Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases

Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity

The comprehensive genome-wide nature of transcriptome studies in Alzheimer's disease (AD) should provide a reliable description of disease molecular states. However, the genes and molecular systems nominated by transcriptomic studies do not always overlap. Even when results do align, it is not clear if those observations represent true consensus across many studies. A couple of sources of variation have been proposed to explain this variability, including tissue-of-origin and cohort type, but its basis remains uncertain. To address this variability and extract reliable results, we utilized all publicly available blood or brain transcriptomic datasets of AD, comprised of 24 brain studies with 4007 samples from six different brain regions, and eight blood studies with 1566 samples. We identified a consensus of AD-associated genes across brain regions and AD-associated gene-sets across blood and brain, generalizable machine learning and linear scoring classifiers, and significant contributors to biological diversity in AD datasets. While AD-associated genes did not significantly overlap between blood and brain, our findings highlighted 15 dysregulated processes shared across blood and brain in AD. The top five most significantly dysregulated processes were DNA replication, metabolism of proteins, protein localization, cell cycle, and programmed cell death. Conversely, addressing the discord across studies, we found that large-scale gene co-regulation patterns can account for a significant fraction of variability in AD datasets. Overall, this study ranked and characterized a compilation of genes and molecular systems consistently identified across a large assembly of AD transcriptome studies in blood and brain, providing potential candidate biomarkers and therapeutic targets.

CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease

BACKGROUND

Sepsis, a life-threatening condition driven by dysregulated host responses to infection, is associated with long-term cognitive impairments resembling Alzheimer's disease (AD). However, the molecular mechanisms linking sepsis-induced cognitive dysfunction and AD remain unclear. We hypothesized that shared genetic pathways underlie cognitive deficits in both conditions.

METHODS

Cecal ligation and puncture (CLP) in C57BL/6 J mice modeled sepsis-induced cognitive decline and amyloid pathology. Brain tissue datasets (GSE33000 for AD; GSE135838 for sepsis) were analyzed via Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and functional enrichment. Key genes were validated through ROC analysis, immune infiltration profiling, and in vivo/in vitro experiments.

RESULTS

Sepsis accelerated cognitive decline and AD-like pathology in mice. Bioinformatics identified CLIC1 and IFITM2 as co-diagnostic genes linked to immune dysregulation in both sepsis and AD. Immune infiltration revealed reduced neutrophils/NK cells, M1 macrophage polarization, and naïve-to-memory B cell shifts in sepsis versus AD. CLIC1 and IFITM2 were upregulated in CLP mice and cytokine-stimulated human cerebral endothelial cells, aligning with bioinformatics predictions.

CONCLUSION

CLIC1 and IFITM2, pivotal in immune cell activation, emerged as shared biomarkers of sepsis-related cognitive impairment and AD. These findings highlight immune-driven molecular intersections in cognitive deficits, offering novel targets for mechanistic research and therapeutic development.

Altered immune response is associated with sex difference in vulnerability to Alzheimer's disease in human prefrontal cortex

Alzheimer's disease (AD) is a neurodegenerative disorder with a higher risk incidence in females than in males, and there are also differences in AD pathophysiology between sexes. The role of sex in the pathogenesis of AD may be crucial, yet the cellular and molecular basis remains unclear. Here, we performed a comprehensive analysis using four public transcriptome datasets of AD patients and age-matched control individuals in prefrontal cortex, including bulk transcriptome (295 females and 402 males) and single-nucleus RNA sequencing (snRNA-seq) data (224 females and 219 males). We found that the transcriptomic profile in female control was similar to those in AD. To characterize the key features associated with both the pathogenesis of AD and sex difference, we identified a co-expressed gene module that positively correlated with AD, sex, and aging, and was also enriched with immune-associated pathways. Using snRNA-seq datasets, we found that microglia (MG), a resident immune cell in the brain, demonstrated substantial differences in several aspects between sexes, such as an elevated proportion of activated MG, altered transcriptomic profile and cell-cell interaction between MG and other brain cell types in female control. Additionally, genes upregulated in female MG, such as TLR2, MERTK, SPP1, SLA, ACSL1, and FKBP5, had high confidence to be identified as biomarkers to distinguish AD status, and these genes also interacted with some approved drugs for treatment of AD. These findings underscore the altered immune response in female is associated with sex difference in susceptibility to AD, and the necessity of considering sex factors when developing AD biomarkers and therapeutic strategies, providing a scientific basis for further in-depth studies on sex differences in AD.

Integration of Single-Cell and Spatial Transcriptomic Data Reveals Spatial Architecture and Potential Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the gradual loss of neurons and the accumulation of amyloid plaques and neurofibrillary tangles. Despite advancements in the understanding of AD's pathophysiology, the cellular organization and interactions in the prefrontal cortex (PFC) remain elusive. Eight single-cell RNA sequencing (scRNA-seq) datasets from both normal controls and individuals with AD were harmonized. Stringent preprocessing protocols were implemented to uphold dataset integrity. Unsupervised clustering and annotation revealed 22 distinct cell clusters corresponding to 19 unique cell types. The spatial architecture of the PFC region was constructed using the CARD tool. Further analyses encompassed trajectory examination of Oligodendrocyte subtypes, evaluation of regulon activity scores, and spot clustering within white matter regions (WM). Differential expression analysis and functional enrichment assays unveiled molecular signatures linked to AD progression and were validated using microarray data sourced from neurodegenerative disorder patients. Our investigation employs scRNA-seq and spatial transcriptomics to uncover the cellular atlas and spatial architecture of the human PFC in AD. Moreover, our results indicate that Oligodendrocytes are more prevalent in AD patients, showcasing diverse subtypes and spatial organization within WM regions. Each subtype appears to be associated with distinct biological processes and transcriptional regulators, shedding light on their involvement in AD pathology. Notably, the Oligodendrocyte_C6 subtype is linked to neurological damage in AD patients, characterized by heightened expression of genes involved in cell-cell connections, cell membrane stability, and myelination. Additionally, 12 target genes regulated by NFIA were identified, which are upregulated in AD patients and associated with disease progression. Elevated PLXDC2 expression in peripheral blood was also identified, suggesting its potential as a non-invasive biomarker for early AD detection. Our study provides novel insights into the role of Oligodendrocytes in AD and highlights the potential of PLXDC2 as a blood biomarker for non-invasive diagnosis and monitoring of AD patients.

Multiomics from Alzheimer's Brains and Mesenchymal Stem Cell-Derived Extracellular Vesicles Identifies Therapeutic Potential of Specific Subpopulations to Target Mitochondrial Proteostasis

Background

Alzheimer's disease (AD) is characterized by complex molecular alterations that complicate its pathogenesis and contribute to the lack of effective treatments. Mesenchymal stem cell-derived extracellular vesicles (EVs) have shown promise in AD models, but results across different EV subpopulations remain inconsistent.

Objectives

This study investigates proteomic and transcriptomic data from publicly available postmortem AD brain datasets to identify molecular changes at both the gene and protein levels. These findings are then compared with the proteomes of various EV subpopulations, differing in size and distribution, to determine the most promising subtype for compensating molecular degeneration in AD.

Design

We conducted a comprehensive analysis of 788 brain samples, including 481 AD cases and 307 healthy controls, examining protein and mRNA levels to uncover AD-associated molecular changes. These findings were then compared with the proteomes of different EV subpopulations to identify potential therapeutic candidates.

Methods

A multi-omics approach was employed, integrating proteomic and transcriptomic data analysis, miRNA and transcription factor profiling, protein-protein network construction, hub gene identification, and enrichment analyses. This approach aimed to explore molecular changes in AD brains and pinpoint the most relevant EV subpopulations for therapeutic intervention.

Results

We identified common alterations in the cAMP signaling pathway and coagulation cascade at both the protein and mRNA levels. Distinct changes in energy metabolism were observed at the protein level but not at the mRNA level. A specific EV subtype, characterized by a broader size distribution obtained through high-speed centrifugation, was identified as capable of compensating for dysregulated mitochondrial proteostasis in AD brains. Network biology analyses further highlighted potential regulators of key therapeutic proteins within this EV subtype.

Conclusion

This study underscores the critical role of proteomic alterations in AD and identifies a promising EV subpopulation, enriched with proteins targeting mitochondrial proteostasis, as a potential therapeutic strategy for AD.

The Osgin Gene Family: Underexplored Yet Essential Mediators of Oxidative Stress

The Osgin gene family consists of two members, Osgin1 and Osgin2, involved in the cellular oxidative stress response. While many members of this essential cellular pathway have been extensively characterized, the Osgin gene family, despite its broad phylogenetic distribution, has received far less attention. Here, we review published articles and open-source databases to synthesize the current research on the evolutionary history, structure, biochemical and physiological functions, expression patterns, and role in disease of the Osgin gene family. Although Osgin displays broad spatiotemporal expression during development and adulthood, there is ambiguity regarding the cellular functions of the OSGIN proteins. A recent study identified OSGIN-1 as a flavin-dependent monooxygenase, but the biochemical role of OSGIN-2 has not yet been defined. Moreover, while the Osgin genes are implicated as mediators of cell proliferation, apoptosis, and autophagy, these functions have not been connected to the enzymatic classification of OSGIN. Misregulation of Osgin expression has long been associated with various disease states, yet recent analyses highlight the mechanistic role of OSGIN in pathogenesis and disease progression, underscoring the therapeutic potential of targeting OSGIN. In light of these findings, we suggest further avenues of research to advance our understanding of this essential, yet underexplored, gene family.

Dissecting genetic regulation of metabolic coordination

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

Integrating Bulk and Single-Cell Transcriptomic Data to Identify Ferroptosis-Associated Inflammatory Gene in Alzheimer's Disease

Background

Ferroptosis is a form of programmed cell death triggered by iron-dependent lipid peroxidation, characterized by iron accumulation and elevated reactive oxygen species (ROS), leading to cell membrane damage. It is associated with a variety of diseases. However, the cellular and molecular links between ferroptosis, immune inflammation, and the brain-peripheral blood axis in Alzheimer's disease (AD) remain unclear.

Methods

We integrated bulk RNA-seq data from AD brain tissue and peripheral blood and refined the screening of AD candidate genes through differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and other approaches. Additionally, we analyzed single-cell RNA-seq (scRNA-seq) data from AD patients' brain tissue and peripheral blood, combined with scRNA-seq data from experimental autoimmune encephalomyelitis (EAE) mouse brain tissue. This enabled us to explore AD-related molecular mechanisms from a cell-type-specific perspective. Finally, candidate genes were validated in ferroptosis models using reverse transcription quantitative PCR (RT-qPCR) and immunofluorescence methods.

Results

Bulk RNA-seq analysis identified SLC11A1, an inflammatory gene associated with AD. Single-cell RNA-seq analysis further revealed that SLC11A1 expression was significantly elevated in the pro-inflammatory (M1-type) microglia and peripheral blood monocytes in AD. Moreover, we identified a microglial subpopulation in AD M1-type microglia that was highly associated with ferroptosis. This subpopulation simultaneously expressed characteristic markers of peripheral blood monocytes, suggesting that these cells may originate from peripheral blood monocytes, thereby triggering neuroinflammation through the ferroptosis pathway. Cell experiments confirmed that SLC11A1 was significantly upregulated in inflammatory microglia induced by ferroptosis.

Conclusion

This study reveals the key role of SLC11A1 in AD, particularly in the context of ferroptosis and immune inflammation. It provides a novel molecular mechanistic perspective and offers potential targets for future therapeutic strategies.

Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental analyses

One of the underlying mechanisms of epilepsy (EP), a brain disease characterized by recurrent seizures, is considered to be cell death. Disulfidptosis, a proposed novel cell death mechanism, is thought to play a part in the pathogenesis of epilepsy, but the exact role is unclear. The gene expression omnibus series (GSE) 33000 and GSE63808 datasets were used to search for differentially expressed disulfidptosis-related molecules (DE-DRMs). A correlation between the DE-DRMs was discovered. Individuals with epilepsy were then used to investigate molecular clusters based on the expression of DE-DRMs. Following that, the best machine learning model which is validated by GSE143272 dataset and predictor molecules were identified. The correlation between predictive molecules and clinical traits was determined. Based on the in vitro and in vivo seizures models, experimental analyses were applied to verify the DE-DRMs expressions and the correlation between them. Nine molecules were identified as DE-DRMs: glycogen synthase 1 (GYS1), solute carrier family 3 member 2 (SLC3A2), solute carrier family 7 member 11 (SLC7A11), NADH:ubiquinone oxidoreductase core subunit S1 (NDUFS1), 3-oxoacyl-ACP synthase, mitochondrial (OXSM), leucine rich pentatricopeptide repeat containing (LRPPRC), NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), NUBP iron‑sulfur cluster assembly factor, mitochondrial (NUBPL), and NCK associated protein 1 (NCKAP1). NDUFS1 interacted with NDUFA11, NUBPL, and LRPPRC, while SLC3A2 interacted with SLC7A11. The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. The experimental analyses suggested an upregulated SLC7A11 expression, an increased number of formed SLC3A2 and SLC7A11 complexes, and a decreased number of formed NDUFS1 and NDUFA11 complexes. This study provides previously undocumented evidence of the relationship between disulfidptosis and EP. In addition to suggesting that SLC7A11 may be a specific DRM for EP, this research demonstrates the alterations in two disulfidptosis-related protein complexes: SLC7A11-SLC3A2 and NDUFS1-NDUFA11.

Chronic pain-induced methylation in the prefrontal cortex targets gene networks associated with cognition and Alzheimer's disease

Chronic pain is prevalent among aging adults. Epidemiologic evidence has demonstrated that individuals with chronic pain have accelerated memory decline and increased probability of dementia. Neurophysiologic, molecular, and pharmacologic hypotheses have been proposed to explain the relationship between chronic pain and cognitive decline, but there remains currently limited evidence supporting any of these. Here, we integrate multi-omic data across human cohorts and rodent species and demonstrate that methylation in the prefrontal cortex induced by chronic pain specifically targets transcriptional networks associated with cognitive ability, memory, and Alzheimer's disease in humans. We validate this with multiple independent data sets and identify cortical microglia as a likely mechanism by which chronic pain can increase dementia risk. Our analyses support the molecular hypothesis for the role of chronic pain in cognitive decline and identifies several potential therapeutic targets.

Novel cuproptosis metabolism-related molecular clusters and diagnostic signature for Alzheimer's disease

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with no effective treatments available. There is growing evidence that cuproptosis contributes to the pathogenesis of this disease. This study developed a novel molecular clustering based on cuproptosis-related genes and constructed a signature for AD patients.

Methods

The differentially expressed cuproptosis-related genes (DECRGs) were identified using the DESeq2 R package. The GSEA, PPI network, GO, KEGG, and correlation analysis were conducted to explore the biological functions of DECRGs. Molecular clusters were performed using unsupervised cluster analysis. Differences in biological processes between clusters were evaluated by GSVA and immune infiltration analysis. The optimal model was constructed by WGCNA and machine learning techniques. Decision curve analysis, calibration curves, receiver operating characteristic (ROC) curves, and two additional datasets were employed to confirm the prediction results. Finally, immunofluorescence (IF) staining in AD mice models was used to verify the expression levels of risk genes.

Results

GSEA and CIBERSORT showed higher levels of resting NK cells, M2 macrophages, naïve CD4+ T cells, neutrophils, monocytes, and plasma cells in AD samples compared to controls. We classified 310 AD patients into two molecular clusters with distinct expression profiles and different immunological characteristics. The C1 subtype showed higher abundance of cuproptosis-related genes, with higher proportions of regulatory T cells, CD8+T cells, and resting dendritic cells. We subsequently constructed a diagnostic model which was confirmed by nomogram, calibration, and decision curve analysis. The values of area under the curves (AUC) were 0.738 and 0.931 for the external datasets, respectively. The expression levels of risk genes were further validated in mouse brain samples.

Conclusion

Our study provided potential targets for AD treatment, developed a promising gene signature, and offered novel insights for exploring the pathogenesis of AD.

Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration

The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

Unveiling disulfidptosis-related biomarkers and predicting drugs in Alzheimer's disease

Alzheimer's disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer's disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer's disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer's disease.

Excitotoxicity, Oxytosis/Ferroptosis, and Neurodegeneration: Emerging Insights into Mitochondrial Mechanisms

Mitochondrial dysfunction plays a pivotal role in the development of age-related diseases, particularly neurodegenerative disorders. The etiology of mitochondrial dysfunction involves a multitude of factors that remain elusive. This review centers on elucidating the role(s) of excitotoxicity, oxytosis/ferroptosis and neurodegeneration within the context of mitochondrial bioenergetics, biogenesis, mitophagy and oxidative stress and explores their intricate interplay in the pathogenesis of neurodegenerative diseases. The effective coordination of mitochondrial turnover processes, notably mitophagy and biogenesis, is assumed to be critically important for cellular resilience and longevity. However, the age-associated decrease in mitophagy impedes the elimination of dysfunctional mitochondria, consequently impairing mitochondrial biogenesis. This deleterious cascade results in the accumulation of damaged mitochondria and deterioration of cellular functions. Both excitotoxicity and oxytosis/ferroptosis have been demonstrated to contribute significantly to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). Excitotoxicity, characterized by excessive glutamate signaling, initiates a cascade of events involving calcium dysregulation, energy depletion, and oxidative stress and is intricately linked to mitochondrial dysfunction. Furthermore, emerging concepts surrounding oxytosis/ferroptosis underscore the importance of iron-dependent lipid peroxidation and mitochondrial engagement in the pathogenesis of neurodegeneration. This review not only discusses the individual contributions of excitotoxicity and ferroptosis but also emphasizes their convergence with mitochondrial dysfunction, a key driver of neurodegenerative diseases. Understanding the intricate crosstalk between excitotoxicity, oxytosis/ferroptosis, and mitochondrial dysfunction holds potential to pave the way for mitochondrion-targeted therapeutic strategies. Such strategies, with a focus on bioenergetics, biogenesis, mitophagy, and oxidative stress, emerge as promising avenues for therapeutic intervention.

Equilibrative nucleoside transporter 3 supports microglial functions and protects against the progression of Huntington's disease in the mouse model

Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by involuntary movements, cognitive deficits, and psychiatric symptoms. Currently, there is no cure, and only limited treatments are available to manage the symptoms and to slow down the disease's progression. The molecular and cellular mechanisms of HD's pathogenesis are complex, involving immune cell activation, altered protein turnover, and disturbance in brain energy homeostasis. Microglia have been known to play a dual role in HD, contributing to neurodegeneration through inflammation but also enacting neuroprotective effects by clearing mHTT aggregates. However, little is known about the contribution of microglial metabolism to HD progression. This study explores the impact of a microglial metabolite transporter, equilibrative nucleoside transporter 3 (ENT3), in HD. Known as a lysosomal membrane transporter protein, ENT3 is highly enriched in microglia, with its expression correlated with HD severity. Using the R6/2 ENT3-/- mouse model, we found that the deletion of ENT3 increases microglia numbers yet worsens HD progression, leading to mHTT accumulation, cell death, and disturbed energy metabolism. These results suggest that the delicate balance between microglial metabolism and function is crucial for maintaining brain homeostasis and that ENT3 has a protective role in ameliorating neurodegenerative processes.

Therapeutic targeting of immunometabolism reveals a critical reliance on hexokinase 2 dosage for microglial activation and Alzheimer's progression

Neuroinflammation is a prominent feature of Alzheimer's disease (AD). Activated microglia undergo a reprogramming of cellular metabolism necessary to power their cellular activities during disease. Thus, selective targeting of microglial immunometabolism might be of therapeutic benefit for treating AD. In the AD brain, the levels of microglial hexokinase 2 (HK2), an enzyme that supports inflammatory responses by promoting glycolysis, are significantly increased. In addition, HK2 displays non-metabolic activities that extend its inflammatory role beyond glycolysis. The antagonism of HK2 affects microglial phenotypes and disease progression in a gene-dose-dependent manner. HK2 complete loss fails to improve pathology by exacerbating inflammation, while its haploinsufficiency reduces pathology in 5xFAD mice. We propose that the partial antagonism of HK2 is effective in slowing disease progression by modulating NF-κB signaling through its cytosolic target, IKBα. The complete loss of HK2 affects additional inflammatory mechanisms related to mitochondrial dysfunction.

Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning

The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD.

Application of Multiomics Approach to Investigate the Therapeutic Potentials of Stem Cell-derived Extracellular Vesicle Subpopulations for Alzheimer's Disease

Alzheimer's disease (AD) presents a complex interplay of molecular alterations, yet understanding its pathogenesis remains a challenge. In this study, we delved into the intricate landscape of proteome and transcriptome changes in AD brains compared to healthy controls, examining 788 brain samples revealing common alterations at both protein and mRNA levels. Moreover, our analysis revealed distinct protein-level changes in aberrant energy metabolism pathways in AD brains that were not evident at the mRNA level. This suggests that the changes in protein expression could provide a deeper molecular representation of AD pathogenesis. Subsequently, using a comparative proteomic approach, we explored the therapeutic potential of mesenchymal stem cell-derived extracellular vehicles (EVs), isolated through various methods, in mitigating AD-associated changes at the protein level. Our analysis revealed a particular EV-subtype that can be utilized for compensating dysregulated mitochondrial proteostasis in the AD brain. By using network biology approaches, we further revealed the potential regulators of key therapeutic proteins. Overall, our study illuminates the significance of proteome alterations in AD pathogenesis and identifies the therapeutic promise of a specific EV subpopulation with reduced pro-inflammatory protein cargo and enriched proteins to target mitochondrial proteostasis.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

Cross-Talking Pathways of Rapidly Accelerated Fibrosarcoma-1 (RAF-1) in Alzheimer's Disease

Alzheimer's disease (AD) becomes one of the main global burden diseases with the aging population. This study was to investigate the potential molecular mechanisms of rapidly accelerated fibrosarcoma-1 (RAF-1) in AD through bioinformatics analysis. Differential gene expression analysis was performed in GSE132903 dataset. We used weight gene correlation network analysis (WGCNA) to evaluate the relations among co-expression modules and construct global regulatory network. Cross-talking pathways of RAF-1 in AD were identified by functional enrichment analysis. Totally, 2700 differentially expressed genes (DEGs) were selected between AD versus non-dementia control and RAF-1-high versus low group. Among them, DEGs in turquoise module strongly associated with AD and high expression of RAF-1 were enriched in vascular endothelial growth factor (VEGF), neurotrophin, mitogen-activated protein kinase (MAPK) signaling pathway, oxidative phosphorylation, GABAergic synapse, and axon guidance. Moreover, cross-talking pathways of RAF-1, including MAPK, VEGF, neurotrophin signaling pathways, and axon guidance, were identified by global regulatory network. The performance evaluation of AUC was 84.2%. The gene set enrichment analysis (GSEA) indicated that oxidative phosphorylation and synapse-related biological processes were enriched in RAF-1-high and AD group. Our findings strengthened the potential roles of high RAF-1 level in AD pathogenesis, which were mediated by MAPK, VEGF, neurotrophin signaling pathways, and axon guidance.

Large-scale gene expression changes in APP/PSEN1 and GFAP mutation models exhibit high congruence with Alzheimer's disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder with both genetic and non-genetic causes. Animal research models are available for a multitude of diseases and conditions affecting the central nervous system (CNS), and large-scale CNS gene expression data exist for many of these. Although there are several models specifically for AD, each recapitulates different aspects of the human disease. In this study we evaluate over 500 animal models to identify those with CNS gene expression patterns matching human AD datasets. Approaches included a hypergeometric based scoring system that rewards congruent gene expression patterns but penalizes discordant gene expression patterns. The top two models identified were APP/PS1 transgenic mice expressing mutant APP and PSEN1, and mice carrying a GFAP mutation that is causative of Alexander disease, a primary disorder of astrocytes in the CNS. The APP/PS1 and GFAP models both matched over 500 genes moving in the same direction as in human AD, and both had elevated GFAP expression and were highly congruent with one another. Also scoring highly were the 5XFAD model (with five mutations in APP and PSEN1) and mice carrying CK-p25, APP, and MAPT mutations. Animals with the APOE3 and 4 mutations combined with traumatic brain injury ranked highly. Bulbectomized rats scored high, suggesting anosmia could be causative of AD-like gene expression. Other matching models included the SOD1G93A strain and knockouts for SNORD116 (Prader-Willi mutation), GRID2, INSM1, XBP1, and CSTB. Many top models demonstrated increased expression of GFAP, and results were similar across multiple human AD datasets. Heatmap and Uniform Manifold Approximation Plot results were consistent with hypergeometric ranking. Finally, some gene manipulation models, including for TYROBP and ATG7, were identified with reversed AD patterns, suggesting possible neuroprotective effects. This study provides insight for the pathobiology of AD and the potential utility of available animal models.

Investigating the Potential Shared Molecular Mechanisms between COVID-19 and Alzheimer's Disease via Transcriptomic Analysis

SARS-CoV-2 caused the COVID-19 pandemic. COVID-19 may elevate the risk of cognitive impairment and even cause dementia in infected individuals; it may accelerate cognitive decline in elderly patients with dementia, possibly in Alzheimer's disease (AD) patients. However, the mechanisms underlying the interplay between AD and COVID-19 are still unclear. To investigate the underlying mechanisms and associations between AD progression and SARS-CoV-2 infection, we conducted a series of bioinformatics research into SARS-CoV-2-infected cells, COVID-19 patients, AD patients, and SARS-CoV-2-infected AD patients. We identified the common differentially expressed genes (DEGs) in COVID-19 patients, AD patients, and SARS-CoV-2-infected cells, and these DEGs are enriched in certain pathways, such as immune responses and cytokine storms. We constructed the gene interaction network with the signaling transduction module in the center and identified IRF7, STAT1, STAT2, and OAS1 as the hub genes. We also checked the correlations between several key transcription factors and the SARS-CoV-2 and COVID-19 pathway-related genes. We observed that ACE2 expression is positively correlated with IRF7 expression in AD and coronavirus infections, and interestingly, IRF7 is significantly upregulated in response to different RNA virus infections. Further snRNA-seq analysis indicates that NRGN neurons or endothelial cells may be responsible for the increase in ACE2 and IRF7 expression after SARS-CoV-2 infection. The positive correlation between ACE2 and IRF7 expressions is confirmed in the hippocampal formation (HF) of SARS-CoV-2-infected AD patients. Our findings could contribute to the investigation of the molecular mechanisms underlying the interplay between AD and COVID-19 and to the development of effective therapeutic strategies for AD patients with COVID-19.

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning

BACKGROUND

Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis.

METHODS

In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors.

RESULTS

We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes.

CONCLUSION

This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Identification of Molecular Correlations of GSDMD with Pyroptosis inAlzheimer's Disease

AIM

An analysis of bioinformatics and cell experiments was performed to verify the relationship between gasdermin D (GSDMD), an executive protein of pyroptosis, and Alzheimer's disease (AD).

METHODS

The training set GSE33000 was utilized to identify differentially expressed genes (DEGs) in both the AD group and control group, as well as in the GSDMD protein high/low expression group. Subsequently, the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) regression analysis were conducted, followed by the selection of the key genes for the subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The association between GSDMD and AD was assessed and confirmed in the training set GSE33000, as well as in the validation sets GSE5281 and GSE48350. Immunofluorescence (IF) was employed to detect the myelin basic protein (MBP), a distinctive protein found in the rat oligodendrocytes (OLN-93 cells). A range of concentrations (1-15 μmol/L) of β-amyloid 1-42 (Aβ1-42) were exposed to the cells, and the subsequent observations were made regarding cell morphology. Additionally, the assessments were conducted to evaluate the cell viability, the lactate dehydrogenase (LDH) release, the cell membrane permeability, and the GSDMD protein expression.

RESULTS

A total of 7,492 DEGs were screened using GSE33000. Subsequently, WGCNA analysis identified 19 genes that exhibited the strongest correlation with clinical traits in AD. Additionally, LASSO regression analysis identified 13 key genes, including GSDMD, AFF1, and ATOH8. Furthermore, the investigation revealed that the key genes were associated with cellular inflammation based on GO and KEGG analyses. Moreover, the area under the curve (AUC) values for the key genes in the training and validation sets were determined to be 0.95 and 0.70, respectively. Significantly, GSDMD demonstrated elevated levels of expression in AD across both datasets. The positivity of MBP expression in cells exceeded 95%. As the concentration of Aβ1-42 action gradually escalated, the detrimental effects on cells progressively intensified, resulting in a gradual decline in cell survival rate, accompanied by an increase in lactate dehydrogenase release, cell membrane permeability, and GSDMD protein expression.

CONCLUSION

The association between GSDMD and AD has been observed, and it has been found that Aβ1-42 can induce a significant upregulation of GSDMD in OLN-93 cells. This suggests that Aβ1-42 has the potential to induce cellular pyroptosis and can serve as a valuable cellular pyroptosis model for the study of AD.

Development of a diagnostic and risk prediction model for Alzheimer's disease through integration of single-cell and bulk transcriptomic analysis of glutamine metabolism

Background

In this study, we present a novel system for quantifying glutamine metabolism (GM) to enhance the effectiveness of Alzheimer's disease (AD) diagnosis and risk prediction.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was utilized to comprehensively assess the expression patterns of GM. The WGCNA algorithm was applied to investigate the most significant genes related to GM. Subsequently, three machine learning algorithms (Boruta, LASSO, and SVM-RFE) were employed to identify GM-associated characteristic genes and develop a risk model. Patients were divided into high- and low-risk groups based on this model. Moreover, we explored biological properties, distinct signaling pathways, and immunological characteristics of AD patients at different risk levels. Finally, in vitro and in vivo models of AD were constructed to validate the characteristics of the feature genes.

Results

Both scRNA-seq and bulk transcriptomic analyses revealed increased GM activity in AD patients, specifically in certain cell subsets (pDC, Tem/Effector helper T cells (LTB), and plasma cells). Cells with higher GM scores demonstrated more significant numbers and strengths of interactions with other cell types. The WGCNA algorithm identified 360 genes related to GM, and a risk score was constructed based on nine characteristic genes (ATP13A4, PIK3C2A, CD164, PHF1, CES2, PDGFB, LCOR, TMEM30A, and PLXNA1) identified through multiple machine learning algorithms displayed reliable diagnostic efficacy for AD onset. Nomograms, calibration curves, and decision curve analysis (DCA) based on these characteristic genes provided significant clinical benefits for AD patients. High-risk AD patients exhibited higher levels of immune-related functions and pathways, increased immune cell infiltration, and elevated expressions of immune modulators. RT-qPCR analysis revealed that the majority of the nine characteristic genes were differentially expressed in AD-induced rat neurons. Knocking down PHF1 could protect against neurite loss and alleviate cell injury in AD neurons. In vivo, down-regulation of PHF1 in AD models decreases GM metabolism levels and modulates the immunoinflammatory response in the brain.

Conclusion

This comprehensive identification of gene expression patterns contributes to a deeper understanding of the underlying pathological mechanisms driving AD pathogenesis. Furthermore, the risk model based on the nine-gene signature offers a promising theoretical foundation for developing individualized treatments for AD patients.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Identification and verification of genes associated with hypoxia microenvironment in Alzheimer's disease

As the incidence of Alzheimer's disease (AD) increases year by year, more people begin to study this disease. In recent years, many studies on reactive oxygen species (ROS), neuroinflammation, autophagy, and other fields have confirmed that hypoxia is closely related to AD. However, no researchers have used bioinformatics methods to study the relationship between AD and hypoxia. Therefore, our study aimed to screen the role of hypoxia-related genes in AD and clarify their diagnostic significance. A total of 7681 differentially expressed genes (DEGs) were identified in GSE33000 by differential expression analysis and cluster analysis. Weighted gene co-expression network analysis (WGCNA) was used to detect 9 modules and 205 hub genes with high correlation coefficients. Next, machine learning algorithms were applied to 205 hub genes and four key genes were selected. Through the verification of external dataset and quantitative real-time PCR (qRT-PCR), the AD diagnostic model was established by ANTXR2, BDNF and NFKBIA. The bioinformatics analysis results suggest that hypoxia-related genes may increase the risk of AD. However, more in-depth studies are still needed to investigate their association, this article would guide the insights and directions for further research.

PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease

Introduction

Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability.

Methods

To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD.

Results

The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation.

Discussion

By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.

A beginner's guide into curated analyses of open access datasets for biomarker discovery in neurodegeneration

The discovery of surrogate biomarkers reflecting neuronal dysfunction in neurodegenerative diseases (NDDs) remains an active area of research. To boost these efforts, we demonstrate the utility of publicly available datasets for probing the pathogenic relevance of candidate markers in NDDs. As a starting point, we introduce the readers to several open access resources, which contain gene expression profiles and proteomics datasets from patient studies in common NDDs, including proteomics analyses of cerebrospinal fluid (CSF). Then, we illustrate the method for curated gene expression analyses across select brain regions from four cohorts of Parkinson disease patients (and from one study in common NDDs), probing glutathione biogenesis, calcium signaling and autophagy. These data are complemented by findings of select markers in CSF-based studies in NDDs. Additionally, we enclose several annotated microarray studies, and summarize reports on CSF proteomics across the NDDs, which the readers can utilize for translational purposes. We anticipate that this "beginner's guide" will benefit the research community in NDDs, and would serve as a useful educational tool.

Identification of novel genes for age-at-onset of Alzheimer's disease by combining quantitative and survival trait analyses

INTRODUCTION

Our understanding of the genetic predisposition for age-at-onset (AAO) of Alzheimer's disease (AD) is limited. Here, we sought to identify genes modifying AAO and examined whether any have sex-specific effects.

METHODS

Genome-wide association analysis were performed on imputed genetic data of 9219 AD cases and 10,345 controls from 20 cohorts of the Alzheimer's Disease Genetics Consortium. AAO was modeled from cases directly and as a survival outcome.

RESULTS

We identified 11 genome-wide significant loci (P < 5 × 10-8 ), including six known AD-risk genes and five novel loci, UMAD1, LUZP2, ARFGEF2, DSCAM, and 4q25, affecting AAO of AD. Additionally, 39 suggestive loci showed strong association. Twelve loci showed sex-specific effects on AAO including CD300LG and MLX/TUBG2 for females and MIR4445 for males.

DISCUSSION

Genes that influence AAO of AD are excellent therapeutic targets for delaying onset of AD. Several loci identified include genes with promising functional implications for AD.

Transcriptome analysis identifies the role of Class I histone deacetylase in Alzheimer's disease

Epigenetics modification is a process that does not change the sequence of deoxyribonucleic acid (DNA) in disease progression but can alter the genetic expression of the brain in Alzheimer's disease (AD). In this study, we deployed the weighted gene co-expression network analysis (WGCNA) to explore the role of Class I histone deacetylases (HDACs) in AD, which included HDAC1, HDAC2, HDAC3, and HDAC8. The aim of the study was to find how Class I HDACs affected AD pathology by analyzing the Gene Expression Omnibus (GEO) microarray datasets GSE33000. We found that HDAC1 and HDAC8 were more highly expressed in the cortex of AD patients than in Controls, while HDAC2 and HDAC3 were lower expressed. By WGCNA analysis, we found the blue module was associated with HDAC1 and HDAC8, and the turquoise module was related to HDAC2 and HDAC3. Functional enrichment analysis revealed that the Wnt signaling pathway and synaptic plasticity played an important role in the modification of HDAC1 and HDAC8 while gap junction and cell-cell junction were involved in the regulation of HDAC2 and HDAC3 in the disease progression of AD. By Receiver Operating Characteristics (ROC) analysis, we concluded that HDAC1 might be the most probable diagnostic biomarker of Class I HDACs for AD. Our study provided a comprehensive understanding of Class I HDACs and provided new insight into the function of HDAC1 in AD disease progression.

Common molecular signatures between coronavirus infection and Alzheimer's disease reveal targets for drug development

Cognitive decline has been reported as a common consequence of COVID-19, and studies have suggested a link between COVID-19 infection and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear. To shed light on this link, we conducted an integrated genomic analysis using a novel Robust Rank Aggregation method to identify common transcriptional signatures of the frontal cortex, a critical area for cognitive function, between individuals with AD and COVID-19. We then performed various analyses, including the KEGG pathway, GO ontology, protein-protein interaction, hub gene, gene-miRNA, and gene-transcription factor interaction analyses to identify molecular components of biological pathways that are associated with AD in the brain also show similar changes in severe COVID-19. Our findings revealed the molecular mechanisms underpinning the association between COVID-19 infection and AD development and identified several genes, miRNAs, and TFs that may be targeted for therapeutic purposes. However, further research is needed to investigate the diagnostic and therapeutic applications of these findings.

Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning

Alzheimer's disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.

Identification of TAC1 Associated with Alzheimer's Disease Using a Robust Rank Aggregation Approach

BACKGROUND

Alzheimer's disease (AD) brings heavy burden to society and family. There is an urgent need to find effective methods for disease diagnosis and treatment. The robust rank aggregation (RRA) approach that could aggregate the resulting gene lists has been widely utilized in genomic data analysis.

OBJECTIVE

To identify hub genes using RRA approach in AD.

METHODS

Seven microarray datasets in frontal cortex from GEO database were used to identify differential expressed genes (DEGs) in AD patients using RRA approach. STRING was performed to explore the protein-to-protein interaction (PPI). Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were utilized for enrichment analysis. Human Gene Connectome and Gene Set Enrichment Analysis were used for functional annotation. Finally, the expression levels of hub genes were validated in the cortex of 5xFAD mice by quantitative real-time polymerase chain reaction.

RESULTS

After RRA analysis, 473 DEGs (216 upregulated and 257 downregulated) were identified in AD samples. PPI showed that DEGs had a total of 416 nodes and 2750 edges. These genes were divided into 17 clusters, each of which contains at least three genes. After functional annotation and enrichment analysis, TAC1 is identified as the hub gene and may be related to synaptic function and inflammation. In addition, Tac1 was found downregulated in cortices of 5xFAD mice.

CONCLUSION

In the current study, TAC1 is identified as a key gene in the frontal cortex of AD, providing insight into the possible pathogenesis and potential therapeutic targets for this disease.

Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data

Alzheimer's disease (AD) is the most uncertain form of Dementia in terms of finding out the mechanism. AD does not have a vital genetic factor to relate to. There were no reliable techniques and methods to identify the genetic risk factors associated with AD in the past. Most of the data available were from the brain images. However, recently, there have been drastic advancements in the high-throughput techniques in bioinformatics. It has led to focused researches in discovering the AD causing genetic risk factors. Recent analysis has resulted in considerable prefrontal cortex data with which classification and prediction models can be developed for AD. We have developed a Deep Belief Network-based prediction model using the DNA Methylation and Gene Expression Microarray Data, with High Dimension Low Sample Size (HDLSS) issues. To overcome the HDLSS challenge, we performed a two-layer feature selection considering the biological aspects of the features as well. In the two-layered feature selection approach, first the differentially expressed genes and differentially methylated positions are identified, then both the datasets are combined using Jaccard similarity measure. As the second step, an ensemble-based feature selection approach is implemented to further narrow down the gene selection. The results show that the proposed feature selection technique outperforms the existing commonly used feature selection techniques, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Correlation-based Feature Selection (CBS). Furthermore, the Deep Belief Network-based prediction model performs better than the widely used Machine Learning models. Also, the multi-omics dataset shows promising results compared to the single omics.

Deletion of equilibrative nucleoside transporter 2 disturbs energy metabolism and exacerbates disease progression in an experimental model of Huntington's disease

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease, characterized by motor dysfunction and abnormal energy metabolism. Equilibrative nucleoside transporter 1 (ENT1) and ENT2 are the major nucleoside transporters in cellular plasma membrane of the brain. Yet, unlike ENT1 whose function has been better investigated in HD, the role of ENT2 in HD remains unclear. The present study aimed to investigate the impacts of ENT2 deletion on HD using a well-characterized mouse model (R6/2). Microarray analysis, quantitative real-time polymerase chain reaction, and immunostaining of ENT2 in postmortem human brain tissues were conducted. R6/2 mice with or without genetic deletion of ENT2 were generated. Motor functions, including rotarod performance and limb-clasping test, were examined at the age of 7 to 12 weeks. Biochemical changes were evaluated by immunofluorescence staining and immunoblotting at the age of 12 to 13 weeks. In regard to energy metabolism, levels of striatal metabolites were determined by liquid chromatography coupled with the fluorescence detector or quadrupole time-of-flight mass spectrometer. Mitochondrial bioenergetics was assessed by the Seahorse assay. The results showed that ENT2 protein was detected in the neurons and astrocytes of human brains and the levels in the postmortem brain tended to be higher in patients with HD. In mice, ENT2 deletion did not alter the phenotype of the non-HD controls. Yet, ENT2 deletion deteriorated motor function and increased the number of aggregated mutant huntingtin in the striatum of R6/2 mice. Notably, disturbed energy metabolism with decreased ATP level and increased AMP/ ATP ratio was observed in R6/2-Ent2^-/- mice, compared with R6/2-Ent2^+/+ mice, resulting in the activation of AMPK in the late disease stage. Furthermore, ENT2 deletion reduced the NAD⁺/NADH ratio and impaired mitochondrial respiration in the striatum of R6/2 mice. Taken together, these findings indicate the crucial role of ENT2 in energy homeostasis, in which ENT2 deletion further impairs mitochondrial bioenergetics and deteriorates motor function in R6/2 mice.

A Nile rat transcriptomic landscape across 22 organs by ultra-deep sequencing and comparative RNA-seq pipeline (CRSP)

RNA sequencing (RNA-seq) has been a widely used high-throughput method to characterize transcriptomic dynamics spatiotemporally. However, RNA-seq data analysis pipelines typically depend on either a sequenced genome and/or corresponding reference transcripts. This limitation is a challenge for species lacking sequenced genomes and corresponding reference transcripts. The Nile rat (Arvicanthis niloticus) has two key features - it is daytime active, and it is prone to diet-induced diabetes, which makes it more similar to humans than regular laboratory rodents. However, at the time of this study, neither a Nile rat genome nor a reference transcript set were available, making it technically challenging to perform large-scale RNA-seq based transcriptomic studies. This genome-independent work progressed concurrently with our generation of a Nile rat genome. A well-annotated genome requires several iterations of manually reviewing curated transcripts and takes years to achieve. Here, we developed a Comparative RNA-Seq Pipeline (CRSP), integrating a comparative species strategy independent of a specific sequenced genome or species-matched reference transcripts. We performed benchmarking to validate that our CRSP tool can accurately quantify gene expression levels. In this study, we generated the first ultra-deep (2.3 billion × 2 paired-end) Nile rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial gene expression reference for Nile rat researchers. Importantly, CRSP is not limited to the Nile rat species and can be applied to any species without prior genomic knowledge. To facilitate a general use of CRSP, we also characterized the number of RNA-seq reads required for accurate estimation via simulation studies. CRSP and documents are available at: https://github.com/pjiang1105/CRSP.

Sex, Age, and Regional Differences in CHRM1 and CHRM3 Genes Expression Levels in the Human Brain Biopsies: Potential Targets for Alzheimer's Disease-related Sleep Disturbances

BACKGROUND

Cholinergic hypofunction and sleep disturbance are hallmarks of Alzheimer's disease (AD), a progressive disorder leading to neuronal deterioration. Muscarinic acetylcholine receptors (M1-5 or mAChRs), expressed in hippocampus and cerebral cortex, play a pivotal role in the aberrant alterations of cognitive processing, memory, and learning, observed in AD. Recent evidence shows that two mAChRs, M1 and M3, encoded by CHRM1 and CHRM3 genes, respectively, are involved in sleep functions and, peculiarly, in rapid eye movement (REM) sleep.

METHODS

We used twenty microarray datasets extrapolated from post-mortem brain tissue of nondemented healthy controls (NDHC) and AD patients to examine the expression profile of CHRM1 and CHRM3 genes. Samples were from eight brain regions and stratified according to age and sex.

RESULTS

CHRM1 and CHRM3 expression levels were significantly reduced in AD compared with ageand sex-matched NDHC brains. A negative correlation with age emerged for both CHRM1 and CHRM3 in NDHC but not in AD brains. Notably, a marked positive correlation was also revealed between the neurogranin (NRGN) and both CHRM1 and CHRM3 genes. These associations were modulated by sex. Accordingly, in the temporal and occipital regions of NDHC subjects, males expressed higher levels of CHRM1 and CHRM3, respectively, than females. In AD patients, males expressed higher levels of CHRM1 and CHRM3 in the temporal and frontal regions, respectively, than females.

CONCLUSION

Thus, substantial differences, all strictly linked to the brain region analyzed, age, and sex, exist in CHRM1 and CHRM3 brain levels both in NDHC subjects and in AD patients.

Integration of bulk RNA sequencing and single-cell analysis reveals a global landscape of DNA damage response in the immune environment of Alzheimer's disease

Background

We developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer's disease (AD).

Methods

We thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.

Results

The progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively.

Conclusions

In conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.

Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women

Introduction

Alzheimer's disease (AD) is a neurodegenerative disorder involving interactions between different cell types in the brain. Previous single-cell and bulk expression Alzheimer's studies have reported conflicting findings about the key cell types and cellular pathways whose expression is primarily altered in this disease. We re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings. Our analysis sheds light on the observation that females have higher AD incidence than males.

Methods

We re-analyzed three single-cell transcriptomics datasets. We used the software Model-based Analysis of Single-cell Transcriptomics (MAST) to seek differentially expressed genes comparing AD cases to matched controls for both sexes together and each sex separately. We used the GOrilla software to search for enriched pathways among the differentially expressed genes. Motivated by the male/female difference in incidence, we studied genes on the X-chromosome, focusing on genes in the pseudoautosomal region (PAR) and on genes that are heterogeneous across individuals or tissues for X-inactivation. We validated findings by analyzing bulk AD datasets from the cortex in the Gene Expression Omnibus.

Results

Our results resolve a contradiction in the literature, showing that by comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types. Synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons. PAR genes and X-chromosome heterogeneous genes, including, for example, BEX1 and ELK1, may contribute to the difference in sex incidence of Alzheimer's disease. GRIN1, stood out as an overexpressed autosomal gene in cases versus controls in all three single-cell datasets and as a functional candidate gene contributing to pathways upregulated in cases.

Discussion

Taken together, these results point to a potential linkage between two longstanding questions concerning AD pathogenesis, involving which cell type is the most important and why females have a higher incidence than males.

Highlights

By reanalyzing three, published, single-cell RNAseq datasets, we resolved a contradiction in the literature and showed that when comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types.Further analysis of the published single-cell datasets showed that synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons.Combining analysis of single-cell datasets and publicly available bulk transcriptomics datasets revealed that X-chromosome genes, such as BEX1, ELK1, and USP11, whose X-inactivation status is heterogeneous may contribute to the higher incidence in females of Alzheimer's disease.

Common Molecular Signatures Between Coronavirus Infection and Alzheimer's Disease Reveal Targets for Drug Development

BACKGROUND

Cognitive decline is a common consequence of COVID-19, and studies suggest a link between COVID-19 and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear.

OBJECTIVE

To understand the potential molecular mechanisms underlying the association between COVID-19 and AD development, and identify the potential genetic targets for pharmaceutical approaches to reduce the risk or delay the development of COVID-19-related neurological pathologies.

METHODS

We analyzed transcriptome datasets of 638 brain samples using a novel Robust Rank Aggregation method, followed by functional enrichment, protein-protein, hub genes, gene-miRNA, and gene-transcription factor (TF) interaction analyses to identify molecular markers altered in AD and COVID-19 infected brains.

RESULTS

Our analyses of frontal cortex from COVID-19 and AD patients identified commonly altered genes, miRNAs and TFs. Functional enrichment and hub gene analysis of these molecular changes revealed commonly altered pathways, including downregulation of the cyclic adenosine monophosphate (cAMP) signaling and taurine and hypotaurine metabolism, alongside upregulation of neuroinflammatory pathways. Furthermore, gene-miRNA and gene-TF network analyses provided potential up- and downstream regulators of identified pathways.

CONCLUSION

We found that downregulation of cAMP signaling pathway, taurine metabolisms, and upregulation of neuroinflammatory related pathways are commonly altered in AD and COVID-19 pathogenesis, and may make COVID-19 patients more susceptible to cognitive decline and AD. We also identified genetic targets, regulating these pathways that can be targeted pharmaceutically to reduce the risk or delay the development of COVID-19-related neurological pathologies and AD.

Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and AD models have focused on a small subset of genes identified in familial AD. Microarray studies have identified thousands of dysregulated genes in the brains of patients with AD yet identifying the best gene candidates to both model and treat AD remains a challenge. We performed a meta-analysis of microarray data from the frontal cortex (n = 697) and cerebellum (n = 230) of AD patients and healthy controls. A two-stage artificial intelligence approach, with both unsupervised and supervised machine learning, combined with a functional network analysis was used to identify functionally connected and biologically relevant novel gene candidates in AD. We found that in the frontal cortex, genes involved in mitochondrial energy, ATP, and oxidative phosphorylation, were the most significant dysregulated genes. In the cerebellum, dysregulated genes were involved in mitochondrial cellular biosynthesis (mitochondrial ribosomes). Although there was little overlap between dysregulated genes between the frontal cortex and cerebellum, machine learning models comprised of this overlap. A further functional network analysis of these genes identified that two downregulated genes, ATP5L and ATP5H, which both encode subunits of ATP synthase (mitochondrial complex V) may play a role in AD. Combined, our results suggest that mitochondrial dysfunction, particularly a deficit in energy homeostasis, may play an important role in AD.

Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning

Background

Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.

Methods

ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.

Results

It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.

Conclusion

The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer's disease

Background

To date, the pathogenesis of Alzheimer's disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD.

Materials and methods

We obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters' immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models.

Results

Based on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.

Conclusion

Our study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.

Human microRNA-4433 (hsa-miR-4443) Targets 18 Genes to be a Risk Factor of Neurodegenerative Diseases

BACKGROUND

Neurodegenerative diseases, such as Alzheimer's disease patients (AD), Huntington's disease (HD) and Parkinson's disease (PD), are common causes of morbidity, mortality, and cognitive impairment in older adults.

OBJECTIVE

We aimed to understand the transcriptome characteristics of the cortex of neurodegenerative diseases and to provide an insight into the target genes of differently expressed microRNAs in the occurrence and development of neurodegenerative diseases.

METHODS

The Limma package of R software was used to analyze GSE33000, GSE157239, GSE64977 and GSE72962 datasets to identify the differentially expressed genes (DEGs) and microRNAs in the cortex of neurodegenerative diseases. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis and gene interaction network analysis, were used to explore the biological functions of DEGs. Weighted gene co-expression network analysis (WGCNA) was used to cluster DEGs into modules. RNA22, miRDB, miRNet 2.0 and TargetScan7 databases were performed to predict the target genes of microRNAs.

RESULTS

Among 310 Alzheimer's disease (AD) patients, 157 Huntington's disease (HD) patients and 157 non-demented control (Con) individuals, 214 co-DEGs were identified. Those co-DEGs were filtered into 2 different interaction network complexes, representing immune-related genes and synapserelated genes. The WGCNA results identified five modules: yellow, blue, green, turquoise, and brown. Most of the co-DEGs were clustered into the turquoise module and blue module, which respectively regulated synapse-related function and immune-related function. In addition, human microRNA-4433 (hsa-miR-4443), which targets 18 co-DEGs, was the only 1 co-up-regulated microRNA identified in the cortex of neurodegenerative diseases.

CONCLUSION

214 DEGs and 5 modules regulate the immune-related and synapse-related function of the cortex in neurodegenerative diseases. Hsa-miR-4443 targets 18 co-DEGs and may be a potential molecular mechanism in neurodegenerative diseases' occurrence and development.

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

BACKGROUND

Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD.

MATERIALS AND METHODS

Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers.

RESULTS

Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs.

CONCLUSION

Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease

Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.

Alzheimer's disease large-scale gene expression portrait identifies exercise as the top theoretical treatment

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects multiple brain regions and is difficult to treat. In this study we used 22 AD large-scale gene expression datasets to identify a consistent underlying portrait of AD gene expression across multiple brain regions. Then we used the portrait as a platform for identifying treatments that could reverse AD dysregulated expression patterns. Enrichment of dysregulated AD genes included multiple processes, ranging from cell adhesion to CNS development. The three most dysregulated genes in the AD portrait were the inositol trisphosphate kinase, ITPKB (upregulated), the astrocyte specific intermediate filament protein, GFAP (upregulated), and the rho GTPase, RHOQ (upregulated). 41 of the top AD dysregulated genes were also identified in a recent human AD GWAS study, including PNOC, C4B, and BCL11A. 42 transcription factors were identified that were both dysregulated in AD and that in turn affect expression of other AD dysregulated genes. Male and female AD portraits were highly congruent. Out of over 250 treatments, three datasets for exercise or activity were identified as the top three theoretical treatments for AD via reversal of large-scale gene expression patterns. Exercise reversed expression patterns of hundreds of AD genes across multiple categories, including cytoskeleton, blood vessel development, mitochondrion, and interferon-stimulated related genes. Exercise also ranked as the best treatment across a majority of individual region-specific AD datasets and meta-analysis AD datasets. Fluoxetine also scored well and a theoretical combination of fluoxetine and exercise reversed 549 AD genes. Other positive treatments included curcumin. Comparisons of the AD portrait to a recent depression portrait revealed a high congruence of downregulated genes in both. Together, the AD portrait provides a new platform for understanding AD and identifying potential treatments for AD.

Distinct sex-specific DNA methylation differences in Alzheimer's disease

BACKGROUND

Sex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer's disease (AD).

METHODS

We studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset.

RESULTS

In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10^-5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10^-5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65-0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56-0.82).

CONCLUSIONS

Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.