Peripheral Transcriptomic Signatures Reveal Convergent Neuroinflammatory, Metabolic, and miRNA Dysregulation in Major Psychiatric Disorders

Bipolar disorder (BD) is a complex and serious psychiatric condition primarily characterized by bipolar depression, with the underlying genetic determinants yet to be elucidated. There is a substantial body of literature linking psychiatric disorders, including BD, to oxidative stress (OS). Consequently, this study aims to assess the relationship between BD and OS by identifying key hub genes implicated in OS pathways. We acquired gene microarray data from GSE5392 through the Gene Expression Omnibus (GEO). Our approach encompassed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction (PPI) Network analysis to pinpoint hub genes associated with BD. Subsequently, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify hub genes relevant to OS. To evaluate the diagnostic accuracy of these hub genes, we performed receiver operating characteristic curve (ROC) analysis on both GSE5388 and GSE5389 datasets. Furthermore, we conducted a study involving ten BD patients and ten healthy controls (HCs) who met the special criteria, assessing the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). We identified 411 down-regulated genes and 69 up-regulated genes for further scrutiny. Through WGCNA, we obtained 22 co-expression modules, with the sienna3 module displaying the strongest association with BD. By integrating differential analysis with genes linked to OS, we identified 44 common genes. Subsequent PPI Network and WGCNA analyses confirmed three hub genes as potential biomarkers for BD. Functional enrichment pathway analysis revealed their involvement in neuronal signal transduction, oxidative phosphorylation, and metabolic obstacle pathways. Using the Cytoscape plugin "ClueGo assay," we determined that a majority of these targets regulate neuronal synaptic plasticity. ROC curve analysis underscored the excellent diagnostic value of these three hub genes. Quantitative reverse transcription-PCR (RT-qPCR) results indicated significant changes in the expression of these hub genes in the PBMCs of BD patients compared to HCs. We identified three hub genes (TAC1, MAP2K1, and MAP2K4) in BD associated with OS, potentially influencing the diagnosis and treatment of BD. Based on the GEO database, our study provides novel insights into the relationship between BD and OS, offering promising therapeutic targets.

Objectives Environmental and genetic factors play important roles in the development of schizophrenia (SCZ), bipolar disorder (BPD) or major depressive disorder (MDD). Some risk loci are identified with shared genetic effects on major psychiatric disorders. To investigate whether SNX29 gene played a significant role in these psychiatric disorders in the Han Chinese population. Methods We focussed on 11 single-nucleotide polymorphisms (SNPs) harbouring SNX29 gene and carried out case–control studies in patients with SCZ (n = 1248), BPD (n = 1344), or MDD (n = 1056), and 1248 healthy controls (HC) recruited from the Han Chinese population. We constructed weighted gene co-expression network analysis (WGCNA) and extracted significant modules by R package. Results We found that rs3743592 was significantly associated with MDD and rs6498263 with BPD in both allele and genotype distributions. Before correction, rs3743592 showed allelic and genotypic significance with SCZ, rs6498263 showed allelic significance with SCZ. WGCNA identified top 10 modules of co-expressed genes. Gene Ontology (GO) and pathway analysis were used to examine the functions of SNX29, which revealed that SNX29 was involved in the regulation of a number of biological processes, such as TGF-beta, ErbB, and Wnt signalling pathway, etc. Conclusions Our results supported common risk factors in SNX29 might share among these three mental disorders in the Han Chinese population.

Objective: To identify promising biomarkers for the pathogenesis of major depressive disorder (MDD). Methods: Microarray chips of MDD patients, including the GSE98793, GSE52790, and GSE39653 datasets, were obtained from the Gene Expression Omnibus database. The biological processes and pathways related to MDD were investigated using the GO and KEGG pathway tools. Weighted gene coexpression network analysis was conducted to identify modules related to MDD. The hub genes associated with MDD were obtained via protein-protein interaction analysis. Finally, the expression of hub genes in the hippocampal tissues of depression-like rats was detected by reverse transcription-polymerase chain reaction and Western blotting. Results: A total of 658 differentially expressed genes were identified from the Gene Expression Omnibus datasets; thus, these genes and the GSE98793 dataset were used to conduct weighted gene coexpression network analysis. A total of 244 module-related genes were identified and these genes were highly correlated with MDD. These genes were involved in the Ras signaling pathway, regulation of the actin cytoskeleton, and axon guidance according to the KEGG analysis. Hub genes, including MAPK14, SOCS1, TLR2, PTK2B, and GRB2, were obtained via protein-protein interaction analysis. All these hub genes showed better diagnostic efficiency in the GSE52790, GSE39653, and GSE98793 datasets. In vivo experiments revealed that compared with those in control rats, SOCS1 and MAPK14 expression was significantly decreased; while GRB2, TLR2, and PTK2B expression was increased in the hippocampi of depression-like rats. Conclusions: Our study demonstrates that GRB2, TLR2, SOCS1, PTK2B, and MAPK14 are promising hub genes, and targeting these five genes may be an effective treatment strategy for MDD.

Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents. A total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules "darked", "darkurquoise" and "light yellow" showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2. The study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Postoperative systemic inflammatory dysregulation (PSID) is characterised by strongly interlinked immune and metabolic abnormalities. However, the hub genes responsible for the interconnections between these two systemic alterations remain to be identified. We analysed differentially expressed genes (DEGs) of individual peripheral blood nucleated cells in patients with PSID (n = 21, CRP > 250 mg/L) and control patients (n = 25, CRP < 75 mg/L) following major abdominal surgery, along with their biological functions. Correlation analyses were conducted to explore the interconnections of immune-related DEGs (irDEGs) and metabolism-related DEGs (mrDEGs). Two methods were used to screen hub genes for irDEGs and mrDEGs: we screened for hub genes among DEGs via 12 algorithms using CytoHubba in Cytoscape, and also screened for hub immune-related and metabolic-related genes using weighted gene co-expression network analysis. The hub genes selected were involved in the interaction between changes in immunity and metabolism in PSID. Finally, we validated our results in mice with PSID to confirm the findings. We identified 512 upregulated and 254 downregulated DEGs in patients with PSID compared with controls. Gene enrichment analysis revealed that DEGs were significantly associated with immune- and metabolism-related biological processes and pathways. Correlation analyses revealed a close association between irDEGs and mrDEGs. Fourteen unique hub genes were identified via 12 screening algorithms using CytoHubba in Cytoscape and via weighted gene co-expression network analysis. Among these, CD28, CD40LG, MAPK14, and S100A12 were identified as hub genes among both immune- and metabolism-related genes; these genes play a critical role in the interaction between alterations in immunity and metabolism in PSID. The experimental results also showed that the expression of these genes was significantly altered in PSID mice. This study identified hub genes associated with immune and metabolic alterations in patients with PSID and hub genes that link these alterations. These findings provide novel insights into the mechanisms underlying immune and metabolic interactions and new targets for clinical treatment can be proposed on this basis.

People with major psychiatric disorders (MPDs) have a 10- to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown. To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them. Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018. The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways. Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ: r for genetic = -0.11, P = 2.1 × 10-10; BIP: r for genetic = -0.06, P = .0103; MD: r for genetic = 0.12, P = 6.7 × 10-10). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate <0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways. In this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.

Objective-This retrospective cohort study examined the association between co-occurring serious mental illness and substance use disorders and parole revocation among inmates from the Texas Department of Criminal Justice, the nation's largest state prison system. Methods-The study population included all 8,149 inmates who were released under parole supervision between September 1, 2006, and November 31, 2006.An electronic database was used to identify inmates whose parole was revoked within 12 months of their release.The independent risk of parole revocation attributable to psychiatric disorders, substance use disorders, and other covariates was assessed with logistic regression analysis.Results-Parolees with a dual diagnosis of a major psychiatric disorder (major depressive disorder, bipolar disorder, schizophrenia, or other psychotic disorder) and a substance use disorder had a substantially increased risk of having their parole revoked because of either a technical violation (adjusted odds ratio [OR]=1.7,95% confidence interval [CI]=1.4-2.4) or commission of a new criminal offense (OR=2.8,95% CI=1.7-4.5) in the 12 months after their release.However, parolees with a diagnosis of either a major psychiatric disorder alone or a substance use disorder alone demonstrated no such increased risk.Conclusions-These findings highlight the need for future investigations of specific social, behavioral, and other factors that underlie higher rates of parole revocation among individuals with co-occurring serious mental illness and substance use disorders.Over the past four decades the widespread deinstitutionalization of persons with serious mental illness (1-3), the increase in drug-related arrests (4,5), and the reduction of community-based mental health care (1,2) have resulted in a substantial overrepresentation of persons with serious mental illness in the U.S. correctional system (1,2,6).Approximately 10% to 20% of U.S. prison inmates are estimated to have an axis I major mental disorder of thought or mood, such as major depressive disorder, bipolar disorder, or schizophrenia (7-12).Moreover, a majority of inmates with serious mental illness have a comorbid substance use disorder (7,(12)(13)(14)(15).A number of investigations have examined predictors of recidivism among released inmates (16)(17)(18)(19).Although results of these studies-conducted throughout a variety of criminal justice

Objective To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA). Methods The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate < 0.05 and a log2 fold change ≥ 0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes. Results Fourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally, HLA-DRB1, LRP1, and MMP2 were identified as key genes. Conclusion This was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease.

Bipolar disorder (BD) is a complex mental disorder with high mortality and disability rates worldwide; however, research on its pathogenesis and diagnostic methods remains limited. This study aimed to elucidate potential candidate hub genes and key pathways related to BD in a pre-frontal cortex sample. Raw gene expression profile files of GSE53987, including 36 samples, were obtained from the gene expression omnibus (GEO) database. After data pre-processing, 10,094 genes were selected for weighted gene co-expression network analysis (WGCNA). After dividing highly related genes into 19 modules, we found that the pink, midnight blue, and brown modules were highly correlated with BD. Functional annotation and pathway enrichment analysis for modules, which indicated some key pathways, were conducted based on the Enrichr database. One of the most remarkable significant pathways is the Hippo signaling pathway and its positive transcriptional regulation. Finally, 30 hub genes were identified in three modules. Hub genes with a high degree of connectivity in the PPI network are significantly enriched in positive regulation of transcription. In addition, the hub genes were validated based on another dataset (GSE12649). Taken together, the identification of these 30 hub genes and enrichment pathways might have important clinical implications for BD treatment and diagnosis.

Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.

Shared peripheral blood biomarkers for Alzheimer’s disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis

Anorexia nervosa (AN) is a heritable eating disorder (50–60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the “signal by MST1” pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

There is a gap in understanding the molecular mechanisms behind early relapse / multiple myeloma (MM) progression in the newly diagnosed patient on standard treatment for MM, i.e., lenalidomide, bortezomib, and dexamethasone (RVD). As a result, the relatively high percentage of patients that progress after early treatment with RVD is significant. Therefore, identification of clinically relevant clusters of co-expressed genes or representative biomarkers for MM progression, while on RVD, would help identify new molecular mechanisms and drug targets. The objective of this study is to use weighted gene co-expression network analysis (WGCNA) to identify gene-signaling networks associated with early relapse / MM progression. To this end, we performed a WGCNA to determine module-trait correlations. We next examined the overrepresentation of upstream regulators and signal pathway networks from MM patients in the MMRF CoMMpass dataset (n = 175, gene transcripts= 30,598). WGCNA constructed 48 modules based on the correlations between patients on RVD and death. We are using death as a biomarker for MM progression and two years as cut off for treatment. We identified two modules, Green (p &amp;lt; 4.6 x 10-8), and Pale Turquoise (p &amp;lt; 8.8 x 10-7), that significantly and positively correlated with overall survival of &amp;lt; 2 years following initial treatment with RVD. Further analysis identified HDAC8, PARPBP, HSPG2, MAGE, KIF, and FOXM1 as the top six hub genes having tight variations in positive correlations. The signatures for these cell cycle signaling pathway related hub genes were found to be largely associated with the biological process for proliferation. Future studies will use connectivity mapping to identify drug signatures that can target any of the identified hub genes. The selective targeting of these hub genes is expected to improve response to RVD in MM patients. This transcriptome study is the first using this approach to identify gene signaling networks associated with MM progression. Citation Format: Olayinka O. Adebayo, Tiara Griffen, Corey Young, Eric Dammer, James W. Lillard. Weighted gene co-expression network analysis identified cell cycle signaling pathway associated hub genes correlated with progression and prognosis of multiple myeloma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4388.

Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.