, sirtuins, tryptophan catabolites, DNA repair and oxidative and nitrosative stress, with consequences for classification and cognition. Prog Neuropsychopharmacol Biol Psychiatry. (2018) 80:255–66. doi: 10.1016/j.pnpbp.2017.04.022 PubMed DOI

Styczeń K, Sowa-Kućma M, Siwek M, Dudek D, Reczyński W, Szewczyk B, et al. . The serum zinc concentration as a potential biological marker in patients with major depressive disorder. Metab Brain Dis. (2017) 32:97–103. doi: 10.1007/s11011-016-9888-9 PubMed DOI PMC

Fries GR, Saldana VA, Finnstein J, Rein T. Molecular pathways of major depressive disorder converge on the synapse. Mol Psychiatry. (2023) 28:284–97. doi: 10.1038/s41380-022-01806-1 PubMed DOI PMC

Duarte TT, Spencer CT. Personalized proteomics: the future of precision medicine. Proteomes. (2016) 4:29. doi: 10.3390/proteomes4040029 PubMed DOI PMC

Belzeaux R, Lin C-W, Ding Y, Bergon A, Ibrahim EC, Turecki G, et al. . Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis. J Psychiatr Res. (2016) 81:119–26. doi: 10.1016/j.jpsychires.2016.07.009 PubMed DOI

Martins-de-Souza D. Comprehending depression through proteomics. Int J Neuropsychopharmacol. (2012) 15:1373–4. doi: 10.1017/S146114571200034X PubMed DOI

Silva-Costa LC, Carlson PT, Guest PC, de Almeida V, Martins-de-Souza D. Proteomic markers for depression. Adv Exp Med Biol. (2019) 1118:191–206. doi: 10.1007/978-3-030-05542-4_10 PubMed DOI

Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. . Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. AJP. (2010) 167:748–51. doi: 10.1176/appi.ajp.2010.09091379 PubMed DOI

Gold PW, Chrousos GP. Organization of the stress system and its dysregulation in melancholic and atypical depression: high vs low CRH/NE states. Mol Psychiatry. (2002) 7:254–75. doi: 10.1038/sj.mp.4001032 PubMed DOI

Dooley LN, Kuhlman KR, Robles TF, Eisenberger NI, Craske MG, Bower JE. The role of inflammation in core features of depression: Insights from paradigms using exogenously-induced inflammation. Neurosci Biobehav Rev. (2018) 94:219–37. doi: 10.1016/j.neubiorev.2018.09.006 PubMed DOI PMC

Sharpley CF, Bitsika V. Differences in neurobiological pathways of four “clinical content” subtypes of depression. Behav Brain Res. (2013) 256:368–76. doi: 10.1016/j.bbr.2013.08.030 PubMed DOI

Sharpley CF, Bitsika V. Validity, reliability and prevalence of four “clinical content” subtypes of depression. Behav Brain Res. (2014) 259:9–15. doi: 10.1016/j.bbr.2013.10.032 PubMed DOI

Křenek P, Hořínková J, Bartečků E. Peripheral inflammatory markers in subtypes and core features of depression: A systematized review. Psychopathology. (2023) 56:403–16. doi: 10.1159/000528907 PubMed DOI PMC

World Health Organization . International Classification of Diseases, 10th Revision (ICD-10): Classifications of Mental and Behavioural Disorder: Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organization; (1992).

Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. . The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. (1998) 59:22–33;quiz 34-57. PubMed

Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. (1979) 134:382–9. doi: 10.1192/bjp.134.4.382 PubMed DOI

Zimmerman M, Posternak MA, Chelminski I. Defining remission on the Montgomery-Asberg depression rating scale. J Clin Psychiatry. (2004) 65:163–8. doi: 10.4088/jcp.v65n0204 PubMed DOI

Suzuki A, Aoshima T, Fukasawa T, Yoshida K, Higuchi H, Shimizu T, et al. . A three-factor model of the MADRS in major depressive disorder. Depress Anxiety. (2005) 21:95–7. doi: 10.1002/da.20058 PubMed DOI

Ho TK. (1995). Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition. (Piscataway, NJ, USA: IEEE; ). 1:278–82. doi: 10.1109/ICDAR.1995.598994 DOI

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. (2008) 9:559. doi: 10.1186/1471-2105-9-559 PubMed DOI PMC

Nelder JA, Wedderburn RWM. Generalized linear models. J R Stat Soc Ser A (General). (1972) 135:370–84. doi: 10.2307/2344614 DOI

Smyth GK. limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, New York, NY: (2005). p. 397–420. doi: 10.1007/0-387-29362-0_23 DOI

Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. . DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. (2003) 4:R60. doi: 10.1186/gb-2003-4-9-r60 PubMed DOI

R Core Team . R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; (2022). Available at: https://www.R-project.org/.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. . limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. (2015) 43:e47. doi: 10.1093/nar/gkv007 PubMed DOI PMC

Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). (1995) 57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x DOI

Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. (2012) 46:i11. doi: 10.18637/jss.v046.i11 PubMed DOI PMC

Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. (2015) 67:1–48. doi: 10.18637/jss.v067.i01 DOI

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. . Scikit-learn: machine learning in python(2011). Available online at: https://inria.hal.science/hal-00650905 (Accessed March 23, 2024).

Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. (2010) 26:1340–7. doi: 10.1093/bioinformatics/btq134 PubMed DOI

Lundberg SM, Lee S-I. A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems (2017). Curran Associates, Inc. Available online at: https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html (Accessed March 23, 2024).

Hosack DA, Dennis G, Sherman BT, Lane HC, Lempicki RA. Identifying biological themes within lists of genes with EASE. Genome Biol. (2003) 4:R70. doi: 10.1186/gb-2003-4-10-r70 PubMed DOI PMC

van Haeringen M, Milaneschi Y, Lamers F, Penninx BWJH, Jansen R. Dissection of depression heterogeneity using proteomic clusters. Psychol Med. (2023) 53:2904–12. doi: 10.1017/S0033291721004888 PubMed DOI PMC

Duivis HE, Vogelzangs N, Kupper N, de Jonge P, Penninx BWJH. Differential association of somatic and cognitive symptoms of depression and anxiety with inflammation: findings from the Netherlands Study of Depression and Anxiety (NESDA). Psychoneuroendocrinology. (2013) 38:1573–85. doi: 10.1016/j.psyneuen.2013.01.002 PubMed DOI

Jokela M, Virtanen M, Batty GD, Kivimäki M. Inflammation and specific symptoms of depression. JAMA Psychiatry. (2016) 73:87–8. doi: 10.1001/jamapsychiatry.2015.1977 PubMed DOI

Grosse L, Carvalho LA, Birkenhager TK, Hoogendijk WJ, Kushner SA, Drexhage HA, et al. . Circulating cytotoxic T cells and natural killer cells as potential predictors for antidepressant response in melancholic depression. Restoration of T regulatory cell populations after antidepressant therapy. Psychopharmacol (Berl). (2016) 233:1679–88. doi: 10.1007/s00213-015-3943-9 PubMed DOI

Maes M, Mihaylova I, Kubera M, Ringel K. Activation of cell-mediated immunity in depression: association with inflammation, melancholia, clinical staging and the fatigue and somatic symptom cluster of depression. Prog Neuropsychopharmacol Biol Psychiatry. (2012) 36:169–75. doi: 10.1016/j.pnpbp.2011.09.006 PubMed DOI

Rothermundt M, Arolt V, Fenker J, Gutbrodt H, Peters M, Kirchner H. Different immune patterns in melancholic and non-melancholic major depression. Eur Arch Psychiatry Clin Neurosci. (2001) 251:90–7. doi: 10.1007/s004060170058 PubMed DOI

Martino M, Rocchi G, Escelsior A, Fornaro M. Immunomodulation mechanism of antidepressants: interactions between serotonin/norepinephrine balance and th1/th2 balance. Curr Neuropharmacol. (2012) 10:97–123. doi: 10.2174/157015912800604542 PubMed DOI PMC

Shelton R C, Miller A H. Inflammation in depression: is adiposity a cause? Dialogues Clin Neurosci. (2011) 13:41–53. doi: 10.31887/DCNS.2011.13.1/rshelton PubMed DOI PMC

Anderson KM, Olson KE, Estes KA, Flanagan K, Gendelman HE, Mosley RL. Dual destructive and protective roles of adaptive immunity in neurodegenerative disorders. Transl Neurodegener. (2014) 3:25. doi: 10.1186/2047-9158-3-25 PubMed DOI PMC

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

American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Washington, DC, USA: American Psychiatric Publishing; (2013).

Gaiano N, Fishell G. The role of notch in promoting glial and neural stem cell fates. Annu Rev Neurosci. (2002) 25:471–90. doi: 10.1146/annurev.neuro.25.030702.130823 PubMed DOI

Khalili AA, Ahmad MR. A review of cell adhesion studies for biomedical and biological applications. Int J Mol Sci. (2015) 16:18149–84. doi: 10.3390/ijms160818149 PubMed DOI PMC

Fabbri C, Crisafulli C, Gurwitz D, Stingl J, Calati R, Albani D, et al. . Neuronal cell adhesion genes and antidepressant response in three independent samples. Pharmacogenomics J. (2015) 15:538–48. doi: 10.1038/tpj.2015.15 PubMed DOI

Liu W, Zheng Y, Zhang F, Zhu M, Guo Q, Xu H, et al. . A preliminary investigation on plasma cell adhesion molecules levels by protein microarray technology in major depressive disorder. Front Psychiatry. (2021) 12:627469. doi: 10.3389/fpsyt.2021.627469 PubMed DOI PMC

Kayhan F, Gündüz Ş, Ersoy SA, Kandeğer A, Annagür BB. Relationships of neutrophil-lymphocyte and platelet-lymphocyte ratios with the severity of major depression. Psychiatry Res. (2017) 247:332–5. doi: 10.1016/j.psychres.2016.11.016 PubMed DOI

Lykouras L, Gournellis R. Psychotic (Delusional) major depression: new vistas. Curr Psychiatry Rev. (2009) 5:1–28. doi: 10.2174/157340009787315271 DOI

Van Zaane B, Nur E, Squizzato A, Dekkers OM, Twickler MTB, Fliers E, et al. . Hypercoagulable state in Cushing’s syndrome: a systematic review. J Clin Endocrinol Metab. (2009) 94:2743–50. doi: 10.1210/jc.2009-0290 PubMed DOI

Anastasi C, Rousselle P, Talantikite M, Tessier A, Cluzel C, Bachmann A, et al. . BMP-1 disrupts cell adhesion and enhances TGF-β activation through cleavage of the matricellular protein thrombospondin-1. Sci Signal. (2020) 13:eaba3880. doi: 10.1126/scisignal.aba3880 PubMed DOI

Gettins PGW. Serpin structure, mechanism, and function. Chem Rev. (2002) 102:4751–804. doi: 10.1021/cr010170+ PubMed DOI

National Center for Biotechnology Information (NCBI) . SLPI secretory leukocyte peptidase inhibitor [Homo sapiens (human)] - Gene(2024). Available online at: https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=ShowDetailView&TermToSearch=6590 (Accessed March 23, 2024).

Stanley ER, Berg KL, Einstein DB, Lee PS, Pixley FJ, Wang Y, et al. . Biology and action of colony–stimulating factor-1. Mol Reprod Dev. (1997) 46:4–10. doi: 10.1002/(SICI)1098-2795(199701)46:1<4::AID-MRD2>3.0.CO;2-V PubMed DOI

Edmondson-Stait AJ, Shen X, Adams MJ, Barbu MC, Jones HJ, Miron VE, et al. . Early-life inflammatory markers and subsequent psychotic and depressive episodes between 10 to 28 years of age. Brain Behav Immun Health. (2022) 26:100528. doi: 10.1016/j.bbih.2022.100528 PubMed DOI PMC

Khandaker GM, Pearson RM, Zammit S, Lewis G, Jones PB. Association of serum interleukin 6 and C-reactive protein in childhood with depression and psychosis in young adult life: a population-based longitudinal study. JAMA Psychiatry. (2014) 71:1121–8. doi: 10.1001/jamapsychiatry.2014.1332 PubMed DOI PMC

Perry BI, Zammit S, Jones PB, Khandaker GM. Childhood inflammatory markers and risks for psychosis and depression at age 24: Examination of temporality and specificity of association in a population-based prospective birth cohort. Schizophr Res. (2021) 230:69–76. doi: 10.1016/j.schres.2021.02.008 PubMed DOI PMC

Li X, Zong J, Si S. Complement Factor H related protein 1 and immune inflammatory disorders. Mol Immunol. (2022) 145:43–9. doi: 10.1016/j.molimm.2022.03.117 PubMed DOI

UniProt Consortium . LILRA3 - leukocyte immunoglobulin-like receptor subfamily A member 3 [Homo sapiens (Human)]. UniProtKB(2024). Available online at: https://www.uniprot.org/uniprotkb/Q8N6C8/entry (Accessed March 23, 2024).

National Center for Biotechnology Information (NCBI) . LILRB1 leukocyte immunoglobulin like receptor B1 [Homo sapiens (human)]. Gene ID: 10859(2024). Available online at: https://www.ncbi.nlm.nih.gov/gene/10859 (Accessed March 23, 2024).

Skerka C, Chen Q, Fremeaux-Bacchi V, Roumenina LT. Complement factor H related proteins (CFHRs). Mol Immunol. (2013) 56:170–80. doi: 10.1016/j.molimm.2013.06.001 PubMed DOI

Slyepchenko A, Maes M, Köhler CA, Anderson G, Quevedo J, Alves GS, et al. . T helper 17 cells may drive neuroprogression in major depressive disorder: Proposal of an integrative model. Neurosci Biobehav Rev. (2016) 64:83–100. doi: 10.1016/j.neubiorev.2016.02.002 PubMed DOI

Mittal M, Siddiqui MR, Tran K, Reddy SP, Malik AB. Reactive oxygen species in inflammation and tissue injury. Antioxid Redox Signal. (2014) 20:1126–67. doi: 10.1089/ars.2012.5149 PubMed DOI PMC

Esmon CT. The interactions between inflammation and coagulation. Br J Haematol. (2005) 131:417–30. doi: 10.1111/j.1365-2141.2005.05753.x PubMed DOI

Coppack SW. Pro-inflammatory cytokines and adipose tissue. Proc Nutr Soc. (2001) 60:349–56. doi: 10.1079/pns2001110 PubMed DOI

Siwek M, Sowa-Kućma M, Dudek D, Styczeń K, Szewczyk B, Kotarska K, et al. . Oxidative stress markers in affective disorders. Pharmacol Rep. (2013) 65:1558–71. doi: 10.1016/s1734-1140(13)71517-2 PubMed DOI

Bassoy EY, Walch M, Martinvalet D. Reactive oxygen species: do they play a role in adaptive immunity? Front Immunol. (2021) 12:755856. doi: 10.3389/fimmu.2021.755856 PubMed DOI PMC

Bakunina N, Pariante CM, Zunszain PA. Immune mechanisms linked to depression via oxidative stress and neuroprogression. Immunology. (2015) 144:365–73. doi: 10.1111/imm.12443 PubMed DOI PMC

Wang Q, Zennadi R. Oxidative stress and thrombosis during aging: the roles of oxidative stress in RBCs in venous thrombosis. Int J Mol Sci. (2020) 21:4259. doi: 10.3390/ijms21124259 PubMed DOI PMC

Felger JC, Haroon E, Patel TA, Goldsmith DR, Wommack EC, Woolwine BJ, et al. . What does plasma CRP tell us about peripheral and central inflammation in depression? Mol Psychiatry. (2020) 25:1301–11. doi: 10.1038/s41380-018-0096-3 PubMed DOI PMC

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