Identification of Hub Genes Related to Alzheimer's Disease and Major Depressive Disorder

The relationship between major depressive disorder and dementia: A bidirectional two-sample Mendelian randomization study

BACKGROUND

Major depressive disorder (MDD) and dementia psychiatric and neurological diseases that are clinically widespread, but whether there is a causal link between them is still unclear. In this study, bidirectional two-sample Mendelian randomization (MR) was used to investigate the potential causal relationship between MDD and dementia via a genome-wide association study (GWAS) database, containing samples from the European population.

METHOD

We collected data on MDD and common clinical dementia subtypes from GWAS, including Alzheimer's disease (AD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), Parkinson's disease with dementia (PDD), and vascular dementia (VaD). A series of bidirectional two-sample MR studies and correlation sensitivity analysis were carried out.

RESULTS

In the study of the effect of MDD on dementia subtypes, no causal relationship was found between MDD and dementia subtypes other than VaD, inverse variance weighted (IVW) method: odds ratio (OR), 2.131; 95 % confidence interval (CI), 1.249-3.639, P = 0.006; MDD-AD: OR, 1.000; 95 % CI, 0.999-1.001, P = 0.537; MDD-FTD: OR, 1.476; 95 % CI, 0.471-4.627, P = 0.505; MDD-PDD: OR, 0.592; 95 % CI, 0.204-1.718, P = 0.335; MR-Egger method: MDD-DLB: OR, 0.582; 95 % CI, 0.021-15.962, P = 0.751. In reverse MR analyses, no dementia subtype was found to be a risk factor for MDD.

LIMITATIONS

The results of this study may not be generalizable to non-European populations.

CONCLUSION

MDD was identified as a potential risk factor for VaD, but no dementia subtype was found to be a risk factor for MDD. These results suggest a new avenue for the prevention of VaD.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Secrets and lies of host-microbial interactions: MHC restriction and trans-regulation of T cell trafficking conceal the role of microbial agents on the edge between health and multifactorial/complex diseases

Here we critically discuss data supporting the view that microbial agents (pathogens, pathobionts or commensals alike) play a relevant role in the pathogenesis of multifactorial diseases, but their role is concealed by the rules presiding over T cell antigen recognition and trafficking. These rules make it difficult to associate univocally infectious agents to diseases' pathogenesis using the paradigm developed for canonical infectious diseases. (Cross-)recognition of a variable repertoire of epitopes leads to the possibility that distinct infectious agents can determine the same disease(s). There can be the need for sequential infection/colonization by two or more microorganisms to develop a given disease. Altered spreading of infectious agents can determine an unwanted activation of T cells towards a pro-inflammatory and trafficking phenotype, due to differences in the local microenvironment. Finally, trans-regulation of T cell trafficking allows infectious agents unrelated to the specificity of T cell to modify their homing to target organs, thereby driving flares of disease. The relevant role of microbial agents in largely prevalent diseases provides a conceptual basis for the evaluation of more specific therapeutic approaches, targeted to prevent (vaccine) or cure (antibiotics and/or Biologic Response Modifiers) multifactorial diseases.

Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets.

METHODS

Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). Gene Ontology and pathway enrichment were conducted using DAVID online tool. The STRING database and Cytoscape tools were employed to analyze protein-protein interactions and identify hub genes. The predictive value of hub genes was assessed by principal component analysis and receiver operating characteristic curves. AD mice model was constructed, and histology was then observed by hematoxylin-eosin staining. Gene expression levels were finally determined by real-time quantitative PCR.

RESULTS

We obtained 197 overlapping DEGs from GSE5281 and GSE28146 datasets. After constructing protein-protein interaction network, three highly interconnected clusters were identified and 6 hub genes (RBL1, BUB1, HDAC7, KAT5, SIRT2, and ITGB1) were selected. The hub genes could be used as basis to predict AD. Histological abnormalities of brain were observed, suggesting successful AD model was constructed. Compared with the control group, the mRNA expression levels of RBL1, BUB1, HDAC7, KAT5 and SIRT2 were significantly increased, while the mRNA expression level of ITGB1 was significantly decreased in AD groups.

CONCLUSION

RBL1, BUB1, HDAC7, KAT5, SIRT2 and ITGB1 are promising gene signatures for diagnosis and therapy of AD.

Cerebrospinal fluid exploratory proteomics and ketamine metabolite pharmacokinetics in human volunteers after ketamine infusion

Ketamine is a treatment for both refractory depression and chronic pain syndromes. In order to explore ketamine's potential mechanism of action and whether ketamine or its metabolites cross the blood brain barrier, we examined the pharmacokinetics of ketamine and its metabolites-norketamine (NK), dehydronorketamine (DHNK), and hydroxynorketamines (HNKs)-in cerebrospinal fluid (CSF) and plasma, as well as in an exploratory proteomic analysis in the CSF of nine healthy volunteers who received ketamine intravenously (0.5 mg/kg IV). We found that ketamine, NK, and (2R,6R;2S,6S)-HNK readily crossed the blood brain barrier. Additionally, 354 proteins were altered in the CSF in at least two consecutive timepoints (p < 0.01). Proteins in the classes of tyrosine kinases, cellular adhesion molecules, and growth factors, including insulin, were most affected, suggesting an interplay of altered neurotransmission, neuroplasticity, neurogenesis, synaptogenesis, and neural network functions following ketamine administration.

Genetic analysis of potential biomarkers and therapeutic targets in age-related hearing loss

Age-related hearing loss (ARHL) or presbycusis is the phenomenon of hearing loss due to the aging of auditory organs with age. It seriously affects the cognitive function and quality of life of the elderly. This study is based on comprehensive bioinformatic and machine learning methods to identify the critical genes of ARHL and explore its therapy targets and pathological mechanisms. The ARHL and normal samples were from GSE49543 datasets of the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to obtain significant modules. The Limma R-package was used to identify differentially expressed genes (DEGs). The 15 common genes of the practical module and DEGs were screened. Functional enrichment analysis suggested that these genes were mainly associated with inflammation, immune response, and infection. Cytoscape software created the protein-protein interaction (PPI) layouts and cytoHubba, support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) algorithms screened hub genes. After validating the hub gene expressions in GSE6045 and GSE154833 datasets, Clec4n, Mpeg1, and Fcgr3 are highly expressed in ARHL and have higher diagnostic efficacy for ARHL, so they were identified as hub genes. In conclusion, Clec4n, Mpeg1, and Fcgr3 play essential roles in developing ARHL, and they might become vital targets in ARHL diagnosis and anti-inflammatory therapy.

Genetic analysis of potential biomarkers and therapeutic targets in neuroinflammation from sporadic Creutzfeldt-Jakob disease

This study aimed to identify hub genes and pathological mechanisms related to neuroinflammation in Sporadic Creutzfeldt-Jakob disease (SCJD) based on comprehensive bioinformatics. SCJD and normal samples were collected from GSE160208. Weighted gene co-expression network analysis (WGCNA) and Limma R package were used to obtain key genes, which were used for enrichment and immune cell infiltration analyses. Protein-protein interaction (PPI) network, cytoHubba, and machine learning were used to screen the central genes of SCJD. The chemicals related to hub genes were predicted and explored by molecular docking. 88 candidate genes were screened. Enrichment analysis showed they were mainly related to bacterial and viral infection and immune cell activation. Immune cell infiltration analysis suggested that immune cell activation and altered activity of the immune system are involved in the progression of SCJD. After identifying hub genes, KIT and SPP1 had higher diagnostic efficacy for SCJD (AUC > 0.9), so they were identified as central genes. The molecular docking results showed hub genes both docked well with Tretinoin. KIT, SPP1, and Tretinoin are essential in developing neuroinflammation in SCJD and may provide new ideas for diagnosing and treating SCJD.

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.

Identification of novel biomarkers linking depressive disorder and Alzheimer's disease based on an integrative bioinformatics analysis

BACKGROUND

Previous reports revealed that a history of major depressive disorder (MDD) increased the risk of Alzheimer's disease (AD). The immune disorder is associated with MDD and AD pathophysiology. We aimed to identify differentially expressed immune-related genes (DEIRGs) that are involved in the pathogenesis of MDD and AD.

METHODS

We downloaded mRNA expression profiles (GSE76826 and GSE5281) from the Gene Expression Omnibus (GEO) database. The R software was used to identify DEIRGs for the two diseases separately. Functional enrichment analysis and PPI network of DEIRGs were performed. Finally, the relationship between shared DEIRGs and immune infiltrates of AD and MDD were analyzed, respectively.

RESULTS

A total of 121 DEIRGs linking AD and MDD were identified. These genes were significantly enriched in immune-related pathways, such as the JAK-STAT signaling pathway, regulation of chemotaxis, chemotaxis, cytokine-cytokine receptor interaction, and primary immunodeficiency. Furthermore, three shared DEIRGs (IL1R1, CHGB, and NRG1) were identified. Correlation analysis between DEIRGs and immune cells revealed that IL1R1 and NRG1 had a negative or positive correlation with some immune cells both in AD and MDD.

CONCLUSION

Both DEIRGs and immune cell infiltrations play a vital role in the pathogenesis of AD and MDD. Our findings indicated that there are common genes and biological processes between MDD and AD, which provides a theoretical basis for the study of the comorbidity of MDD and AD.

Control of centrosome distal appendages assembly and disassembly

Centrosomes are microtubule organizing centers involved in chromosome segregation, spindle orientation, cell motility and cilia formation. In recent years, they have also emerged as key modulators of asymmetric cell division. Centrosomes are composed of two centrioles that initiate duplication in S phase. The conservative nature of centriole duplication means that the two centrioles of a G1 cell are of different ages. They are also structurally different as only the older centriole carry appendages, an assembly of a subset of proteins primarily required for cilia formation. In a growing tissue, the non-motile, primary cilium acts as a mechano- and sensory organelle that influences cell behavior via modulation of signaling pathways. Here, we discuss the most recent findings about distal appendage composition and function, as well as cell cycle-specific regulation and their implications in various diseases.

Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer

Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes were validated using GEPIA, OncoDB, and GENT2. For exploring promoter methylation levels and genetic alterations in hub genes, MEXPRESS and cBioPortal were utilized, respectively. Moreover, DAVID, HPA, TIMER, CancerSEA, ENCORI, DrugBank, and GSCAlite were used for gene enrichment analysis, subcellular localization analysis, immune cell infiltration analysis, exploring correlations between hub genes and different diverse states, lncRNA-miRNA-mRNA co-regulatory network analysis, predicting hub gene-associated drugs, and conducting drug sensitivity analysis, respectively. In total, 8947 DEGs were found between OC and normal samples in GSE69428. After STRING and Cytohubba analysis, 4 hub genes including TTK (TTK Protein Kinase), (BUB1 mitotic checkpoint serine/threonine kinase B) BUB1B, (Nucleolar and spindle-associated protein 1) NUSAP1, and (ZW10 interacting kinetochore protein) ZWINT were selected as the hub genes. Further, it was validated that these 4 hub genes were significantly up-regulated in OC samples compared to normal controls, but overexpression of these genes was not associated with overall survival (OS). However, genetic alterations in those genes were found to be linked with OS and disease-free (DFS) survival. Moreover, this study also revealed some novel links between TTK, BUB1B, NUSAP1, and ZWINT overexpression and promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. Four hub genes, including TTK, BUB1B, NUSAP1, and ZWINT, were revealed as tumor-promotive factors in OC, having the potential to be utilized as novel biomarkers and therapeutic targets for OC management.

Identification of hub genes and pathophysiological mechanism related to acute unilateral vestibulopathy by integrated bioinformatics analysis

Background

Although many pathological mechanisms and etiological hypotheses of acute unilateral vestibulopathy (AUVP) have been reported, but the actual etiology remains to be elucidated.

Objective

This study was based on comprehensive bioinformatics to identify the critical genes of AUVP and explore its pathological mechanism.

Methods

Gene expression profiles of AUVP and normal samples were collected from GSE146230 datasets of the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was constructed, and the WGCNA R-package extracted significant modules. The limma R-package was applied to identify differentially expressed genes (DEGs). The common genes of practical modules and DEGs were screened for GO and KEGG pathways analysis. The protein–protein interaction (PPI) layout and hub genes validation was created by Cytoscape software using the link from the STRING database. The functions of hub genes were predicted through the CTD (comparative genetics database).

Results

A total of 332 common genes were screened from practical modules and DEGs. Functional enrichment analysis revealed that these genes were predominantly associated with inflammation and infection. After construction of PPI, expressions of hub genes, and drawing ROC curves, LILRB2, FPR1, AQP9, and LILRA1 are highly expressed in AUVP (p < 0.05) and have a certain diagnostic efficacy for AUVP (AUC > 0.7), so they were selected as hub genes. The functions of hub genes suggested that the occurrence of AUVP may be related to inflammation, necrosis, hepatomegaly, and other conditions in CTD.

Conclusion

LILRB2, FPR1, AQP9, and LILRA1 may play essential roles in developing AUVP, providing new ideas for diagnosing and treating AUVP.

Neurovascular Unit-Derived Extracellular Vesicles: From Their Physiopathological Roles to Their Clinical Applications in Acute Brain Injuries

Extracellular vesicles (EVs) form a heterogeneous group of membrane-enclosed structures secreted by all cell types. EVs export encapsulated materials composed of proteins, lipids, and nucleic acids, making them a key mediator in cell–cell communication. In the context of the neurovascular unit (NVU), a tightly interacting multicellular brain complex, EVs play a role in intercellular communication and in maintaining NVU functionality. In addition, NVU-derived EVs can also impact peripheral tissues by crossing the blood–brain barrier (BBB) to reach the blood stream. As such, EVs have been shown to be involved in the physiopathology of numerous neurological diseases. The presence of NVU-released EVs in the systemic circulation offers an opportunity to discover new diagnostic and prognostic markers for those diseases. This review outlines the most recent studies reporting the role of NVU-derived EVs in physiological and pathological mechanisms of the NVU, focusing on neuroinflammation and neurodegenerative diseases. Then, the clinical application of EVs-containing molecules as biomarkers in acute brain injuries, such as stroke and traumatic brain injuries (TBI), is discussed.