Charting the proteome landscape in major psychiatric disorders: From biomarkers to biological pathways towards drug discovery

Individualized psychiatric care: integration of therapeutic drug monitoring, pharmacogenomics, and biomarkers

Personalized treatment optimization considers individual clinical, genetic, and environmental factors influencing drug efficacy and tolerability. As evidence accumulates, these approaches may become increasingly integrated into standard psychiatric care, potentially transforming the treatment landscape for mental health disorders. While personalized treatment optimization shows promise in enhancing therapeutic outcomes and minimizing adverse effects, further research is needed to establish its clinical utility and cost-effectiveness across various psychiatric disorders. This review examines the potential utility of personalized treatment optimization in psychiatry, addressing the challenge of suboptimal effectiveness and variable patient responses to psychiatric medications. It explores how therapeutic drug monitoring, pharmacogenomics, and biomarker testing can be used to individualize and optimize pharmacotherapy for mental disorders such as depression, bipolar disorder, and schizophrenia.

Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology

Background

Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples.

Methods

Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents.

Results

A total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules "darked", "darkurquoise" and "light yellow" showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2.

Conclusions

The study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Quantitative proteomics combined independent PRM analysis reveals the mitochondrial and synaptic mechanism underlying norisoboldine's antidepressant effects

Major depressive disorder (MDD) is a common disease affecting 300 million people worldwide. The existing drugs are ineffective for approximately 30% of patients, so it is urgent to develop new antidepressant drugs with novel mechanisms. Here, we found that norisoboldine (NOR) showed an antidepressant efficacy in the chronic social defeat stress (CSDS) depression model in the tail suspension, forced swimming, and sucrose consumption tests. We then utilized the drug-treated CSDS mice paradigm to segregate and gain differential protein groups of CSDS versus CON (CSDSCON), imipramine (IMI)-treated versus CSDS (IMICSDS), and NOR-treated versus CSDS (NORCSDS) from the prefrontal cortex. These protein expression alterations were first analyzed by ANOVA with p < 0.05. The protein cluster 1 and cluster 3, in which the pattern of protein levels similar to the mood pattern, showed enrichment in functions and localizations related to mitochondrion, ribosome and synapses. Further GO analysis of the common proteins for NORCSDS groups and NORIMI groups supported the findings from ANOVA analysis. We employed Protein-Protein interaction (PPI) analysis to examine the proteins of NORCSDS and NORIMI, revealing an enrichment of the proteins associated with the mitochondrial ribosomal and synaptic functions. Further independent analysis using parallel reaction monitoring (PRM) revealed that Cox7c, Mrp142, Naa30, Ighm, Apoa4, Ssu72, Mrps30, Apoh, Acbd5, and Cdv3, exhibited regulation in the NOR-treated group to support the homeostasis of mitochondrial functions. Additionally, Dcx, Arid1b, Rnf112, and Fam3c, were also observed to undergo modulation in the NOR-treated groups to support the synaptic formation and functions. These findings suggest that the proteins involved in depression treatment exert effects in strengthen the mitochondrial and synaptic functions in the mice PFC. Western blot analysis supported the data that the levels of Mrpl42, Cox7c, Naa30, Rnf112, Dcx Apoa4, Apoh and Fam3c were altered in the CSDS mice, and rescued by NOR treatment, supporting the PRM data. NOR treatment also rescued the NLRP3 inflammasome activation in CSDS mice. In summary, the current proteomic research conducted on the prefrontal cortex has provided valuable insights into the specific and shared molecular mechanisms underlying pathophysiology and treatment to CSDS-induced depression, shedding light on the therapeutic effects of Norisoboldine.

Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders

N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.

Common protein networks for various drug regimens of major depression are associated with complement and immunity

BACKGROUND

Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD.

AIM

This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20).

METHODS

Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied.

RESULTS

Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity.

CONCLUSIONS

Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Correlation between myelin basic protein levels in cerebrospinal fluid and motor speed in patients with schizophrenia

Alterations in the white matter have been implicated in schizophrenia. Myelin basic protein (MBP), a component of the myelin sheath, in the cerebrospinal fluid (CSF) has been suggested as a biomarker for white matter damage in demyelinating diseases. This prompted us to examine the CSF-MBP levels in patients with schizophrenia. We analyzed the CSF-MBP levels in 152 patients with schizophrenia and 117 age- and sex-matched controls. A significant positive correlation between age and CSF-MBP levels was observed both in the patients (p < 0.001) and controls (p = 0.014). No significant difference was observed in the CSF-MBP levels between the two groups. However, among a subsample of the patients (N = 32), a significantly negative correlation was observed between CSF-MBP and age-adjusted motor speed score of the brief assessment of cognition in schizophrenia (ρ = -0.59, p < 0.001). Further, among patients who underwent diffusional magnetic resonance imaging of the brain (N = 27), the CSF-MBP levels showed a significantly negative correlation with the mean kurtosis value in the right temporo-parietal region (p < 0.001). Our results suggest that the CSF-MBP level has limited utility as a diagnostic marker; however, higher CSF-MBP levels are associated with poorer motor speed, which may be associated with regional white matter damage in the brain in patients with schizophrenia.

Biomarkers as proxies for cognitive reserve: The role of high density lipoprotein cholesterol in first episode of psychosis

INTRODUCTION

The proxies used to compose cognitive reserve (CR) for patients of a first episode of psychosis (FEP) have varied in the literature. The development of FEP is linked to peripheral pathways of the central nervous system, yet despite this knowledge, no research has considered the introduction of biomarkers as proxies for CR. Meanwhile, schizophrenia has been linked to the metabolic system, indicating that alterations in the levels of biological parameters, in particular high-density lipoproteins (HDL), cause worse global functioning and cognitive impairment. For these reasons, the present study aimed to create a quantifiable and objective CR index that adjusts for the multifactorial nature of FEP.

MATERIALS AND METHODS

We included 668 FEP patients and 217 healthy controls. Participants were assessed for sociodemographic information, years of education, employment status, premorbid IQ and biological parameters: waist circumference, hypertension, and levels of HDL, triglycerides, and glucose.

RESULTS

The findings suggest that the years of education proxy showed correlational and higher relationship with HDL levels for both FEP patients (r=0.23, b=0.185) and controls (r=0.31, b=0.342). We found that the CR index composed of years of education and HDL levels showed a higher explanatory power for the phenomenon than the classical CR index composed of years of education, employment status and premorbid IQ.

CONCLUSIONS

This article proposes an objective and quantifiable method to measure CR that is more the multifactorial nature of FEP.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Common changes in rat cortical gene expression after antidepressant drug treatment: Impacts on metabolism of polyamines, mRNA splicing, regulation of RAS by GAPs, neddylation and GPCR ligand binding

OBJECTIVES

This study sought to identify pathways affected by rat cortical RNA that were changed after treatment with fluoxetine or imipramine.

METHODS

We measured levels of cortical RNA in male rats using GeneChip® Rat Exon 1.0 ST Array after treatment with vehicle (0.9% NaCl), fluoxetine (10 mg/kg/day) or imipramine (20 mg/kg/day) for 28 days. Levels of coding and non-coding RNA in vehicle treated rats were compared to those in treated rats using ANOVA in JMP Genomics 13 and the Panther Gene Ontology Classification System was used to identify pathways involving the changed RNAs.

RESULTS

18,876 transcripts were detected; there were highly correlated changes in 1010 levels of RNA after both drug treatments that would principally affect the metabolism of polyamines, mRNA splicing, regulation of RAS by GAPs, neddylation and GPCR ligand binding. Using our previously published data, we compared changes in transcripts after treatment with antipsychotic and mood stabilising drugs.

CONCLUSIONS

Our study shows there are common, correlated, changes in coding and non-coding RNA in the rat cortex after treatment with fluoxetine or imipramine; we propose the pathways affected by these changes are involved in the therapeutic mechanisms of action of antidepressant drugs.

Transcutaneous auricular vagus nerve stimulation ameliorates adolescent depressive- and anxiety-like behaviors via hippocampus glycolysis and inflammation response

BACKGROUND

Transcutaneous auricular vagus nerve stimulation (taVNS) is a crucial neuromodulation therapy for depression, yet its molecular mechanism remains unclear. Here, we aim to unveil the underlying mechanisms of antidepression by systematically evaluating the change of gene expression in different brain regions (i.e., hippocampus, anterior cingulate cortex, and medial prefrontal cortex).

METHODS

The adolescent depression rat model was established by chronic unpredictable mild stress (CUMS), followed by the taVNS treatment for 3 weeks. The open field test (OFT), forced swimming test (FST), elevated plus maze test (EPM), and new object recognition (NOR) test were used to evaluate depressive- and anxiety-like behaviors. Gene expression analysis of three brain regions was conducted by RNA sequencing (RNA-seq) and further bioinformatics methods.

RESULTS

The depressive- and anxiety-like behaviors in CUMS-exposed rats were manifested by decreased spontaneous locomotor activity of OFT, increased immobility time of FST, increased entries and time in the closed arms of EPM, and decreased new object index of NOR. Furthermore, CUMS exposure also led to alterations in gene expression within the hippocampus (HIP), anterior cingulate cortex (ACC), and medial prefrontal cortex (mPFC), suggesting a potential link between adolescent stress and pathological changes within these brain regions. TaVNS could significantly ameliorate depressive- and anxiety-like behaviors. Its effects on these three brain regions were found related to regulation of the metabolism, and there were some brain region-specific findings. Compared with ACC and mPFC, taVNS has a more concrete effect on HIP by regulating the inflammation response and glycolysis.

CONCLUSION

taVNS is capable of ameliorating adolescent depressive- and anxiety-like behaviors by regulating plenty of genes in the three brain regions. Suppressed level of inflammatory response and enhanced glycolysis manifests the dominant role of taVNS in HIP, which provides a theoretical foundation and data support for the molecular mechanism of antidepression by taVNS.

Proteomic insights into mental health status: plasma markers in young adults

Global emphasis on enhancing prevention and treatment strategies necessitates an increased understanding of the biological mechanisms of psychopathology. Plasma proteomics is a powerful tool that has been applied in the context of specific mental disorders for biomarker identification. The p-factor, also known as the "general psychopathology factor", is a concept in psychopathology suggesting that there is a common underlying factor that contributes to the development of various forms of mental disorders. It has been proposed that the p-factor can be used to understand the overall mental health status of an individual. Here, we aimed to discover plasma proteins associated with the p-factor in 775 young adults in the FinnTwin12 cohort. Using liquid chromatography-tandem mass spectrometry, 13 proteins with a significant connection with the p-factor were identified, 8 of which were linked to epidermal growth factor receptor (EGFR) signaling. This exploratory study provides new insight into biological alterations associated with mental health status in young adults.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

Downregulation of FTO in the hippocampus is associated with mental disorders induced by fear stress during pregnancy

Mental disorders (MD), such as anxiety, depression, and cognitive impairment, are very common during pregnancy and predispose to adverse pregnancy outcomes; however, the underlying mechanisms are still under intense investigation. Although the most common RNA modification in epigenetics, N6-methyladenosine (m6A) has been widely studied, its role in MD has not been investigated. Here, we observed that fat mass and obesity-associated protein (FTO) are downregulated in the hippocampus of pregnant rats with MD induced by fear stress and demonstrated that FTO participates in and regulates MD induced by fear stress. In addition, we identified four genes with anomalous modifications and expression (double aberrant genes) that were directly regulated by FTO, namely Angpt2, Fgf10, Rpl21, and Adcy7. Furthermore, we found that these genes might induce MD by regulating the PI3K/Akt and Rap1 signaling pathways. It appears that FTO-mediated m6A modification is a key regulatory mechanism in MD caused by fear stress during pregnancy.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Study on correlations of BDNF, PI3K, AKT and CREB levels with depressive emotion and impulsive behaviors in drug-naïve patients with first-episode schizophrenia

BACKGROUND

The pathogenesis of schizophrenia is still unknown. Nearly a half of schizophrenic patients have depressive symptoms and even some impulsive behaviors. The definite diagnosis of schizophrenia is an immense challenge. Molecular biology plays an essential role in the research on the pathogenesis of schizophrenia.

OBJECTIVE

This study aims to analyze the correlations of serum protein factor levels with depressive emotion and impulsive behaviors in drug-naïve patients with first-episode schizophrenia.

METHODS

Seventy drug-naïve patients with first-episode schizophrenia and sixty-nine healthy volunteers from the health check center in the same period participated in this study. In both the patient group and control group, brain-derived neurotrophic factor (BDNF), phosphatidylin-ositol-3-kinase (PI3K), protein kinase B (AKT), and cAMP-response element binding protein (CREB) levels in the peripheral blood were tested by enzyme-linked immunosorbent assay (ELISA). The depressive emotion and impulsive behaviors were evaluated with Chinese versions of the Calgary Depression Scale for Schizophrenia (CDSS) and Short UPPS-P Impulsive Behavior Scale (S-UPPS-P), respectively.

RESULTS

The serum levels of BDNF, PI3K, and CREB in the patient group were lower than those in the control group, while AKT level, total CDSS score and total S-UPPS-P score were all higher. In the patient group, total CDSS score, and total S-UPPS-P score were both correlated negatively with BDNF, PI3K, and CREB levels but positively with AKT level, and the lack-of-premeditation (PR) sub-scale score was not significantly correlated with BDNF, PI3K, AKT, and CREB levels.

CONCLUSION

Our study results showed that the peripheral blood levels of BDNF, PI3K, AKT, and CREB in drug-naïve patients with first-episode schizophrenia were significantly different from those in the control group. The levels of these serum protein factors are promising biomarkers to predict schizophrenic depression and impulsive behaviors.

Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics

Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = -2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = -2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.