Fibroblasts from patients with major depressive disorder show distinct transcriptional response to metabolic stressors.
Garbett K A,Vereczkei A,Kálmán S,Wang L,Korade Ž,Shelton R C,Mirnics K
Major depressive disorder (MDD) is increasingly viewed as interplay of environmental stressors and genetic predisposition, and recent data suggest that the disease affects not only the brain, but the entire body. As a result, we aimed at determining whether patients with major depression have aberrant molecular responses to stress in peripheral tissues. We examined the effects of two metabolic stressors, galactose (GAL) or reduced lipids (RL), on the transcriptome and miRNome of human fibroblasts from 16 pairs of patients with MDD and matched healthy controls (CNTR). Our results demonstrate that both MDD and CNTR fibroblasts had a robust molecular response to GAL and RL challenges. Most importantly, a significant part (messenger RNAs (mRNAs): 26-33%; microRNAs (miRNAs): 81-90%) of the molecular response was only observed in MDD, but not in CNTR fibroblasts. The applied metabolic challenges uncovered mRNA and miRNA signatures, identifying responses to each stressor characteristic for the MDD fibroblasts. The distinct responses of MDD fibroblasts to GAL and RL revealed an aberrant engagement of molecular pathways, such as apoptosis, regulation of cell cycle, cell migration, metabolic control and energy production. In conclusion, the metabolic challenges evoked by GAL or RL in dermal fibroblasts exposed adaptive dysfunctions on mRNA and miRNA levels that are characteristic for MDD. This finding underscores the need to challenge biological systems to bring out disease-specific deficits, which otherwise might remain hidden under resting conditions.
A pilot integrative genomics study of GABA and glutamate neurotransmitter systems in suicide, suicidal behavior, and major depressive disorder.
Yin Honglei,Pantazatos Spiro P,Galfalvy Hanga,Huang Yung-Yu,Rosoklija Gorazd B,Dwork Andrew J,Burke Ainsley,Arango Victoria,Oquendo Maria A,Mann John J
American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
Gamma-amino butyric acid (GABA) and glutamate are the major inhibitory and excitatory neurotransmitters in the mammalian central nervous system, respectively, and have been associated with suicidal behavior and major depressive disorder (MDD). We examined the relationship between genotype, brain transcriptome, and MDD/suicide for 24 genes involved in GABAergic and glutamatergic signaling. In part 1 of the study, 119 candidate SNPs in 24 genes (4 transporters, 4 enzymes, and 16 receptors) were tested for associations with MDD and suicidal behavior in 276 live participants (86 nonfatal suicide attempters with MDD and 190 non-attempters of whom 70% had MDD) and 209 postmortem cases (121 suicide deaths of whom 62% had MDD and 88 sudden death from other causes of whom 11% had MDD) using logistic regression adjusting for sex and age. In part 2, RNA-seq was used to assay isoform-level expression in dorsolateral prefrontal cortex of 59 postmortem samples (21 with MDD and suicide, 9 MDD without suicide, and 29 sudden death non-suicides and no psychiatric illness) using robust regression adjusting for sex, age, and RIN score. In part 3, SNPs with subthreshold (uncorrected) significance levels below 0.05 for an association with suicidal behavior and/or MDD in part 1 were tested for eQTL effects in prefrontal cortex using the Brain eQTL Almanac (www.braineac.org). No SNPs or transcripts were significant after adjustment for multiple comparisons. However, a protein coding transcript (ENST00000414552) of the GABA A receptor, gamma 2 (GABRG2) had lower brain expression postmortem in suicide (P = 0.01) and evidence for association with suicide death (P = 0.03) in a SNP that may be an eQTL in prefrontal cortex (rs424740, P = 0.02). These preliminary results implicate GABRG2 in suicide and warrant further investigation and replication in larger samples.
Alterations in leukocyte transcriptional control pathway activity associated with major depressive disorder and antidepressant treatment.
Mellon S H,Wolkowitz O M,Schonemann M D,Epel E S,Rosser R,Burke H B,Mahan L,Reus V I,Stamatiou D,Liew C-C,Cole S W
Major depressive disorder (MDD) is associated with a significantly elevated risk of developing serious medical illnesses such as cardiovascular disease, immune impairments, infection, dementia and premature death. Previous work has demonstrated immune dysregulation in subjects with MDD. Using genome-wide transcriptional profiling and promoter-based bioinformatic strategies, we assessed leukocyte transcription factor (TF) activity in leukocytes from 20 unmedicated MDD subjects versus 20 age-, sex- and ethnicity-matched healthy controls, before initiation of antidepressant therapy, and in 17 of the MDD subjects after 8 weeks of sertraline treatment. In leukocytes from unmedicated MDD subjects, bioinformatic analysis of transcription control pathway activity indicated an increased transcriptional activity of cAMP response element-binding/activating TF (CREB/ATF) and increased activity of TFs associated with cellular responses to oxidative stress (nuclear factor erythroid-derived 2-like 2, NFE2l2 or NRF2). Eight weeks of antidepressant therapy was associated with significant reductions in Hamilton Depression Rating Scale scores and reduced activity of NRF2, but not in CREB/ATF activity. Several other transcriptional regulation pathways, including the glucocorticoid receptor (GR), nuclear factor kappa-B cells (NF-κB), early growth response proteins 1-4 (EGR1-4) and interferon-responsive TFs, showed either no significant differences as a function of disease or treatment, or activities that were opposite to those previously hypothesized to be involved in the etiology of MDD or effective treatment. Our results suggest that CREB/ATF and NRF2 signaling may contribute to MDD by activating immune cell transcriptome dynamics that ultimately influence central nervous system (CNS) motivational and affective processes via circulating mediators.
Transcriptomics Evidence for Common Pathways in Human Major Depressive Disorder and Glioblastoma.
Xie Yongfang,Wang Ling,Xie Zengyan,Zeng Chuisheng,Shu Kunxian
International journal of molecular sciences
Depression as a common complication of brain tumors. Is there a possible common pathogenesis for depression and glioma? The most serious major depressive disorder (MDD) and glioblastoma (GBM) in both diseases are studied, to explore the common pathogenesis between the two diseases. In this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma.
Altered neuro-inflammatory gene expression in hippocampus in major depressive disorder.
Mahajan Gouri J,Vallender Eric J,Garrett Michael R,Challagundla Lavanya,Overholser James C,Jurjus George,Dieter Lesa,Syed Maryam,Romero Damian G,Benghuzzi Hamed,Stockmeier Craig A
Progress in neuro-psychopharmacology & biological psychiatry
Major Depressive Disorder (MDD) is a common psychiatric disorder for which available medications are often not effective. The high prevalence of MDD and modest response to existing therapies compels efforts to better understand and treat the disorder. Decreased hippocampal volume with increasing duration of depression suggests altered gene expression or even a decrease in neurogenesis. Tissue punches from the dentate gyrus were collected postmortem from 23 subjects with MDD and 23 psychiatrically-normal control subjects. Total RNA was isolated and whole transcriptome paired-end RNA-sequencing was performed using an Illumina NextSeq 500. For each sample, raw RNA-seq reads were aligned to the Ensembl GRCh38 human reference genome. Analysis revealed 30 genes differentially expressed in MDD compared to controls (FDR<0.05). Down-regulated genes included several with inflammatory function (ISG15, IFI44L, IFI6, NR4A1/Nur-77) and GABBR1 while up-regulated genes included several with cytokine function (CCL2/MCP-1), inhibitors of angiogenesis (ADM, ADAMTS9), and the KANSL1 gene, a histone acetyltransferase. Similar analyses of specific subsets of MDD subjects (suicide vs. non-suicide, single vs. multiple episodes) yielded similar, though not identical, results. Enrichment analysis identified an over-representation of inflammatory and neurogenesis-related (ERK/MAPK) signaling pathways significantly altered in the hippocampal dentate gyrus in MDD. Together, these data implicate neuro-inflammation as playing a crucial role in MDD. These findings support continued efforts to identify adjunctive approaches towards the treatment of MDD with drugs including anti-inflammatory and neuroprotective properties.
The effect of childhood trauma on blood transcriptome expression in major depressive disorder.
Minelli Alessandra,Magri Chiara,Giacopuzzi Edoardo,Gennarelli Massimo
Journal of psychiatric research
Childhood trauma (CT) increases the likelihood of developing severe mental illnesses, such as major depressive disorder (MDD), during adulthood. Several studies have suggested an inflammatory immune system dysregulation as a biological mediator; however, the molecular mechanisms underlying this relationship remain largely undetermined. Moreover, different types of CT, in particular, emotional abuse and neglect, confer a higher risk of developing MDD, and recent meta-analyses showed that each CT can be associated with different pro-inflammatory biomarkers. However, no studies using a hypothesis-free approach have been performed. For this reason, we carried out a reanalysis of transcriptome data from a large mRNA sequencing dataset to investigate different types of CT in MDD patients. Gene expression analysis followed by principal component and gene-set enrichment analyses were carried out to identify genes and pathways differentially expressed in 368 patients who experienced four different types of CT (sexual abuse, physical abuse, emotional abuse and neglect). Expression analysis of single genes revealed a significant association between the neglect CT and the MED22 gene (p = 1.11 × 10; FDR = 0.016). Furthermore, analyses of the principal components of expression data support a dysregulation of cytokine system pathways, such as interferon (IFN) α/β and γ signaling, as a consequence of emotional abuse in depressed patients. Our results corroborate the hypothesis that specific types of CT affect distinct molecular pathways, and in particular, emotional abuse and neglect exert the strongest impact on gene expression in MDD.
Integrated profiling of phenotype and blood transcriptome for stress vulnerability and depression.
Hori Hiroaki,Nakamura Seiji,Yoshida Fuyuko,Teraishi Toshiya,Sasayama Daimei,Ota Miho,Hattori Kotaro,Kim Yoshiharu,Higuchi Teruhiko,Kunugi Hiroshi
Journal of psychiatric research
Etiology of depression and its vulnerability remains elusive. Using a latent profile analysis on dimensional personality traits, we previously identified 3 different phenotypes in the general population, namely stress-resilient, -vulnerable, and -resistant groups. Here we performed microarray-based blood gene expression profiling of these 3 groups (n = 20 for each group) in order to identify genes involved in stress vulnerability as it relates to the risk of depression. Identified differentially expressed genes among the groups were most markedly enriched in ribosome-related pathways. These ribosomal genes, which included ribosomal protein L17 (RPL17) and ribosomal protein L34 (RPL34), were upregulated in relation to the stress vulnerability. Protein-protein interaction and correlational co-expression analyses of the differentially expressed genes/non-coding RNAs consistently showed that functional networks involving ribosomes were affected. The significant upregulation of RPL17 and RPL34 was also observed in depressed patients compared to healthy controls, as confirmed in 2 independent case-control datasets by using pooled microarray data and qPCR experiments (total number of subjects was 122 and 166, respectively). Moreover, the upregulation of RPL17 and RPL34 was most marked in DSM-IV major depressive disorder, followed by in bipolar disorder, and then in schizophrenia, suggesting some diagnostic specificity of these markers as well as their general roles in stress vulnerability. These results suggest that ribosomal genes, particularly RPL17 and RPL34, can play integral roles in stress vulnerability and depression across nonclinical and clinical conditions. This study presents an opportunity to understand how multiple psychological traits and underlying molecular mechanisms interact to render individuals vulnerable to depression.
Depression and suicide risk prediction models using blood-derived multi-omics data.
Bhak Youngjune,Jeong Hyoung-Oh,Cho Yun Sung,Jeon Sungwon,Cho Juok,Gim Jeong-An,Jeon Yeonsu,Blazyte Asta,Park Seung Gu,Kim Hak-Min,Shin Eun-Seok,Paik Jong-Woo,Lee Hae-Woo,Kang Wooyoung,Kim Aram,Kim Yumi,Kim Byung Chul,Ham Byung-Joo,Bhak Jong,Lee Semin
More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R values of 0.961 and 0.943 for Hamilton Rating Scale for Depression-17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.
The importance of long non-coding RNAs in neuropsychiatric disorders.
Hosseini Ebrahim,Bagheri-Hosseinabadi Zahra,De Toma Ilario,Jafarisani Moslem,Sadeghi Iman
Molecular aspects of medicine
In the last decade, transcriptome analyses have discovered thousands of long non-coding RNAs (lncRNAs) which are assumed as a fundamental part of the gene regulatory networks in the cell. Intriguingly, lncRNAs are abundantly enriched in the brain, displaying elaborate spatiotemporal expression profiles and modulation. They diversely participate in the delicate regulation of the central nervous system (CNS) development including self-renewal maintenance, cell fate decision, synapse plasticity, synaptogenesis and memory formation. Moreover, lncRNAs have vastly demonstrated correlations with mental illnesses such as neuropsychiatric disorders (NPDs), implying the vital jobs of these yet poorly-understood transcripts. Here, we underlie the accumulating evidence for the significance of lncRNAs in neural networks and their impairment in several NPDs including autism spectrum disorder (ASD), schizophrenia (SZ), intellectual disability (ID), major depressive disorder (MDD), Rett syndrome (RTT) and others.
Sex differences: Transcriptional signatures of stress exposure in male and female brains.
Brivio Elena,Lopez Juan Pablo,Chen Alon
Genes, brain, and behavior
More than two-thirds of patients suffering from stress-related disorders are women but over two-thirds of suicide completers are men. These are just some examples of the many sex differences in the prevalence and manifestations of stress-related disorders, such as major depressive disorder, post-traumatic stress disorder, and anxiety disorders, which have been extensively documented in clinical research. Nonetheless, the molecular origins of this sex dimorphism are still quite obscure. In response to this lack of knowledge, the NIH recently advocated implementing sex as biological variable in the design of preclinical studies across disciplines. As a result, a newly emerging field within psychiatry is trying to elucidate the molecular causes underlying the clinically described sex dimorphism. Several studies in rodents and humans have already identified many stress-related genes that are regulated by acute and chronic stress in a sex-specific fashion. Furthermore, current transcriptomic studies have shown that pathways and networks in male and female individuals are not equally affected by stress exposure. In this review, we give an overview of transcriptional studies designed to understand how sex influences stress-specific transcriptomic changes in rodent models, as well as human psychiatric patients, highlighting the use of different methodological techniques. Understanding which mechanisms are more affected in males, and which in females, may lead to the identification of sex-specific mechanisms, their selective contribution to stress susceptibility, and their role in the development of stress-related psychiatric disorders.
Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach.
Ciobanu Liliana G,Sachdev Perminder S,Trollor Julian N,Reppermund Simone,Thalamuthu Anbupalam,Mather Karen A,Cohen-Woods Sarah,Stacey David,Toben Catherine,Schubert K Oliver,Baune Bernhard T
Journal of affective disorders
BACKGROUND:At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations. METHODS:We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space. RESULTS:By adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%. LIMITATIONS:Although we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription. CONCLUSIONS:We found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.
Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies.
Wittenberg Gayle M,Greene Jon,Vértes Petra E,Drevets Wayne C,Bullmore Edward T
BACKGROUND:Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder. METHODS:We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods. RESULTS:We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions. CONCLUSIONS:Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.
Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders.
Hernandez Leanna M,Kim Minsoo,Hoftman Gil D,Haney Jillian R,de la Torre-Ubieta Luis,Pasaniuc Bogdan,Gandal Michael J
Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight and clinical translation continue to lag the pace of risk variant identification, hindered by the sheer number of targets and their predominant noncoding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of "causal" genetic variants and their proximal biological consequences; placing variants within biologically defined functional contexts, reflecting specific molecular pathways, cell types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in the human brain, and how transcriptomic approaches can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies for risk gene prioritization through integration of genome-wide association studies with expression quantitative trait loci. We outline transcriptomic results that inform our understanding of the brain-level molecular pathology of psychiatric disorders, including autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Finally, we discuss systems-level approaches for integration of distinct genetic, genomic, and phenotypic levels, including combining spatially resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci to rare variants enriched for gene expression regulatory pathways.
Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.
Baird Denis A,Liu Jimmy Z,Zheng Jie,Sieberts Solveig K,Perumal Thanneer,Elsworth Benjamin,Richardson Tom G,Chen Chia-Yen,Carrasquillo Minerva M,Allen Mariet,Reddy Joseph S,De Jager Philip L,Ertekin-Taner Nilufer,Mangravite Lara M,Logsdon Ben,Estrada Karol,Haycock Philip C,Hemani Gibran,Runz Heiko,Smith George Davey,Gaunt Tom R,
Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.
Molecular pathology associated with altered synaptic transcriptome in the dorsolateral prefrontal cortex of depressed subjects.
Yoshino Yuta,Roy Bhaskar,Kumar Nilesh,Shahid Mukhtar M,Dwivedi Yogesh
Disrupted synaptic plasticity is the hallmark of major depressive disorder (MDD), with accompanying changes at the molecular and cellular levels. Often, the maladaptive molecular changes at the synapse are the result of global transcriptional reprogramming dictated by activity-dependent synaptic modulation. Thus far, no study has directly studied the transcriptome-wide expression changes locally at the synapse in MDD brain. Here, we have examined altered synaptic transcriptomics and their functional relevance in MDD with a focus on the dorsolateral prefrontal cortex (dlPFC). RNA was isolated from total fraction and purified synaptosomes of dlPFC from well-matched 15 non-psychiatric controls and 15 MDD subjects. Transcriptomic changes in synaptic and total fractions were detected by next-generation RNA-sequencing (NGS) and analyzed independently. The ratio of synaptic/total fraction was estimated to evaluate a shift in gene expression ratio in MDD subjects. Bioinformatics and network analyses were used to determine the biological relevance of transcriptomic changes in both total and synaptic fractions based on gene-gene network, gene ontology (GO), and pathway prediction algorithms. A total of 14,005 genes were detected in total fraction. A total of 104 genes were differentially regulated (73 upregulated and 31 downregulated) in MDD group based on 1.3-fold change threshold and p < 0.05 criteria. In synaptosomes, out of 13,236 detectable genes, 234 were upregulated and 60 were downregulated (>1.3-fold, p < 0.05). Several of these altered genes were validated independently by a quantitative polymerase chain reaction (qPCR). GO revealed an association with immune system processes and cell death. Moreover, a cluster of genes belonged to the nervous system development, and psychological disorders were discovered using gene-gene network analysis. The ratio of synaptic/total fraction showed a shift in expression of 119 genes in MDD subjects, which were primarily associated with neuroinflammation, interleukin signaling, and cell death. Our results suggest not only large-scale gene expression changes in synaptosomes, but also a shift in the expression of genes from total to synaptic fractions of dlPFC of MDD subjects with their potential role in immunomodulation and cell death. Our findings provide new insights into the understanding of transcriptomic regulation at the synapse and their possible role in MDD pathogenesis.