Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression

Sex-specific transcriptional signatures in the medial prefrontal cortex underlying sexually dimorphic behavioural responses to stress in rats

BACKGROUND

Converging evidence suggests that stress alters behavioural responses in a sex-specific manner; however, the underlying molecular mechanisms of stress remain largely unknown.

METHODS

We adapted unpredictable maternal separation (UMS) and adult restraint stress (RS) paradigms to mimic stress in rats in early life or adulthood, respectively. The sexual dimorphism of the prefrontal cortex was noted, and we performed RNA sequencing (RNA-Seq) to identify specific genes or pathways responsible for sexually dimorphic responses to stress. We then performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the results of RNA-Seq.

RESULTS

Female rats exposed to either UMS or RS showed no negative effects on anxiety-like behaviours, whereas the emotional functions of the PFC were impaired markedly in stressed male rats. Leveraging differentially expressed genes (DEG) analyses, we identified sex-specific transcriptional profiles associated with stress. There were many overlapping DEGs between UMS and RS transcriptional data sets, where 1406 DEGs were associated with both biological sex and stress, while only 117 DEGs were related to stress. Notably, Uba52 and Rpl34-ps1 were the first-ranked hub gene in 1406 and 117 DEGs respectively, and Uba52 was higher than Rp134-ps1, suggesting that stress may have led to a more pronounced effect on the set of 1406 DEGs. Pathway analysis revealed that 1406 DEGs were primarily enriched in ribosomal pathway. These results were confirmed by qRT-PCR.

LIMITATIONS

Sex-specific transcriptional profiles associated with stress were identified in this study, but more in-depth experiments, such as single-cell sequencing and manipulation of male and female gene networks in vivo, are needed to verify our findings.

CONCLUSION

Our findings show sex-specific behavioural responses to stress and highlight sexual dimorphism at the transcriptional level, shedding light on developing sex-specific therapeutic strategies for stress-related psychiatric disorders.

Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents

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.

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

BACKGROUND

The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.

METHODS

The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.

RESULTS

Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.

CONCLUSION

The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.

Genetic association between major depressive disorder and type 2 diabetes mellitus: Shared pathways and protein networks

BACKGROUND

Major depressive disorder (MDD) and type 2 diabetes mellitus (T2DM) are common public health disorders that often co-occur. This study aims to determine whether gene expression profiles from individuals with MDD or T2DM overlap and if there are any functional interconnectivity between identified genes using protein-protein interaction (PPI).

METHODS

The DNA microarray datasets were extracted from the Gene Expression Omnibus. Gene expression dataset GSE98793 from a case-control study of MDD (64 healthy control subjects, 128 patients) and dataset GSE15653 from a case-control study of T2DM (nine controls, nine individuals with T2DM) were used for this secondary and post-hoc analysis. GO enrichment analyses and Reactome pathway enrichment analysis were performed for functional enrichment analyses with the shared genes. PPI networks, PPI clusters and hub genes were performed to detect the potential relationships among differentially expressed genes (DEG) -encoding proteins in both MDD and T2DM.

RESULTS

A total of 3640 DEGs were identified in the MDD group when compared to the control group, whereas 3700 DEGs were identified in the T2DM group when compared to the control groups, among which 244 DEGs were overlap genes. The identified DEGs were enriched for Interleukin-4 and Interleukin-13 signaling, neutrophil degranulation, as well as other select species of the innate immune system. The biological processes of neurofibrillary tangle assembly regulation, tau-protein kinase activity regulation, amyloid-beta clearance regulation, amyloid-beta formation regulation and neuron apoptotic processes were also identified. Molecular function analysis indicated that identified genes were mainly enriched for amyloid-beta binding. 925 out of 1006 protein-protein interactions and six sub-networks were identified reflecting the disparate biological domains of overlapping genes. Ten hub genes further highlight the putative importance of tau-protein kinase activity, inflammatory response and neuron apoptotic regulatory processes across MDD and T2DM.

CONCLUSIONS

Our results indicate that an overlapping genetic architecture subserves MDD and T2DM. Genes relevant to the innate immune system, tau protein formation, and cellular aging were identified. Results indicate that the common, often comorbid, conditions of MDD and T2DM have a pathoetiologic nexus.

Potential biomarkers: differentially expressed proteins of the extrinsic coagulation pathway in plasma samples from patients with depression

Depression is a severe disabling psychiatric illness and the pathophysiological mechanisms remain unknown. In previous work, we found the changes in extrinsic coagulation (EC) pathway proteins in depressed patients compared with healthy subjects were significant. In this study, we screened differentially expressed proteins (DEPs) in the EC pathway, and explored the molecular mechanism by constructing a protein-protein interaction (PPI) network. The DEPs of the EC pathwaywere initially screened by isobaric tags for relative and absolute quantification (iTRAQ) in plasma samples obtained from 20 depression patients and 20 healthy controls, and were then identified by Enzyme-linked immunosorbent assays (ELISAs). Ingenuity Pathway Analysis (IPA) software was used to analyse pathway. The differentially expressed genes (DEGs) were identified by analyzing the GSE98793 microarray data from the Gene Expression Omnibus database using the Significance Analysis for Microarrays (SAM, version 4.1) statistical method. Cytoscape version 3.4.0 software was used to construct and visualize PPI networks. The results show that Fibrinogen alpha chain (FGA), Fibrinogen beta chain (FGB), Fibrinogen gamma chain (FGG) and Coagulation factor VII (FVII) were screened in the EC pathway from depression patient samples. FGA, FGB, and FGG were significantly up-regulated, and FVII was down-regulated. Thirteen DEGs related to depression and EC pathways were identified from the microarray database. Among them NF-κB Inhibitor Beta (NFKBIB) and Heat shock protein family B (small) member 1 (HSPB1) were highly correlated with EC pathway. We conclude that EC pathway is associated with depression, which provided clues for the biomarker development and the pathogenesis of depression.

New Molecular Targets for Antidepressant Drugs

Major depressive disorder (MDD) is a common and severe mental disorder that is usually recurrent and has a high risk of suicide. This disorder manifests not only with psychological symptoms but also multiple changes throughout the body, including increased risks of obesity, diabetes, and cardiovascular disease. Peripheral markers of oxidative stress and inflammation are elevated. MDD is therefore best described as a multisystem whole-body disease. Pharmacological treatment with antidepressants usually requires several weeks before the desired effects manifest. Previous theories of depression, such as the monoamine or neurogenesis hypotheses, do not explain these characteristics well. In recent years, new mechanisms of action have been discovered for long-standing antidepressants that also shed new light on depression, including the sphingolipid system and the receptor for brain-derived neurotrophic factor (BDNF).

Integrating Clinical and Genomic Analyses of Hippocampal-Prefrontal Circuit Disorder in Depression

Major depressive disorder (MDD) is a prevalent, devastating and recurrent mental disease. Hippocampus (HIP)-prefrontal cortex (PFC) neural circuit abnormalities have been confirmed to exist in MDD; however, the gene-related molecular features of this circuit in the context of depression remain unclear. To clarify this issue, we performed gene set enrichment analysis (GSEA) to comprehensively analyze the genetic characteristics of the two brain regions and used weighted gene correlation network analysis (WGCNA) to determine the main depression-related gene modules in the HIP-PFC network. To clarify the regional differences and consistency for MDD, we also compared the expression patterns and molecular functions of the key modules from the two brain regions. The results showed that candidate modules related to clinical MDD of HIP and PFC, which contained with 363 genes and 225 genes, respectively. Ninety-five differentially expressed genes (DEGs) were identified in the HIP candidate module, and 51 DEGs were identified in the PFC candidate module, with only 11 overlapping DEGs in these two regional modules. Combined with the enrichment results, although there is heterogeneity in the molecular functions in the HIP-PFC network of depression, the regulation of the MAPK cascade, Ras protein signal transduction and Ephrin signaling were significantly enriched in both brain regions, indicating that these biological pathways play important roles in MDD pathogenesis. Additionally, the high coefficient protein–protein interaction (PPI) network was constructed via STRING, and the top-10 coefficient genes were identified as hub genes via the cytoHubba algorithm. In summary, the present study reveals the gene expression characteristics of MDD and identifies common and unique molecular features and patterns in the HIP-PFC network. Our results may provide novel clues from the gene function perspective to explain the pathogenic mechanism of depression and to aid drug development. Further research is needed to confirm these findings and to investigate the genetic regulation mechanisms of different neural networks in depression.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

Interleukin-6 Gene Expression Changes after a 4-Week Intake of a Multispecies Probiotic in Major Depressive Disorder-Preliminary Results of the PROVIT Study

Major depressive disorder (MDD) is a prevalent disease, in which one third of sufferers do not respond to antidepressants. Probiotics have the potential to be well-tolerated and cost-efficient treatment options. However, the molecular pathways of their effects are not fully elucidated yet. Based on previous literature, we assume that probiotics can positively influence inflammatory mechanisms. We aimed at analyzing the effects of probiotics on gene expression of inflammation genes as part of the randomized, placebo-controlled, multispecies probiotics PROVIT study in Graz, Austria. Fasting blood of 61 inpatients with MDD was collected before and after four weeks of probiotic intake or placebo. We analyzed the effects on gene expression of tumor necrosis factor (TNF), nuclear factor kappa B subunit 1 (NFKB1) and interleukin-6 (IL-6). In IL-6 we found no significant main effects for group (F(1,44) = 1.33, p = ns) nor time (F(1,44) = 0.00, p = ns), but interaction was significant (F(1,44) = 5.67, p < 0.05). The intervention group showed decreasing IL-6 gene expression levels while the placebo group showed increasing gene expression levels of IL-6. Probiotics could be a useful additional treatment in MDD, due to their anti-inflammatory effects. Results of the current study are promising, but further studies are required to investigate the beneficial effects of probiotic interventions in depressed individuals.