Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Alterations in T-Cell Transcription Factors and Cytokine Gene Expression in Late-Onset Alzheimer's Disease

BACKGROUND

Late-onset Alzheimer's disease (LOAD) is associated with many environmental and genetic factors. The effect of systemic inflammation on the pathogenesis of neurodegenerative diseases such as AD has been strongly suggested. T helper cells (Th) are one of the important components of the immune system and can easily infiltrate the brain in pathological conditions. The development of each Th-subset depends on the production of unique cytokines and their main regulator.

OBJECTIVE

This study aimed to compare the mRNA levels of Th-related genes derived from peripheral blood mononuclear cells of LOAD patients with control. Also, the identification of the most important Th1/Th2 genes and downstream pathways that may be involved in the pathogenesis of AD was followed by computational approaches.

METHODS

This study invloved 30 patients with LOAD and 30 non-demented controls. The relative expression of T-cell cytokines (IFN-γ, TNF-α, IL-4, and IL-5) and transcription factors (T-bet and GATA-3) were assessed using real-time PCR. Additionally, protein-protein interaction (PPI) was investigated by gene network construction.

RESULTS

A significant decrease at T-bet, IFN-γ, TNF-α, and GATA-3 mRNA levels was detected in the LOAD group, compared to the controls. However, there was no significant difference in IL-4 or IL-5 mRNA levels. Network analysis revealed a list of the highly connected protein (hubs) related to mitogen-activated protein kinase (MAPK) signaling and Th17 cell differentiation pathways.

CONCLUSION

The findings point to a molecular dysregulation in Th-related genes, which can promising in the early diagnosis or targeted interventions of AD. Furthermore, the PPI analysis showed that upstream off-target stimulation may involve MAPK cascade activation and Th17 axis induction.

Identification of MEDAG as a Hub Candidate Gene in the Onset and Progression of Type 2 Diabetes Mellitus by Comprehensive Bioinformatics Analysis

Objectives

We conducted the present study to identify novel hub candidate genes in the pathogenesis of type 2 diabetes mellitus (T2DM) and provide potential biomarkers or therapeutic targets for dealing with the disease.

Methods

We conducted weighted gene coexpression network analysis on a series of the expression profiles of the pancreas islet of T2DM patients obtained from the Gene Expression Omnibus database to construct a weighted coexpression network. After dividing genes into separated coexpression modules, we identified a T2DM-related module using Pearson's correlation analysis. Then, hub genes were identified from the T2DM-related module using the Maximal Clique Centrality method and validated by correlation analysis with clinical traits, differentially expressed gene analysis, validation in other datasets, and single-gene gene set enrichment analysis (GSEA).

Results

Genes were divided into 16 coexpression modules, and one module was identified as a T2DM-related module. Four hub candidate genes were identified, and MEDAG was a novel hub candidate gene. The expression level of MEDAG was positively correlated with hemoglobin A1c (HbA1c) and was evidently overexpressed in the pancreas islet tissue of T2DM patients compared with normal control. Analyses on two other datasets supported the results. GSEA verified that MEDAG plays essential roles in T2DM.

Conclusions

MEDAG is a novel hub candidate of T2DM, and its irregular expression in the pancreas islet plays vital roles in the pathogenesis of T2DM. MEDAG is a potential target of intervention in the future for the treatment of T2DM.

Role of Adaptive Immune and Impacts of Risk Factors on Adaptive Immune in Alzheimer's Disease: Are Immunotherapies Effective or Off-Target?

The pathogenesis of Alzheimer's disease (AD) is complex. Still it remains unclear, which resulted in all efforts for AD treatments with targeting the pathogenic factors unsuccessful over past decades. It has been evidenced that the innate immune is strongly implicated in the pathogenesis of AD. However, the role of adaptive immune in AD remains mostly unknown and the results obtained were controversial. In the review, we summarized recent studies and showed that the molecular and cellular alterations in AD patients and its animal models involving T cells and B cells as well as immune mediators of adaptive immune occur not only in the peripheral blood but also in the brain and the cerebrospinal fluid. The risk factors that cause AD contribute to AD progress by affecting the adaptive immune, indicating that adaptive immunity proposes a pivotal role in this disease. It may provide a possible basis for applying immunotherapy in AD and further investigates whether the immunotherapies are effective or off-target?

Effect of Aging on Homeostasis in the Soft Tissue of the Periodontium: A Narrative Review

Aging is characterized by a progressive decline or loss of physiological functions, leading to increased susceptibility to disease or death. Several aging hallmarks, including genomic instability, cellular senescence, and mitochondrial dysfunction, have been suggested, which often lead to the numerous aging disorders. The periodontium, a complex structure surrounding and supporting the teeth, is composed of the gingiva, periodontal ligament, cementum, and alveolar bone. Supportive and protective roles of the periodontium are very critical to sustain life, but the periodontium undergoes morphological and physiological changes with age. In this review, we summarize the current knowledge of molecular and cellular physiological changes in the periodontium, by focusing on soft tissues including gingiva and periodontal ligament.