Plasma proteomic signature of major depressive episode in the elderly

Characterizing metabolomic and proteomic changes in depression: a systematic analysis

Despite the widespread use of metabolomics and proteomics to explore the molecular landscape of depression, there is a lack of consensus regarding dysregulated molecules with replicable evidence. Thus, this study aimed to identify robust metabolomic and proteomic features in depression by integrating evidence from large-scale studies. In this study, a knowledge base-mining approach was adopted to compile a list of dysregulated molecules derived from metabolomic and proteomic studies. A vote-counting approach was performed to identify consistently altered molecules in the blood and urine samples of patients with depression. A total of 2398 molecular entries were selected, comprising 857 unique metabolites and 468 unique proteins from 143 metabolomic and 23 proteomic studies in depression. The results of vote-counting analyses revealed that 11 metabolites in blood and 5 metabolites in urine exhibited consistent disturbances across studies. Circulating levels of glutamic acid and phosphatidylcholine (32:0) were elevated in depressive patients, whereas the levels of tryptophan, kynurenic acid, kynurenine, acetylcarnitine, serotonin, creatinine, inosine, phenylalanine, and valine were lower. Urinary levels of isobutyric acid, alanine, and nicotinic acid were higher, whereas the levels of N-methylnicotinamide and tyrosine were lower. Moreover, analysis of the proteomic dataset identified only one circulating protein, ceruloplasmin, that was consistently dysregulated. Convergence comparison prioritized tryptophan as the top-ranked circulating metabolite, followed by kynurenic acid, acetylcarnitine, creatinine, serotonin, and valine. Collectively, robust evidence of metabolomic changes was observed in patients with depression, pointing to a role as potential biomarkers. Further investigation of consensus proteomic features for depression is necessitated.

Serum KNG and FVIII may serve as potential biomarkers for depression

BACKGROUND

The global burden of major depressive disorder (MDD) is rising, with current diagnostic methods hindered by significant subjectivity and low inter-rater reliability. Several studies have implied underlying link between coagulation-related proteins, such as kininogen (KNG) and coagulation factor VIII (FVIII), and depressive symptoms, offering new insights into the exploration of depression biomarkers. This study aims to elucidate the roles of KNG and FVIII in depression, potentially providing a foundational basis for biomarker research in this field.

METHODS

A three-part experiment was conducted: (1) we measured serum levels of KNG and FVIII in the chronic unpredictable mild stress (CUMS) model; (2) KNG adeno-associated-virus overexpression (KNG-AAV-OE) model was constructed to further investigate the roles of KNG and FVIII. Meanwhile, quantity PCR, western blotting and immunofluorescence staining detected the KNG-FVIII pathway. (3) Peripheral blood samples were gathered from healthy control (HC, N = 21), as well as first-episode drug-naive patients with MDD (FEDN-MDD, N = 21), to further confirm the association between KNG, FVIII and depression.

RESULTS

Firstly, serum KNG and FVIII levels were significantly elevated in the CUMS model. Then, the rats exhibited pronounced depressive-like behaviors in the KNG-AAV-OE model, with corresponding increases in serum KNG and FVIII. Lastly, clinical data showed increased KNG and FVIII levels in FEDN-MDD compared to HC. Furthermore, KNG and FVIII levels exhibited a strong positive correlation with the scores of the 24-item Hamilton Depression Scale and the 14-item Hamilton Anxiety Scale.

CONCLUSION

To sum up, this study highlights critical roles of serum KNG and FVIII in depression and the KNG-AAV-OE may lead the augment of FVIII in serum. Consequently, our research may offer new evidence and foundation for depression biomarkers research in the future.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Pathophysiology of chikungunya virus infection associated with fatal outcomes

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes acute, subacute, and chronic human arthritogenic diseases and, in rare instances, can lead to neurological complications and death. Here, we combined epidemiological, virological, histopathological, cytokine, molecular dynamics, metabolomic, proteomic, and genomic analyses to investigate viral and host factors that contribute to chikungunya-associated (CHIK) death. Our results indicate that CHIK deaths are associated with multi-organ infection, central nervous system damage, and elevated serum levels of pro-inflammatory cytokines and chemokines compared with survivors. The histopathologic, metabolite, and proteomic signatures of CHIK deaths reveal hemodynamic disorders and dysregulated immune responses. The CHIKV East-Central-South-African lineage infecting our study population causes both fatal and survival cases. Additionally, CHIKV infection impairs the integrity of the blood-brain barrier, as evidenced by an increase in permeability and altered tight junction protein expression. Overall, our findings improve the understanding of CHIK pathophysiology and the causes of fatal infections.

Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study

Background

Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.

Methods

Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.

Results

There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.

Conclusion

This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.

Association between suicide attempts and anemia in late-life depression inpatients

BACKGROUND

Anemia is strongly associated with late-life depression (LLD), however, few studies have investigated the relationship between anemia and suicide attempts in LLD patients. It is still challenging to predict suicide risk in patients with depression. Therefore, there is growing interest in potential biomarkers of depressive disorders and suicidal behavior, which may play a significant role in the early diagnosis and treatment of depression. This study aimed to compare serum ferritin, folate, vitamin B12, and erythrocyte parameter levels in patients with LLD with those in healthy older adults, and the relationship between serum ferritin, folate, vitamin B12, and suicide attempts in patients.

METHODS

Serum ferritin, folate, vitamin B12, and erythrocyte parameter levels were measured in 66 hospitalized LLD patients (30 without suicide attempt, 36 with suicide attempt) and 47 healthy individuals. All participants were surveyed for basic conditions and suicide attempts, and depression was assessed in LLD patients.

RESULTS

Serum ferritin, folate, vitamin B12, red blood cell count, hemoglobin, hematocrit, mean platelet volume and plateletcrit levels were significantly lower in LLD patients compared with healthy older adults (P < 0.05). Further analysis of the relationship between serum ferritin, folate, and vitamin B12 levels and LLD patients' suicide attempts and showed a significant negative association between serum folate and vitamin B12 and suicide attempts (P < 0.05).

CONCLUSIONS

Serum ferritin, folate, vitamin B12, red blood cell count, hemoglobin, hematocrit, mean platelet volume and plateletcrit levels were significantly lower in LLD patients than in healthy older adults. In addition, reduced serum folate and vitamin B12 levels in patients may have some effect on suicide attempts. More mechanistic studies are needed to further explain this association.