Identification of key genes in late-onset major depressive disorder through a co-expression network module

A Transcriptomic Signature of Depressive Symptoms in Late Life

Background

Depressive symptoms in late life can impair daily function and accompany cognitive decline. However, the molecular mechanisms that underlie these changes in the brain remain poorly understood.

Methods

Differential expression analysis was performed on bulk-tissue RNA sequencing data generated from dorsolateral prefrontal cortex samples of elderly participants in ROS/MAP (Religious Orders Study and Memory and Aging Project; N = 998, mean age at death = 89.7 years). Bulk tissue RNA sequencing was analyzed against depressive symptoms measured prior to death, controlling for Alzheimer's disease neuropathology, medication status, and lifestyle factors. Sex-stratified models were also tested.

Results

Increased abundance of the Prader-Willi syndrome-associated gene PWAR1 (corrected p = 5.47 × 10-3) and CTDSPL2 (corrected p = .03) were associated with a higher burden of depressive symptoms in the combined sample. An additional 14 genes showed suggestive associations, including several with known links to neuropsychiatric illness (e.g., ACVR2B-AS1, COL19A1). Functional enrichment analysis revealed downregulation of aerobic metabolism and upregulation of both amino acid catabolism and DNA modification processes. Differential expression signatures were poorly correlated between males and females (Pearson r = 0.12; 95% CI, 0.10 to 0.13), and only the male group showed independently significant differential expression. Little overlap was found with previously published analyses of major depressive disorder.

Conclusions

Building on recently published single-nucleus profiling, we present the largest-ever study of transcriptomic correlates of depressive symptoms in late life, revealing new insights into sex-specific regulators. PWAR1 and CTDSPL2 were identified as putative markers of late-life depression in the dorsolateral prefrontal cortex and warrant further study.

Construction and validation of an immune gene-based model for diagnosis and risk prediction of severe asthma

OBJECTIVE

Severe asthma (SA) is a serious disease with limited treatment options, which is closely linked to immune dysfunction. Therefore, immune-associated biomarkers may diagnose SA and offer therapeutic targets for SA.

METHODS

The gene expression profiles of SA patients and matched controls were from the National Center for Biotechnology Information database. Immune genes were downloaded from the ImmPort database. After screening for differentially expressed genes (DEGs) between SA patients and controls, and identifying gene modules highly associated with SA, immune-related DEGs were obtained. Then, protein-protein interaction analysis, Cytoscape software and receiver operating characteristic (ROC) curves were used to identify hub genes. Next, the relationship between hub genes and immune cells was explored, and single-sample gene set enrichment analysis (ssGSEA) was applied to conduct pathway enrichment analyses. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) combined with ROC analysis were used to confirm the diagnostic value of the hub genes.

RESULTS

Forty immune-related DEGs were obtained, and RNASE3, CAMP and LTF were determined as hub genes. The hub genes were closely associated with immune cells, and ssGSEA showed that lysosome was associated with high expressions of the hub genes, while primary immunodeficiency was related to low expressions of the hub genes. LASSO combined with ROC analysis confirmed the immune gene-based model (RNASE3, CAMP, LTF, and CD79A) could distinguish SA patients from healthy individuals with high sensitivity.

CONCLUSIONS

RNASE3, CAMP, LTF, and CD79A could act as diagnostic markers for SA, providing a theoretical basis for developing diagnostic targets for SA.

Identification of blood transcriptome modules associated with suicidal ideation in patients with major depressive disorder

The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD_SI), 82 MDD patients without SI (MDD_nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD_SI from MDD_nSI (p = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD_SI from HC (p = 0.004). Additionally, three modules showed differences between MDD_nSI and HC: magenta (p = 0.009), brown (related to innate immunity and mitochondrial metabolism; p = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD.

Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex

Background

Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation.

Methods

We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls.

Results

Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power.

Conclusions

The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.

Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.