Plasma proteomics identifies proteins and pathways associated with incident depression
Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
Grand Ballroom 101-102
Poster No:
854
Submission Type:
Abstract Submission
Authors:
Jujiao Kang1, Liu Yang1, Tianye Jia1, Jintai Yu1, Wei Cheng1, Jianfeng Feng2
Institutions:
1Fudan University, Shanghai, Shanghai, 2Institute of Science and Technology for Brain inspired Intelligence, Shanghai, Shanghai
First Author:
Co-Author(s):
Jianfeng Feng
Institute of Science and Technology for Brain inspired Intelligence
Shanghai, Shanghai
Institute of Science and Technology for Brain inspired Intelligence
Shanghai, Shanghai
Introduction:
Depression, a growing global concern with a prevalence surpassing 5%(Collins, Patel et al. 2011), gravely impairs the wellbeing and quality of life of affected individuals and posing substantial societal burdens(Herrman, Patel et al. 2022). The limited success in achieving consistent remission is intricately linked to our incomplete understanding of its pathogenesis(Yuan, Yang et al. 2023). Unraveling these elusive mechanisms is paramount, setting the stage for more effective therapeutic interventions.
While several studies have delved into the association between plasma proteins and depression(Zhang, Guo et al. 2022), their insights, albeit valuable, are constrained by small sample sizes or limited proteomic scope. Thus, it is crucial to explore the profiling protein dysregulations prior to depression onset using large biobanks. Besides, given that depression arises from a sophisticated interplay of biological and environmental elements(Yuan, Yang et al. 2023), examining these proteins within diverse biological and environmental factors and understanding their linked pathways is essential.
While several studies have delved into the association between plasma proteins and depression(Zhang, Guo et al. 2022), their insights, albeit valuable, are constrained by small sample sizes or limited proteomic scope. Thus, it is crucial to explore the profiling protein dysregulations prior to depression onset using large biobanks. Besides, given that depression arises from a sophisticated interplay of biological and environmental elements(Yuan, Yang et al. 2023), examining these proteins within diverse biological and environmental factors and understanding their linked pathways is essential.
Methods:
Utilizing the prospective UKB cohort, we assessed associations between 1448 baseline plasma protein levels and incident depression among 45,505 participants. We initially employed survival analysis to profile proteomic concentrations associated with incident depression. Accordingly, we related these depression-linked proteins to neuroimaging data, genetic indicators, and environmental variables, and subsequently characterized the biological pathways. To underscore the clinical implications, we applied MR to establish causal links, spotlighting potential therapeutic targets. Overall, we aim to identify risk signatures, decipher underlying pathogenesis, and inform tailored therapeutic.
Results:
We examined the association between plasma protein level and incident depression using the cox proportional hazard model and identified 212 proteins significantly associated with incident depression (Fig. 1A). Notably, growth/differentiation factor 15 (GDF15); tumor necrosis factor receptor superfamily member 10B (TNFRSF10B); neurofilament light polypeptide (NEFL); urokinase plasminogen activator surface receptor (PLAUR); and insulin-like growth factor-binding protein 4 (IGFBP4) had the most significant associations. (Fig. 1B). Overall, 206 of the 212 depression-associated proteins showed cross-sectional associations with baseline PHQ4 scores after Bonferroni correction (Fig. 1C). After further adjustment for the PHQ4 score, 95 of the 212 proteins remained significantly associated with the incident depression (Fig. 1D).
We examined the associations between the 95 identified depression-associated proteins and brain structures. After FDR correction, 43 of the 95 proteins showed at least one association with the four global brain structures (Fig. 2A). We also identified 43 proteins that exhibited significant associations with at least one brain regional measure. Several brain regions implicated in depression, including left middle temporal gyrus, left medial orbitofrontal gyrus, bilateral hippocampus, bilateral thalamus, were associated with at least 3 proteins (Fig. 2B). In addition, bilateral posterior thalamic radiation, bilateral anterior thalamic radiation, left cingulate gyrus part of cingulum, were associated with at least 3 proteins (Fig. 2C).
Furthermore, these protein alterations showed stronger correlations with stress-related events than genetic factors. Pathway analysis highlighted the central role of the immune response and TNF emerged as a key hub in the protein-protein expression network. BTN3A2 was identified as having a causal association with depression.
We examined the associations between the 95 identified depression-associated proteins and brain structures. After FDR correction, 43 of the 95 proteins showed at least one association with the four global brain structures (Fig. 2A). We also identified 43 proteins that exhibited significant associations with at least one brain regional measure. Several brain regions implicated in depression, including left middle temporal gyrus, left medial orbitofrontal gyrus, bilateral hippocampus, bilateral thalamus, were associated with at least 3 proteins (Fig. 2B). In addition, bilateral posterior thalamic radiation, bilateral anterior thalamic radiation, left cingulate gyrus part of cingulum, were associated with at least 3 proteins (Fig. 2C).
Furthermore, these protein alterations showed stronger correlations with stress-related events than genetic factors. Pathway analysis highlighted the central role of the immune response and TNF emerged as a key hub in the protein-protein expression network. BTN3A2 was identified as having a causal association with depression.
·Figure 1: Proteome-wide associations with incident depression.
·Figure 2: Association of depression-associated proteins with brain structure.
Conclusions:
Our findings bridge critical knowledge gaps, emphasizing the potential of proteomic markers in both predicting and possibly mitigating depression's onset, setting the stage for more personalized and effective therapeutic strategies.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Genetics:
Genetic Association Studies 1
Genetics Other
Keywords:
Affective Disorders
Psychiatric Disorders
1|2Indicates the priority used for review
Provide references using author date format
Choi, H., et al. (2021). "Serum proteomic analysis of major depressive disorder patients and their remission status: Novel biomarker set of zinc-alpha-2-glycoprotein and keratin type II cytoskeletal 1." Int J Biol Macromol 183: 2001-2008.
Collins, P. Y., et al. (2011). "Grand challenges in global mental health." Nature 475(7354): 27-30.
Herrman, H., et al. (2022). "Time for united action on depression: a Lancet-World Psychiatric Association Commission." Lancet 399(10328): 957-1022.
Li, S., et al. (2021). "Multiregional profiling of the brain transmembrane proteome uncovers novel regulators of depression." Sci Adv 7(30).
Malhi, G. S. and J. J. Mann (2018). "Depression." Lancet 392(10161): 2299-2312.
Marx, W., et al. (2023). "Major depressive disorder." Nat Rev Dis Primers 9(1): 44.
Shin, D., et al. (2022). "Integrating proteomic and clinical data to discriminate major psychiatric disorders: Applications for major depressive disorder, bipolar disorder, and schizophrenia." Clin Transl Med 12(6): e929.
Thase, M. E. and T. L. Schwartz (2015). "Choosing medications for treatment-resistant depression based on mechanism of action." J Clin Psychiatry 76(6): 720-727; quiz 727.
Yuan, M., et al. (2023). "Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential." Signal Transduct Target Ther 8(1): 309.
Zhang, M. M., et al. (2022). "IL-1R/C3aR signaling regulates synaptic pruning in the prefrontal cortex of depression." Cell Biosci 12(1): 90.
Zhdanava, M., et al. (2021). "The Prevalence and National Burden of Treatment-Resistant Depression and Major Depressive Disorder in the United States." J Clin Psychiatry 82(2).