Identification of Differential Genes of DNA Methylation Associated With Alzheimer's Disease Based on Integrated Bioinformatics and Its Diagnostic Significance

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

The role of PIMT in Alzheimer's disease pathogenesis: A novel hypothesis

There are multiple theories of Alzheimer's disease pathogenesis. One major theory is that oxidation of amyloid beta (Aβ) promotes plaque deposition that directly contributes to pathology. A competing theory is that hypomethylation of DNA (due to altered one carbon metabolism) results in pathology through altered gene regulation. Herein, we propose a novel hypothesis involving L-isoaspartyl methyltransferase (PIMT) that unifies the Aβ and DNA hypomethylation hypotheses into a single model. Importantly, the proposed model allows bidirectional regulation of Aβ oxidation and DNA hypomethylation. The proposed hypothesis does not exclude simultaneous contributions by other mechanisms (e.g., neurofibrillary tangles). The new hypothesis is formulated to encompass oxidative stress, fibrillation, DNA hypomethylation, and metabolic perturbations in one carbon metabolism (i.e., methionine and folate cycles). In addition, deductive predictions of the hypothesis are presented both to guide empirical testing of the hypothesis and to provide candidate strategies for therapeutic intervention and/or nutritional modification. HIGHLIGHTS: PIMT repairs L-isoaspartyl groups on amyloid beta and decreases fibrillation. SAM is a common methyl donor for PIMT and DNA methyltransferases. Increased PIMT activity competes with DNA methylation and vice versa. The PIMT hypothesis bridges a gap between plaque and DNA methylation hypotheses.

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.

Identification of key lipid metabolism-related genes in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression.

METHODS

Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined.

RESULTS

Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters.

CONCLUSION

This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.

Perinatal S-Adenosylmethionine Supplementation Represses PSEN1 Expression by the Cellular Epigenetic Memory of CpG and Non-CpG Methylation in Adult TgCRD8 Mice

DNA methylation, the main epigenetic modification regulating gene expression, plays a role in the pathophysiology of neurodegeneration. Previous evidence indicates that 5'-flanking hypomethylation of PSEN1, a gene involved in the amyloidogenic pathway in Alzheimer's disease (AD), boosts the AD-like phenotype in transgenic TgCRND8 mice. Supplementation with S-adenosylmethionine (SAM), the methyl donor in the DNA methylation reactions, reverts the pathological phenotype. Several studies indicate that epigenetic signatures, driving the shift between normal and diseased aging, can be acquired during the first stages of life, even in utero, and manifest phenotypically later on in life. Therefore, we decided to test whether SAM supplementation during the perinatal period (i.e., supplementing the mothers from mating to weaning) could exert a protective role towards AD-like symptom manifestation. We therefore compared the effect of post-weaning vs. perinatal SAM treatment in TgCRND8 mice by assessing PSEN1 methylation and expression and the development of amyloid plaques. We found that short-term perinatal supplementation was as effective as the longer post-weaning supplementation in repressing PSEN1 expression and amyloid deposition in adult mice. These results highlight the importance of epigenetic memory and methyl donor availability during early life to promote healthy aging and stress the functional role of non-CpG methylation.

Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease

Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.