تحلیل بیوانفورماتیکی نیم‌رخ بیان ژن در زنان دارای اختلال افسردگی اساسی

مقدمه:اختلال افسردگی اساسی، یک اختلال روانی است که در زنان دو برابر مردان رخ می دهد. در هر دو جنس، میانگین سن مبتلایان به اختلال افسردگی اساسی حدود 25 سال است. مطالعات خانوادگی و دوقلویی و اپیدمیولوژیک همه به ویژگی ‌های چند عاملی و چند ژنی صفات روان ‌پزشکی اختلال افسردگی اساسی اشاره دارند. هدف از این مطالعه، غربال گری ژن ‌های مرتبط با بیماری ‌زایی اختلال افسردگی اساسی توسط بیوانفورماتیک بود.روش کار: با استفاده از داده‌های ریزآرایه gse98793 از پایگاه داده geo، 223 ژن متفاوت بیان شده (degs) از مقایسه نمونه‌ های زن بیمار با کنترل توسط نرم ‌افزار tac به دست آمد. ژن ‌های hub از طریق string و cytoscape و سپس روش غنی ‌سازی kegg غربال گری شدند. یافته‌ ها: در مقایسه نمونه ‌های زن بیمار با گروه کنترل 103 ژن افزایش بیان و120 ژن کاهش بیان نشان دادند. نتایج تجزیه و تحلیل غنی ‌سازی مسیر kegg و panther از مقایسه نمونه ‌های زن بیمار با گروه کنترل نشان داد که deg ها عمدتاً در مسیر سیگنال ‌رسانی hif1، مسیر سیگنال ‌رسانی foxo، مسیر تمایز سلول ‌های th17، مسیر سیگنال ‌رسانی pi3kakt، مسیر مرگ برنامه ‌ریزی شده سلول (فروپتوزیز) و مسیر سنتز پورین ‌ها مهم بودند. نتایج این مطالعه نشان داد که ژن ‌های igf1r و atm با افزایش بیان و ژن gmps با کاهش بیان برای زنان این بیماری نیز می ‌تواند گزینه مناسبی جهت اهداف درمانی باشند.نتیجه ‌گیری: ژن ‌های کلیدی که با تجزیه و تحلیل داده ‌های ریزآرایه در مطالعه حاضر به دست آمد، سرنخ ‌های مهمی برای آشکار کردن ‌ساز و کار مولکولی و درمان هدفمند بالینی افسردگی در زنان فراهم می کند.

Bioinformatics analysis of gene expression profile in women with major depressive disorder

IntroductionMajor depressive disorder (MDD) is a mental disorder occurring in women twice as much as men. In both sexes, the average age of people with MDD is about 25 years. Family, twin, and epidemiological studies all point to the multifactorial and polygenetic characteristics of the psychiatric traits of major depressive disorder. In recent years, many efforts have been made to identify biomarkers for diagnosing, preventing, and treating depression. Bioinformatics is a new science that uses computers, computer software, and databases to try to answer biological questions, especially in the cellular and molecular fields, proteins, and genes. In this way, biological networks analysis is widely used to calculate and model intracellular interactions to identify cellular mechanisms. A biological network is any type of network that can depict a biological system. Biological networks can be used at three levels of the genome, transcriptome and proteome, to identify biological markers associated with various diseases. In the present study, expression data related to major depressive disorder were extracted and used to identify key genes of the disease, gene networks, and related metabolic pathways of major depressive disorder by bioinformatics. MethodsBy referring to the GEO database (http://www.ncbi.nlm.nih.gov/geo) and searching for the expression profile of MDDrelated data, the data were extracted with the access number GSE98793 and the platform number GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) in CEL format. The DEGs were determined by using Affymetrix Transcriptome Analysis Console (TAC), following the software guidelines. The adjusted Pvalues (adj. P) and Benjamini and Hochberg false discovery rate were applied to balance between discoveries of statistically significant genes. Two hundred twentythree different genes (DEGs) were expressed by comparing female patient samples with controls by TAC software. LogFC (fold change) >2 and adj. Pvalue <0.05 were considered statistically significant.In order to obtain the biological function and signaling pathways of DEGs, EnrichR (http://david.ncifcrf.gov) was used to GO annotation and KEGG pathways enrichment of DEGs. P<0.05 was considered statistically significant. The top 100 genes of DEGs were used for gene set enrichment analysis. EnrichR is a webbased gene function enrichment analysis software. It can provide a comprehensive set of functional annotation information of genes and proteins. GO annotation is a main bioinformatics tool to annotate genes and analyze the biological process of DEGs. KEGG is a database resource for understanding highlevel functions and biological systems from largescale molecular datasets generated by highthroughput experimental technologies.The proteinprotein interaction (PPI) network was constructed using the STRING (Search Tool for the Retrieval of Interacting Genes http://stringdb.org) online database alongside Cytoscape software, followed by identifying hub genes.The STRING database was used to obtain the predicted interactions to gain the interaction between DEGs. The STRING database constructed the PPI network of DEGs in the current study. The interaction with a combined score >0.7 was considered statistically significant. The visualization of the PPI network was used by Cytoscape software and Gephi. Besides, Cytoscape software (version 3.6.1), which can display molecular interaction networks, is an opensource bioinformatics software platform. Accordingly, proteinprotein interaction networks of key hub genes were obtained from Gephi software. ResultsAccording to the obtained results, comparing female patients with control of 103 genes showed increased expression, and 120 genes identified as decreased expression. In women with depressive disorder, ATM, IGF1R, BCBP2, VHL, and EIF4G2 genes were highly expressed hub genes of a gene network, GMPS, PPP2R1A LCK, and HSP90AB1 gene was as hub genes of lowexpressed genes network. The results of KEGG and panther pathway enrichment analysis comparing female patient samples with control showed that DEGs are mainly in the HIF1 signaling pathway, FOXO signaling pathway, Th17 cell differentiation pathway, pathway PI3KAkt signaling, programmed cell death pathway (Ferptosis), and purine synthesis pathway were important. ConclusionAccording to the specific study of women with depression with healthy women and finding different differential genes and different pathways in this group, it can be concluded that for women with this disease, IGFIR and ATM gene with increased expression and GMPS gene. IGF1R gene encodes the insulinlike growth factor receptor. Increased expression of insulinlike growth factors has a direct effect on the development of major depression. The ATM gene is involved in the p53 signaling pathway. Due to the function of this gene in apoptosis, it can be indirectly associated with depression. GMP signaling cascade is also expressed in the brain. The activity of this pathway is involved in learning and memory processes. Further studies have shown that the cGMP cascade in the brain acts as an antidepressant. The HIF signaling pathway is one of the pathways that were jointly identified through both KEGG and Panther databases in relation to the increase in gene expression in depressed women compared to healthy women in this study. In the future, this pathway can be studied with more confidence in depression in women for diagnostic and therapeutic purposes. Therefore, regarding the key genes obtained by microarray analysis and MDD DEGs and interpretation of their function, some genes showed significant differences in expression in people with depression compared to healthy individuals that their association with major depression has not been reported in previous studies. The results of this study showed that IGF1R and ATM genes with increased expression and GMPS genes with decreased expression for women with this disease could also be a good option for therapeutic purposes. These genes could be suitable and new candidates for future studies on major depression, as well as the optimization of treatment methods. The effective pathways identified in the present study were primarily involved in the brain pathways. In addition, dysfunction of one part of the brain causes depression. The key genes involved in this disease are influential in several diseases, which leads to people with this disease have an increased risk of developing other diseases. Bioinformatics examines the link between these genes, depression, and other diseases. Accordingly, these genes provide essential clues for revealing the molecular mechanism and could be suitable and new candidates for future studies on major depression, as well as the optimization of treatment methods. Ethical Considerations Compliance with ethical guidelinesThe authors of this research declared that there are no data fabrications and falsifications or data manipulation in their submitted article, and all authors have observed the research ethics. The authors have cited any source in this study.Authors ’ contributionsFarzaneh Esmaeili planned and designed the experiments, performed them out, analyzed the data, produced figures, and tables contributed literature review, authored and reviewed drafts of the article, and approved the final manuscript. Samaneh Zolghadri is the corresponding author that reviewed drafts of the article and approved the final manuscript.FundingThis research has no financial support and has been done at personal expense.AcknowledgmentsWe are grateful to the ViceChancellor of the Islamic Azad University of Jahrom for conducting this research.Conflicts of interestThe authors declare that there is no conflict of interest.