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. 2026 Feb 11;16:8336. doi: 10.1038/s41598-026-39284-y

Systems-level transcriptomic analysis reveals synapse-related gene dysregulation in peripheral leukocytes of MDD patients

Anny Silva Adri 1,8,✉, Adriel Leal Nóbile 1,8, Débora Gomes de Albuquerque 2,7,8, Pedro Marçal Barcelos 1,8, Fernando Yuri Nery do Vale 1,8, Roseane Galdioli Nava 2,8, Yohan Lucas G Correa 1,8, Lena Friederick Schimke 5,8, Luiz Fernando Onuchic 5, Rodrigo Dalmolin 4,9, Rafael Machado Rezende 6, Haroldo Dutra Dias 5,8, Igor Salerno Filgueiras 3,7,8, Otavio Cabral-Marques 1,2,3,4,5,7,8,9,✉
PMCID: PMC12966306  PMID: 41673267

Abstract

Major depressive disorder (MDD) involves both central nervous system dysfunction and systemic immune alterations. Using a systems biology approach, we investigated whether peripheral leukocytes exhibit transcriptional changes in genes annotated to synaptic processes, molecular functions typically associated with neurons but also possibly implicated in immune cell biology. A meta-analysis of transcriptomic data from 3072 individuals identified 1383 meta-differentially expressed genes (metaDEGs) in leukocytes, including 73 whose known functions are linked to synaptic biology. Among them, functional enrichment analysis indicated synapse-related metaDEGs (48 downregulated, 25 upregulated) involved in synaptic vesicle cycling, neurotransmitter signaling, synaptic assembly, and neurogenesis. Linear discriminant analysis (LDA) identified 18 of these genes that robustly distinguished MDD patients from healthy controls across independent datasets. Notably, we identified metaDEGs shared between leukocytes and brain regions associated with MDD, indicating that genes traditionally linked to neuronal pathways are also expressed in immune cells, where they may contribute to immune-related mechanisms relevant to the disorder. These findings highlight potential systemic molecular patterns that warrant further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-39284-y.

Subject terms: Computational biology and bioinformatics, Databases, Gene ontology, Neuroimmunology, Depression

Introduction

Major depressive disorder (MDD) is a multifactorial psychiatric disorder1 characterized by persistent low mood, anhedonia, cognitive impairments, and a wide range of somatic symptoms that markedly interfere with daily functioning2, making it one of the leading causes of disability worldwide3. Due to its clinical and biological heterogeneity, understanding the pathophysiology of MDD requires an integrative approach encompassing genetic, environmental, and neurobiological factors4–6. Among these, increasing attention has been given to the interaction between the nervous and immune systems7–9, a complex interface through which inflammatory mediators can modulate neural processes such as synaptic plasticity, neurogenesis, and neurotransmission, while neural activity may influence immune responses10,11.

These interactions suggest the hypothesis that MDD may involve systemic dysregulation, in which peripheral immune cells could exhibit altered expression of genes annotated to pathways typically associated with neuronal functions2. Notably, peripheral blood leukocytes are not passive immune responders; they express several components of neurotransmitter-related pathways7,12, including receptors and enzymes for dopamine, serotonin, glutamate, and acetylcholine11,13,14. While these molecules are classically linked to neuronal signaling15, they also participate in immune modulation16,17. Although the complete structural organization of synaptic-like assemblies in leukocytes remains unconfirmed, studies indicate that neurotransmitter signaling components in these cells can influence immune activity13,18. Alterations in leukocyte neurotransmitter pathways have been observed in psychiatric conditions such as MDD19,20 suggesting that immune cells may exhibit molecular features commonly described in the central nervous system.

Building on these findings, we hypothesized that MDD may involve transcriptional overlaps between immune and neuronal gene programs, where leukocytes express genes annotated to synaptic and neuronal processes that are also active in brain regions implicated in the disorder. Such annotations, revealed by enrichment analyses, do not imply that immune cells form functional synapses, but rather that they share molecular components with neuronal pathways, a feature that warrants future mechanistic validation. Our hypothesis is that systemic transcriptomic signatures may capture molecular parallels with brain alterations in MDD. To address this, we performed an observational, integrative systems biology study, combining transcriptomic datasets from peripheral leukocytes with comparative analyses of brain tissues.

Results

Synaptic-related networks in peripheral leukocytes modulated by MDD

To examine whether peripheral leukocytes11,15,21 display an intrinsic network of synaptic-related metaDEGs in MDD, we analyzed publicly available bulk RNA-seq datasets from these cells, a key component of the peripheral immune system (Supplementary Fig. 1a). Their accessibility and role in mediating immune interactions with the central nervous system make peripheral leukocytes22 suitable for investigating molecular associations relevant to MDD pathology23–25.

A meta-analysis of two independent datasets, using the MetaVolcanoR framework to combine differential gene expression results, identified 903 downregulated and 480 upregulated metaDEGs (p < 0.05; Supplementary Fig. 22 Supplementary Table 14 and 15), respectively. Most of these metaDEGs primarily code for proteins. Gene Ontology (GO) enrichment analysis of the metaDEGs revealed biological processes (BPs) associated with both immune and nervous system functions (Fig. 1a; Supplementary Table 1), such as significantly enriched pathways (e.g., synaptic vesicle cycle and interleukin-27-mediated signaling pathway; Fig. 1b; Supplementary Table 2).

Fig. 1.

Fig. 1

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Meta-analysis of peripheral leukocytes and enrichment of biological processes. (a) Scatterplot of enriched biological processes (BPs) related to the immune and nervous systems, with color intensity representing statistical significance. (b) Heatmap of the semantic similarity matrix for enriched BPs, generated with the rrvgo package. Both axes represent enriched Gene Ontology (GO) terms, and the color scale indicates their semantic similarity (from low in blue to high in red). “Parent” labels correspond to the higher-level GO categories assigned by rrvgo to group related terms.

To facilitate interpretation, we utilized GO annotation to assess the biological relevance of DEGs within peripheral leukocytes, leveraging the rrvgo26 package to reduce redundancy in enriched GO terms. In addition to identifying dysregulation in immune-related BPs, such as granulocyte activation, our results also highlighted the presence of nervous system-related BPs within peripheral leukocytes as parent terms in the GO hierarchy (Fig. 1b), which includes vital processes such as the synaptic vesicle cycle. These terms suggest transcriptional overlap between immune and neuronal molecular programs, indicating that genes annotated to synaptic processes may also participate in immune-related mechanisms relevant to MDD23–25.

MetaDEGs in peripheral leukocytes annotated to synaptic-related processes

To further explore these nervous system-related BPs, we focused on characterizing the enrichment of annotated DEGs in synaptic-related BPs. To achieve this, we performed enrichment analysis across multiple platforms, enabling the systematic annotation of synaptic-related DEGs.

Our annotation identified 73 synapse-related metaDEGs in peripheral leukocytes (48 downregulated and 25 upregulated; Fig. 2a). Among these, we prioritized the top 15 genes enriched in synaptic BPs (padj < 0.05): NLGN1, CDK5, OTOF, GIT1, STX1A, PLD2, BCR, IQSEC2, CACNA1B, MAPK8IP2, DOC2A, STX2, KIF1A, LZTS3, and MX1. These DEGs are involved in key functions such as synaptic vesicle cycling, modulation of chemical synaptic transmission, synaptic exocytosis, and dendritic spine morphogenesis. The network analysis (Fig. 2b; Supplementary Table 3) places these genes within their associated BPs, underscoring their dual annotation in immune- and nervous system–related pathways.

Fig. 2.

Fig. 2

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Synaptic-related DEGs. (a) Schematic representation of the identification of synapse-related differentially expressed genes (DEGs). From the meta-analysis of differentially expressed genes (metaDEGs; p < 0.05), we performed an enrichment analysis using the EnrichR environment to access ontologies and pathways from different databases. As a result, we identified 73 metaDEGs associated with synaptic pathways and/or ontologies present in at least two databases, of which 48 were down-regulated and 25 up-regulated. (b) Network of biological processes (BPs) associated with the 73 synaptic-related metaDEGs, highlighting their interactions and synapse-related functions. Node shape indicates DEG regulation status (triangle = upregulated; inverted triangle = downregulated), and node size corresponds to the degree value (number of direct connections to other nodes).

Notably, the enrichment analysis for synaptic-related metaDEGs identified in the Wittenberg et al.27 study revealed 25 synaptic-related metaDEGs, including two that overlap with the metaDEGs obtained from our peripheral leukocytes meta-analysis (Supplementary Fig. 3; Supplementary Table 16–18). This supports the presence of synaptic-related transcriptional alterations in immune cells of MDD patients, although their precise functional role in immune biology remains to be determined.

Network analysis revealed interconnected relationships among synapse-related metaDEGs and BPs, underscoring their potential relevance to both immune and nervous system functions (Fig. 2b).

MetaDEGs in peripheral leukocytes annotated to synaptic-related processes differentiate MDD patients from healthy controls

Investigating the metaDEGs in peripheral leukocytes annotated to synaptic-related processes provides a systems-level view of potential neuroimmune interactions in MDD. This approach allows the identification of coordinated transcriptional patterns that may bridge immune and nervous pathways, contributing to the systemic characterization of the disorder. To pursue this, we applied the stratification technique Linear Discriminant Analysis (LDA), after identifying the most relevant metaDEGs. This selection was based on enrichment analysis of synaptic terms from the 73 synaptic-related metaDEGs using the EnrichR database in R, followed by identification of genes shared between the Cathomas et al. and Trang et al. datasets (Fig. 3a).

Fig. 3.

Fig. 3

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Variable selection by LDA. (a) Diagram illustrating the identification of the meta-analysis of differentially expressed genes (metaDEGs) that most contributed to the discrimination between the MDD and control groups. From the 73 metaDEGs related to the synapse, linear discriminant analysis (LDA) was performed, resulting in 32 metaDEGs selected from the Trang et al., 2018 study and 38 from the Cathomas et al., 2022 study. Of these, 18 metaDEGs were common between both studies. (b) The 18 metaDEGs related to the synapse that were common between the two studies and most contributed to the separation between major depressive disorder (MDD) and control. In red, the genes that most contributed to MDD (positive values), and in blue, those that most contributed to the control (negative values).

LDA analysis identified 18 metaDEGs that distinguished MDD patients from healthy controls, which were shared between both datasets (Figs. 3b, c; Supplementary Table 4). Among these, 32 DEGs were present in the Trang et al. dataset and 39 in the Cathomas et al. dataset. The reproducibility of these DEGs across independent cohorts highlights their potential value for future biomarker studies.

Synaptic-related DEGs shared between peripheral leukocytes and brain regions in MDD

To explore transcriptomic parallels between blood and brain, we conducted a consensus analysis of 18 common synaptic-related DEGs, assessing their expression across seven brain regions (Fig. 5a). These included the anterior cingulate cortex (ACC) from our meta-analysis (Fig. 4; Supplementary Table 5–8) and six additional areas from the Labonté et al. (2017) dataset: anterior insula (aINS), cingulate gyrus Area 25 (Cg25), dorsolateral prefrontal cortex (DLPFC), nucleus accumbens (Nac), orbitofrontal cortex (OFC), and subiculum (Sub). This approach identified seven synaptic-related DEGs shared between peripheral leukocytes and these brain regions (Figs. 5a, b; Supplementary Table 9): BCR28, NSMF29–31, PICK132,33, MX134, MDGA135, MYLK36 and GNB337. These shared genes participate in molecular pathways essential for maintaining synaptic organization and network integrity, functions that may be disrupted in MDD.

Fig. 5.

Fig. 5

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Overlap of DEGs from peripheral leukocytes and brain regions in major depressive disorder. (a) Venn diagram showing the intersection of the 18 shared meta-analysis of differentially expressed genes (metaDEGs) identified in peripheral leukocytes cells with the total differentially expressed genes (DEGs) from brain regions. (b) Dot plot highlighting the 7 DEGs common to both peripheral leukocytes and each brain region. (c) Disease network analysis of the 7 DEGs common to peripheral leukocytes and brain regions. Node size corresponds to the degree value (number of direct gene–disease or gene–gene associations; padj < 0.05).

Fig. 4.

Fig. 4

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Meta-analysis and characterization of biological process interactions in the anterior cingulate cortex. (a) Volcano plot displaying the distribution of the meta-analysis of differentially expressed genes (metaDEGs). (b) Distribution of up-regulated (red) and down-regulated (blue) metaDEGs. (c) Enrichment analysis of metaDEGs, showing the results for down-regulated DEGs (left) and up-regulated DEGs (right). (d) Heatmap illustrating the similarity matrix of biological processes (BPs) derived from rrvgo analysis, highlighting the relationships among the enriched processes.

Beyond the shared metaDEGs between peripheral leukocytes and brain tissues, our analysis revealed additional region-specific DEGs that may reflect aspects of immune–brain communication in MDD. Using the OmniPathR-derived interaction network, we identified connections between immune blood cell metaDEGs and DEGs expressed in each of the seven MDD-relevant brain regions. Additionally, interactions between the 18 synaptic-related metaDEGs and DEGs expressed in these brain regions were implicated in MDD. Among these, the aINS showed the highest number of interactions with blood cell DEGs, followed by the aINS (n = 55), ACC (n = 32) and the Sub; (n = 31) (Fig. 6a; Supplementary Table 11). Each brain region displayed a distinct interaction profile, with specific synaptic-related metaDEGs demonstrating higher frequencies of interaction (Fig. 6b; Supplementary Table 11).

Fig. 6.

Fig. 6

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Interaction between brain region DEGs and metaDEGs from peripheral leukocytes. (a) Total interactions between the 18 shared meta-analysis of differentially expressed genes (metaDEGs) from linear discriminant analysis (LDA) and brain regions. (b) Frequency of each meta-analysis of differentially expressed genes (metaDEGs) interacting with brain regions. (c) Network of interactions between each metaDEG from peripheral leukocytes and brain regions. (d) Anatomical locations of the brain regions analyzed in the study, including the Anterior Cingulate Cortex (ACC), Anterior Insula (aINS), Cingulate Gyrus Area 25 (Cg25), Dorsolateral Prefrontal Cortex (DLPFC), Nucleus Accumbens (Nac), Orbitofrontal Cortex (OFC), and Subiculum (Sub).

The interaction network illustrates how the peripheral leukocyte-related metaDEGs map onto gene sets from multiple brain regions (Fig. 6c; Supplementary Table 12), suggesting parallel transcriptional patterns between peripheral immune alterations and region-specific expression profiles in the brains of MDD patients. The anatomical locations of the brain regions analyzed in this study are depicted in Fig. 6d.

Diseasome associations of shared genes

We mapped the seven shared genes to the DisGeNET database, identifying associations not only expressed in MDD but also with other psychiatric disorders (e.g., mood disorders38–40, bipolar disorder39,41, psychoses42, vascular diseases43, cancer (e.g., malignant neoplasm of prostate44 and lymphoma45,46), and reproductive disorders (e.g., erectile dysfunction47,48 and absence of pubertal development49) (Fig. 5c; Supplementary Table 10). The presence of these genes in multiple disease contexts suggests that some molecular pathways relevant to MDD may also have broader systemic roles. In the network visualization, these DEGs are connected to several disease terms, with MDGA1, BCR, and GNB3 appearing directly linked to MDD, while others show associations with both psychiatric and non-psychiatric conditions.

Discussion

This work provides a systems-level overview of transcriptional alterations in peripheral leukocytes from MDD patients, focusing on genes annotated to synaptic processes. Rather than implying equivalence between blood and brain expression, our approach identifies in peripheral leukocytes a reproducible subset of synaptic-related genes that discriminate MDD patients from controls across independent datasets. These findings offer a new perspective on how traditionally neuronal-associated genes may participate in immune-related processes that warrant further investigation.

In our meta-analysis, we identified 73 synaptic-related metaDEGs in peripheral leukocytes, including genes involved in the synaptic vesicle cycle50 and synaptic assembly. These findings suggest that the enrichment results can indicate that immune cells can express genes involved in nervous system processes, potentially with distinct immune-specific roles previously unidentified. Notably, 18 of these synaptic-related metaDEGs were found consistently replicated across datasets and exhibited discriminative power between MDD and controls.

Previous research has shown that neurotransmitters such as dopamine, glutamate, serotonin (5-HT), acetylcholine (ACh), norepinephrine (NE), and GABA can modulate immune functions11,18 in addition to their roles in neuronal signaling. Immune cells themselves, such as dendritic cells51,52, macrophages53, and lymphocytes, can synthesize, store, and release neurotransmitters in a paracrine or autocrine fashion11. In our data, the 18 LDA-selected metaDEGs were significantly enriched in GABAergic and glutamatergic synapse pathways (APBB1, BCR, MDGA1 and STX4) (Supplementary Fig. 4; Supplementary Table 19). Glutamate and GABA, the major excitatory and inhibitory neurotransmitters in the central nervous system, are critical key regulators of synaptic plasticity, neuronal connectivity, and the excitation-inhibition balance54, and are also known to play immunomodulatory roles55,56. Both can be synthesized or taken up by immune cells, acting through specific receptors to modulate cytokine production, immune cell activation, and migration57,58. For example, glutamate released by dendritic cells can signal through metabotropic glutamate receptors on T cells to influence their proliferation and effector function59, while GABAergic signaling can suppress inflammatory responses57.

In addition to their roles in neuronal signaling, some synapse-related genes identified here are involved in essential immune and cellular functions that could influence the systemic pathophysiology of MDD. For instance, BCR and MYLK regulate cytoskeletal remodeling and cell migration, mechanisms that shape leukocyte motility, endothelial permeability, and neurovascular interactions during inflammation60–62. Others, such as HSPA8, contribute to antigen processing and presentation through chaperone-mediated autophagy63 linking cellular stress responses to adaptative imune activation. Genes like MX1 and PLD2 are central to antiviral and cytokine-mediated signaling64,65, reflecting pathways commonly dysregulated in MDD patients with heightened inflammatory and interferon responses. Additionally, STX4 and GNB3 participate in vesicle trafficking and G-protein–coupled receptor signaling66,67 connecting neurotransmitter-like signaling events with immune cell communication. Finally, NSMF (also known as NELF) supports genomic stability under replication stress68, a process often altered in stress-related and mood disorders.

Seven DEGs were shared between peripheral leukocytes and brain regions associated with mood regulation. Notably, genes such as MDGA135, PICK169, BCR28,37, MX134, MYLK36 and GNB337 are involved in synaptic processes, including the regulation of presynapse assembly and trans-synaptic signaling, and glutamate neurotransmission, where molecular pathways are essential for maintaining synaptic organization and network integrity70. Both functions are frequently impaired in MDD. Although only a few of the seven synaptic-related metaDEGs identified in peripheral leukocytes were directly shared with gene expression profiles in mood-related brain regions, an expected outcome given the distinct transcriptomic landscapes of blood and brain tissues, and even among different brain regions, our interaction analysis revealed a deeper layer of connectivity. Interaction, analyses demonstrated that the 18 synaptic-related peripheral metaDEGs exhibited multiple connections with DEGs from mood-related brain regions, suggesting potential co-expression patterns, regulatory relationships, or involvement in shared signaling pathways.

Furthermore, the diseasome network analysis revealed associations of these seven shared genes with various psychiatric and physiological conditions, including mood disorders38–40, autoimmune diseases71, and cardiovascular issues72–75. This overlap supports the idea that molecular pathways implicated in MDD may also participate in systemic processes, possibly contributing to comorbidities frequently observed in affected individuals76.

This systems biology study highlights network-level transcriptomic associations between immune and neuronal gene programs. The integration with brain datasets was exploratory, and the results should therefore be interpreted as molecular associations rather than as evidence of functional synapses or direct equivalence between immune and neuronal processes. In this context, our findings suggest pleiotropic activities (Fig. 7), indicating that synapse-related genes expressed in leukocytes may participate in cellular pathways involved in immune regulation and stress adaptation, generating hypotheses for future studies aimed at elucidating the systemic molecular mechanisms underlying MDD. Further multi-omic integration, combined with longitudinal and experimental validation, will be essential to determine the biological relevance of these transcriptomic patterns.

Fig. 7.

Fig. 7

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Pleiotropic synapse-related genes expressed in peripheral leukocytes of patients with major depressive disorder. The figure summarizes molecular pathways shared between peripheral immune alterations and central synaptic processes in major depressive disorder (MDD). On the left, peripheral leukocytes display altered gene expression and increased production of pro-inflammatory cytokines (TNF, IL-6, IL-1β, IL-8), along with the expression of neurotransmitter-related receptors. Differentially expressed genes (e.g., MDGA1, FXYD6, TUBB2A, TMUB1, PICK1) are highlighted for their possible dual roles in immune regulation and synaptic function, reflecting potential pleiotropic activity. On the right, dysregulated neurotransmission typically observed in MDD is shown.

Methods

Data curation

Eligible studies were identified using PubMed, Google Scholar, and Gene Expression Omnibus (GEO) by searching for peer-reviewed datasets indexed with the terms “major depressive disorder,” “Homo sapiens,” and expression profiles from “high throughput sequencing” (bulk or single-cell RNA-seq), containing healthy controls and a minimum of 10 total samples. Initially, 58 studies reporting transcriptional data from human subjects were identified (Supplementary Fig. 1a; Supplementary Table 13).

Studies were excluded based on the following criteria: 1) studies focused on other categories of depression or not identified as MDD datasets (n = 36) and other categories of depression (n = 1); 2) studies not available in the databases, i.e., only found in original articles (n = 4); 3) studies that used single-nucleus RNA sequencing (snRNA-Seq) (n = 3); 4) datasets containing neurons, astrocytes, or immortalized B lymphocyte cell lines (n = 5); 5) datasets with fewer than 50 significant DEGs (n = 2); 6) datasets with fewer than 10 samples (n = 1); and 7) datasets associated with other psychiatric disorders, such as schizophrenia or bipolar disorder (n = 1). After applying these exclusion criteria, five datasets remained eligible in our workflow (Supplementary Table 77, along with four datasets from the Wittenberg meta-analysis27 used for comparison. Together, these datasets included a total of 3072 participants: 1864 with MDD and 1208 healthy controls (Supplementary Fig. 1b).

These datasets were derived from six publications and were accessible for analysis: peripheral blood mononuclear cells (PBMCs) from Trang et al.77 (https://github.com/insilico/DepressionGeneModules/tree/master/secondary_expression_data), and peripheral blood lymphocytes (PBLs) from Cathomas et al. (GSE185855)78; ACC from Ramaker et al. (GSE80655)79 and Oh et al. (GSE193417)80; Labonté et al. (GSE102556)81, the study by Wittenberg et al.82, which includes the datasets by Leday et al. (GSK-HiTDIP cohort)83, Mostafavi et al.84, Jansen et al.82, and Leday et al. (Janssen-BCR cohort)80, included here for comparison (Supplementary Fig. 1b; Supplementary Table 20).

To comprehensively evaluate the transcriptional overlap between metaDEGs (obtained through meta-analysis as described below) in peripheral leukocytes and DEGs from some brain regions, we used the dataset from Labonté et al.85 alongside the metaDEGs from ACC.

All data/tables utilized to generate and visualize the results of our study are provided as supplementary files. For better understanding, the workflow of the methodology is illustrated in Supplementary Fig. 5.

Differential expression analysis

DEGs between groups were identified using DESeq286. A statistical cutoff of adjusted p-value < 0.05, using FDR adjustment, was applied to define significant expression changes. For the Labonté et al.85 dataset, a p-value < 0.05 was used, consistent with the methodology of the original study. The analysis was conducted in R version 4.3. To address confounding variables (e.g., age, sex, medication, and other available metadata parameters), we employed DESeq2’s built-in model design, ensuring that these factors did not confound the results. No significant effect of these factors on gene expression was observed beyond the primary influence of diagnosis (Control vs. MDD), affirming that the findings reflect genuine biological variations rather than technical artifacts.

Meta-analysis of gene expression datasets

A comprehensive gene expression meta-analysis was conducted using the ‘MetaVolcanoR’ package77, applying the ‘Combining MetaVolcano’ approach between Trang et al.77 and Cathomas et al.87 and ACCs (Ramaker et al.79 and Oh et al.80. (Trang et al.77 and Cathomas et al.87) or ACCs (Ramaker et al.79 and Oh et al.80). Standard parameters were applied uniformly to ensure consistency across the results. The ACC region was selected for meta-analysis as it was the only brain region with more than two datasets that met the inclusion criteria and was consistent with findings from other studies, which highlight the fundamental role of this region in disorders such as MDD88. This approach combines DEGs from multiple bulk RNA-seq studies by calculating the mean and synthesizing gene differential expression using Fisher’s combined p-value method for each study (peripheral leukocytes and ACC). The identified metaDEGs were further characterized and classified into gene types (e.g., protein-coding, miRNA, snoRNA, lncRNA, non-coding) using the ShinyGO89 online platform, providing a detailed understanding of the genomic landscape underlying the observed expression changes.

Functional enrichment and clustering

GO enrichment analysis was conducted using the EnrichR90 tool to identify enriched BPs with a significance threshold of p-value < 0.05. The enrichment analyses were performed in R using the EnrichR package, which integrates results from multiple annotation databases (GO, KEGG, Reactome, and others) to ensure consistent cross-platform interpretation. This enrichment and clustering approach follows current recommendations for pathway analysis in multi-omic studies90. To reduce redundancy in GO terms, we clustered similar terms based on semantic similarity, grouping BPs into parent categories to provide a more concise topological visualization of nervous and immunological BPs. This was achieved using the ‘rrvgo’26 package in R. For evaluating the nervous and immune clusters formed by BPs, the ‘appyters’91 tool, integrated with EnrichR, was used. This approach allowed for identifying enriched BPs, organized into clusters on a scatter plot. The Leiden algorithm was applied for clustering, which reduced dimensions using uniform manifold approximation and projection (UMAP). Additionally, a network analysis of metaDEGs and BPs was performed to examine the neuroimmunological interactome, utilizing the ‘ggnet2’92 package in R.

Characterization of synapse-related BPs

To characterize synapse-related metaDEGs, an analysis was conducted using the EnrichR93 platform, which provides access to a variety of enrichment databases containing biological and functional terms. Initially, a selection of enrichment databases, widely used in biological research and encompassing categories directly related to synapse biology, was made. These databases included GO Biological Process94 (BPs; 2025), GO Cellular Component94 (2025), GO Molecular Function94 (2025), SynGO95 (2024), KEGG96,97 (2021), WikiPathways98 (2024), Reactome Pathways99 (2024), MSigDB Hallmark100(2024), Human Phenotype Ontology101, and Jensen Compartments102. These databases were selected based on their relevance to synaptic processes and cellular biology, ensuring a thorough and accurate analysis.

Using the enrichr function from the enrichR R package, a search was performed for metaDEGs associated with the terms “synapse” and “synaptic” in each of the selected databases. For each queried database, the terms were filtered to include only those containing the relevant keywords. The metaDEGs associated with these terms were then extracted and stored for further analysis.

After identifying the synapse-related metaDEGs, an additional filtering step was applied to retain only those genes that appeared in at least two different databases. This approach was used to ensure that the selected genes were consistent across multiple data sources, rather than being specific to a single enrichment database, yielding a final list of 73 synapse-related metaDEGs. The selection of genes was based on enrichment significance across multiple annotation databases, ensuring that only genes consistently associated with synaptic-related ontologies were included, rather than arbitrary selection.

Subsequently, enrichment analysis was conducted on the 73 synapse-related genes using GO terms. This analysis aimed to identify significantly overrepresented BPs (adjusted p-value < 0.05) and to map their associations with the metaDEGs. The resulting network was visualized using the ‘ggnet2′92 package in R, providing an integrative view of how these genes interact within key biological pathways.

Linear discriminant and principal component analyses of synapse-related genes

To assess the impact of synapse-related metaDEGs in stratifying MDD and healthy individuals, we applied LDA103,104 to PBMCs and PBLs datasets (Trang et al.77 and Cathomas et al.87, respectively). LDA, a dimensionality reduction technique105, identifies the linear combinations of variables that maximize the separation between predefined groups (Control vs. MDD).

To enhance the model’s performance and minimize noise, we first excluded genes that were highly correlated (with a correlation coefficient greater than 0.9) and removed genes with low variance. Dimensionality reduction was then performed using Principal Component Analysis (PCA), which reduced the complexity of the data while preserving its variance. Using a supervised learning approach, we trained the LDA model with 70% of the samples and tested it on the remaining 30%105,106. We employed a supervised learning approach. We identified 18 common DEGs between both datasets that effectively differentiated the groups.

Identification of interactions between peripheral leukocytes and brain regions

To investigate the transcriptomic relationships between immune blood cell synaptic-related metaDEGs and brain regions associated with MDD, specifically the ACC, aINS, Cg25, DLPFC, Nac, OFC, and Sub, we focused on the 18 common metaDEGs identified through LDA between two datasets (Trang et al., 2018) and77.

We used the OmniPathR package107 to retrieve all known gene interactions from the OmniPath108 database, which provides comprehensive data on various molecular interactions, such as protein–protein interactions, signaling pathways, and gene associations. These comparisons helped identify potential transcriptomic correspondences between the DEGs of interest.

Next, we filtered the interactions to focus on those involving the 18 common peripheral leukocyte metaDEGs and genes from the brain regions. Specifically, we identified interactions where either the source gene (the gene initiating the interaction) or the target gene (the gene being affected by the interaction) was present in both the list of the 18 immune-related metaDEGs and the brain region DEG list.

Construction of the “diseasome” network

To build the diseasome109,110 network mapping associations between genes and diseases based on shared genetic and molecular factors, we selected seven DEGs common to the immune blood cells meta-analysis from LDA and seven brain regions (ACC meta-analysis, aINs, Cg25, DLPFC, OFC, Nac, and Sub). Using the EnrichR interface, we accessed the DisGeNET110 database to retrieve information on gene-disease associations. For the network visualization, we focused on diseases with a p-value < 0.05, enabling us to highlight the most statistically significant and relevant disease connections.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (3.2MB, pdf)
Supplementary Material 2 (13.6MB, xlsx)

Acknowledgements

We thank the São Paulo Research Foundation (FAPESP grants 2018/18886-9 to O.C.M., 2024/21635-9 to A.S.A., 2023/07806-2 to I.S.F., 2023/06086-6 to P.M.B.) for financial support. We acknowledge the National Council for Scientific and Technological Development (CNPq) (grants: 309482/2022-4 to O.C.M., 140013/2025-3 to A.S.A., 102430/2022-5 to L.F.S, and 130027/2023-5 to Y.L.G.C.), the Foundation Coordination for the Improvement of Higher Education Personnel (CAPES) (grants: 88887.801068/2023-00 to A.L.N., 88887.699840/2022-00 to F.Y.N.V.), and the Serrapilheira fellowship (grant: R2111-39828 to D.G.A.).

Author contributions

A.S.A. and O.C.M. wrote the manuscript. P.M.B., I.S.F., O.C.M., F.Y.N.V., Y.L.G.C., A.L.N., D.G.A., H.D.D., L.F.S., R.D., L.F.O., R.M.R., R.G.N., and A.S.A. provided scientific insights. A.S.A. performed data and bioinformatics analyses, while O.C.M. revised and edited the manuscript.

Data availability

All data used in this study are publicly available. RNA-seq datasets were obtained from the GEO DataSets repository, accessible via the National Library of Medicine Data Portal ([https://www.ncbi.nlm.nih.gov/gds/]). The dataset from Trang et al. is available through the following GitHub repository: [https://github.com/insilico/DepressionGeneModules/tree/master/secondary_expression_data]. The code used for the analyses is available at: [https://github.com/annysadri/Major-depression-article].

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Anny Silva Adri, Email: annyadri@usp.br.

Otavio Cabral-Marques, Email: otavio.cmarques@usp.br.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (3.2MB, pdf)
Supplementary Material 2 (13.6MB, xlsx)

Data Availability Statement

All data used in this study are publicly available. RNA-seq datasets were obtained from the GEO DataSets repository, accessible via the National Library of Medicine Data Portal ([https://www.ncbi.nlm.nih.gov/gds/]). The dataset from Trang et al. is available through the following GitHub repository: [https://github.com/insilico/DepressionGeneModules/tree/master/secondary_expression_data]. The code used for the analyses is available at: [https://github.com/annysadri/Major-depression-article].

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