A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Research progress on ferroptosis in gliomas (Review)

Glioma is the most prevalent type of brain tumor characterized by a poor 5-year survival rate and a high mortality rate. Malignant gliomas are commonly treated by surgery, chemotherapy and radiotherapy. However, due to toxicity and resistance to chemoradiotherapy, these treatments can be ineffective. Anxiety and depression are highly prevalent in patients with glioma, adversely affecting disease prognosis and posing societal concerns. Ferroptosis is a type of non-apoptotic, iron-dependent cell death characterized by the accumulation of lethal reactive oxygen species produced by iron metabolism, and it serves a key role in numerous diseases. Regulation of iron phagocytosis may serve as a therapeutic strategy for the development of novel glioma treatments. The present review discusses the mechanisms underlying the occurrence and regulation of ferroptosis, its role in the genesis and evolution of gliomas, and its association with glioma-related anxiety and depression. By exploring potential targets for glioma treatment, the present review provides a theoretical basis for the development of novel therapeutic strategies against glioma.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Establishment of an Artificial Neural Network Model Using Immune-Infiltration Related Factors for Endometrial Receptivity Assessment

Background: A comprehensive clinical strategy for infertility involves treatment and, more importantly, post-treatment evaluation. As a component of assessment, endometrial receptivity does not have a validated tool. This study was anchored on immune factors, which are critical factors affecting embryonic implantation. We aimed at establishing novel approaches for assessing endometrial receptivity to guide clinical practice. Methods: Immune-infiltration levels in the GSE58144 dataset (n = 115) from GEO were analysed by digital deconvolution and validated by immunofluorescence (n = 23). Then, modules that were most associated with M1/M2 macrophages and their hub genes were selected by weighted gene co-expression network as well as univariate analyses and validated using the GSE5099 macrophage dataset and qPCR analysis (n = 19). Finally, the artificial neural network model was established from hub genes and its predictive efficacy validated using the GSE165004 dataset (n = 72). Results: Dysregulation of M1 to M2 macrophage ratio is an important factor contributing to defective endometrial receptivity. M1/M2 related gene modules were enriched in three biological processes in macrophages: antigen presentation, interleukin-1-mediated signalling pathway, and phagosome acidification. Their hub genes were significantly altered in patients and associated with ribosomal, lysosomal, and proteasomal pathways. The established model exhibited an excellent predictive value in both datasets, with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945–1). Conclusions: M1/M2 polarization influences endometrial receptivity by regulating three gene modules, while the established ANN model can be used to effectively assess endometrial receptivity to inform pregnancy and individualized clinical management strategies.

miR-323a regulates ERBB4 and is involved in depression

Major depressive disorder (MDD) is a complex and debilitating illness whose etiology remains unclear. Small RNA molecules, such as micro RNAs (miRNAs) have been implicated in MDD, where they display differential expression in the brain and the periphery. In this study, we quantified miRNA expression by small RNA sequencing in the anterior cingulate cortex and habenula of individuals with MDD and psychiatrically-healthy controls. Thirty-two miRNAs showed significantly correlated expression between the two regions (False Discovery Rate < 0.05), of which four, miR-204-5p, miR-320b, miR-323a-3p, and miR-331-3p, displayed upregulated expression in MDD. We assessed the expression of predicted target genes of differentially expressed miRNAs in the brain, and found that the expression of erb-b2 receptor tyrosine kinase 4 (ERBB4), a gene encoding a neuregulin receptor, was downregulated in both regions, and was influenced by miR-323a-3p in vitro. Finally, we assessed the effects of manipulating miRNA expression in the mouse ACC on anxiety- and depressive-like behaviors. Mice in which miR-323-3p was overexpressed or knocked-down displayed increased and decreased emotionality, respectively. Additionally, these mice displayed significantly downregulated and upregulated expression of Erbb4, respectively. Overall, our findings indicate the importance of brain miRNAs in the pathology of MDD, and emphasize the involvement of miR-323a-3p and ERBB4 in this phenotype. Future studies further characterizing miR-323a-3p and neuregulin signaling in depression are warranted.

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Background

Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.

Materials and Methods

We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.

Results

Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.

Conclusion

This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Ferroptosis resistance mechanisms in endometriosis for diagnostic model establishment

RESEARCH QUESTION

Does ferroptosis resistance occur in patients with endometriosis, and can it be used as an indicator for diagnosis?

DESIGN

Six datasets including 164 expression arrays from endometriosis studies were selected from the National Center for Biotechnology Information Gene Expression Omnibus. Surrogate variable analysis was used for data integration after RobustRankAggreg to determine ferroptosis-associated gene expression trends. Differential genes between eutopic and normal endometrium, as well as between ectopic and eutopic endometrium, were determined. Ferroptosis resistance mechanisms during the development of endometriosis were determined by intergenic co-expression and the Kyoto Encyclopedia of Genes and Genomes pathway. Independent factors were then screened by least absolute shrinkage and selection operator regression to build a nomogram diagnostic model. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were used to verify model consistency and diagnostic efficacy, respectively.

RESULTS

Differential analysis revealed a progressive increase in changes of ferroptosis-related genes in endometriosis patients during the process of endometrium development from eutopic to ectopic. In the subsequent regression selection, nine ferroptosis-associated genes in the eutopic endometrium could, in most cases, distinguish endometriosis patients from healthy patients. Therefore, these genes can be used as independent factors for endometriosis diagnosis. The nomogram model established by these molecular markers had an area under the ROC curve of 0.979.

CONCLUSIONS

The eutopic and ectopic endometrium of patients with endometriosis is associated with ferroptosis resistance genetic alterations. Ferroptosis-associated genes have excellent clinical value in endometriosis diagnosis, achieved by the nomogram model.

Neuronal Development-Related miRNAs as Biomarkers for Alzheimer's Disease, Depression, Schizophrenia and Ionizing Radiation Exposure

Radiation exposure may induce Alzheimer's disease (AD), depression or schizophrenia. A number of experimental and clinical studies suggest the involvement of miRNA in the development of these diseases, and also in the neuropathological changes after brain radiation exposure. The current literature review indicated the involvement of 65 miRNAs in neuronal development in the brain. In the brain tissue, blood, or cerebral spinal fluid (CSF), 11, 55, or 28 miRNAs are involved in the development of AD respectively, 89, 50, 19 miRNAs in depression, and 102, 35, 8 miRNAs in schizophrenia. We compared miRNAs regulating neuronal development to those involved in the genesis of AD, depression and schizophrenia and also those driving radiation-induced brain neuropathological changes by reviewing the available data. We found that 3, 11, or 8 neuronal developmentrelated miRNAs from the brain tissue, 13, 16 or 14 miRNAs from the blood of patient with AD, depression and schizophrenia respectively were also involved in radiation-induced brain pathological changes, suggesting a possibly specific involvement of these miRNAs in radiation-induced development of AD, depression and schizophrenia respectively. On the other hand, we noted that radiationinduced changes of two miRNAs, i.e., miR-132, miR-29 in the brain tissue, three miRNAs, i.e., miR- 29c-5p, miR-106b-5p, miR-34a-5p in the blood were also involved in the development of AD, depression and schizophrenia, thereby suggesting that these miRNAs may be involved in the common brain neuropathological changes, such as impairment of neurogenesis and reduced learning memory ability observed in these three diseases and also after radiation exposure.

Common genes and pathways involved in the response to stressful stimuli by astrocytes: A meta-analysis of genome-wide expression studies

Astrocytes play pivotal roles in the brain and they become reactive under stress conditions. Here, we carried out, for the first time, an integrative meta-analysis of genome-wide expression profiling of astrocytes from human and mouse exposed to different stressful stimuli (hypoxia, infections by virus and bacteria, cytokines, ethanol, among others). We identified common differentially expressed genes and pathways in human and murine astrocytes. Our results showed that astrocytes induce expression of genes associated with stress response and immune system regulation when they are exposed to stressful stimuli, whereas genes related to neurogenesis are found as downregulated. Several of the identified genes showed to be important hubs in the protein-protein interaction analysis (TRAF2, CDC37 and PAX6). This work demonstrates that despite astrocytes are highly heterogeneous and complex, there are common gene expression signatures that can be triggered under distinct detrimental stimuli, which opens an opportunity for exploring other possible markers of reactivity.

Depressive and anxiety disorders worsen the prognosis of glioblastoma

Glioblastoma multiforme (GBM) is one of the most malignant tumors. Depressive and anxiety disorders may co-exist with GBM. We investigated whether depression and anxiety influenced the outcomes of GBM. The Patient Health Questionnaire 9-item (PHQ-9) and Generalized Anxiety Disorder 7-item (GAD-7) scales were used to investigate the mental condition of GBM patients in our department, and the overall survival times of these patients were monitored. The scores on both scales were higher in GBM patients than in healthy controls. For each scale, GBM patients were divided into high- and low-score groups based on the average score. The prognosis was poorer for GBM patients in the high-score groups than for those in the low-score groups. Moreover, magnetic resonance imaging revealed that tumor necrosis was more prevalent among high-scored GBM patients. Cellular experiments were performed on primary GBM cells from patients with either high or low scores on both scales. Sphere formation, EdU and wound healing assays revealed greater proliferation and invasion capacities in GBM cells from patients with high scores on both scales. Western blotting assay revealed significantly different expression of epithelial and mesenchymal markers between the two groups. Thus, our analysis revealed a clinically important correlation between depression/anxiety and GBM prognosis.

Discovery of gene module acting on ubiquitin-mediated proteolysis pathway by co-expression network analysis for endometriosis

RESEARCH QUESTION

Is abnormal gene module expression in the eutopic endometrium related to the occurrence of endometriosis?

DESIGN

Nine datasets of normal and eutopic endometrium were searched and collected through the National Center for Biotechnology Information Gene Expression Omnibus, which included genome-wide expression studies of 71 normal cases and 142 endometriosis cases. Surrogate variable analysis was used for dataset integration. The network module and hub genes were selected by weighted gene co-expression network analysis. Machine learning was used to establish a diagnostic model of endometriosis.

RESULTS

A gene module that was most relevant to endometriosis was selected through weighted gene co-expression network analysis. After further analysis of this module, four hub genes that represent the function of this module were selected: SCAF11, KRAS, MDM2 and KIF3A. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the four hub genes revealed that all of them were most highly correlated with genes enriched in the ubiquitin-mediated proteolysis pathway. Moreover, in the correlation analysis between hub genes and Jab1, SCAF11 was found to be closely related to Jab1. Furthermore, hub genes were effective indicators for clinical diagnosis. The deep machine learning diagnostic model based on hub genes was highly sensitive.

CONCLUSIONS

The gene module identified is highly correlated with endometriosis. The four hub genes in this module degrade p27^kip1 through the ubiquitin-mediated proteolysis pathway to regulate the endometrium cell cycle and affect the development of endometriosis. The hub genes and the deep learning model based on them are valuable for clinical diagnosis.

Integration of postmortem amygdala expression profiling, GWAS, and functional cell culture assays: neuroticism-associated synaptic vesicle glycoprotein 2A (SV2A) gene is regulated by miR-133a and miR-218

Recent genome-wide studies have begun to identify gene variants, expression profiles, and regulators associated with neuroticism, anxiety disorders, and depression. We conducted a set of experimental cell culture studies of gene regulation by micro RNAs (miRNAs), based on genome-wide transcriptome, proteome, and miRNA expression data from twenty postmortem samples of lateral amygdala from donors with known neuroticism scores. Using Ingenuity Pathway Analysis and TargetScan, we identified a list of mRNA-protein-miRNA sets whose expression patterns were consistent with miRNA-based translational repression, as a function of trait anxiety. Here, we focused on one gene from that list, which is of particular translational significance in Psychiatry: synaptic vesicle glycoprotein 2A (SV2A) is the binding site of the anticonvulsant drug levetiracetam ((S)-α-Ethyl-2-oxo-1-pyrrolidineacetamide), which has shown promise in anxiety disorder treatments. We confirmed that SV2A is associated with neuroticism or anxiety using an original GWAS of a community cohort (N = 1,706), and cross-referencing a published GWAS of multiple cohorts (Ns ranging from 340,569 to 390,278). Postmortem amygdala expression profiling implicated three putative regulatory miRNAs to target SV2A: miR-133a, miR-138, and miR-218. Moving from association to experimental causal testing in cell culture, we used a luciferase assay to demonstrate that miR-133a and miR-218, but not miR-138, significantly decreased relative luciferase activity from the SV2A dual-luciferase construct. In human neuroblastoma cells, transfection with miR-133a and miR-218 reduced both endogenous SV2A mRNA and protein levels, confirming miRNA targeting of the SV2A gene. This study illustrates the utility of combining postmortem gene expression data with GWAS to guide experimental cell culture assays examining gene regulatory mechanisms that may contribute to complex human traits. Identifying specific molecular mechanisms of gene regulation may be useful for future clinical applications in anxiety disorders or other forms of psychopathology.

Functional Genomics of Epileptogenesis in Animal Models and Humans

It has been estimated that epilepsies are among the top five neurological diseases with the highest burden of disease. In recent years, genome-wide expression studies (GWES) have been carried out in experimental models of epilepsy and in samples from human patients. In this study, I carried out meta-analyses and analyses of convergence for available GWES for epileptogenesis in humans and in mouse, rat, zebrafish and fruit fly models. Multiple lines of evidence (such as genome-wide association data and known druggable genes) were integrated to prioritize top candidate genes for epileptogenesis and a functional enrichment analysis was carried out. Several top candidate genes, which are supported by multiple lines of genomic evidence, such as GRIN1, KCNAB1 and STX1B, were identified. Druggable genes of potential interest (such as GABRA2, GRIK1, KCNAB1 and STX4) were also identified. An enrichment of genes regulated by the MEF2 and SOX5 transcription factors and the miR-106b-5p and miR-101-3p miRNAs was found. The current work is the first meta-analysis and convergent analysis of GWES for epileptogenesis in humans and in multiple animal models, integrating results from several genomic studies. Novel candidate genes and pathways for epileptogenesis were identified in this analysis.

Bioinformatic analysis reveals that the OAS family may play an important role in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. However, the molecular mechanisms of LN remain unclear. The lack of understanding hinders the development of specific targeted therapy for this progressive disease.

OBJECTIVES

In the present study, we used bioinformatics analysis of gene expression profiles from the Gene Expression Omnibus to identify novel targets and potential biomarkers for LN.

MATERIAL AND METHODS

A GSE32591 dataset, which included 31 LN glomerular biopsy tissues and 14 living donors' glomerular tissues, was downloaded for further analysis. Differentially expressed genes in LN were analyzed by the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differentially expressed genes by using the Disease Ontology Semantic and Enrichment and the clusterProfiler software. The protein-protein interaction (PPI) network was then formed using STRING online tool.

RESULTS

440 genes, including 310 upregulated genes and 130 downregulated genes, were found as differentially expressed genes. GO and KEGG analyses revealed that immune response is significantly enriched in such genes. The PPI network showed that ISG15, MX1, OAS1, OAS2, and OAS3 were the hub genes enriched in LN. Along with literature review, the OAS family genes were revealed to be closely associated with LN progression.

CONCLUSIONS

our studies provided new insight into the molecular pathogenesis of LN. The OAS family may play an important role in LN and act as a novel molecular candidate for the further study of LN.

Association Between a Functional Polymorphism in the Monoamine Oxidase A (MAOA) Gene and Both Emotional Coping Style and Neuroticism

Background:

Identification of novel genetic factors for Depressive Disorders (DD) represents a major challenge around the world. Molecular studies of endophenotypes associated with DD, such as personality traits and coping, are powerful strategies for finding genetic markers.

Objective:

The main objective of this work was to confirm the potential relationship between a functional polymorphism in the monoamine oxidase A (MAOA) gene and scores in coping and neuroticism in young adults.

Methods:

A Colombian sample of two hundred fifty-one young participants was evaluated with the short forms of the Coping Inventory for Stressful Situations (CISS-SF) and the Big Five Inventory (BFI-S). Genotypes for MAOA-VNTR polymorphism were obtained by PCR.

Results:

A significant relationship between the functional MAOA-VNTR polymorphism and scores in both emotion-oriented coping and neuroticism was found. Individuals carrying the 4 allele (3/4 or 4/4 genotypes) had higher scores for both emotion-oriented coping and neuroticism than individuals with a 3/3 genotype.

Conclusion:

Our current findings are novel in terms of being the first report of a relationship between a functional polymorphism in the MAOA gene and coping and add evidence to the association of this gene with neuroticism. Our results expand the associations between MAOA gene and multiple dimensions of human emotion and personality.

Integrative In Silico Analysis of Genome-Wide DNA Methylation Profiles in Schizophrenia

Schizophrenia (SZ) is a complex and severe psychiatric disorder, which has a global lifetime prevalence of 0.4% and a heritability of around 0.81. A number of epigenome-wide association studies (EWAS) have been carried out for SZ, with discordant results. The main aim of this study was to carry out an integrative in silico analysis of available genome-wide DNA methylation profiles in schizophrenia. In this work, an integration of multiple lines of evidence (top candidate genes from several EWAS and genome-wide expression and association data) was carried out, in order to identify top differentially methylated (DM) genes for SZ. In addition, functional enrichment and protein-protein interaction analyses were carried out. Several top differentially methylated genes, such as APC, CACNB2, and PRKN, were found, and an enrichment of binding sites for brain-expressed transcription factors, such as FOXO1, MYB, and ZIC3, was also observed. Moreover, a protein-protein interaction network showed a central role for DISC1 and ZNF688 genes, and experimentally validated targets of MIR-137, such as and KCNB2, NRXN1, and SYN2, were identified among DM genes. This is the first integrative in silico analysis of available genome-wide DNA methylation profiles in schizophrenia. This work identified novel candidate genes and pathways for SZ and provides the basis to explore their role in the pathogenesis of SZ in future studies.

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

INTRODUCTION

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD.

METHODS

We searched and downloaded gene expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in MDD. Then, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Ontology analysis, and protein-protein interaction (PPI) network were applied to investigate the biological function of identified DEGs. The quantitative real-time polymerase chain reaction and a published dataset were used to validate the result of bioinformatics analysis.

RESULTS

A total of 514 DEGs were identified in MDD. In the PPI network, some hub genes with high degrees were identified, such as EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1. The result of in vitro validation of RPL26L1, RSRC1, TOMM20L, RPLPO, PRPF8, AP2B1, STIP1, and C5orf45 was consistent with the bioinformatics analysis. Electronic validation of C5orf45, STIP1, PRPF8, AP2B1, and SLC35E1 was consistent with the bioinformatics analysis.

DISCUSSION

The deregulated genes could be used as potential pathological factors of MDD. In addition, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1 might be therapeutic targets for MDD.

Available Software for Meta-analyses of Genome-wide Expression Studies

Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

BACKGROUND

Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN.

RESULTS

Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features.

CONCLUSIONS

This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

Background

Major depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD.

Methods

In this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes.

Results

We identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD.

Conclusions

Our findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.

Integrated bioinformatics analysis reveals novel hub genes closely associated with pathological mechanisms of immunoglobulin A nephropathy

Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated. In the present study, an integrated bioinformatics analysis was applied to further explore novel potential gene targets for IgAN. The mRNA expression profile datasets GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis of DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, and hub genes were identified through the degree of connectivity in the PPI. The hub genes were subjected to Kyoto Encyclopedia of Genes and Genomes pathway analysis, and co-expression analysis was performed. A total of 298 DEGs between IgAN and control groups were identified, and 148 and 150 of these DEGs were upregulated and downregulated, respectively. The DEGs were enriched in distinct GO terms for Biological Process, including cell growth, epithelial cell proliferation, ERK1 and ERK2 cascades, regulation of apoptotic signaling pathway and ECM organization. The top 10 hub genes were then screened from the PPI network by Cytoscape. As novel hub genes, Fos proto-oncogene, AP-1 transcription factor subunit and early growth response 1 were determined to be closely associated with apoptosis and cell proliferation in IgAN. Tumor protein 53, integrin subunit β2 and fibronectin 1 may also be involved in the occurrence and development of IgAN. Co-expression analysis suggested that these hub genes were closely linked with each other. In conclusion, the present integrated bioinformatics analysis provided novel insight into the molecular events and novel candidate gene targets of IgAN.

Distinct Expression Pattern of Epigenetic Machinery Genes in Blood Leucocytes and Brain Cortex of Depressive Patients

In major depressive disorder (MDD), altered gene expression in brain cortex and blood leucocytes may be due to aberrant expression of epigenetic machinery coding genes. Here, we explore the expression of these genes both at the central and peripheral levels. Using real-time quantitative PCR technique, we first measured expression levels of genes encoding DNA and histone modifying enzymes in the dorsolateral prefrontal cortex (DLPFC) and cingulate cortex (CC) of MDD patients (n = 24) and healthy controls (n = 12). For each brain structure, transcripts levels were compared between subject groups. In an exploratory analysis, we then compared the candidate gene expressions between a subgroup of MDD patients with psychotic characteristics (n = 13) and the group of healthy subjects (n = 12). Finally, we compared transcript levels of the candidate genes in blood leucocytes between separate samples of MDD patients (n = 17) and healthy controls (n = 16). In brain and blood leucocytes of MDD patients, we identified an overexpression of genes encoding enzymes which transfer repressive transcriptional marks: HDAC4-5-6-8 and DNMT3B in the DLPFC, HDAC2 in the CC and blood leucocytes. In the DLPFC of patients with psychotic characteristics, two genes (KAT2A and UBE2A) were additionally overexpressed suggesting a shift to a more transcriptionally permissive conformation of chromatin. Aberrant activation of epigenetic repressive systems may be involved in MDD pathogenesis both in brain tissue and blood leucocytes.

Genome-Wide Expression Profiles for Ischemic Stroke: A Meta-Analysis

BACKGROUND

Genome-wide expression studies (GWES), using microarray platforms, have allowed a deeper understanding of the molecular factors involved in the pathophysiology of ischemic stroke (IS), one of the main global causes of mortality and disability.

METHODS

In the current work, we carried out a meta-analysis of available GWES for IS. Bioinformatics and computational biology analyses were applied to identify enriched functional categories and convergence with other genomic datasets for IS.

RESULTS

Three primary datasets were included and in the meta-analyses for GWES and IS, 41 differentially expressed (DE) genes were identified using a random effects model. Thirteen of these genes were downregulated and 28 were upregulated. An analysis of functional categories found a significant enrichment for the Gene Ontology Term "Inflammatory Response" and for binding sites for the PAX2 transcription factor.

CONCLUSIONS

The list of DE genes identified in this meta-analysis of GWES for IS is useful for future genetic and molecular studies, which would allow the identification of novel mechanisms involved in the pathophysiology of IS. Several of the DE genes found in this meta-analysis have known functional roles related to mechanisms involved in the pathophysiology of IS. It is recognized the role of the inflammatory response in the pathophysiology of IS.

Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis

Treatment response to antidepressants is limited and varies among patients with major depressive disorder (MDD). To discover genes and mechanisms related to the pathophysiology of MDD and antidepressant treatment response, we performed gene expression analyses using peripheral blood specimens from 38 MDD patients and 14 healthy individuals at baseline and at 6 weeks after the initiation of either selective serotonin reuptake inhibitor (SSRI) or mirtazapine treatment. The results were compared with results from public microarray data. Seven differentially expressed genes (DEGs) between MDD patients and controls were identified in our study and in the public microarray data: CD58, CXCL8, EGF, TARP, TNFSF4, ZNF583, and ZNF587. CXCL8 was among the top 10 downregulated genes in both studies. Eight genes related to SSRI responsiveness, including BTNL8, showed alterations in gene expression in MDD. The expression of the FCRL6 gene differed between SSRI responders and nonresponders and changed after SSRI treatment compared to baseline. In evaluating the response to mirtazapine, 21 DEGs were identified when comparing MDD patients and controls and responders and nonresponders. These findings suggest that the pathophysiology of MDD and treatment response to antidepressants are associated with a number of processes, including DNA damage and apoptosis, that can be induced by immune activation and inflammation.

Differences in the expression of several genes before and after different antidepressant treatments were found in patients with major depressive disorder (MDD), and may help identify patients most likely to benefit from specific drugs. Researchers in South Korea led by Doh Kwan Kim and Soo-Youn Lee at Samsung Medical Center, Seoul, examined gene expression across the 28,869 genes in 38 patients with MDD and 14 healthy individuals. They also validated their findings using existing databases of gene expression in patients with MDD and healthy controls. The research suggests that genes involved in the immune response and inflammation are significantly alternated in MDD and are predictable in which patients respond well to antidepressants. These findings may help develop new approaches to antidepressant therapies, and assist tailoring of treatment to the specific needs of different patients.

Depression and Obesity: Integrating the Role of Stress, Neuroendocrine Dysfunction and Inflammatory Pathways

Literature on depression and obesity describes the relevance of the hypothalamic pituitary adrenal axis dysfunction, sympathetic nervous system (SNS) activation, and inflammatory processes as well as the interaction of genetic and environmental factors. Recent investigation in obesity highlights the involvement of several regulation systems, particularly in white adipose tissue. The hypothalamic pituitary adrenal axis, gonadal, growth hormone, leptin, sympathetic nervous system and adrenergic, dopaminergic, and serotoninergic central pathways, all seem interconnected and involved in obesity. From another perspective, the role of psychosocial chronic stressors, determining poor mental and physical health, is well documented. Empirical data can support biologically conceivable theories describing how perceptions of the external social environment are transduced into cellular inflammation and depression. Although in neurobiological models of depression, stress responses are associated with neuroendocrine and neuro-inflammatory processes, concerning similar pathways to those described in obesity, an integrating model is still lacking. The aim of this mini-review is to offer a reflexion on the interplay between the neuroendocrine dysfunctions related to chronic stress and the nature of the shared biologic mechanisms in the pathophysiology of both clinical entities, depression and obesity. We highlight dysfunctional answers of mind body systems that are usually activated to promote regulation and adaptation. Stress response, as a mediator between different level phenomena, may undertake the role of a plausible link between psychological and biological determinants of disease. Depression and obesity are major public health issues, urging for new insights and novel interventions and this discussion points to the need of a more in-depth approach.

MTHFR gene methylation is associated with perceived stress in healthy young adults

BACKGROUND

Epigenetic factors have been identified in the past years as interesting candidates for psychiatric disorders and related endophenotypes. It has been found that the methylenetetrahydrofolate reductase (MTHFR) gene is associated with major depressive disorder, and the aim of the current study was to examine the possible association between perceived stress and MTHFR methylation, taking into account depressive symptoms as a covariate.

PARTICIPANTS AND METHODS

Seventy-eight healthy Colombian participants (mean age=20.9 years; SD=3.0) were evaluated with the Perceived Stress Scale and with the Patient Health Questionnaire-9 for depressive symptomatology. MTHFR methylation levels were measured with a methylation-sensitive high-resolution melting method. A multiple regression analysis (adjusting for age, sex, and depressive symptoms) was carried out to assess the association between MTHFR methylation and perceived stress scores.

RESULTS

We found a significant inverse correlation between MTHFR methylation levels and perceived stress scores (r=-0.502; P=5.9×10(-5)), which remained significant after being adjusted for age, sex, and depressive symptomatology.

CONCLUSION

To our knowledge, this is the first study that reports an association between perceived stress and MTHFR methylation levels. This report adds evidence to the emerging role of epigenetic changes in endophenotypes related to affective disorders.