Transcriptomic analysis uncovers the shared and unique biological foundations acrossSchizophrenia, Bipolar and Major Depressive Disorders

Transcriptomic analysis uncovers the shared and unique biological foundations acrossSchizophrenia, Bipolar and Major Depressive Disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptomic analysis uncovers the shared and unique biological foundations acrossSchizophrenia, Bipolar and Major Depressive Disorders Priyanka Priyanka, Rajesh Kumar, Sandeep Singh Rana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5907225/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Psychiatric disorders, including Schizophrenia (SCZ), Bipolar Disorder (BD), and Major Depressive Disorder (MDD), represent complex neuropsychiatric conditions with significant overlap in clinical presentation yet distinct pathophysiological mechanisms. Understanding the molecular underpinnings of major psychiatric disorders remains a significant challenge in neuroscience. This study conducted a comprehensive transcriptomic analysis integrating publicly available 538 RNA-seq datasets from post-mortem samples across multiple brain regions to elucidate shared and unique biological foundations underlying these disorders. We employed systematic bioinformatic approaches to analyze differential gene expression patterns and pathway dysregulation across the disorders and the brain regions. ​​The identified differentially expressed genes were further analyzed for shared biological pathways, candidate drugs, and transcription factors. Protein-protein interaction (PPI) network analysis and transcription factor ranking were performed to understand the regulatory mechanisms governing unique and shared molecular behaviors across these disorders. Our findings revealed distinct transcriptional signatures with notable overlap between SCZ and BD, identifying 373 shared differentially expressed genes (DEGs) and 12 common hub genes. BD exhibited the highest number of unique DEGs, followed by SCZ and MDD, suggesting disorder-specific molecular mechanisms. Brain region-specific analyses demonstrated distinctive transcriptional patterns, particularly in the hippocampus and DLPFC, highlighting the spatial heterogeneity of gene expression changes. Pathway analysis uncovered disorder-specific dysregulation patterns: MDD showed predominant alterations in stress response and metabolic pathways; BD demonstrated robust immune system activation and cellular growth signaling perturbations; and SCZ exhibited a complex interplay of immune dysregulation, oxidative stress, and metabolic disruptions. Network analysis identified key transcription factors, including STAT3, NF-κB, and CREB1, as major regulators of the disease-specific gene expression patterns. Notably, our drug-gene interaction analysis using DGIdb revealed promising therapeutic implications, with key genes like SERPINA3 interacting with antipsychotic agents, and inflammatory mediators such as IL6 and CCL2 showing potential interactions with immunomodulators. These findings suggest novel drug repurposing strategies and targeted therapeutic approaches for psychiatric disorders. These findings provide crucial insights into the molecular underpinnings of major psychiatric disorders, revealing both shared biological mechanisms and disorder-specific pathways. The identification of common hub genes and key transcription factors suggests potential therapeutic targets for intervention. Furthermore, our results emphasize the importance of considering both shared and unique molecular signatures in developing targeted treatment strategies for psychiatric disorders, potentially leading to more personalized therapeutic approaches. Bipolar Disorder Major Depressive Disorder Schizophrenia Transcriptomics Pathways Vulnerability Mental Health Psychiatry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Psychiatric disorders represent a significant global health burden, affecting millions worldwide and posing substantial challenges to healthcare systems, social structures, and economic frameworks ( 1 ). Among these, Schizophrenia (SCZ), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) stand as particularly impactful conditions, characterized by complex symptomatology and often devastating effects on individual functioning and quality of life ( 2 ). Despite their clinical significance, the underlying molecular mechanisms driving these disorders remain incompletely understood, hindering the development of more effective therapeutic strategies. The global burden of these disorders is substantial, with approximately 1% of the population affected by SCZ, 2.4% by BD, and 3.8% by MDD ( 1 , 3 ). These conditions account for a significant proportion of disability-adjusted life years (DALYs) worldwide. The overlapping symptomatology and high comorbidity rates among these disorders suggest shared biological underpinnings, yet each condition also exhibits unique clinical features that point to distinct pathophysiological mechanisms ( 4 ). Recent advances in high-throughput sequencing technologies and bioinformatics have provided unprecedented opportunities to explore these biological foundations at the molecular level ( 5 ). Transcriptomic analysis, in particular, has emerged as a powerful tool for understanding the complex interplay of genetic and environmental factors in psychiatric disorders ( 6 ). The complexity of psychiatric disorders is further compounded by the involvement of multiple brain regions and neural circuits. The dorsolateral prefrontal cortex (DLPFC) and hippocampus have been consistently implicated in the pathophysiology of SCZ, BD, and MDD, yet their relative contributions to each disorder remain debated ( 7 ). Understanding region-specific transcriptional changes is crucial for developing targeted therapeutic approaches and identifying biomarkers for early diagnosis and intervention. Previous studies have typically focused on individual disorders or specific brain regions, limiting our understanding of the broader biological landscape across psychiatric conditions ( 5 , 8 – 10 ). The integration of data from multiple studies and brain regions offers a unique opportunity to identify both shared and unique molecular signatures, potentially revealing novel therapeutic targets and biological pathways ( 11 ). This comprehensive approach is particularly relevant given the growing recognition of psychiatric disorders as existing along a biological continuum rather than as discrete entities. Recent technological advances have enabled more sophisticated analyses of gene expression patterns and pathway dysregulation, providing new insights into the molecular basis of psychiatric disorders ( 5 ). The application of advanced bioinformatic approaches to large-scale transcriptomic data has revealed complex patterns of gene expression changes and pathway perturbations that may underlie the development and progression of these conditions. The integration of machine learning approaches with transcriptomic analysis has further enhanced our ability to identify complex patterns and relationships within large-scale genomic data ( 12 ). These computational advances have enabled more robust identification of disease-specific signatures and pathway interactions. The present study aims to address these knowledge gaps through a comprehensive transcriptomic analysis of SCZ, BD, and MDD, integrating data from multiple brain regions and studies. Our objectives include: (i) identifying shared and unique genomic signatures across disorders, (ii) characterizing brain region-specific transcriptional patterns, (iii) elucidating distinct molecular mechanisms through pathway analysis, and (iv) exploring the implications of these findings for therapeutic development. Understanding the molecular intersection of these disorders is crucial for several reasons. First, it may reveal common therapeutic targets that could benefit multiple conditions. Second, identifying disorder-specific signatures could lead to more precise diagnostic tools and personalized treatment approaches. Finally, this knowledge contributes to our broader understanding of the biological basis of psychiatric disorders, potentially informing future research directions and therapeutic strategies. This study represents a significant step forward in psychiatric research by providing a comprehensive analysis of the molecular landscape across major psychiatric disorders. By integrating multiple datasets and employing advanced analytical approaches, we aim to provide new insights into the biological foundations of these conditions and contribute to the development of more effective therapeutic strategies. Methodology Dataset Collection There is evidence of a common underlying pathophysiology in neuropsychological disorders. We selected and searched the publicly available dataset on BD, SCZ, and MDD, with considerations of the PRISMA guidelines, ensuring a systematic and thorough review of the available data. The selection criteria include - i) Study should involve human subjects ("Homo sapiens"), ii) Study should contain a minimum of 12 samples to ensure statistical robustness in downstream analysis, iii) data should be publicly available. The following exclusion criteria was used - i) Study should include the strict patient vs control analysis, ii) Should contain experimental samples other than post-mortem brain samples, iii) experimental data excluding cellular populations. Following these data guidelines and criteria, we have been left with 5 RNA-Sequencing datasets - GSE138082, GSE174407, GSE80655, GSE78936, and GSE379666. However, for the GSE174407 study, the raw sequence read files were not publicly accessible and consequently, we had to exclude this dataset from our further analysis. Transcriptomic Dataset Processing We have downloaded the raw fastq files of the above-mentioned dataset using NCBI SRA-Toolkit by developing a custom bash script. After downloading the raw fastq files, the sequencing data was further processed and analyzed using the nf-core RNA-seq pipeline version 3.14.0 ( https://nf-co.re/rnaseq/3.14.0/ ) ( 13 ). Default parameters were employed for running the pipeline; this includes a broad range of software packages for quality control, read processing, alignment, and quantification. Initial steps in the processing of raw reads included quality assessment using FastQC and adapter trimming using Trim Galore. The trimmed reads were then aligned to the human reference genome (GRCh38) version 113 using STAR and quantified at both gene and transcript levels using Salmon. Other quality control criteria included computation of the transcript integrity number, gene body coverage analysis done with Qualimap, and an assessment of read distribution through RSeQC. Alignment quality metrics were produced through Picard Tools, while normalized bigWig files were prepared for visualization. We applied the default minimum threshold of mapped reads for sample inclusion: 5. All these processes have been orchestrated using Nextflow to ensure that the reproduction and scalability of our analysis across the different datasets that compose this study are run. Batch Correction and Visualization The limma R package (version 3.20) was used to remove any potential batch effects in the dataset. The batch effect correction for each study was done by using the known variable such as sequencing library preparations ( 14 ). Unknown batch effects were further estimated using the checking of the surrogate variables by the limma R package. For plotting and visualization of the batch effect, the first two principal components are considered. Further downstream analysis has been done with a batch-corrected data matrix which consists of 19447 human protein-coding genes. To represent the latent projection of all the samples in a 2-dimensional space we have also performed the UMAP projection on a batch corrected data matrix. UMAP analysis was performed on each tissue and the disease class which the sample belongs to. In addition, we also performed the UMAP analysis for each distinct brain region, examining samples across disease conditions. Differential Expression Analysis and Identification of Key Genes and Pathways To investigate transcriptional changes across different brain regions and disease conditions, we performed comprehensive differential expression analysis using DESeq2 ( 15 ). This robust analytical approach enabled us to identify genes that showed significant alterations in expression patterns when compared to control samples. For statistical stringency and biological relevance, we established dual filtering criteria: an absolute log2 fold change threshold exceeding 1 (corresponding to a minimum two-fold change in expression) and a statistical significance threshold of P < 0.05. This balanced approach helped us identify genes that showed both substantial magnitude of change and statistical reliability. The differentially expressed genes were then examined for their functional implications through Gene Set Enrichment Analysis (GSEA). We incorporated pathway information from two complementary databases: the Reactome database, which provides detailed molecular pathway annotations, and the Molecular Signatures Database (MSigDB), offering a broader spectrum of functional gene sets ( 16 ). This dual-database approach enabled us to capture both specific molecular mechanisms and broader biological processes affected in each disease condition. By analyzing each disease group separately, we could identify both unique pathway perturbations specific to individual conditions and common pathway alterations shared across multiple disease states. Transcription Factor Enrichment To elucidate the regulatory mechanisms underlying the identified pathways, we performed transcription factor (TF) enrichment analysis using ChIP-X Enrichment Analysis 3 (ChEA3) ( 17 ). This analysis was crucial for understanding the upstream regulators that orchestrate the observed pathway-specific gene expression patterns. By analyzing the leading genes derived from MSigDB and Reactome pathway analyses, we sought to identify both unique and shared transcriptional regulators across different pathways. ChEA3's integrative approach, which combines multiple lines of evidence including RNA-seq-based TF-gene co-expression data, ChIP-seq-derived TF-target associations, and TF-gene co-occurrence patterns from crowd-sourced gene lists, provided a comprehensive view of the regulatory landscape. The composite ranking system of ChEA3 enhanced the reliability of our TF predictions by synthesizing evidence from these diverse data sources, offering insights into the hierarchical organization of the transcriptional networks governing these pathways. This approach enabled us to identify key regulatory nodes that could explain the observed pathway-specific gene expression patterns and potential crosstalk between different biological processes. Identification of Key Drugs for Therapeutics We have utilized the DGIdb resource ( https://www.dgidb.org/search_interactions ) for the purpose of studying the therapeutic potentials of the identified leading genes governing each pathway and thus sheds light upon a unique treatment-targeted approach. This database maintains the latest information on drug-gene interactions identified from experimental studies. We have searched our signature genes in the database and identified their association, in order to search for disease specific therapeutic interventions. Results Dataset Characteristics and Study Overview Following PRISMA guidelines, we left with four studies, to be included in our analysis. The study PRJNA314463 (GSE78936) consists of samples from 24 controls, 30 BD and 28 SCZ, whereas Study PRJNA319583 (GSE80655) have 86 control, 84 BD, 89 MDD, and 83 SCZ patients, study PRJNA379666 (GSE379666) consisted of 24 control and 22 SCZ samples, and study PRJNA574470 (GSE138082) was made up of 34 control and 34 SCZ. The complete description of the dataset used in the present study has been added to Table 1 (Supplementary File 1). Table 1 Summary of the dataset used in the present study. ID Disease Brain Areas Platform File Type Reference GSE78936 Bipolar and Schizophrenia Orbitofrontal Cortex Illumina HiSeq 2000 (Homo sapiens) Raw Fastq ( 18 ) GSE80655 Bipolar, Schizophrenia and Major Depressive Disorder Anterior Cingulate Cortex, Dorsolateral Prefrontal Cortex Illumina HiSeq 2000 (Homo sapiens) Raw Fastq ( 19 ) GSE379666 Schizophrenia Amygdala Illumina HiSeq 2000 (Homo sapiens) Raw Fastq ( 20 ) GSE138082 Schizophrenia Hippocampus Illumina HiSeq 2000 (Homo sapiens) Raw Fastq ( 21 ) Collectively, the present study consists of a cohort of 538 public RNA-seq dataset of BD, SCZ and MDD from different regions of the brain and accessed their genomic expression profile for the identification of unique and shared features. All samples were processed using standardized RNA-seq protocols on the Illumina HiSeq 2000 platform, ensuring technical consistency across studies. The brain regions analyzed represent key areas implicated in psychiatric disorders: the orbitofrontal cortex, involved in decision-making and emotional processing; the anterior cingulate and dorsolateral prefrontal cortex, crucial for executive function and emotional regulation; the amygdala, central to fear and emotional responses; and the hippocampus, essential for memory formation and emotional processing. This diverse regional sampling allows for a comprehensive analysis of disease-specific molecular signatures across different functional brain areas. Figure 1 . provides the complete architecture of the study performed. Figure 1 . Schematic representation of the overall integrative bioinformatics pipeline used in the study. Dimensionality Reduction Uncovers Disease Specific Features Across Psychological Conditions The raw FASTQ files were processed using a uniform bioinformatics pipeline to minimize technical variability in the analysis workflow. The raw count matrix of 538 cases and control dataset were used for the batch correction to mitigate technical variations across the datasets. Following batch correction, the Principal Component Analysis (PCA) revealed distinct patterns across psychiatric conditions, brain regions, and study groups. The study group specific PCA clustering, validates our batch correction approach, as samples from all studies (PRJNA314463, PRJNA319583, PRJNA379666, and PRJNA574470) showed uniform distribution without distinct study-specific clustering, confirming successful removal of batch effects (Fig. 2 A, top). Since the dataset consists of different psychological conditions and across several brain regions, we extend the PCA analysis across conditions and different brain regions. PCA clustering based on the disease status (Fig. 2 A, middle), we observed partial overlapping between psychiatric conditions, with SCZ and BD samples showing closer molecular signatures compared to MDD, suggesting a molecular continuum among these disorders. Control samples showed a more diffuse distribution, indicating natural biological variability in healthy brain tissue ( 22 ). The brain region-specific analysis (bottom panel A) revealed interesting biological clustering, particularly in the hippocampus and DLPFC regions, which formed more distinct clusters compared to other brain areas, suggesting strong region-specific transcriptional signatures (Fig. 2 A, bottom). However, the limited variance explained by the first two principal components (PC1: 27.6%, PC2: 10.9%) indicated additional complexity in the data structure not captured by linear dimensional technique. To further uncover the more biological variations in the dataset, we extended our analysis with non-linear dimensionality reduction techniques. The Uniform Manifold Approximation and Projection (UMAP) analysis reveals more nuanced biological patterns, particularly in disease-specific manner (Fig. 2 B). The UMAP projection shows that while there is partial overlap between SCZ and BD, MDD forms a distinct cluster with partial or no overlap with SCZ. These complementary analyses suggest that while these psychiatric disorders share common molecular features, they also possess unique transcriptional programs particularly for major depressive disorder, as evidenced by both PCA and UMAP. Figure 2 . Dimensionality Reduction Validates Disease Specific Pattern Across Conditions Shared and Unique Genomic Signatures Across Psychiatric Disorders Our transcriptomic analysis revealed both unique and overlapping genomic signatures across psychiatric disorders. BD exhibited the largest number of unique differentially expressed genes (DEG’s), followed by SCZ and MDD. Notably, BD and SCZ shared substantial molecular overlap with 373 common DEGs, suggesting significant biological convergence between these conditions (Fig. 3 A). We also identified 12 common hub genes across all the three disorders. The brain region-specific analysis demonstrated distinct patterns of transcriptional dysregulation. The amygdala and hippocampus demonstrated unique transcriptional signatures, especially in SCZ, highlighting the region-specific nature of psychiatric pathology (Fig. 3 B). This comprehensive analysis suggests a complex interplay between shared and unique genomic vulnerabilities across psychiatric disorders, with substantial overlap between BD and SCZ, while MDD shows more distinct molecular patterns. Figure 3 . Transcriptomic Analysis Identify Shared and Unique Genomic Vulnerability across Psychosis Disorders Pathway Analysis Reveals Distinct Molecular Mechanisms in Psychiatric Disorders The pathway enrichment analysis uncovered distinct biological mechanisms underlying each psychiatric disorder. MDD showed predominant dysregulation in stress response and metabolic pathways, with significant enrichment in KRAS signaling, unfolded protein response, and glycolysis, suggesting cellular stress as a key pathogenic mechanism. BD demonstrated the most robust immune system activation, characterized by strong enrichment in interferon response pathways and JAK-STAT signaling cascades, alongside significant involvement of the PI3K-AKT-mTOR pathway, indicating a complex interplay between immune regulation and cellular growth signaling. SCZ exhibited a unique combination of immune dysregulation, oxidative stress, and metabolic perturbations, with notable enrichment in interferon responses and reactive oxygen species pathways (Fig. 4 ). Thus, the pathway enrichment analysis distinct molecular signatures with both convergent and divergent patterns across psychiatric disorders. While BD and SCZ showed striking similarities in immune system activation, with significant upregulation of interferon gamma and alpha response pathways, suggesting shared inflammatory mechanisms in their pathophysiology. In contrast, MDD exhibited a distinct pattern with downregulation of inflammatory responses and TNFα signaling, indicating that while immune system dysregulation is common across these disorders, the directional changes are disorder specific. Similar to the immune system the metabolic pathways show disease specific patterns. MDD demonstrated upregulation of fundamental metabolic processes including glycolysis, hypoxia response, and estrogen signaling, suggesting cellular stress and altered energy metabolism as key features. BD uniquely showed strong activation of PI3K-AKT-mTOR and JAK-STAT signaling cascades, alongside upregulated cholesterol homeostasis and epithelial-mesenchymal transition pathways, indicating disrupted cellular signaling and plasticity. SCZ exhibited a distinct profile with upregulation of xenobiotic metabolism and reactive oxygen species pathways, suggesting oxidative stress as a central mechanism, along with altered cholesterol and androgen responses. Furthermore, Gene Ontology (GO) analysis confirms the same molecular convergence as observed with the MsigDb hallmark analysis. These findings suggest shared inflammatory and signaling pathway disruptions across these psychiatric conditions, while also revealing disorder-specific molecular signatures that could inform targeted therapeutic approaches. These molecular signatures correlate remarkably with clinical presentations: the dysregulated stress response and metabolic pathways in MD align with observed neurovegetative symptoms and stress sensitivity ( 23 ); the oscillating cellular signaling patterns in BD mirror the cyclic nature of mood states ( 24 ); and the combination of immune activation and oxidative stress in Schizophrenia may underlie the progressive nature of cognitive symptoms ( 25 ). Of therapeutic relevance, these findings suggest that while immune-modulating strategies might benefit BD and SCZ patients, alternative approaches targeting metabolic and stress response pathways might be more effective for MDD. Furthermore, the identification of disorder-specific pathway dysregulation provides potential novel therapeutic targets: mTOR pathway modulators for BD, antioxidant strategies for SCZ, and metabolic pathway interventions for MDD. Figure 4 . Pathway Analysis Uncovers the shared and unique biological pathway across Psychosis Disorders Transcription Factor Enrichment Analysis Uncovers Distinctive Regulatory Programs To investigate the observed disease specific gene expression and pathway, we aim to prioritize the transcription factor (TF) which governs this behavior. We identified unique TF signatures across conditions, with notable disease-specific patterns. Our analysis revealed ASCL3, MYOG, HNF1B, RUNX3, FOXA1 and STAT4 as predominant regulators of observed gene expression change in MDD; FOSL1, FOSL2, PLSCR1, RELB, BATF3, IRF and NFKB1 emerged as key regulatory factors, potentially orchestrating immune-related gene expression changes in BD; ATF5, CREB3L3, SNAI1, NFIL3, CEBPB, RELB, IRF as unique signature potentially indicating their involvement in regulating genes associated with immune function and neurodevelopment in SCZ. Figure 5 . Transcription Factor Uncovers the Unique biology of disease Literature Validation of the Key Genes and Identification of Drug Targets To further explore the key biological difference observed at the disease level, we sought to validate our findings with literature search. Using the DGE list, we searched the genes across the web for their involvement in pathophysiology across the disease status. In BD, several of our identified genes such as SERPINA3 ( 26 ), CCL2 ( 27 ), SOCS3 ( 27 ), S100A3 ( 28 ), FOSL1 ( 29 ) (Fig. 4 , volcano plot) have been previously implicated in pathophysiology observed bipolar patients. Notably, in SCZ, genes such as SERPINA3 ( 30 ), CHI3L1 ( 31 ), SOCS3 ( 32 ), CASP1 ( 32 ), IL1RL1 ( 33 ), IL6 ( 33 ), HBG2 ( 34 ), GRIN2A ( 35 ) and GRIA3 ( 36 ) have established associations with inflammation, synaptic plasticity and disease severity. For MDD, our key genes, including CHI3L1 ( 37 ), SERPINA3 ( 38 ), CP, which involved in metabolism ( https://psychiatry-psychopharmacology.com/en/ceruloplasmin-levels-before-and-after-treatment-in-patients-with-depression-a-case-control-study-132758 ) align with published studies demonstrating their roles in stress and mood regulation. Next, to explore the therapeutic potential of our DGE gene list in a disease-specific manner, we utilized the DGIdb resource ( https://www.dgidb.org/search_interactions ), which catalogs the experimentally validated drug-gene interaction. Our drug-gene interaction analysis using DGIdb uncovered several promising therapeutic implications across psychiatric disorders. Notably, SERPINA3, a key dysregulated gene in our analysis, showed interactions with multiple established antipsychotic agents including risperidone, olanzapine, and clozapine, validating its relevance in psychiatric pathophysiology. The inflammatory mediators identified in our study, particularly IL6 and CCL2, demonstrated interactions with various therapeutic agents, including immunomodulators and antipsychotics, CP with antidepressants etc. The DGIdb analysis uncovered several promising drug-gene interactions beyond our curated psychiatric gene set. For example - SLC22A12 demonstrated interactions with multiple therapeutic agents, including losartan and antineoplastics, suggesting potential metabolic pathway interventions. PTGIR showed significant associations with cardiovascular agents like selexipag and epoprostenol, highlighting possible vascular-related therapeutic approaches. The CALCA pathway revealed interactions with novel therapeutic antibodies (galcanezumab, fremanezumab) and traditional medications, suggesting its potential role in pain and neurotransmitter modulation. CHRNG's interactions with multiple neuromuscular blocking agents point to possible therapeutic implications for motor symptoms. PLA2G2A's connections to anti-inflammatory agents and corticosteroids, along with CXCR1/2's interaction profile with anti-inflammatory compounds, suggest additional inflammatory pathway intervention possibilities. Notably, NPC1L1's interaction with lipid-modulating drugs like ezetimibe indicates potential metabolic therapeutic approaches. These previously unexplored drug-gene interactions reveal additional therapeutic opportunities and potential drug repurposing strategies for psychiatric disorders, particularly through modulation of inflammatory, metabolic, and neurotransmitter pathways. The summarized list of drug-gene interaction has been added into the Supplementary File 1. Discussion Our comprehensive transcriptomic analysis reveals both shared and unique molecular signatures across SCZ, BD, and MDD, providing crucial insights into the biological foundations of these psychiatric conditions. The identification of 373 common differentially expressed genes (DEGs) between SCZ and BD and 12 common hub genes across all three disorders, supports the hypothesis of shared pathophysiological mechanisms, while distinct transcriptional patterns highlight disorder-specific molecular pathways. This substantial molecular overlap between BD and SCZ provides a molecular basis for the clinical similarities often observed between these disorders and may explain the challenges clinicians face in differential diagnosis. The observation that BD exhibited the largest number of unique DEGs suggests a particularly complex molecular landscape, potentially reflecting the disorder's characteristic oscillation between manic and depressive states. This finding aligns with the robust immune system activation and cellular growth signaling perturbations observed in BD patients, suggesting potential therapeutic targets specific to this condition. Brain region-specific transcriptional patterns, particularly in the hippocampus and DLPFC, underscore the spatial heterogeneity of gene expression changes in psychiatric disorders. These findings suggest that therapeutic approaches may need to consider both disorder-specific and region-specific molecular alterations. Our analysis of pathway disruption reveals complex interactions between different biological systems, particularly notable in the immune system's involvement across all three disorders, albeit with varying patterns and intensity. The distinct pathway dysregulation patterns observed for each disorder – stress response and metabolic pathways in MDD, immune system activation in BD, and a combination of immune dysregulation and oxidative stress in SCZ – provide potential targets for tailored therapeutic interventions. This finding suggests that immune modulation might represent a promising therapeutic avenue, though the approach would need to be carefully tailored to each disorder's specific immune signature. The disorder-specific molecular signatures could guide the development of novel therapeutic agents, potentially leading to more precise treatment strategies that address the unique pathophysiological mechanisms of each condition. Several limitations of this study warrant consideration. First, the use of publicly available datasets introduces potential heterogeneity in sample collection and processing methods. Second, transcriptomic analysis of post-mortem tissue provides only a terminal snapshot of gene expression, potentially missing the dynamic molecular changes that occur throughout disease progression. Future longitudinal studies could help address this limitation. Additionally, the focus on specific brain regions, while providing detailed insights, may not capture the full complexity of brain-wide network disruptions in psychiatric disorders. Future research directions should address these limitations through multiple approaches. Validation of key molecular signatures could be pursued through complementary methods such as single-cell RNA sequencing of post-mortem tissue, which might better account for cellular heterogeneity and provide higher resolution of cell-type-specific changes. The development of improved methods for handling post-mortem tissue and standardizing collection procedures across brain banks would enhance data quality and reproducibility. Integration of these findings with other molecular data types especially proteomic, metabolomic, and epigenetic data could provide a more comprehensive understanding of the biological mechanisms underlying psychiatric disorders. Additionally, future studies should consider alternative approaches such as patient-derived induced pluripotent stem cells (iPSCs) and brain organoids, which could help overcome some limitations of post-mortem studies by enabling longitudinal analysis and investigation of developmental aspects of these disorders. These models, while having their own limitations, could complement post-mortem studies and provide insights into the temporal dynamics of disease progression. In conclusion, our study provides valuable insights into the molecular landscape of major psychiatric disorders, revealing a complex interplay of shared biological mechanisms and disorder-specific pathways. The identification of distinct transcriptional signatures and key regulatory networks contributes significantly to our understanding of the biological continuum across psychiatric conditions. These findings not only suggest potential targets for therapeutic intervention but also emphasize the importance of considering both common and unique molecular features in treatment development. As we move forward, the integration of these molecular insights with clinical practices could pave the way for more personalized treatment approaches, potentially improving outcomes for patients with psychiatric disorders. Future research building on these results, particularly through the integration of multi-omics data and advanced therapeutic targeting strategies, may lead to more effective, precision medicine approaches in psychiatric care. Declarations Ethics Approval and Consent to Participate This study does not involve any human or animal subjects, and therefore, ethics approval and consent to participate are not applicable. Consent for Publication All authors have read and approved the final version of the manuscript for submission and publication. Author’s Contribution Priyanka: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, writing original draft, Reviewing and editing original draft. Rajesh Kumar: Formal analysis, Investigation, Reviewing and editing original draft Sandeep Singh Rana: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, writing original draft, Reviewing and editing original draft. Funding All funding has been granted by the Guru Jambheshwar University of Science & Technology, Hisar, India. Data Availability All the datasets generated for this study are either included in this article. The script will be uploaded to the github page. Declaration of Competing Interest There is no potential conflict of interest among the authors of the manuscript. Acknowledgment The authors are thankful to the University Grants Commission (UGC), Guru Jambheshwar University of Science & Technology for providing the necessary facility and infrastructure to carry out this research work. References Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9, 137–150 (2022). B. Yin, Y. Cai, T. Teng, X. Wang, X. Liu, X. Li, J. Wang, H. Wu, Y. He, F. Ren, T. Kou, Z. J. Zhu, X. 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Pharmacol Rep 73, 1052–1062 (2021). Y. Fang, S. F. Xiao, S. Y. Zhang, Q. Qiu, T. Wang, X. Li, Increased Plasma S100β Level in Patients with Major Depressive Disorder. CNS Neurosci Ther 22, 248–250 (2016). G. Hu, S. Yu, C. Yuan, W. Hong, Z. Wang, R. Zhang, D. Wang, Z. Li, Z. Yi, Y. Fang, Gene expression signatures differentiating major depressive disorder from subsyndromal symptomatic depression. Aging 13, 13124–13137 (2021). H. F. North, C. Weissleder, J. M. Fullerton, R. Sager, M. J. Webster, C. S. Weickert, A schizophrenia subgroup with elevated inflammation displays reduced microglia, increased peripheral immune cell and altered neurogenesis marker gene expression in the subependymal zone. Transl Psychiatry 11 (2021). M. S. Yang, D. W. Morris, G. Donohoe, E. Kenny, C. T. O’Dushalaine, S. Schwaiger, J. M. Nangle, S. Clarke, P. Scully, J. Quinn, D. Meagher, P. Baldwin, N. Crumlish, E. O’Callaghan, J. L. Waddington, M. Gill, A. 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Harrison, D. M. Bannerman, GRIN2A (NR2A): a gene contributing to glutamatergic involvement in schizophrenia. Mol Psychiatry 28, 3568–3572 (2023). C. Magri, R. Gardella, P. Valsecchi, S. D. Barlati, L. Guizzetti, L. Imperadori, C. Bonvicini, G. B. Tura, M. Gennarelli, E. Sacchetti, S. Barlati, Study on GRIA2, GRIA3 and GRIA4 genes highlights a positive association between schizophrenia and GRIA3 in female patients. Am J Med Genet B Neuropsychiatr Genet 147B, 745–753 (2008). B. M. Andrus, K. Blizinsky, P. T. Vedell, K. Dennis, P. K. Shukla, D. J. Schaffer, J. Radulovic, G. A. Churchill, E. E. Redei, Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models. Mol Psychiatry 17, 49–61 (2012). K. Ikubo, K. Yamanishi, N. Doe, T. Hashimoto, M. Sumida, Y. Watanabe, Y. El-Darawish, W. Li, H. Okamura, H. Yamanishi, H. Matsunaga, Molecular analysis of the mouse brain exposed to chronic mild stress: The influence of hepatocyte nuclear factor 4α on physiological homeostasis. Mol Med Rep 16, 301–309 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5907225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407687031,"identity":"6f77fa82-c4f1-4a17-9ef8-82c20fce5637","order_by":0,"name":"Priyanka Priyanka","email":"data:image/png;base64,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","orcid":"","institution":"Guru Jambheshwar University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Priyanka","suffix":""},{"id":407687033,"identity":"6adf9374-ed54-4a4f-b537-16b0a3e571d2","order_by":1,"name":"Rajesh Kumar","email":"","orcid":"","institution":"CSIR – Institute of Microbial Technology","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Kumar","suffix":""},{"id":407687039,"identity":"89bcd39c-5cb1-47c3-bdd5-eb7abb66b26f","order_by":2,"name":"Sandeep Singh Rana","email":"","orcid":"","institution":"Guru Jambheshwar University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"Singh","lastName":"Rana","suffix":""}],"badges":[],"createdAt":"2025-01-26 15:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5907225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5907225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74983628,"identity":"254359b1-44e0-4a27-b376-86bcc2a3c818","added_by":"auto","created_at":"2025-01-29 05:31:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86098,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the overall integrative bioinformatics pipeline used in the study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/1dfb59b2482d6e71597d2b70.png"},{"id":74983629,"identity":"1d311990-13d3-4e65-9ce1-224de9451b6f","added_by":"auto","created_at":"2025-01-29 05:31:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279838,"visible":true,"origin":"","legend":"\u003cp\u003eDimensionality Reduction Validates Disease Specific Pattern Across Conditions\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/7a981bd57c9fea76714b7e2f.png"},{"id":74983630,"identity":"e6e28588-c755-4ccc-940b-56162a4240b9","added_by":"auto","created_at":"2025-01-29 05:31:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163492,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic Analysis Identify Shared and Unique Genomic Vulnerability across Psychosis Disorders\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/e20631a3305d747d2dfd73a5.png"},{"id":74983635,"identity":"e1da78d4-a811-4130-9a42-b7b88ed64c86","added_by":"auto","created_at":"2025-01-29 05:31:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":455947,"visible":true,"origin":"","legend":"\u003cp\u003ePathway Analysis Uncovers the shared and unique biological pathway across Psychosis Disorders\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/976ff1286a53e9d364e3f732.png"},{"id":74984148,"identity":"2072c245-1308-4068-8019-779293ed5561","added_by":"auto","created_at":"2025-01-29 05:39:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":186341,"visible":true,"origin":"","legend":"\u003cp\u003eTranscription Factor Uncovers the Unique biology of disease\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/6093cf7b787f5b9be0026fa3.png"},{"id":75054204,"identity":"6dbb40f9-f7d5-4b3a-ac3e-9a3694a8b40d","added_by":"auto","created_at":"2025-01-30 01:46:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2004599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5907225/v1/db8febbe-5232-4f2b-a75a-3a8e275139ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic analysis uncovers the shared and unique biological foundations acrossSchizophrenia, Bipolar and Major Depressive Disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychiatric disorders represent a significant global health burden, affecting millions worldwide and posing substantial challenges to healthcare systems, social structures, and economic frameworks (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Among these, Schizophrenia (SCZ), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) stand as particularly impactful conditions, characterized by complex symptomatology and often devastating effects on individual functioning and quality of life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite their clinical significance, the underlying molecular mechanisms driving these disorders remain incompletely understood, hindering the development of more effective therapeutic strategies. The global burden of these disorders is substantial, with approximately 1% of the population affected by SCZ, 2.4% by BD, and 3.8% by MDD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These conditions account for a significant proportion of disability-adjusted life years (DALYs) worldwide.\u003c/p\u003e \u003cp\u003eThe overlapping symptomatology and high comorbidity rates among these disorders suggest shared biological underpinnings, yet each condition also exhibits unique clinical features that point to distinct pathophysiological mechanisms (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Recent advances in high-throughput sequencing technologies and bioinformatics have provided unprecedented opportunities to explore these biological foundations at the molecular level (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Transcriptomic analysis, in particular, has emerged as a powerful tool for understanding the complex interplay of genetic and environmental factors in psychiatric disorders (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe complexity of psychiatric disorders is further compounded by the involvement of multiple brain regions and neural circuits. The dorsolateral prefrontal cortex (DLPFC) and hippocampus have been consistently implicated in the pathophysiology of SCZ, BD, and MDD, yet their relative contributions to each disorder remain debated (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Understanding region-specific transcriptional changes is crucial for developing targeted therapeutic approaches and identifying biomarkers for early diagnosis and intervention.\u003c/p\u003e \u003cp\u003ePrevious studies have typically focused on individual disorders or specific brain regions, limiting our understanding of the broader biological landscape across psychiatric conditions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The integration of data from multiple studies and brain regions offers a unique opportunity to identify both shared and unique molecular signatures, potentially revealing novel therapeutic targets and biological pathways (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This comprehensive approach is particularly relevant given the growing recognition of psychiatric disorders as existing along a biological continuum rather than as discrete entities.\u003c/p\u003e \u003cp\u003eRecent technological advances have enabled more sophisticated analyses of gene expression patterns and pathway dysregulation, providing new insights into the molecular basis of psychiatric disorders (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The application of advanced bioinformatic approaches to large-scale transcriptomic data has revealed complex patterns of gene expression changes and pathway perturbations that may underlie the development and progression of these conditions. The integration of machine learning approaches with transcriptomic analysis has further enhanced our ability to identify complex patterns and relationships within large-scale genomic data (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These computational advances have enabled more robust identification of disease-specific signatures and pathway interactions.\u003c/p\u003e \u003cp\u003eThe present study aims to address these knowledge gaps through a comprehensive transcriptomic analysis of SCZ, BD, and MDD, integrating data from multiple brain regions and studies. Our objectives include: (i) identifying shared and unique genomic signatures across disorders, (ii) characterizing brain region-specific transcriptional patterns, (iii) elucidating distinct molecular mechanisms through pathway analysis, and (iv) exploring the implications of these findings for therapeutic development.\u003c/p\u003e \u003cp\u003eUnderstanding the molecular intersection of these disorders is crucial for several reasons. First, it may reveal common therapeutic targets that could benefit multiple conditions. Second, identifying disorder-specific signatures could lead to more precise diagnostic tools and personalized treatment approaches. Finally, this knowledge contributes to our broader understanding of the biological basis of psychiatric disorders, potentially informing future research directions and therapeutic strategies.\u003c/p\u003e \u003cp\u003eThis study represents a significant step forward in psychiatric research by providing a comprehensive analysis of the molecular landscape across major psychiatric disorders. By integrating multiple datasets and employing advanced analytical approaches, we aim to provide new insights into the biological foundations of these conditions and contribute to the development of more effective therapeutic strategies.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDataset Collection\u003c/h2\u003e \u003cp\u003eThere is evidence of a common underlying pathophysiology in neuropsychological disorders. We selected and searched the publicly available dataset on BD, SCZ, and MDD, with considerations of the PRISMA guidelines, ensuring a systematic and thorough review of the available data.\u003c/p\u003e \u003cp\u003eThe selection criteria include - i) Study should involve human subjects (\"Homo sapiens\"), ii) Study should contain a minimum of 12 samples to ensure statistical robustness in downstream analysis, iii) data should be publicly available. The following exclusion criteria was used - i) Study should include the strict patient vs control analysis, ii) Should contain experimental samples other than post-mortem brain samples, iii) experimental data excluding cellular populations. Following these data guidelines and criteria, we have been left with 5 RNA-Sequencing datasets - GSE138082, GSE174407, GSE80655, GSE78936, and GSE379666. However, for the GSE174407 study, the raw sequence read files were not publicly accessible and consequently, we had to exclude this dataset from our further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTranscriptomic Dataset Processing\u003c/h3\u003e\n\u003cp\u003eWe have downloaded the raw fastq files of the above-mentioned dataset using NCBI SRA-Toolkit by developing a custom bash script. After downloading the raw fastq files, the sequencing data was further processed and analyzed using the nf-core RNA-seq pipeline version 3.14.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nf-co.re/rnaseq/3.14.0/\u003c/span\u003e\u003cspan address=\"https://nf-co.re/rnaseq/3.14.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Default parameters were employed for running the pipeline; this includes a broad range of software packages for quality control, read processing, alignment, and quantification. Initial steps in the processing of raw reads included quality assessment using FastQC and adapter trimming using Trim Galore. The trimmed reads were then aligned to the human reference genome (GRCh38) version 113 using STAR and quantified at both gene and transcript levels using Salmon. Other quality control criteria included computation of the transcript integrity number, gene body coverage analysis done with Qualimap, and an assessment of read distribution through RSeQC. Alignment quality metrics were produced through Picard Tools, while normalized bigWig files were prepared for visualization. We applied the default minimum threshold of mapped reads for sample inclusion: 5. All these processes have been orchestrated using Nextflow to ensure that the reproduction and scalability of our analysis across the different datasets that compose this study are run.\u003c/p\u003e\n\u003ch3\u003eBatch Correction and Visualization\u003c/h3\u003e\n\u003cp\u003eThe limma R package (version 3.20) was used to remove any potential batch effects in the dataset. The batch effect correction for each study was done by using the known variable such as sequencing library preparations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Unknown batch effects were further estimated using the checking of the surrogate variables by the limma R package. For plotting and visualization of the batch effect, the first two principal components are considered. Further downstream analysis has been done with a batch-corrected data matrix which consists of 19447 human protein-coding genes. To represent the latent projection of all the samples in a 2-dimensional space we have also performed the UMAP projection on a batch corrected data matrix. UMAP analysis was performed on each tissue and the disease class which the sample belongs to. In addition, we also performed the UMAP analysis for each distinct brain region, examining samples across disease conditions.\u003c/p\u003e\n\u003ch3\u003eDifferential Expression Analysis and Identification of Key Genes and Pathways\u003c/h3\u003e\n\u003cp\u003eTo investigate transcriptional changes across different brain regions and disease conditions, we performed comprehensive differential expression analysis using DESeq2 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This robust analytical approach enabled us to identify genes that showed significant alterations in expression patterns when compared to control samples. For statistical stringency and biological relevance, we established dual filtering criteria: an absolute log2 fold change threshold exceeding 1 (corresponding to a minimum two-fold change in expression) and a statistical significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This balanced approach helped us identify genes that showed both substantial magnitude of change and statistical reliability. The differentially expressed genes were then examined for their functional implications through Gene Set Enrichment Analysis (GSEA). We incorporated pathway information from two complementary databases: the Reactome database, which provides detailed molecular pathway annotations, and the Molecular Signatures Database (MSigDB), offering a broader spectrum of functional gene sets (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This dual-database approach enabled us to capture both specific molecular mechanisms and broader biological processes affected in each disease condition. By analyzing each disease group separately, we could identify both unique pathway perturbations specific to individual conditions and common pathway alterations shared across multiple disease states.\u003c/p\u003e\n\u003ch3\u003eTranscription Factor Enrichment\u003c/h3\u003e\n\u003cp\u003eTo elucidate the regulatory mechanisms underlying the identified pathways, we performed transcription factor (TF) enrichment analysis using ChIP-X Enrichment Analysis 3 (ChEA3) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This analysis was crucial for understanding the upstream regulators that orchestrate the observed pathway-specific gene expression patterns. By analyzing the leading genes derived from MSigDB and Reactome pathway analyses, we sought to identify both unique and shared transcriptional regulators across different pathways. ChEA3's integrative approach, which combines multiple lines of evidence including RNA-seq-based TF-gene co-expression data, ChIP-seq-derived TF-target associations, and TF-gene co-occurrence patterns from crowd-sourced gene lists, provided a comprehensive view of the regulatory landscape. The composite ranking system of ChEA3 enhanced the reliability of our TF predictions by synthesizing evidence from these diverse data sources, offering insights into the hierarchical organization of the transcriptional networks governing these pathways. This approach enabled us to identify key regulatory nodes that could explain the observed pathway-specific gene expression patterns and potential crosstalk between different biological processes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Key Drugs for Therapeutics\u003c/h2\u003e \u003cp\u003eWe have utilized the DGIdb resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dgidb.org/search_interactions\u003c/span\u003e\u003cspan address=\"https://www.dgidb.org/search_interactions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the purpose of studying the therapeutic potentials of the identified leading genes governing each pathway and thus sheds light upon a unique treatment-targeted approach. This database maintains the latest information on drug-gene interactions identified from experimental studies. We have searched our signature genes in the database and identified their association, in order to search for disease specific therapeutic interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDataset Characteristics and Study Overview\u003c/h2\u003e \u003cp\u003eFollowing PRISMA guidelines, we left with four studies, to be included in our analysis. The study PRJNA314463 (GSE78936) consists of samples from 24 controls, 30 BD and 28 SCZ, whereas Study PRJNA319583 (GSE80655) have 86 control, 84 BD, 89 MDD, and 83 SCZ patients, study PRJNA379666 (GSE379666) consisted of 24 control and 22 SCZ samples, and study PRJNA574470 (GSE138082) was made up of 34 control and 34 SCZ. The complete description of the dataset used in the present study has been added to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Supplementary File 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the dataset used in the present study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain Areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFile Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE78936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBipolar and Schizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrbitofrontal Cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina HiSeq 2000 (Homo sapiens)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Fastq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE80655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBipolar, Schizophrenia and Major Depressive Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnterior Cingulate Cortex, Dorsolateral Prefrontal Cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina HiSeq 2000 (Homo sapiens)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Fastq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE379666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmygdala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina HiSeq 2000 (Homo sapiens)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Fastq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE138082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina HiSeq 2000 (Homo sapiens)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Fastq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCollectively, the present study consists of a cohort of 538 public RNA-seq dataset of BD, SCZ and MDD from different regions of the brain and accessed their genomic expression profile for the identification of unique and shared features. All samples were processed using standardized RNA-seq protocols on the Illumina HiSeq 2000 platform, ensuring technical consistency across studies. The brain regions analyzed represent key areas implicated in psychiatric disorders: the orbitofrontal cortex, involved in decision-making and emotional processing; the anterior cingulate and dorsolateral prefrontal cortex, crucial for executive function and emotional regulation; the amygdala, central to fear and emotional responses; and the hippocampus, essential for memory formation and emotional processing. This diverse regional sampling allows for a comprehensive analysis of disease-specific molecular signatures across different functional brain areas. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. provides the complete architecture of the study performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Schematic representation of the overall integrative bioinformatics pipeline used in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDimensionality Reduction Uncovers Disease Specific Features Across Psychological Conditions\u003c/h2\u003e \u003cp\u003eThe raw FASTQ files were processed using a uniform bioinformatics pipeline to minimize technical variability in the analysis workflow. The raw count matrix of 538 cases and control dataset were used for the batch correction to mitigate technical variations across the datasets. Following batch correction, the Principal Component Analysis (PCA) revealed distinct patterns across psychiatric conditions, brain regions, and study groups. The study group specific PCA clustering, validates our batch correction approach, as samples from all studies (PRJNA314463, PRJNA319583, PRJNA379666, and PRJNA574470) showed uniform distribution without distinct study-specific clustering, confirming successful removal of batch effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, top). Since the dataset consists of different psychological conditions and across several brain regions, we extend the PCA analysis across conditions and different brain regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePCA clustering based on the disease status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, middle), we observed partial overlapping between psychiatric conditions, with SCZ and BD samples showing closer molecular signatures compared to MDD, suggesting a molecular continuum among these disorders. Control samples showed a more diffuse distribution, indicating natural biological variability in healthy brain tissue (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The brain region-specific analysis (bottom panel A) revealed interesting biological clustering, particularly in the hippocampus and DLPFC regions, which formed more distinct clusters compared to other brain areas, suggesting strong region-specific transcriptional signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, bottom). However, the limited variance explained by the first two principal components (PC1: 27.6%, PC2: 10.9%) indicated additional complexity in the data structure not captured by linear dimensional technique. To further uncover the more biological variations in the dataset, we extended our analysis with non-linear dimensionality reduction techniques. The Uniform Manifold Approximation and Projection (UMAP) analysis reveals more nuanced biological patterns, particularly in disease-specific manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The UMAP projection shows that while there is partial overlap between SCZ and BD, MDD forms a distinct cluster with partial or no overlap with SCZ. These complementary analyses suggest that while these psychiatric disorders share common molecular features, they also possess unique transcriptional programs particularly for major depressive disorder, as evidenced by both PCA and UMAP.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Dimensionality Reduction Validates Disease Specific Pattern Across Conditions\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eShared and Unique Genomic Signatures Across Psychiatric Disorders\u003c/h2\u003e \u003cp\u003eOur transcriptomic analysis revealed both unique and overlapping genomic signatures across psychiatric disorders. BD exhibited the largest number of unique differentially expressed genes (DEG\u0026rsquo;s), followed by SCZ and MDD. Notably, BD and SCZ shared substantial molecular overlap with 373 common DEGs, suggesting significant biological convergence between these conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We also identified 12 common hub genes across all the three disorders. The brain region-specific analysis demonstrated distinct patterns of transcriptional dysregulation. The amygdala and hippocampus demonstrated unique transcriptional signatures, especially in SCZ, highlighting the region-specific nature of psychiatric pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This comprehensive analysis suggests a complex interplay between shared and unique genomic vulnerabilities across psychiatric disorders, with substantial overlap between BD and SCZ, while MDD shows more distinct molecular patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Transcriptomic Analysis Identify Shared and Unique Genomic Vulnerability across Psychosis Disorders\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePathway Analysis Reveals Distinct Molecular Mechanisms in Psychiatric Disorders\u003c/h2\u003e \u003cp\u003eThe pathway enrichment analysis uncovered distinct biological mechanisms underlying each psychiatric disorder. MDD showed predominant dysregulation in stress response and metabolic pathways, with significant enrichment in KRAS signaling, unfolded protein response, and glycolysis, suggesting cellular stress as a key pathogenic mechanism. BD demonstrated the most robust immune system activation, characterized by strong enrichment in interferon response pathways and JAK-STAT signaling cascades, alongside significant involvement of the PI3K-AKT-mTOR pathway, indicating a complex interplay between immune regulation and cellular growth signaling. SCZ exhibited a unique combination of immune dysregulation, oxidative stress, and metabolic perturbations, with notable enrichment in interferon responses and reactive oxygen species pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, the pathway enrichment analysis distinct molecular signatures with both convergent and divergent patterns across psychiatric disorders. While BD and SCZ showed striking similarities in immune system activation, with significant upregulation of interferon gamma and alpha response pathways, suggesting shared inflammatory mechanisms in their pathophysiology. In contrast, MDD exhibited a distinct pattern with downregulation of inflammatory responses and TNFα signaling, indicating that while immune system dysregulation is common across these disorders, the directional changes are disorder specific. Similar to the immune system the metabolic pathways show disease specific patterns. MDD demonstrated upregulation of fundamental metabolic processes including glycolysis, hypoxia response, and estrogen signaling, suggesting cellular stress and altered energy metabolism as key features. BD uniquely showed strong activation of PI3K-AKT-mTOR and JAK-STAT signaling cascades, alongside upregulated cholesterol homeostasis and epithelial-mesenchymal transition pathways, indicating disrupted cellular signaling and plasticity. SCZ exhibited a distinct profile with upregulation of xenobiotic metabolism and reactive oxygen species pathways, suggesting oxidative stress as a central mechanism, along with altered cholesterol and androgen responses. Furthermore, Gene Ontology (GO) analysis confirms the same molecular convergence as observed with the MsigDb hallmark analysis. These findings suggest shared inflammatory and signaling pathway disruptions across these psychiatric conditions, while also revealing disorder-specific molecular signatures that could inform targeted therapeutic approaches.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese molecular signatures correlate remarkably with clinical presentations: the dysregulated stress response and metabolic pathways in MD align with observed neurovegetative symptoms and stress sensitivity (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e); the oscillating cellular signaling patterns in BD mirror the cyclic nature of mood states (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e); and the combination of immune activation and oxidative stress in Schizophrenia may underlie the progressive nature of cognitive symptoms (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Of therapeutic relevance, these findings suggest that while immune-modulating strategies might benefit BD and SCZ patients, alternative approaches targeting metabolic and stress response pathways might be more effective for MDD. Furthermore, the identification of disorder-specific pathway dysregulation provides potential novel therapeutic targets: mTOR pathway modulators for BD, antioxidant strategies for SCZ, and metabolic pathway interventions for MDD.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Pathway Analysis Uncovers the shared and unique biological pathway across Psychosis Disorders\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTranscription Factor Enrichment Analysis Uncovers Distinctive Regulatory Programs\u003c/h2\u003e \u003cp\u003eTo investigate the observed disease specific gene expression and pathway, we aim to prioritize the transcription factor (TF) which governs this behavior. We identified unique TF signatures across conditions, with notable disease-specific patterns. Our analysis revealed ASCL3, MYOG, HNF1B, RUNX3, FOXA1 and STAT4 as predominant regulators of observed gene expression change in MDD; FOSL1, FOSL2, PLSCR1, RELB, BATF3, IRF and NFKB1 emerged as key regulatory factors, potentially orchestrating immune-related gene expression changes in BD; ATF5, CREB3L3, SNAI1, NFIL3, CEBPB, RELB, IRF as unique signature potentially indicating their involvement in regulating genes associated with immune function and neurodevelopment in SCZ.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Transcription Factor Uncovers the Unique biology of disease\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLiterature Validation of the Key Genes and Identification of Drug Targets\u003c/h2\u003e \u003cp\u003eTo further explore the key biological difference observed at the disease level, we sought to validate our findings with literature search. Using the DGE list, we searched the genes across the web for their involvement in pathophysiology across the disease status. In BD, several of our identified genes such as SERPINA3 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), CCL2 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), SOCS3 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), S100A3 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), FOSL1 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, volcano plot) have been previously implicated in pathophysiology observed bipolar patients. Notably, in SCZ, genes such as SERPINA3 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), CHI3L1 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), SOCS3 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), CASP1 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), IL1RL1 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), IL6 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), HBG2 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), GRIN2A (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and GRIA3 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) have established associations with inflammation, synaptic plasticity and disease severity. For MDD, our key genes, including CHI3L1 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), SERPINA3 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), CP, which involved in metabolism (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psychiatry-psychopharmacology.com/en/ceruloplasmin-levels-before-and-after-treatment-in-patients-with-depression-a-case-control-study-132758\u003c/span\u003e\u003cspan address=\"https://psychiatry-psychopharmacology.com/en/ceruloplasmin-levels-before-and-after-treatment-in-patients-with-depression-a-case-control-study-132758\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) align with published studies demonstrating their roles in stress and mood regulation. Next, to explore the therapeutic potential of our DGE gene list in a disease-specific manner, we utilized the DGIdb resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dgidb.org/search_interactions\u003c/span\u003e\u003cspan address=\"https://www.dgidb.org/search_interactions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which catalogs the experimentally validated drug-gene interaction. Our drug-gene interaction analysis using DGIdb uncovered several promising therapeutic implications across psychiatric disorders. Notably, SERPINA3, a key dysregulated gene in our analysis, showed interactions with multiple established antipsychotic agents including risperidone, olanzapine, and clozapine, validating its relevance in psychiatric pathophysiology. The inflammatory mediators identified in our study, particularly IL6 and CCL2, demonstrated interactions with various therapeutic agents, including immunomodulators and antipsychotics, CP with antidepressants etc. The DGIdb analysis uncovered several promising drug-gene interactions beyond our curated psychiatric gene set. For example - SLC22A12 demonstrated interactions with multiple therapeutic agents, including losartan and antineoplastics, suggesting potential metabolic pathway interventions. PTGIR showed significant associations with cardiovascular agents like selexipag and epoprostenol, highlighting possible vascular-related therapeutic approaches. The CALCA pathway revealed interactions with novel therapeutic antibodies (galcanezumab, fremanezumab) and traditional medications, suggesting its potential role in pain and neurotransmitter modulation. CHRNG's interactions with multiple neuromuscular blocking agents point to possible therapeutic implications for motor symptoms. PLA2G2A's connections to anti-inflammatory agents and corticosteroids, along with CXCR1/2's interaction profile with anti-inflammatory compounds, suggest additional inflammatory pathway intervention possibilities. Notably, NPC1L1's interaction with lipid-modulating drugs like ezetimibe indicates potential metabolic therapeutic approaches. These previously unexplored drug-gene interactions reveal additional therapeutic opportunities and potential drug repurposing strategies for psychiatric disorders, particularly through modulation of inflammatory, metabolic, and neurotransmitter pathways. The summarized list of drug-gene interaction has been added into the Supplementary File 1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur comprehensive transcriptomic analysis reveals both shared and unique molecular signatures across SCZ, BD, and MDD, providing crucial insights into the biological foundations of these psychiatric conditions. The identification of 373 common differentially expressed genes (DEGs) between SCZ and BD and 12 common hub genes across all three disorders, supports the hypothesis of shared pathophysiological mechanisms, while distinct transcriptional patterns highlight disorder-specific molecular pathways. This substantial molecular overlap between BD and SCZ provides a molecular basis for the clinical similarities often observed between these disorders and may explain the challenges clinicians face in differential diagnosis.\u003c/p\u003e \u003cp\u003eThe observation that BD exhibited the largest number of unique DEGs suggests a particularly complex molecular landscape, potentially reflecting the disorder's characteristic oscillation between manic and depressive states. This finding aligns with the robust immune system activation and cellular growth signaling perturbations observed in BD patients, suggesting potential therapeutic targets specific to this condition. Brain region-specific transcriptional patterns, particularly in the hippocampus and DLPFC, underscore the spatial heterogeneity of gene expression changes in psychiatric disorders. These findings suggest that therapeutic approaches may need to consider both disorder-specific and region-specific molecular alterations. Our analysis of pathway disruption reveals complex interactions between different biological systems, particularly notable in the immune system's involvement across all three disorders, albeit with varying patterns and intensity. The distinct pathway dysregulation patterns observed for each disorder \u0026ndash; stress response and metabolic pathways in MDD, immune system activation in BD, and a combination of immune dysregulation and oxidative stress in SCZ \u0026ndash; provide potential targets for tailored therapeutic interventions. This finding suggests that immune modulation might represent a promising therapeutic avenue, though the approach would need to be carefully tailored to each disorder's specific immune signature. The disorder-specific molecular signatures could guide the development of novel therapeutic agents, potentially leading to more precise treatment strategies that address the unique pathophysiological mechanisms of each condition.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study warrant consideration. First, the use of publicly available datasets introduces potential heterogeneity in sample collection and processing methods. Second, transcriptomic analysis of post-mortem tissue provides only a terminal snapshot of gene expression, potentially missing the dynamic molecular changes that occur throughout disease progression. Future longitudinal studies could help address this limitation. Additionally, the focus on specific brain regions, while providing detailed insights, may not capture the full complexity of brain-wide network disruptions in psychiatric disorders.\u003c/p\u003e \u003cp\u003eFuture research directions should address these limitations through multiple approaches. Validation of key molecular signatures could be pursued through complementary methods such as single-cell RNA sequencing of post-mortem tissue, which might better account for cellular heterogeneity and provide higher resolution of cell-type-specific changes. The development of improved methods for handling post-mortem tissue and standardizing collection procedures across brain banks would enhance data quality and reproducibility. Integration of these findings with other molecular data types especially proteomic, metabolomic, and epigenetic data could provide a more comprehensive understanding of the biological mechanisms underlying psychiatric disorders. Additionally, future studies should consider alternative approaches such as patient-derived induced pluripotent stem cells (iPSCs) and brain organoids, which could help overcome some limitations of post-mortem studies by enabling longitudinal analysis and investigation of developmental aspects of these disorders. These models, while having their own limitations, could complement post-mortem studies and provide insights into the temporal dynamics of disease progression.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides valuable insights into the molecular landscape of major psychiatric disorders, revealing a complex interplay of shared biological mechanisms and disorder-specific pathways. The identification of distinct transcriptional signatures and key regulatory networks contributes significantly to our understanding of the biological continuum across psychiatric conditions. These findings not only suggest potential targets for therapeutic intervention but also emphasize the importance of considering both common and unique molecular features in treatment development. As we move forward, the integration of these molecular insights with clinical practices could pave the way for more personalized treatment approaches, potentially improving outcomes for patients with psychiatric disorders. Future research building on these results, particularly through the integration of multi-omics data and advanced therapeutic targeting strategies, may lead to more effective, precision medicine approaches in psychiatric care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve any human or animal subjects, and therefore, ethics approval and consent to participate are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript for submission and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePriyanka: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, writing original draft, Reviewing and editing original draft.\u003c/p\u003e\n\u003cp\u003eRajesh Kumar: Formal analysis, Investigation, Reviewing and editing original draft\u003c/p\u003e\n\u003cp\u003eSandeep Singh Rana: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, writing original draft, Reviewing and editing original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll funding has been granted by the Guru Jambheshwar University of Science \u0026amp; Technology, Hisar, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the datasets generated for this study are either included in this article. The script will be uploaded to the github page.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no potential conflict of interest among the authors of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the University Grants Commission (UGC), Guru Jambheshwar University of Science \u0026amp; Technology for providing the necessary facility and infrastructure to carry out this research work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Psychiatry\u003c/em\u003e 9, 137\u0026ndash;150 (2022).\u003c/li\u003e\n\u003cli\u003eB. Yin, Y. Cai, T. Teng, X. Wang, X. Liu, X. 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Wray, J. A. Zwart, J. M. Biernacka, J. I. Nurnberger, S. Cichon, H. J. Edenberg, E. A. Stahl, A. McQuillin, A. Di Florio, R. A. Ophoff, O. A. Andreassen, Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. \u003cem\u003eNat Genet\u003c/em\u003e 53, 817\u0026ndash;829 (2021).\u003c/li\u003e\n\u003cli\u003eK. Curzytek, M. Leśkiewicz, Targeting the CCL2-CCR2 axis in depressive disorders. \u003cem\u003ePharmacol Rep\u003c/em\u003e 73, 1052\u0026ndash;1062 (2021).\u003c/li\u003e\n\u003cli\u003eY. Fang, S. F. Xiao, S. Y. Zhang, Q. Qiu, T. Wang, X. Li, Increased Plasma S100\u0026beta; Level in Patients with Major Depressive Disorder. \u003cem\u003eCNS Neurosci Ther\u003c/em\u003e 22, 248\u0026ndash;250 (2016).\u003c/li\u003e\n\u003cli\u003eG. Hu, S. Yu, C. Yuan, W. Hong, Z. Wang, R. Zhang, D. Wang, Z. Li, Z. Yi, Y. 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Churchill, E. E. Redei, Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models. \u003cem\u003eMol Psychiatry\u003c/em\u003e 17, 49\u0026ndash;61 (2012).\u003c/li\u003e\n\u003cli\u003eK. Ikubo, K. Yamanishi, N. Doe, T. Hashimoto, M. Sumida, Y. Watanabe, Y. El-Darawish, W. Li, H. Okamura, H. Yamanishi, H. Matsunaga, Molecular analysis of the mouse brain exposed to chronic mild stress: The influence of hepatocyte nuclear factor 4\u0026alpha; on physiological homeostasis. \u003cem\u003eMol Med Rep\u003c/em\u003e 16, 301\u0026ndash;309 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bipolar Disorder, Major Depressive Disorder, Schizophrenia, Transcriptomics, Pathways, Vulnerability, Mental Health, Psychiatry","lastPublishedDoi":"10.21203/rs.3.rs-5907225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5907225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsychiatric disorders, including Schizophrenia (SCZ), Bipolar Disorder (BD), and Major Depressive Disorder (MDD), represent complex neuropsychiatric conditions with significant overlap in clinical presentation yet distinct pathophysiological mechanisms. Understanding the molecular underpinnings of major psychiatric disorders remains a significant challenge in neuroscience. This study conducted a comprehensive transcriptomic analysis integrating publicly available 538 RNA-seq datasets from post-mortem samples across multiple brain regions to elucidate shared and unique biological foundations underlying these disorders.\u003c/p\u003e \u003cp\u003eWe employed systematic bioinformatic approaches to analyze differential gene expression patterns and pathway dysregulation across the disorders and the brain regions. ​​The identified differentially expressed genes were further analyzed for shared biological pathways, candidate drugs, and transcription factors. Protein-protein interaction (PPI) network analysis and transcription factor ranking were performed to understand the regulatory mechanisms governing unique and shared molecular behaviors across these disorders.\u003c/p\u003e \u003cp\u003eOur findings revealed distinct transcriptional signatures with notable overlap between SCZ and BD, identifying 373 shared differentially expressed genes (DEGs) and 12 common hub genes. BD exhibited the highest number of unique DEGs, followed by SCZ and MDD, suggesting disorder-specific molecular mechanisms. Brain region-specific analyses demonstrated distinctive transcriptional patterns, particularly in the hippocampus and DLPFC, highlighting the spatial heterogeneity of gene expression changes. Pathway analysis uncovered disorder-specific dysregulation patterns: MDD showed predominant alterations in stress response and metabolic pathways; BD demonstrated robust immune system activation and cellular growth signaling perturbations; and SCZ exhibited a complex interplay of immune dysregulation, oxidative stress, and metabolic disruptions. Network analysis identified key transcription factors, including STAT3, NF-κB, and CREB1, as major regulators of the disease-specific gene expression patterns. Notably, our drug-gene interaction analysis using DGIdb revealed promising therapeutic implications, with key genes like SERPINA3 interacting with antipsychotic agents, and inflammatory mediators such as IL6 and CCL2 showing potential interactions with immunomodulators. These findings suggest novel drug repurposing strategies and targeted therapeutic approaches for psychiatric disorders.\u003c/p\u003e \u003cp\u003eThese findings provide crucial insights into the molecular underpinnings of major psychiatric disorders, revealing both shared biological mechanisms and disorder-specific pathways. The identification of common hub genes and key transcription factors suggests potential therapeutic targets for intervention. Furthermore, our results emphasize the importance of considering both shared and unique molecular signatures in developing targeted treatment strategies for psychiatric disorders, potentially leading to more personalized therapeutic approaches.\u003c/p\u003e","manuscriptTitle":"Transcriptomic analysis uncovers the shared and unique biological foundations acrossSchizophrenia, Bipolar and Major Depressive Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 05:31:54","doi":"10.21203/rs.3.rs-5907225/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00e61bf2-9717-4090-b767-f665efdf0835","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T01:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 05:31:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5907225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5907225","identity":"rs-5907225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}