Identification of Immune Cells and Key Genes associated with Alzheimer's Disease

KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Bradykinin promotes immune responses in differentiated embryonic neurospheres carrying APPswe and PS1dE9 mutations

BACKGROUND

Neural progenitor cells (NPCs) can be cultivated from developing brains, reproducing many of the processes that occur during neural development. They can be isolated from a variety of animal models, such as transgenic mice carrying mutations in amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN 1 and 2), characteristic of familial Alzheimer's disease (fAD). Modulating the development of these cells with inflammation-related peptides, such as bradykinin (BK) and its antagonist HOE-140, enables the understanding of the impact of such molecules in a relevant AD model.

RESULTS

We performed a global gene expression analysis on transgenic neurospheres treated with BK and HOE-140. To validate the microarray data, quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) was performed on 8 important genes related to the immune response in AD such as CCL12, CCL5, CCL3, C3, CX3CR1, TLR2 and TNF alpha and Iba-1. Furthermore, comparative analysis of the transcriptional profiles was performed between treatments, including gene ontology and reactome enrichment, construction and analysis of protein-protein interaction networks and, finally, comparison of our data with human dataset from AD patients. The treatments affected the expression levels of genes mainly related to microglia-mediated neuroinflammatory responses, with BK promoting an increase in the expression of genes that enrich processes, biological pathways, and cellular components related to immune dysfunction, neurodegeneration and cell cycle. B2 receptor inhibition by HOE-140 resulted in the reduction of AD-related anomalies caused in this system.

CONCLUSIONS

BK is an important immunomodulatory agent and enhances the immunological changes identified in transgenic neurospheres carrying the genetic load of AD. Bradykinin treatments modulate the expression rates of genes related to microglia-mediated neuroinflammation. Inhibiting bradykinin activity in Alzheimer's disease may slow disease progression.

Hiding in plain sight: Do recruited dendritic cells surround amyloid plaques in Alzheimer's disease?

Over the past decade, human genome-wide association and expression studies have strongly implicated dysregulation of the innate immune system in the pathogenesis of Alzheimer's disease (AD). Single cell mRNA sequencing studies have identified innate immune cell subtypes that are minimally present in normal healthy brain, but whose numbers greatly increase in association with AD pathology. These AD pathology-associated immune cells are putatively the locus for the immune-related AD risk. While the prevailing view is that these immune cells arise from transformation of resident brain microglia, studies across several decades and using multiple techniques and strategies suggest instead that the pathology-associated immune cells are bone-marrow derived hematopoietic cells that are recruited into brain. We critically review this translational literature, emphasizing the strengths and limitations of techniques used to address recruitment and the experimental designs employed. We conclude that the aggregate evidence points toward recruitment into brain of innate immune cells of the myeloid dendritic cell lineage. Recruitment of dendritic cells and their role in AD pathogenesis has broad implications for our understanding of the etiology and pathobiology of AD that impact the strategies to develop new, immune system-targeted therapeutics for this devastating disease.

Causal relationship between circulating immune cells and the risk of Alzheimer's disease: A Mendelian randomization study

BACKGROUND

Increasing evidence has shown a link between immune cells and Alzheimer's disease (AD). Comprehensive two-sample Mendelian randomization (MR) analysis was performed to determine the causal association between 731 immune cell signatures and AD in this study.

METHODS

We extracted genetic variants of 731 immune cell traits and AD from the publicly available GWAS dataset. The immune features included median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC) and morphological parameters (MP). The inverse variance weighted (IVW) method was the main MR analysis method, and sensitivity analyses were used to validate the robustness, heterogeneity and horizontal pleiotropy of the results.

RESULTS

After FDR adjustment, seven immune phenotypes were found to be associated significantly with AD risk: HLA DR on CD33-HLA DR+ (OR = 0.938, PFDR = 0.001), Secreting Treg %CD4 (OR = 0.972, PFDR = 0.021), HLA DR+T cell AC (OR = 0.928, PFDR = 0.041), Activated & resting Treg % CD4 Treg (OR = 1.031, PFDR = 0.002), CD33 on CD33dim HLA DR+CD11b+ (OR = 1.025, PFDR = 0.025), CD33 on CD14+monocyte (OR = 1.026, PFDR = 0.027) and CD33 on CD66b++myeloid cell (OR = 1.027, PFDR = 0.036).

CONCLUSIONS

These findings demonstrated seven immune phenotypes were significantly associated with AD risk. This may provide researchers with a new perspective in exploring the biological mechanisms of AD and may lead to the exploration of earlier treatment.

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.

Reduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice

Background

Working memory deficits in Alzheimer's disease (AD) are linked to impairments in the retrieval of stored memory information. However, research on the mechanism of impaired working memory retrieval in Alzheimer's disease is still lacking.

Objective

The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval. The purpose of this study is to investigate the functional interactions and information transmission between mPFC and MD in the AD model.

Methods

We recorded local field potentials from mPFC and MD while the mice (APP/PS1 transgenic model and control) performed a T-maze spatial working memory task. The temporal dynamics of oscillatory activity and bidirectional information flow between mPFC and MD were assessed during the task phases.

Results

We mainly found a significant decrease in theta flow from mPFC to MD in APP/PS1 mice during retrieval.

Conclusions

Our results indicate an important role of the mPFC-MD input for retrieval and the disrupted information transfer from mPFC to MD may be the underlying mechanism of working memory deficits in APP/PS1 mice.

Identification of genes related to glucose metabolism and analysis of the immune characteristics in Alzheimer's disease

OBJECTIVE

Glucose metabolism plays a crucial role in the progression of Alzheimer's disease (AD). The purpose of this study is to identify genes related to glucose metabolism in AD by bioinformatics, construct an early AD prediction model from the perspective of glucose metabolism, and analyze the characteristics of immune cell infiltration.

METHODS

AD-related modules and genes were screened by weighted gene co-expression network analysis (WGCNA). The GO and KEEG enrichment analysis were used to explore the potential biological functions of glucose metabolism related genes (GMRGs) in AD. The Least Absolute Shrinkage Selection Operator (LASSO) method was used to construct an early AD prediction model based on GMRGs. Then, the receiver operating characteristic curve (ROC) and nomogram were introduced to evaluate the effectiveness of this model. Finally, CIBERSORT and single-cell analysis were applied for illustrating the immune characteristics in AD patients.

RESULTS

A total of 462 differential expressed genes (DEGs) were obtained between Non-Alzheimer's disease (ND,) and AD groups. The genes in the blue module had the highest correlation with AD by WGCNA analysis. We found 18 intersected genes among DEGs, blue model genes and GMRGs according to the Venn diagram. The GO and KEEG enrichment analysis showed that these 18 genes were mainly involved in the production of metabolites and energy, glycolysis, amino acid biosynthesis and so on. The early AD prediction model including ENO2, TPI1, AEBP1, HERC1, PCSK1, PREPL, SLC25A4, UQCRC2, CHST6, DDIT4, ACSS1 and SUCLA2 was constructed by LASSO analysis. The area under the curve (AUC) of this model in brain tissues was 0.942. Then, we draw the nomogram of this model and the C-index was 0.942. The model was further validated in blood samples and the AUC was 0.644. Immune cell infiltration analysis showed that the proportion of plasma cells, T cells follicular helper and activated NK cells in AD group were significantly lower than ND group, while the proportion of M1 macrophages, neutrophils, T cells CD4 naive and γ-δ T cells was significantly increased when compared with the ND group. Additionally, the specific GMRGs such as ENO2, DDIT4, and SUCLA2 are significantly correlated with certain immune cells such as plasma cells, follicular helper T cells, and M1 macrophages. Single-cell analysis results suggested that the increased macrophages in AD was associated with the up-regulation of AEBP1, DDIT4 and ACSS1.

CONCLUSIONS

The diagnosis model based on the twelve GMRGs has strong predictive ability and can be used as early diagnosis biomarkers for AD. In addition, these GMRGs closely associate with AD development by influencing the glucose metabolism of immune cells.

Transcriptomics Meta-Analysis Reveals Phagosome and Innate Immune System Dysfunction as Potential Mechanisms in the Cortex of Alzheimer's Disease Mouse Strains

Immune-related pathways can affect the immune system directly, such as the chemokine signaling pathway, or indirectly, such as the phagosome pathway. Alzheimer's disease (AD) is reportedly associated with several immune-related pathways. However, exploring its underlying mechanism is challenging in animal studies because AD mouse strains differentially express immune-related pathway characteristics. To overcome this problem, we performed a meta-analysis to identify significant and consistent immune-related AD pathways that are expressed in different AD mouse strains. Next-generation RNA sequencing (RNA-seq) and microarray datasets for the cortex of AD mice from different strains such as APP/PSEN1, APP/PS2, 3xTg, TREM, and 5xFAD were collected from the NCBI GEO database. Each dataset's quality control and normalization were already processed from each original study source using various methods depending on the high-throughput analysis platform (FastQC, median of ratios, RMA, between array normalization). Datasets were analyzed using DESeq2 for RNA-seq and GEO2R for microarray to identify differentially expressed (DE) genes. Significantly DE genes were meta-analyzed using Stouffer's method, with significant genes further analyzed for functional enrichment. Ten datasets representing 20 conditions were obtained from the NCBI GEO database, comprising 116 control and 120 AD samples. The DE analysis identified 284 significant DE genes. The meta-analysis identified three significantly enriched immune-related AD pathways: phagosome, the complement and coagulation cascade, and chemokine signaling. Phagosomes-related genes correlated with complement and immune system. Meanwhile, phagosomes and chemokine signaling genes overlapped with B cells receptors pathway genes indicating potential correlation between phagosome, chemokines, and adaptive immune system as well. The transcriptomic meta-analysis showed that AD is associated with immune-related pathways in the brain's cortex through the phagosome, complement and coagulation cascade, and chemokine signaling pathways. Interestingly, phagosome and chemokine signaling pathways had potential correlation with B cells receptors pathway.

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Objectives

Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.

Methods

Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.

Results

Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.

Conclusion

A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Comprehensive analysis of immune-related genes associated with the microenvironment of patients with unexplained infertility

Background

Disturbances in immunological responses and modulation lead to implantation and pregnancy failure and might be involved in the pathogenesis of infertility. This project aimed to screen and identify immune-related genes as potential biomarkers for treatment.

Methods

Gene expression profiles were obtained from Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were screened using GEO 2R to explore potential biomarkers. Protein-protein interaction (PPI) network analysis and functional enrichment analysis were applied to explore possible mechanisms. The deconvolution algorithm [referred to as Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)] was employed to assess tissue-infiltrating immune cells. Western blot analysis and immunohistochemistry (IHC) were conducted for determination of protein levels.

Results

In this research, we identified 24 candidate immune-related DEGs via combined DEGs and functional analysis. We also found that the ratio of M0 macrophages and resting mast cells was higher in infertile group (P<0.05), whereas the amounts of activated natural killer (NK) cells was significantly lower compared with the control group (P<0.05). Furthermore, we evaluated the relationship between immune cells and candidate genes and found that 17 genes were related to M0 macrophages, resting mast cells, or activated NK cells. The genes CD40, PRF1, and EDN3 were chosen based on validation from independent datasets. Finally, our clinical samples confirmed the expression of the 3 genes.

Conclusions

The study recognized 3 genes that are signatures and could be potential biomarkers for unexplained infertility. These genes might guide the immunotherapy of these patients and become new treatment targets.

Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer's Disease

BACKGROUND

The underlying pathogenic genes and effective therapeutic agents of Alzheimer's disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models.

OBJECTIVE

To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy.

METHODS

The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored.

RESULTS

A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD.

CONCLUSIONS

The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.

Dual-Stream Subspace Clustering Network for revealing gene targets in Alzheimer's disease

The rapid development of scRNA-seq technology in recent years has enabled us to capture high-throughput gene expression profiles at single-cell resolution, reveal the heterogeneity of complex cell populations, and greatly advance our understanding of the underlying mechanisms in human diseases. Traditional methods for gene co-expression clustering are limited to discovering effective gene groups in scRNA-seq data. In this paper, we propose a novel gene clustering method based on convolutional neural networks called Dual-Stream Subspace Clustering Network (DS-SCNet). DS-SCNet can accurately identify important gene clusters from large scales of single-cell RNA-seq data and provide useful information for downstream analysis. Based on the simulated datasets, DS-SCNet successfully clusters genes into different groups and outperforms mainstream gene clustering methods, such as DBSCAN and DESC, across different evaluation metrics. To explore the biological insights of our proposed method, we applied it to real scRNA-seq data of patients with Alzheimer's disease (AD). DS-SCNet analyzed the single-cell RNA-seq data with 10,850 genes, and accurately identified 8 optimal clusters from 6673 cells. Enrichment analysis of these gene clusters revealed functional signaling pathways including the ILS signaling, the Rho GTPase signaling, and hemostasis pathways. Further analysis of gene regulatory networks identified new hub genes such as ELF4 as important regulators of AD, which indicates that DS-SCNet contributes to the discovery and understanding of the pathogenesis in Alzheimer's disease.

Transcriptomic analysis identifies shared biological foundations between ischemic stroke and Alzheimer's disease

AIM

Alzheimer's disease (AD) and ischemic stroke (IS), two major neurological diseases, are suggested to be associated in clinical and pathophysiological levels. Previous studies have provided some insights into the possible genetic mechanisms behind the correlation between AD and IS, but this issue is still not clear. We implemented transcriptomic analysis to detect common hub genes and pathways to help promote the understanding of this issue.

MATERIALS AND METHODS

Four gene expression profiling datasets (GSE16561, GSE58294, GSE63060, and GSE63061) of peripheral whole blood, which contain 108 IS samples, 284 AD samples, and 285 matched controls, were employed to detect differentially expressed genes (DEGs) for AD and IS, which were further analyzed for shared biological pathways, candidate drugs, and transcription factors. Protein-protein interaction (PPI) network and drug-target interaction analysis were applied to identify hub genes and drug targets, respectively. Result verification was done with other independent datasets (GSE37587, GSE46480, and GSE140829). The difference in proportions of various immune cells in the peripheral blood of AD and IS patients were evaluated using CIBERSORT.

RESULTS

We identified 74 DEGs and 18 biological processes with statistical significance shared by AD and IS, 9 of which were immune-related pathways. Five hub genes scored high in the topological analysis of the PPI network, and we also found eight drug target genes and candidate drugs which were associated with AD and IS. As for immunological changes, an increase in the proportion of M0 macrophages was found in the peripheral circulation of both AD and IS patients, and SOD1 expression was significantly correlated with this change.

CONCLUSION

Collectively, the common DEGs and shared pathways found in this study suggest a potential shared etiology between AD and IS, behind which immune system, particularly the M0 macrophage elevation, might have important roles. While, the shared hub genes, potential therapeutic gene targets and drugs reported in this study provide promising treatment strategies for AD and IS.

Composition of the infiltrating immune cells in the brain of healthy individuals: effect of aging

Immune cells infiltrating the central nervous system (CNS) are involved in the defense against invading microbes as well as in the pathogenesis of neuroinflammatory diseases. In these conditions, the presence of several types of immune and inflammatory cells have been demonstrated. However, some studies have also reported low amounts of immune cells that have been detected in the CNS of healthy individuals, but the cell types present have not been systematically analyzed. To do this, we now used brain samples from The Genotype- Tissue Expression (GTEx) project to analyze the relative abundance of 22 infiltrating leukocyte types using a digital cytometry tool (CIBERSORTx). To characterize cell proportions in different parts of the CNS, samples from 13 different anatomic brain regions were used. The data obtained demonstrated that several leukocyte types were present in the CNS. Six leukocyte types (CD4 memory resting T cells, M0 macrophages, plasma cells, CD8 T cells, CD4 memory activated T cells, and monocytes) were present with a proportion higher than 0.05, i.e. 5%. These six cell types were present in most brain regions with only insignificant variation. A consistent association with age was seen with monocytes, CD8 T cells, and follicular helper T cells. Taken together, these data show that several infiltrating immune cell types are present in the non-diseased CNS tissue and that the proportions of infiltrating cells are affected by age in a manner that is consistent with literature on immunosenecence and inflammaging.

Integrated analysis and identification of hub genes as novel biomarkers for Alzheimer’s disease

Alzheimer’s disease (AD) is an intractable and progressive neurodegenerative disorder that can lead to severe cognitive decline, impaired speech, short-term memory loss, and finally an inability to function in daily life. For patients, their families, and even all of society, AD can impart great emotional pressure and economic costs. Therefore, this study aimed to investigate potential diagnostic biomarkers of AD. Using the Gene Expression Omnibus (GEO) database, the expression profiles of genes were extracted from the GSE5281, GSE28146, and GSE48350 microarray datasets. Then, immune-related genes were identified by the intersections of differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA), were performed. Subsequently, random forest models and least absolute shrinkage and selection operator regression were used to further screen hub genes, which were then validated using receiver operating characteristic (ROC) curve analysis. Finally, 153 total immune-related DEGs were identified in relation to AD. DO analysis of these immune-related DEGs showed that they were enriched in “lung disease,” “reproductive system disease,” and “atherosclerosis.” Single GSEA of hub genes showed that they were particularly enriched in “oxidative phosphorylation.” ROC analysis of AGAP3 yielded an area under the ROC curve of 0.878 for GSE5281, 0.727 for GSE28146, and 0.635 for GSE48350. Moreover, immune infiltration analysis demonstrated that AGAP3 was related to follicular helper T cells, naïve CD4 T cells, naïve B cells, memory B cells, macrophages M0, macrophages M1, macrophages M2, resting natural killer (NK) cells, activated NK cells, monocytes, neutrophils, eosinophils, and activated mast cells. These results indicate that identifying immune-related DEGs might enhance the current understanding of the development and prognosis of AD. Furthermore, AGAP3 not only plays a vital role in AD progression and diagnosis but could also serve as a valuable target for further research on AD.

Differential microRNA expression analyses across two brain regions in Alzheimer's disease

Dysregulation of microRNAs (miRNAs) is involved in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease (AD). Hitherto, sample sizes from differential miRNA expression studies in AD are exceedingly small aggravating any biological inference. To overcome this limitation, we investigated six candidate miRNAs in a large collection of brain samples. Brain tissue was derived from superior temporal gyrus (STG) and entorhinal cortex (EC) from 99 AD patients and 91 controls. MiRNA expression was examined by qPCR (STG) or small RNA sequencing (EC). Brain region-dependent differential miRNA expression was investigated in a transgenic AD mouse model using qPCR and FISH. Total RNA sequencing was used to assess differential expression of miRNA target genes. MiR-129-5p, miR-132-5p, and miR-138-5p were significantly downregulated in AD vs. controls both in STG and EC, while miR-125b-5p and miR-501-3p showed no evidence for differential expression in this dataset. In addition, miR-195-5p was significantly upregulated in EC but not STG in AD patients. The brain region-specific pattern of miR-195-5p expression was corroborated in vivo in transgenic AD mice. Total RNA sequencing identified several novel and functionally interesting target genes of these miRNAs involved in synaptic transmission (GABRB1), the immune-system response (HCFC2) or AD-associated differential methylation (SLC16A3). Using two different methods (qPCR and small RNA-seq) in two separate brain regions in 190 individuals we more than doubled the available sample size for most miRNAs tested. Differential gene expression analyses confirm the likely involvement of miR-129-5p, miR-132-5p, miR-138-5p, and miR-195-5p in AD pathogenesis and highlight several novel potentially relevant target mRNAs.

Identification and immunological characterization of cuproptosis-related molecular clusters in Alzheimer's disease

Introduction

Alzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.

Methods

Based on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.

Results

The dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.

Conclusion

Our study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.

Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease

Alzheimer's disease (AD), the most common neurodegenerative disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. Meanwhile, abnormalities in iron metabolism have been demonstrated in patients and mouse models with AD. Therefore, this study sought to find hub genes based on iron metabolism that can influence the diagnosis and treatment of AD. First, gene expression profiles were downloaded from the GEO database, including non-demented (ND) controls and AD samples. Fourteen iron metabolism-related gene sets were downloaded from the MSigDB database, yielding 520 iron metabolism-related genes. The final nine hub genes associated with iron metabolism and AD were obtained by differential analysis and WGCNA in brain tissue samples from GSE132903. GO analysis revealed that these genes were mainly involved in two major biological processes, autophagy and iron metabolism. Through stepwise regression and logistic regression analyses, we selected four of these genes to construct a diagnostic model of AD. The model was validated in blood samples from GSE63061 and GSE85426, and the AUC values showed that the model had a relatively good diagnostic performance. In addition, the immune cell infiltration of the samples and the correlation of different immune factors with these hub genes were further explored. The results suggested that these genes may also play an important role in immunity to AD. Finally, eight drugs targeting these nine hub genes were retrieved from the DrugBank database, some of which were shown to be useful for the treatment of AD or other concomitant conditions, such as insomnia and agitation. In conclusion, this model is expected to guide the diagnosis of patients with AD by detecting the expression of several genes in the blood. These hub genes may also assist in understanding the development and drug treatment of AD.