Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease

Biomarkers of Synaptic Degeneration in Alzheimer's Disease

Synapse has been considered a critical neuronal structure in the procession of Alzheimer's disease (AD), attacked by two pathological molecule aggregates (amyloid-β and phosphorylated tau) in the brain, disturbing synaptic homeostasis before disease manifestation and subsequently causing synaptic degeneration. Recently, evidence has emerged indicating that soluble oligomeric amyloid-β (AβO) and tau exert direct toxicity on synapses, causing synaptic damage. Synaptic degeneration is closely linked to cognitive decline in AD, even in the asymptomatic stages of AD. Therefore, the identification of novel, specific, and sensitive biomarkers involved in synaptic degeneration holds significant promise for early diagnosis of AD, reducing synaptic degeneration and loss, and controlling the progression of AD. Currently, a range of biomarkers in cerebrospinal fluid (CSF), such as synaptosome-associated protein 25 (SNAP-25), synaptotagmin-1, growth-associated protein-43 (GAP-43), and neurogranin (Ng), along with functional brain imaging techniques, can detect variations in synaptic density, offering high sensitivity and specificity for Alzheimer's disease (AD) diagnosis. However, these methods face challenges, including invasiveness, high cost, and limited accessibility. In contrast, biomarkers found in blood or urine provide a minimally invasive, cost-effective, and more accessible alternative to traditional diagnostic methods. Notably, neuron-derived exosomes in blood, which contain synaptic proteins, show variations in concentration that can serve as indicators of synaptic injury, providing an additional, less invasive approach to AD diagnosis and monitoring.

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.

Epigenetic biomarkers in Alzheimer's disease: Diagnostic and prognostic relevance

Alzheimer's disease (AD) is a leading cause of cognitive decline in the aging population, presenting a critical need for early diagnosis and effective prognostic tools. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNAs, have emerged as promising biomarkers for AD due to their roles in regulating gene expression and potential for reversibility. This review examines the current landscape of epigenetic biomarkers in AD, emphasizing their diagnostic and prognostic relevance. DNA methylation patterns in genes such as APP, PSEN1, and PSEN2 are highlighted for their strong associations with AD pathology. Alterations in DNA methylation at specific CpG sites have been consistently observed in AD patients, suggesting their utility in early detection. Histone modifications, such as acetylation and methylation, also play a crucial role in chromatin remodelling and gene expression regulation in AD. Dysregulated histone acetylation and methylation have been linked to AD progression, making these modifications valuable biomarkers. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), further contribute to the epigenetic regulation in AD. miRNAs can modulate gene expression post-transcriptionally and have been found in altered levels in AD, while lncRNAs can influence chromatin structure and gene expression. The presence of these non-coding RNAs in biofluids like blood and cerebrospinal fluid positions them as accessible and minimally invasive biomarkers. Technological advancements in detecting and quantifying epigenetic modifications have propelled the field forward. Techniques such as next-generation sequencing, bisulfite sequencing, and chromatin immunoprecipitation assays offer high sensitivity and specificity, enabling the detailed analysis of epigenetic changes in clinical samples. These tools are instrumental in translating epigenetic research into clinical practice. This review underscores the potential of epigenetic biomarkers to enhance the early diagnosis and prognosis of AD, paving the way for personalized therapeutic strategies and improved patient outcomes. The integration of these biomarkers into clinical workflows promises to revolutionize AD management, offering hope for better disease monitoring and intervention.

MGST3 regulates BACE1 protein translation and amyloidogenesis by controlling the RGS4-mediated AKT signaling pathway

Microsomal glutathione transferase 3 (MGST3) regulates eicosanoid and glutathione metabolism. These processes are associated with oxidative stress and apoptosis, suggesting that MGST3 might play a role in the pathophysiology of Alzheimer's disease. Here, we report that knockdown (KD) of MGST3 in cell lines reduced the protein level of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) and the resulting amyloidogenesis. Interestingly, MGST3 KD did not alter intracellular reactive oxygen species level but selectively reduced the expression of apoptosis indicators which could be associated with the receptor of cysteinyl leukotrienes, the downstream metabolites of MGST3 in arachidonic acid pathway. We then showed that the effect of MGST3 on BACE1 was independent of cysteinyl leukotrienes but involved a translational mechanism. Further RNA-seq analysis identified that regulator of G-protein signaling 4 (RGS4) was a target gene of MGST3. Silencing of RGS4 inhibited BACE1 translation and prevented MGST3 KD-mediated reduction of BACE1. The potential mechanism was related to AKT activity, as the protein level of phosphorylated AKT was significantly reduced by silencing of MGST3 and RGS4, and the AKT inhibitor abolished the effect of MGST3/RGS4 on phosphorylated AKT and BACE1. Together, MGST3 regulated amyloidogenesis by controlling BACE1 protein expression, which was mediated by RGS4 and downstream AKT signaling pathway.

Alzheimer's disease risk associated with changes in Epstein-Barr virus nuclear antigen 1-specific epitope targeting antibody levels

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder influenced by age, sex, genetic factors, immune alterations, and infections. Multiple lines of evidence suggest that changes in antibody response are linked to AD pathology.

METHODS

To elucidate the mechanisms underlying AD development, we investigated antibodies that target autoimmune epitopes using high-resolution epitope microarrays. Our study compared two groups: individuals with AD (n = 19) and non-demented (ND) controls (n = 19). To validate the results, we measured antibody levels in plasma samples from AD patients (n = 96), mild cognitive impairment (MCI; n = 91), and ND controls (n = 97). To further explore the invlovement of EBV, we performed epitope masking immunofluorescence microscopy analysis and tests to induce lytic replication using the B95-8 cell line.

RESULTS

In this study, we analyzed high-resolution epitope-specific serum antibody levels in AD, revealing significant disparities in antibodies targeting multiple epitopes between the AD and control groups. Particularly noteworthy was the significant down-regulation of antibody (anti-DG#29) targeting an epitope of Epstein-Barr virus nuclear antigen 1 (EBNA1). This down-regulation increased AD risk in female patients (odds ratio up to 6.6), but not in male patients. Our investigation further revealed that the down-regulation of the antibody (anti-DG#29) is associated with EBV reactivation in AD, as indicated by the analysis of EBV VCA IgG or IgM levels. Additionally, our data demonstrated that the epitope region on EBNA1 for the antibody is hidden during the EBV lytic reactivation of B95-8 cells.

CONCLUSION

Our findings suggest a potential relationship of EBV in the development of AD in female. Moreover, we propose that antibodies targeting the epitope (DG#29) of EBNA1 could serve as valuable indicators of AD risk in female.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer's disease

Background

Ferroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer's disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).

Methods

The microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R "limma" package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.

Results

We identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.

Conclusion

These results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

Integrated molecular-network analysis reveals infertility-associated key genes and transcription factors in the non-obstructive azoospermia

BACKGROUND

Male infertility is a multifactorial reproductive health problem with complex causes. Non-obstructive azoospermia (NOA) is characterized by failure of spermatogenesis, leading to the absence of spermatozoa in ejaculates. The molecular mechanism underlying the NOA is still not well understood.

OBJECTIVES

This study aims to identify the key genes involved in male infertility that could be a potential biomarker in the diagnosis and prognosis of azoospermia.

STUDY DESIGN

The microarray expression profiles dataset GSE45885 and GSE45887 were downloaded from the NCBI's Gene Expression Omnibus (GEO) database and analyzed for male infertility-associated differentially expressed genes (DEGs) using the GEO2R tool. The common DEGs between the two datasets were combined and their protein-protein interaction (PPI) network was constructed using Cytoscape to reveal the hub genes by topology and module analysis. In addition, transcription factors (TFs) and protein kinases regulating the hub genes were identified using the X2K tool. Then, the expression of the hub genes was validated by analyzing the GSE190752 microarray dataset. Further, the PPI network was screened for biological roles and enriched pathways using DAVID software.

RESULTS

About 256 DEGs associated with NOA were identified and constructed the PPI network to find the infertility-associated proteins. The biological processes linked with these proteins were spermatogenesis, cell differentiation, flagellated sperm motility, and spermatid development. The topology and module analysis of the infertility-associated protein network identified the hub genes TEX38, FAM71F, PRR30, FAM166A, LYZL6, TPPP2, ARMC12, SPACA4, and FAM205A, which were found to be upregulated in the non-obstructive azoospermia. In addition, a total of 23 transcription factors and 3 protein kinases that are regulating these key hub genes were identified. Further these hub genes expression was validated using the microarray data and found that their expression was increased in the testicular biopsies obtained from NOA subjects, compared to healthy individuals.

CONCLUSION

The identified key genes and its associated transcription factors are known to regulate the infertility-related processes in the non-obstructive azoospermia. Also, the clinical sample-based microarray data validation for the expression of these key hub genes indicates their potentiality to develop them as diagnostic or prognostic biomarkers for NOA.

Genetic and Epigenetic Sexual Dimorphism of Brain Cells during Aging

In recent years, much of the attention paid to theoretical and applied biomedicine, as well as neurobiology, has been drawn to various aspects of sexual dimorphism due to the differences that male and female brain cells demonstrate during aging: (a) a dimorphic pattern of response to therapy for neurodegenerative disorders, (b) different age of onset and different degrees of the prevalence of such disorders, and (c) differences in their symptomatic manifestations in men and women. The purpose of this review is to outline the genetic and epigenetic differences in brain cells during aging in males and females. As a result, we hereby show that the presence of brain aging patterns in males and females is due to a complex of factors associated with the effects of sex chromosomes, which subsequently entails a change in signal cascades in somatic cells.