Network analysis of plasma proteomes in affective disorders

Abstract

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19–65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = −0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

HIV-2 mediated effects on target and bystander cells induce plasma proteome remodeling

“…This method was developed for analysis of transcriptomic data, 43 but has recently also been used to analyze mass spectrometry based proteomics data to identify protein modules associated to clinical traits and biological processes. 44 , 45 , 46 , 47 , 48 Gene ontology analyses are commonly performed to elucidate the common biological functions of the proteins found in each module. However, the blood plasma proteome contains both secreted and leakage proteins.…”

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

“…Proteomics analysis is a powerful approach to mitigate conundrums of diseases in clinical research, which has been demonstrated in previous studies. − Particularly, targeted proteomics using multiple reaction monitoring-mass spectrometry (MRM-MS) is regarded as a powerful quantitative method that is advantageous compared with antibody-based assays in terms of multiplex ability, high throughput, and reproducibility. − Recently, we have been constructing diagnostic multiprotein models for various diseases using MRM-MS and machine learning (ML) methods. − When it comes to discrimination of psychiatric disorders, we previously developed models to distinguish between MDD, BD, and SCZ with high accuracy using MRM-MS in depleted plasma samples. , Although the constructed models had high distinguishability, the depletion process of high abundant proteins is time-consuming and labor-intensive, which takes around half of the total time of sample preparation. − In this study, we improved our LC-MRM-MS method to detect and quantify as many proteins as possible with nondepleted plasma in a single MRM-MS assay. Next, we aimed to demonstrate the potential of nondepleted plasma multiprotein-based models to discriminate psychiatric disorders using ML approaches, and to compare these with our previous models .…”