Novel therapeutic targets for major depressive disorder related to oxidative stress identified by integrative multi-omics and multi-trait study

doi:10.21203/rs.3.rs-4641375/v1

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Novel therapeutic targets for major depressive disorder related to oxidative stress identified by integrative multi-omics and multi-trait study

https://doi.org/10.21203/rs.3.rs-4641375/v1

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Oxidative stress (OS) is strongly implicated in the pathophysiology of major depressive disorder (MDD) but the molecular mechanisms remain largely unknown. The purpose of this study is to identify genes related to both OS and MDD, and further to evaluate the utility of these genes as diagnostic markers and potential treatment targets. We searched datasets related to MDD from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) also related to OS according to GeneCards. Bioinformatics analyses and machine learning algorithms were used to identify hub genes mediating OS–MDD interactions. A summary data-based Mendelian randomization (SMR) approach was employed to identify possible causal genes for MDD from blood tissue eQLT data. These investigations identified 32 genes mediating OS–MDD interactions, while SMR analysis identified KCNE1 (OR = 1.057, 95%CI = 1.013–1.102, P = 0.010), MAPK3 (OR = 1.023, 95%CI = 1.004–1.043, P = 0.020), and STIP1 (OR = 0.792, 95%CI = 0.641–0.979, P = 0.031) as OS-related causal genes for MDD. These genes may thus serve as useful diagnostic markers and potential therapeutic targets.

Health sciences/Diseases/Psychiatric disorders/Depression

Health sciences/Biomarkers/Diagnostic markers

Oxidative stress

Depression

Machine learning

Integrative Omics

Mendelian randomization

Major depressive disorder (MDD) is a common psychiatric illness characterized by persistent depressed mood accompanied by heterogenous cognitive, behavioral, and physical symptoms(1). According to the World Health Organization, approximately 280 million people worldwide are currently afflicted with MDD(2). A nationally representative cross-sectional survey from China (the China Mental Health Survey) revealed a weighted lifetime prevalence of 3.4% and weighted 12-month prevalence of 2.1%(3), with substantially higher weighted lifetime prevalence and weighted 12-month prevalence among adult females (8.0% and 4.2%, respectively) compared to males (5.7% and 3.0%)(4). It is generally accepted that genetic, biological, psychosocial, and personality traits all contribute to MDD risk, termed the diversified disease hypothesis of MDD(5). Pathological processes implicated in MDD include glutamatergic excitotoxicity, brain-derived neurotrophic factor/ tyrosine receptor kinase B signaling insufficiency, neuroinflammation, and gut microbiota–brain axis disturbance(6, 7). However, there are no effective treatments based on these mechanisms, implying complex multidimensional pathogenesis.

Cellular metabolism and various signaling mechanisms result in the formation of highly reactive free radicals, including reactive oxygen species (OS) and reactive nitrogen species (RNS). Under normal physiological conditions, these species are neutralized by endogenous antioxidants; however, an imbalance between ROS or RNS production and scavenging will result in oxidative stress (OS), which leads to the oxidative damage of cellular lipids, proteins, and genomic DNA(8), while severe OS can trigger necrotic or apoptotic cell death. Oxidative stress and ensuing cell death is strongly implicated in neurodegenerative diseases such as the Alzheimer’s disease and Parkinson’s disease(9, 10), and recent studies have suggested that OS also contributes to the pathophysiology and treatment response of MDD(11, 12). For instance, a positive correlation was found between and the amplitude of low-frequency fluctuations in key MDD-associated brain regions, such as the thalamus, anterior cingulate gyrus, and superior frontal gyrus, and plasma concentrations of the antioxidant enzymes superoxide dismutase (SOD) and glutathione reductase(13). Furthermore, depression severity and working memory impairment were associated with higher plasma concentrations of malondialdehyde (MDA), an indicator of lipid peroxidation from oxidative stress, among recurrent MDD patients both before and after antidepressant treatment(14). Reduced SOD activity, lower levels of the non-enzyme antioxidant glutathione (GSH), and elevated lipid peroxidation products have been proposed as reliable biomarkers for MDD(15, 16). In experiment animals as well, chronic unpredictable mild stress can induce depression-like symptoms and concomitant abnormalities in redox balance across brain subregions, including decreased GSH and SOD activities and higher levels of ROS, MDA, and carbonyl in prefrontal cortex and hippocampus(17). Despite a growing number of studies implicating OS in MDD, there are still discrepancies(11, 18). An early meta-analysis of 23 studies with 4980 individuals found increased OS among MDD patients compared to matched individuals without depression, but some included studies found lower antioxidant biomarker concentrations (MDA and 8-oxo-2′-deoxyguanosine) in MDD(19). A recent meta-analysis also concluded that OS status was more severe in MDD, but found no significant differences in catalase, SOD, glutathione peroxidase, and uric acid between MDD patients and healthy controls(20).Therefore, the precise contributions of OS to MDD pathogenesis remained unclear.

To address this issue, we conducted a comprehensive multi-omics analysis of the association between OS and MDD and identified multiple differentially expressed genes (DEGs) linking the two conditions. Machine learning models trained using these DEGs reliably distinguished patients from controls in an independent dataset. Thus, these genes may serve as feasible biomarkers or treatment targets. Furthermore, we utilized a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal OS-related genes for MDD.

An overview of the analytical methods is illustrated in Fig. 1.

No ethics committee approval was required for this summary-level study. The gene array data of three study cohorts were extracted from the Gene Expression Omnibus (GEO) database: GSE32280 (platform: GPL570)(21), GSE39653 (platform: GPL10558)(22) and GSE98793 (platform: GPL570)(23). In total these datasets included gene expression profiles for 165 MDD patients and 96 health controls. Genes related to OS were identified from the GeneCards database (https://www.genecards.org) using the keyword “oxidative stress”. Those with a relevance score ≥ 7 were included according to previous methods(24).

Identification of differentially expressed genes (DEGs) associated with MDD and OS

We identified genes common among the disease datasets GSE32280, GSE39653, GSE98793, and GeneCards using the “cbind ()” function of R, and then removed batch bias and performed log2(X + 1) normalization using the R sva function. These standardized gene expression data formed the basis for all further analysis. To analyze the effect of OS-related gene expression level on MDD risk, the R limma package was used to identify DEGs between disease and normal control samples, with the absolution value of log2-fold change (log2FC) > 1 and P < 0.05 set as thresholds. These DEGs were visualized by volcano plot and a heat map was constructed using the R packages heatmaps (version 1.0.12) and ggplot2 (version 3.3.2).

Screening of hub genes mediating crosstalk between MDD and OS

Univariate logistic regression and multivariate logistic regression analyses of the GSE39653 dataset were used to identify hub genes within the MDD- and OS-related gene set (Hub MDD-OS DEGs) with P < 0.05 considered statistically significant. This was followed by dimensionality reduction analysis.

Identification of key modules and functional genes

To reveal biological relationships among key genes and gene clusters related to OS and MDD, Gene Ontology (GO) enrichment analysis was conducted using the R package clusterProfiler. Genes were assessed for molecular function (MF), cell component (CC), and biological process (BP) annotations with a False Discovery Rate (FDR)-corrected P < 0.05 considered statistically significant. The R package cowplot was then used to conduct Spearman correlation analyses between key OS- and MDD-related genes as well as to draw heat maps, scatter plots, and correlation curves. Again, P < 0.05 was considered statistically significant. Key genes were then mapped to chromosomal locations using the R package RCircos based on data from the R package and downloaded from the ENSEMBL database. The open-source STRING database was then used to build a protein–protein interaction (PPI) network(25), which was subsequently visualized using Cytoscape.

In-depth investigation of key genes

Key genes mediating MDD–OS interactions were further screened and evaluated using 6 machine learning algorithms, Bagged Trees, Bayesian, Random Forest, Wrapper (Bpruta), Learning Vector Quantification (LQV), and 1000 iterations 10-fold cross-validation Least Absolute Shrinkage and Selection Operator (LASSO). A DEG identified as a feature gene by at least 5 algorithms based on classification performance was considered an important Hub DEG for MDD–OS interactions. The interactions among these key intersecting genes were then visualized using the R package “Upset” application. To explore the associations of individual hub gene pairs in the GSE39653 and GSE9873 datasets, we conducted Spearman’s correlation analyses and plotting of heat maps, scatter plots, and correlation curves using cowplot. Finally, RCircos was used to draw the chromosome localization map for display.

Based on these analyses, we constructed a nomogram based on the GSE39653 dataset using the R package rms. A nomogram model was then constructed based on the hub genes to predict the prevalence of OS-associated MDD. Calibration curve analysis was performed to evaluate the accuracy and resolution of the nomogram. Finally, receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the diagnostic model for the onset of OS-associated MDD using the R package pROC. Thorough composition validation techniques were employed to confirm model performance for distinguishing MDD from control samples.

We also developed a classification prediction model based on expression analysis of blood utilizing a combination of the aforementioned 6 machine learning algorithms. The performance of each model was evaluated by calculating the area under the ROC curve (AUC), followed by visual representation of the results (predictive genes) using heat maps. Optimal model performance was assessed using calibration curve and decision curve analysis (DCA).

The R ConsensusClusterPlus package was used to identify subpopulations with distinct molecular phenotypes based on hub genes for MDD–OS interactions(26). Principal co-ordinates analysis (PCoA) was then conducted to verify consistency of clustering. The R package ggpubr was used to draw box plots with sample cluster labels as groups, and the differences between groups were evaluated for statistical significance by the Wilcoxon rank sum test.

Immuno-infiltration analysis

The relative abundances of specific infiltrating immune cells were estimated using CIBERSORT (https://cibersort.stanford.edu/)(27), an analytical tool designed to reveal the distribution levels of LM22 immune cells based on gene expression profiles. Distinct enrichment fractions of immune cells were then compared using the Wilcox test. We further performed quantitative Single Sample Gene Set Enrichment Analysis (ssGSEA) to calculate the abundance of immune cells associated with MDD–OS interactions. Finally, differences in immune cell infiltration were visualized using ggplot2 with P < 0.05 set as the threshold for significance.

SMR analysis

We gathered genome-wide association study (GWAS) summary statistics of 170,756 MDD cases and 329,443 controls (8,483,301 genetic variants in total) from the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc), one of the largest, most innovative, and productive platforms in psychiatry(28). Single nucleotide polymorphisms (SNPs) associated with expression quantitative trait loci (eQTLs) were selected as instrumental variables (IVs) to infer direct causal effects of gene expression or protein levels on MDD. Whole-blood eQTL summary statistics for 15,882 genes were obtained from 31,684 individuals in the eQTLGen database(29). The current study focused only on cis-eQTLs within 1-Mb from the start or end of the gene.

Statistical analysis

All statistical analyses were performed using R 4.2.0. Continuous variables were compared between two groups using the Wilcoxon rank sum test and among three or more groups using the Kruskal–Wallis test. Categorical variables were compared by the chi-square test or Fisher's exact test. Associations between immune cell abundance and gene expression levels were evaluated using Spearman's correlation tests. A P < 0.05 was considered statistically significant for all tests.

Causal inferences between GWAS and cis-eQTLs were evaluated using the SMR multi-tool. The top associated cis-QTLs were selected by considering a window centered around the corresponding gene (± 1000 kb) and surpassing a P-value threshold of 5.0 ×10^− 8. All SNPs with allele frequency differences larger than the specified threshold (0.2 in the current study) between datasets, including the LD reference sample, the QTL summary data, and the outcome summary data, were excluded. All SMR analysis were implemented using SMR v1.3.1 and included SNPs as instruments, key genes for MDD–OS interactions as exposures, and MDD as the outcome (SMR P < 0.05, cis-eQTLs, and GWAS P < 1 × 10^− 5).

Sensitivity analysis

The strengths of SNPs used as instruments were assessed using the F-statistic, and we included only SNPs with an F-statistic > 10 to minimize weak instrument bias(30). The heterogeneity in the dependent instrument (HEIDI) test was applied using SMR v1.3.1 to distinguish pleiotropy from linkage. All instruments with P-HEIDI < 0.01 (indicating significant heterogeneity) were omitted from the analysis.

Identification of DEGs between MDD cases and controls

The GEO datasets GSE32280, GSE39653, and GSE98793 were combined using the R “cbind ()” function and the batch bias mitigated using the R “sva” package (see Fig. 2 and Supplementary Table 1). In total, 596 DEGs were identified between MDD patients and healthy controls (HCs) using Limma, including 294 upregulated genes and 302 downregulated genes in MDD. The magnitudes of differential expression for significant DEGs (P < 0.05) are shown as a volcano map in Fig. 3A and as a heatmap in Fig. 3B and details in Supplementary Table 2. These DEGs were enriched in GO CC terms ‘specific granule’, ‘specific granule membrane’, ‘secretory granule membrane’, ‘specific granule lumen’, and ‘tertiary granule’, BP terms ‘receptor signaling pathway via JAK-STAT and STAT’, ‘placenta development’, and ‘carbohydrate catabolic process’, and MF terms ‘identical protein binding’, ‘immune receptor activity’, ‘cytokine receptor activity’, ‘phospholipase activity’, and ‘1-phosphatidylinositol-3-kinase regulator activity’ (Fig. 3C, D and Supplementary Table 3).

To identify MDD-associated genes also related to OS, these DEGs were searched against the 817 genes in GeneCards with relevance score ≥ 7 for OS, yielding 38 potential MDD–OS interaction or crosstalk genes (Supplementary Table 4). The Venn diagram of these overlapping DEGs is shown in Fig. 4A and the chromosomal positions in Fig. 4B. Among these 38 DEGs, AMD, ALPP, CAMK2G, DDAH1, KCNE1, LEP, MAPK3, IL10, PINK1, and SLC2A1 were significantly upregulated in the MDD–OS samples (Fig. 4C). More details were shown in Supplementary Table 5.

Identification of key MDD–OS interaction genes via machine learning

Machine learning algorithms were trained to deduce the associations between the 38 MDD–OS DEGs and MDD pathogenesis using the GSE39653 dataset, and model performance was validated using the GSE98793 dataset. Intersection of results yielded by the Bagged Trees algorithm (Fig. 5A), Bayesian algorithm (Fig. 5B), Random Forest algorithm (Fig. 5C), Wrapper algorithm (Fig. 5D), LQV (Fig. 5E) and 1000 times 10-fold cross-validation LASSO Logistic model (Fig. 5F) identified 32 of these genes as closely related to the pathogenesis of MDD and OS (Fig. 5G): ADM, AKR1C3, ALPP, CAMK2G, CREBBP, DDAH1, DNM1L, F5, FKBP5, GADD45A, GATB, GDF15, HSP90AA1, HSP90AB1, HSP90B1, IL10, INSR, KCNE1 (encoding Potassium Voltage-Gated Channel Subfamily E Regulatory Subunit 1), KLF2, LEP, MAP2K1, MAPK3 (encoding mitogen-activated protein kinase 3), MGST1, PLA2G7, PLAU, PTK2B, RETN, SLC2A1, STIP1 (encoding stress-induced phosphoprotein 1), TNF, UGT1A1, and VDR. Subsequently, we constructed a PPI network with these 32 genes as hubs using the STRING database. Based on the criteria |Logfc| < 0.05 and P value < 0.05, this 32-node PPI network included 341 edges, indicating complex multilevel interactions between OS and MDD pathogenesis.

A diagnostic model was then constructed based on the GSE39653 dataset (Fig. 6A), and discrimination was verified on both the training set GSE39653 and validation set GSE98793. The recall curve and Hosmer − Lemoeshow goodness-of-fit test results were equal to 1 for the training set (GSE39653), indicating a low probability of type I error, that prediction results were close to the real data, and that the calibration degree of the model was high (Fig. 6B). The AUC of the diagnostic model was also 1.00 for the training set GSE39653 (Fig. 6C). The C-index of the diagnostic model was 0.99 for the training set (GSE39653) and 1 for the validation set (GSE98793) (Fig. 6D), and the recall curve and Hosmer − Lemeshow test result P values were equal to 1.00 (Fig. 6E). Finally, the AUC of the diagnostic model was 0.876 for the validation set (GSE98793) (Fig. 6F).

Associations of key genes with MDD subtypes

The physical, behavioral, and cognitive symptoms of MDD can vary substantially among patients, so we examined the potential functions of these 32 key genes in distinct MDD phenotypes by cluster analysis using ConsensusClusterPlus. This analysis yielded two patient clusters in the GSE39653 dataset, Cluster A and Cluster B, based on DEG profiles (Fig. 8A). The slope of consistency index for different classifications (Fig. 7B) further confirmed that two was the optimal cluster number for this cohort, while the scree plot in Fig. 8C revealed the inflection point for best classification. We further validated the stability of this MDD patient subtyping in GSE39653 by PCoA (Fig. 7D).

To explore differences in pathogenesis between the two subtypes, the total DEGs for Cluster A and Cluster B were first identified. In total, 3560 DEGs were found, including 1367 expressed at higher levels in Cluster A and 1223 expressed at higher levels in Cluster B (Fig. 7E). Then, GO enrichment analysis was conducted to reveal differential enrichment of biological functions. Enriched BP terms in Cluster A included ‘leukocyte degranulation’, ‘aerobic electron transport chain’, and ‘ATP synthesis coupled electron transport’, while enriched CC terms included ‘respiratory chain complex’ and ‘ficolin-1-rich granule lumen’, and enriched MF terms included ‘antioxidant activity’ and ‘oxidoreduction-driven active transmembrane transporter activity’ (Fig. 7F and Supplementary Table 6).

Analysis of immune characteristics

Immune cell infiltration is a major driver of OS in the brain, suggesting an important contribution to OS-related MDD pathogenesis. We employed Cibersort analysis to calculate the infiltration status of 22 immune cells in the GSE39653 dataset, and the relative abundance of each immune cell type was then compared by the Wilcoxon sign rank test (Fig. 8A). The CIBERSORT algorithm was also used to compare immune cell infiltration profiles between Cluster A and Cluster B. The results revealed that dendritic cells and activated mast cells were highly abundant in Cluster B (Fig. 8B), and that the relative abundances of most immune cell subsets differed between clusters (Fig. 8C). Moreover, the ssGSEA algorithm indicated greater infiltration of activated CD8 + T cells, effector memory CD8 + T cells, regulatory T cells, Type 1 T helper cell, eosinophils, macrophages, and monocytes in MDD samples of Cluster A (Fig. 9A) (P < 0.05). Furthermore, abundances were correlated with the expression levels of the 32 most important DEGs (all P < 0.05) (Fig. 9B).

SMR analysis of characteristic genes

We also performed SMR analysis to evaluate the association strengths of these 32 key genes with MDD (with P-smr < 0.05 set as the threshold for statistical significance). Results revealed an association between elevated expression of KCNE1 and MDD odds (OR = 1.057, 95% CI = 1.013–1.102, P-smr = 0.010) (Fig. 10A, B, and G). Similarly, elevated MAPK3 expression was associated with greater MDD odds (OR = 1.023, 95% CI = 1.004–1.043, P-smr = 0.020) (Fig. 10C, D, and G), while upregulation of STIP1 was associated with reduced MDD odds (OR = 0.792, 95% CI = 0.641–0.979, P-smr = 0.031) (Fig. 10E, F, and G). Supplementary Table 7 showed the SMR association between expression of gene MAPK3, KCNE1, STIP1 and MDD.

Sensitivity analysis

The F-statistic of all SNPs included in the analysis ranged from 29.855 to 3394.048, indicating a powerful instrumental variable–exposure association (threshold set at 10) (Supplementary Table 7). Results of the HEIDI test further suggested that all observed associations were not due to linkage (p > 0.01).

To our best knowledge, this is the first study on the contributions of oxidative stress-related genes to MDD pathogenesis using integrated multi-omics, machine learning, infiltrated immune cell profiling, genome-wide association, and summary data-based Mendelian randomization analysis. We identified 38 genes differentially expressed between MDD patients and controls that were also associated with OS, of which 32 were deemed important to the influence of OS on MDD pathogenesis (MDD–OS interaction genes) in training and validation cohorts by 6 separate machine learning algorithms. Further screening of blood tissue expression profiles by SMR analysis identified KCNE1, MAPK3, and STIP1 as key linkage genes between OS and MDD. These DEGs may thus be convenient biomarkers for MDD as well as potential treatment targets.

Neuroinflammation in strongly implicated in MDD as evidenced by elevated inflammatory marker concentrations, infiltrating immune cell numbers, and antibody titers(31). These inflammatory processes both generate and are promoted by ROS and RNS (hence the OS–inflammation interaction is also known as “evil twins of aging”), further implicating OS in MDD pathogenesis(32). Elevated ROS production leads to GSH depletion, oxidative damage, and ultimately enhanced inflammation(33). Excessive ROS can promote the expression of proinflammatory cytokines through several pathways, including activation of promoting protein-1 and nuclear factor kappa-B (NFκB), increased histone acetylation, and activation of caspase-1 and NOD-like receptor thermal protein domain associated protein 3(34–36). Inflammatory reactions induce expression and release of peroxiredoxin 2, which in turn stimulates macrophages to release pro-inflammatory tumor necrosis factor-α (TNF-α)(37). Elevated TNF-α had been detected in serum and in multiple brain subregions (including the anterior cingulate cortex, prefrontal cortex, and hippocampus) of MDD patients(38, 39). Neuroinflammation also promotes the kynurenine pathway and ensuing quinolinic acid generation by activating indoleamine 2,3-dioxygenase (IDO), tryptophan-2,3-dioxygenase (TDO), and kynurenine 3-mono-oxygenase, which induces mitochondrial damage and results in further ROS production, glutamate release, N-methyl-D-aspartic acid (NMDA) receptor activation, Ca²⁺ influx, and mitochondrial calcium overload, the end result of which is loss of mitochondrial membrane potential, reduced ATP generation, and accelerated ROS generation(34, 40, 41). In addition, IDO and TDO activation may reduce 5-HT biosynthesis, and 5-HT insufficiency is widely believed to result in low mood(42). These relationships also appear to be bidirectional, such that OS can promote neuroinflammation and vice versa in MDD. In accord with previous studies, we found that multiple immune-inflammatory gene pathways were activated in MDD compared to controls. These genes may in turn mediate the reciprocal exacerbation of OS generation and neuroinflammation leading to MDD. The OS–MDD crosstalk genes identified in this study are primarily involved in immune cell function, including activated CD8 + T cells, effector memory CD8 + T cells, regulatory T cells, type 1 T helper cells, eosinophils, macrophages, and monocytes, further supporting shared immune-inflammatory mechanisms in OS and MDD.

We also conducted SMR analysis to identify new causal genes for MDD as such genes may be prime drug targets. Upregulation of KCNE1 and MAPK3 were found to increase MDD risk, potentially by promoting pathogenic mechanisms involving OS. The MAPK3 product extracellular signal-regulated kinase 1 (ERK1) regulates cell proliferation, differentiation, and cell cycle progression among other vital processes(43). It has been reported that ERK signaling is significantly downregulated in the prefrontal cortex and hippocampus of both human patients and animal models of chronic depression(44–46). The ERK1/2 isoforms are the most thoroughly investigated and well characterized isoforms in the central nervous system(42, 47, 48), and both have been found to promote OS via ROS production and to amplify the inflammatory response through activation of the stress-responsive transcription factor NFκB(49, 50). At present, most studies on the role of MAPK3 in MDD have focused on the brain, while few studies have investigated expression changes in more accessible blood samples. Moreover, most studies have focused on ERK1/2, but few specifically on ERK1. We found higher MAPK3 expression in the blood tissue of MDD patients compared to controls, consistent with previous findings. One prospective case–control study reported that a MAPK3 SNP enhanced interferon-α-induced depression, possibly by increasing the propensity for glutamate dysregulation(51). A bioinformatics analysis identified 5 genes including MAPK3 as key modulators of post-stroke depression risk, disease biomarkers, and therapeutic targets of acupuncture(52). Others have found significant associations of MAPK3 with schizophrenia, and a recent genome-wide Mendelian randomization analysis identified MAPK3 as a potential drug target for schizophrenia treatment(53), in line with previous studiess(54, 55). Based on these and our own findings, we speculate that MAPK3 may be a critical mediator of OS effects on MDD pathogenesis and thus a promising therapeutic target. However, in our present study, MAPK3 appeared to make only a limited contribution (OR = 1.023, 95% CI = 1.004–1.043). Nonetheless, the contributions of MAPK3 to OS and MDD warrant further exploration.

In contrast to MAPKs, few studies have examined the genetic association of KCNE1 with MDD, although McCaffery and colleagues proposed that KCNE1 is associated with longer-term changes in depressive symptoms(56). The KCNE family proteins are regulatory subunits of voltage-gated K(+) channels(57), and are implicated in multiple arrhythmogenic cardiac myocardium diseases(58). The KCNE1 subunit regulates the neuronal membrane potential through modulation of K(+) channels, including KCNQ channels(59). Further, the KCNQ channel modulator retigabine has been shown to improve depressive symptoms, suggesting therapeutic potential for MDD(60). Another study also included KCNE1 expression in a diagnostic model for MDD(61), although no causal association was suggested. In the current study, preliminary genomic analysis indicated that KCNE1 was upregulated in MDD and positioned as a linker gene between MDD and OS, while according to SMR analysis, KCNE1 upregulation increases the risk of MDD. We speculate that drugs targeting KCNE1 could show therapeutic efficacy against MDD.

These same genomics analyses also revealed downregulation of STIP1, which encodes a co-chaperone that interacts with heat-shock proteins 70 and 90, in the blood tissue of MDD patients, in accord with previous reports(62, 63). Further, SMR identified STIP1 as a protective target against MDD (OR = 0.792, 95% CI = 0.641–0.979). Thus, activation of STIP1 expression may be a useful therapeutic strategy against MDD. In addition to acting as a chaperone, extracellular STIP1 acts as a trophic factor to engage PrP^C, thereby enhancing neuritogenesis and neuronal survival(64, 65). Studies have also implicated STIP1 in functional recovery after stroke and regulation of Aβ peptide toxicity in Alzheimer's disease models. Moreover, a GWAS analysis identified a STIP1 polymorphism as a potential risk factor for attention-deficit disorder(66). Mice with elevated STIP1 levels (up to nearly fivefold) showed no neuropathology, anxiety-like behaviors, depression-like behaviors, spatial memory deficits, or attention deficits(67), suggesting that STIP1 augmentation may be a feasible strategy for antidepressant treatment; however, the detailed underlying mechanisms remain unclarified.

Study limitations:

This study has several limitations. First, it is possible that differences in gene expression between MDD patients and controls reflect the influences of factors such as age, sex, smoking, medications, and other health conditions. Second, we only focused on the cis-regions of OS and MDD genes, despite the possibility that trans-eQTL SNPs (SNPs > 5 Mb from the gene) may have a widespread impact on regulatory networks. Finally, functional experiments are still needed to confirm the importance of these DEGs in MDD pathogenesis through OS-dependent or OS-independent pathways.

This integrative multi-omics and multi-trait study identified numerous genes linking OS to MDD pathogenesis, including three genes, KCNE1, MAPK3, and STIP1, causally associated with MDD. These gene in particular could serve as diagnostic markers and drug targets for MDD treatment. In addition, the dozens of other genes identify may provide clues to novel pathological mechanisms for MDD.

Declaration of interests

All authors declare no competing interests.

Data sharing

Original data of major depressive depression could be retrieved from References(21–23, 28). The raw data of oxidative stress could be obtained from the GeneCards database (https://www.genecards.org). The data analyzed in this paper were publicly available and the specific information and links of source data of all Figures and Tables are showed in Supplementary files.

Funding

This work was supported by grants awarded by the Major Project of the Department of Science & Technology, Liaoning Province (2019JH8/10300019).

Code availability

R Code for the current analysis was available at Supplementary material- “Source code. R”. Please contact the corresponding author if you would like to see any data that are not included in the Article or the Appendix.

Contributors

GZ and XJS conceived and planned the study. XJS, YW, ZLG, GML, XTZ, LJ, MM and LD selected the articles and extracted data. XJS and YW did the statistical analyses. XJS wrote the report. GZ had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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The authors have declared there is NO conflict of interest to disclose

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Novel therapeutic targets for major depressive disorder related to oxidative stress identified by integrative multi-omics and multi-trait study

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Abstract

Figures

Introduction

Methods and materials

Identification of differentially expressed genes (DEGs) associated with MDD and OS

Screening of hub genes mediating crosstalk between MDD and OS

Identification of key modules and functional genes

In-depth investigation of key genes

Immuno-infiltration analysis

SMR analysis

Statistical analysis

Sensitivity analysis

Results

Identification of DEGs between MDD cases and controls

Identification of key MDD–OS interaction genes via machine learning

Associations of key genes with MDD subtypes

Analysis of immune characteristics

SMR analysis of characteristic genes

Sensitivity analysis

Discussion

Study limitations:

Conclusion

Declarations

Declaration of interests

Data sharing

Funding

Code availability

Contributors

References

Additional Declarations

Supplementary Files

Status:

Version 1