Key Risk Genes Identified From the Postmortem Brain of Patients With Major Depressive Disorder and Their Potential Clinical Applications.

BACKGROUND: Major depressive disorder (MDD) is a type of emotional dysfunction, and its pathogenesis has not been fully elucidated. Specifically, the key molecules in depression-related brain regions involved in this disease and their contributions to this disease are currently unclear. METHODS: GSE53987 and GSE54568 were selected from the Gene Expression Omnibus database. The data were standardized to identify the common differentially expressed genes (DEGs) in the cortex of MDD patients in the 2 datasets. The DEGs were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The STRING database was used to build protein-protein interaction networks, and the cytoHubba plugin was used to identify hub genes. Furthermore, we selected another blood transcriptome dataset that included 161 MDD and 169 control samples to explore the changes in the screened hub genes. Mice were subjected to 4 weeks of chronic unpredictable mild stress to establish an animal model of depression, and the expression of these hub genes in tissues of the prefrontal cortex was then detected by quantitative real time polymerase chain reaction (qRT-PCR). We subsequently predicted the possible posttranscriptional regulatory networks and traditional Chinese medicine according to the hub genes using a few online databases. RESULTS: The analysis identified 147 upregulated genes and 402 downregulated genes were identified in the cortex of MDD patients compared with that of the controls. Enrichment analyses revealed that DEGs were predominantly enriched in synapse-related cell functions, linoleic acid metabolism, and other pathways. Protein-protein interaction analysis identified 20 hub genes based on the total score. The changes in KDM6B, CUX2, NAAA, PHKB, NFYA, GTF2H1, CRK, CCNG2, ACER3, and SLC4A2 in the peripheral blood of MDD patients were consistent with those in the brain. Furthermore, the prefrontal cortex of mice with depressive-like behaviors showed significantly increased Kdm6b, Aridb1, Scaf11, and Thoc2 expression and decreased Ccng2 expression compared with that of normal mice, which was consistent with the results found for the human brain. Potential therapeutic candidates, such as citron, fructus citri, leaves of Panax Notoginseng, sanchi flower, pseudoginseng, and dan-shen root, were selected via traditional Chinese medicine screening. CONCLUSIONS: This study identified several novel hub genes in specific brain regions involved in the pathogenesis of MDD, which may not only deepen our understanding of depression but may also provide new ideas for its diagnosis and treatment.

Identification of the shared molecular mechanisms between major depressive disorder and COVID-19 from postmortem brain transcriptome analysis.

OBJECTIVES: This study aims to investigate the molecular mechanisms underlying the interaction of major depressive disorder (MDD) and COVID-19, and on this basis, diagnostic biomarkers and potential therapeutic drugs are further explored. METHODS: Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify common key genes involved in the pathogenesis of COVID-19 and MDD. Correlations with clinical features were explored. Detailed mechanisms were further investigated through protein interaction networks, GSEA, and immune cell infiltration analysis. Finally, Enrichr's Drug Signature Database and Coremine Medical were used to predict the potential drugs associated with key genes. RESULTS: The study identified 18 genes involved in both COVID-19 and MDD. Four key genes (MBP, CYP4B1, ERMN, and SLC26A7) were selected based on clinical relevance. A multi-gene prediction model showed good diagnostic efficiency for the two diseases: AUC of 0.852 for COVID-19 and 0.915 for MDD. GO and GSEA analyses identified specific biological functions and pathways associated with key genes in COVID-19 (axon guidance, metabolism, stress response) and MDD (neuron ensheathment, biosynthesis, glutamatergic neuron differentiation). The key genes also affected immune infiltration. Potential therapeutic drugs, including small molecules and traditional Chinese medicines, targeting these genes were identified. CONCLUSION: This study provides insights into the complex biological mechanisms underlying COVID-19 and MDD, develops an effective diagnostic model, and predicts potential therapeutic drugs, which may contribute to the prevention and treatment of these two prevalent diseases.

Protective effects of a novel bicyclic γ-butyrolactone compound against H2O2 or corticosterone-induced neural cell apoptosis.

Oxidative stress plays a pivotal role in various neurological disorders, encompassing both neurodegenerative diseases such as Alzheimer's and Parkinson's, and mood disorders like depression. The balance between the generation of reactive oxygen species (ROS) and the cell's antioxidant defenses, when disrupted, can lead to neuronal damage and neurologic dysfunction. In this study, we focused on the pathogenic role of oxidative stress in various neurologic disease models in vitro and investigated the neuroprotective capabilities of some novel bicyclic γ-butyrolactone compounds, with particular emphasis on the compound designated as 'bd'. Our investigation leveraged the HT22 and SH-SY5Y cells to model oxidative stress induced by H2O2 or corticosterone (CORT), common triggers of neuronal damage in neurodegenerative and mood disorders. We discovered that compound bd robustly reduced ROS production and suppressed neuronal apoptosis, suggesting its potential in treating a wider array of neurological conditions influenced by oxidative stress. In conclusion, our research underscores the importance of addressing oxidative stress in the context of diverse neurological disorders. The identification of compound bd as a neuroprotective agent with potential efficacy against ROS-induced apoptosis in neural cells opens new horizons for therapeutic development, offering hope for patients suffering from neurodegenerative diseases, depression, and other stress-related neurological conditions.

Screening Traditional Chinese Medicine Combination for Cotreatment of Alzheimer's Disease and Type 2 Diabetes Mellitus by Network Pharmacology.

BACKGROUND: In recent years, the efficacy of type 2 diabetes mellitus (T2DM) drugs in the treatment of Alzheimer's disease (AD) has attracted extensive interest owing to the close associations between the two diseases. OBJECTIVE: Here, we screened traditional Chinese medicine (TCM) and multi-target ingredients that may have potential therapeutic effects on both T2DM and AD from T2DM prescriptions. METHODS: Network pharmacology and molecular docking were used. RESULTS: Firstly, the top 10 frequently used herbs and corresponding 275 active ingredients were identified from 263 T2DM-related TCM prescriptions. Secondly, through the comparative analysis of 208 potential targets of ingredients, 1,740 T2DM-related targets, and 2,060 AD-related targets, 61 common targets were identified to be shared. Thirdly, by constructing pharmacological network, 26 key targets and 154 representative ingredients were identified. Further enrichment analysis showed that common targets were involved in regulating multiple pathways related to T2DM and AD, while network analysis also found that the combination of Danshen (Radix Salviae)-Gancao (Licorice)-Shanyao (Rhizoma Dioscoreae) contained the vast majority of the representative ingredients and might be potential for the cotreatment of the two diseases. Fourthly, MAPK1, PPARG, GSK3B, BACE1, and NR3C1 were selected as potential targets for virtual screening of multi-target ingredients. Further docking studies showed that multiple natural compounds, including salvianolic acid J, gancaonin H, gadelaidic acid, icos-5-enoic acid, and sigmoidin-B, exhibited high binding affinities with the five targets. CONCLUSION: To summarize, the present study provides a potential TCM combination that might possess the potential advantage of cotreatment of AD and T2DM.

Associations Between Obesity and Alzheimer's Disease: Multiple Bioinformatic Analyses.

BACKGROUND: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer's disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. OBJECTIVE: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. METHODS: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. RESULTS: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. CONCLUSION: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

Identification of the Similarities and Differences of Molecular Networks Associated With Fear Memory Formation, Extinction, and Updating in the Amygdala.

Abnormality of fear memory is one of the important pathogenic factors leading to post-traumatic stress disorder (PTSD), anxiety disorder, and other mental disorders. Clinically, although exposure therapy, which is based on the principle of fear memory extinction, has a certain effect on these diseases, it still relapses frequently in some cases. These troubles can be effectively solved by retrieving the memory in a certain time window before the extinction of fear memory. Therefore, it is generally believed that the extinction of fear memory is the result of forming new safe memory to competitively inhibit the original fear memory, while the retrieval-extinction operation is the updating or erasure of the original fear memory, thus, which has greater clinical therapeutic potential. However, what are the detailed molecular networks, specifically the circular RNAs (circRNAs), involved in fear memory updating, and the differences with fear extinction, are still unknown. In this study, we systematically observed the expression of mRNAs, microRNAs (miRNA), long non-coding RNAs (lncRNAs), and circRNAs in the basolateral amygdala of mice after fear memory formation, extinction, and updating by whole-transcriptional sequencing, then a variety of inter-group comparison and bioinformatics analysis were used to find the differential expressed RNAs, enrich the function of them, and construct the molecular interaction networks. Moreover, competing endogenous RNA (ceRNA) molecular networks and transcriptional regulatory networks for the candidate circRNAs were constructed. Through these analyses, we found that about 10% of molecules were both involved in the fear memory extinction and formation, but the molecules and their signaling pathways were almost completely different between fear memory extinction and updating. This study describes a relatively detailed molecular network for fear memory updating, which might provide some novel directions for further mechanism research, and help to develop a specific physical method for fear memory intervention, based on the regulation of these key molecules.

Quantitative assessment of the bidirectional relationships between diabetes and depression.

Diabetes and depression impose an enormous public health burden and the present study aimed to assess quantitatively the bidirectional relationships between the two disorders. We searched databases for eligible articles published until October 2016. A total of 51 studies were finally included in the present bidirectional meta-analysis, among which, 32 studies were about the direction of depression leading to diabetes, and 24 studies about the direction of diabetes leading to depression. Pooled results of the 32 eligible studies covering 1274337 subjects showed that depression patients were at higher risk for diabetes (odds ratio (OR) = 1.34, 95% confidence intervals (CI) = [1.23, 1.46]) than non-depressive subjects. Further gender-subgroup analysis found that the strength of this relationship was stronger in men (OR = 1.63, 95%CI = [1.48, 1.78]) than in women (OR = 1.29, 95%CI = [1.07, 1.51]). For the direction of diabetes leading to depression, pooled data of 24 articles containing 329658 subjects showed that patients with diabetes were at higher risk for diabetes (OR = 1.28, 95%CI = [1.15, 1.42]) than non-diabetic subjects. The available data supports that the relationships between diabetes and depression are bidirectional and the overall strengths are similar in both directions. More mechanistic studies are encouraged to explore the molecular mechanisms underlying the relationships between the two diseases.

Detecting the genetic link between Alzheimer's disease and obesity using bioinformatics analysis of GWAS data.

Alzheimer's disease (AD) represents the major form of dementia in the elderly. In recent years, accumulating evidence indicate that obesity may act as a risk factor for AD, while the genetic link between the two conditions remains unclear. This bioinformatics analysis aimed to detect the genetic link between AD and obesity on single nucleotide polymorphisms (SNPs), gene, and pathway levels based on genome-wide association studies data. A total of 31 SNPs were found to be shared by AD and obesity, which were linked to 7 genes. These genes included PSMC3, CELF1, MYBPC3, SPI1, APOE, MTCH2 and RAPSN. Further functional enrichment analysis of these genes revealed the following biological pathways, including proteasome, osteoclast differentiation, hypertrophic cardiomyopathy, dilated cardiomyopathy, Epstein-Barr virus and TLV-I infection, as well as several cancer associated pathways, to be common among AD and obesity. The findings deepened our understanding on the genetic basis linking obesity and AD and may help shape possible prevention and treatment strategies.

Genetic overlap between type 2 diabetes and major depressive disorder identified by bioinformatics analysis.

Our study investigated the shared genetic etiology underlying type 2 diabetes (T2D) and major depressive disorder (MDD) by analyzing large-scale genome wide association studies statistics. A total of 496 shared SNPs associated with both T2D and MDD were identified at p-value ≤ 1.0E-07. Functional enrichment analysis showed that the enriched pathways pertained to immune responses (Fc gamma R-mediated phagocytosis, T cell and B cell receptors signaling), cell signaling (MAPK, Wnt signaling), lipid metabolism, and cancer associated pathways. The findings will have potential implications for future interventional studies of the two diseases.

Assessment of vitamin D levels in type 1 and type 2 diabetes patients: Results from metaanalysis.

SCOPE: Accumulating evidence indicates that vitamin D deficiency is prevalent in patients with type 1 (T1D) and type 2 diabetes (T2D). The present study aims to assess 25-hydroxyvitamin D [25(OH)D] levels in T1D and T2D patients compared with controls through a metaanalysis. METHODS AND RESULTS: We searched databases for articles published until January 2015. A total of 12 studies covering 2003 patients and 1882 controls and 11 studies covering 2236 patients and 2438 controls were included to metaanalyze 25(OH)D levels in patients with T1D and T2D, respectively. Pooled data showed that T1D patients had lower levels of 25(OH)D than controls (summary standardized mean difference (SMD) -0.70, 95% confidence interval (CI) -1.02 to -0.37). Further age-subgroup analysis found that 25(OH)D levels in T1D patients was also significantly lower than controls in subgroup aged ≤ 14 years (summary SMD -1.04, 95% CI -1.55 to -0.53), while the association is not statistically significant in the subgroup aged > 14 years. Similarly, T2D patients had lower 25(OH)D levels compared with controls (summary SMD -0.58, 95%CI -1.16 to -0.00). CONCLUSION: Available data indicated that both T1D and T2D patients had lower levels of 25(OH)D than controls overall. The mechanistic underpinnings of this association warrant further elucidation.