Integrated transcriptomics reveals the brain and blood biomarkers in Alzheimer's disease

Identification of Autophagy-Related Candidate Genes in the Early Diagnosis of Alzheimer's Disease and Exploration of Potential Molecular Mechanisms

This study aimed to identify autophagy-related candidate genes for the early diagnosis of Alzheimer's disease (AD) and elucidate their potential molecular mechanisms. Differentially expressed genes (DEGs) and phenotype-associated significant module genes were obtained using the "limma" package and weighted gene co-expression network analysis (WGCNA) based on hippocampal tissue datasets from AD patients and control samples. The intersection between the list of autophagy-related genes (ATGs), DEGs, and module genes was further investigated to obtain AD-autophagy-related differential expression genes (ATDEGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to identify hub genes, and a second intersection was performed with important module genes from the protein-protein interaction (PPI) network to obtain co-hub genes. Finally, a diagnostic model was constructed by receiver operating characteristic (ROC) analysis to determine the candidate genes with high diagnostic efficacy in the external validation set. Moreover, immune infiltration analysis was performed on AD patient brain tissues and explore the correlation between candidate genes and immune cells. We further analyzed the expression level of candidate genes in the SH-SY5Y cells with Aβ25-35 (25 µM). Among the 17 identified AD-ATDEGs, ATP6V1E1 stood out with area under the curve (AUC) values of 0.869, 0.817, and 0.714 in the external validation set, underscoring its high diagnostic efficacy in both hippocampal and peripheral blood contexts for AD patients. Meanwhile, ATP6V1E1 expression was positively correlated with effector memory CD4 + T cells, while negatively correlated with natural killer T cells and activated CD4 + T cells. Results from quantitative PCR (qPCR) and immunofluorescence assays indicated a reduction in ATP6V1E1 expression, aligning with our database analysis findings. In summary, ATP6V1E1 as a candidate gene provides a new perspective for the early identification and pathogenesis of AD.

Inhibition of CXCR4: A perspective on miracle fruit seed for Alzheimer's disease treatment

Alzheimer's disease (AD) is the most prevalent type of dementia, and its causes are currently diverse and not fully understood. In a previous study, we discovered that short-term treatment with miracle fruit seed (MFS) had a therapeutic effect on AD model mice, however, the precise mechanism behind the effect remains unclear. In this research, we aimed to establish the efficacy and safety of long-term use of MFS in AD model mice. A variety of cytokines and chemokines have been implicated in the development of AD. Previous studies have validated a correlation between the expression levels of C-X-C chemokine receptor type 4 (CXCR4) and disease severity in AD. In this research, we observed an upregulation of CXCR4 expression in hippocampal tissues in the AD model group, which was then reversed after MFS treatment. Moreover, CXCR4 knockout led to improving cognitive function in AD model mice, and MFS showed the ability to regulate CXCR4 expression. Finally, our findings indicate that CXCR4 knockout and long-term MFS treatment produce comparable effects in treating AD model mice. In conclusion, this research demonstrates that therapeutic efficacy and safety of long-term use of MFS in AD model mice. MFS treatment and the subsequent reduction of CXCR4 expression exhibit a neuroprotective role in the brain, highlighting their potential as therapeutic targets for AD.

Advances in Blood Biomarkers for Alzheimer's Disease: Ultra-Sensitive Detection Technologies and Impact on Clinical Diagnosis

Alzheimer's disease has escalated into a critical public health concern, marked by its neurodegenerative nature that progressively diminishes cognitive abilities. Recognized as a continuously advancing and presently incurable condition, AD underscores the necessity for early-stage diagnosis and interventions aimed at delaying the decline in mental function. Despite the proven efficacy of cerebrospinal fluid and positron emission tomography in diagnosing AD, their broader utility is constrained by significant costs and the invasive nature of these procedures. Consequently, the innovation of blood biomarkers such as Amyloid-beta, phosphorylated-tau, total-tau et al, distinguished by their high sensitivity, minimal invasiveness, accessibility, and cost-efficiency, emerges as a promising avenue for AD diagnosis. The advent of ultra-sensitive detection methodologies, including single-molecule enzyme-linked immunosorbent assay and immunoprecipitation-mass spectrometry, has revolutionized the detection of AD plasma biomarkers, supplanting previous low-sensitivity techniques. This rapid advancement in detection technology facilitates the more accurate quantification of pathological brain proteins and AD-associated biomarkers in the bloodstream. This manuscript meticulously reviews the landscape of current research on immunological markers for AD, anchored in the National Institute on Aging-Alzheimer's Association AT(N) research framework. It highlights a selection of forefront ultra-sensitive detection technologies now integral to assessing AD blood immunological markers. Additionally, this review examines the crucial pre-analytical processing steps for AD blood samples that significantly impact research outcomes and addresses the practical challenges faced during clinical testing. These discussions are crucial for enhancing our comprehension and refining the diagnostic precision of AD using blood-based biomarkers. The review aims to shed light on potential avenues for innovation and improvement in the techniques employed for detecting and investigating AD, thereby contributing to the broader field of neurodegenerative disease research.

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease

Background

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients.

Methods

Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene.

Result

A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well.

Conclusion

The NAP1L1 gene is a potential predictive biomarker for AD.

SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease

The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.

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.