Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that presents a significant global health challenge. To explore drugs targeting key genes in AD, R software was used to analyze the data of single nuclei transcriptome from human cerebral frontal cortex in AD, and the differentially expressed genes (DEGs) were screened. Then the gene ontology (GO) analysis, Kyoto gene and genome encyclopedia (KEGG) pathway enrichment and protein-protein interaction (PPI) network were analyzed. The hub genes were calculated by Cytoscape software. Molecular docking and molecular dynamics simulation were used to evaluate and visualize the binding between candidate drugs and key genes. A total of 564 DEGs were screened, and the hub genes were ISG15, STAT1, MX1, IFIT3, IFIT2, RSAD2, IFIT1, IFI44, IFI44L and DDX58. Enrichment terms mainly included response to virus, IFN-γ signaling pathway and virus infection. Diclofenac had good binding effect with IFI44 and IFI44L. Potential drugs may act on key gene targets and then regulate biological pathways such as virus response and IFN-γ-mediated signal pathway, so as to achieve anti-virus, improve immune balance and reduce inflammatory response, and thus play a role in anti-AD.

The dysregulation of lipid metabolism has been strongly associated with Alzheimer's disease (AD) and has intricate connections with various aspects of disease progression, such as amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. Here, a comprehensive bioinformatic assessment was conducted on lipid metabolism genes in the brains and peripheral blood of AD-derived transcriptome datasets, characterizing the correlation betweendifferentially expressed genes (DEGs) of lipid metabolism and disease pathologies, as well as immune cell preferences. Through the application of weighted geneco-expression network analysis (WGCNA),modules eigengenes related to lipid metabolism were pinpointed, and the examination oftheir molecular functions within biological processes, molecular pathways, andtheir associations with pathological phenotypes and molecular networkshas been characterized. Analysis of biological networks indicates notable discrepancies in the expression patterns of the DEGs between neuronal and immune cells, as well as variations in cell type enrichments within both brain tissue and peripheral blood. Additionally, drugs targeting the DEGs from central and peripheral and a diagnostic model for hub genes from the blood were retrieved and assessed, some of which were shown to be useful for therapeutic and diagnostic.These results revealed the distinctive pattern of transcriptionally abnormal lipid metabolism in central, peripheral, and immune cell activation, providing valuable insight into lipid metabolism for diagnosing and guiding more effectivetreatment forAD.

Previous studies have demonstrated a brain volume decrease linked to long-term starvation in patients with anorexia nervosa (AN). Food intake is critically diminished in this disorder, leading to one of the highest mortality rates within the psychiatric disease spectrum. As reported in animal models, astrocytes seem to be the most affected cell type in AN. In a recently established primary cell culture model, an elevated unfolded protein response (UPR) was observed in long-term glucose semi-starved astrocytes. A well-functioning protein machinery is essential for every cell, and prolonged UPR will lead to cell death. As a nucleic acid stress-sensing pathway with the activator located in the endoplasmic reticulum, the regulation of the cGAS-STING pathway (cyclic GMP-AMP synthase/stimulator of interferon genes) was additionally investigated in the starvation context. In the current study, a glucose semi-starvation protocol of 15 days, during which cells were supplied with 2 mM glucose in the medium, was prolonged with an additional 6-day long recovery period. Our findings showed that increased UPR mRNA expression was reversible after re-establishing the standard glucose concentration of 25 mM. Furthermore, we were able to verify the presence of cGAS and STING in astrocytes with a characteristic presence of cGAS in the astrocyte nucleus during starvation. A correlation between STING and the glial fibrillary acidic protein (GFAP) could be established, hinting at a conditional presence of STING with a specific astrocyte phenotype.Graphical

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia. Programmed cell death (PCD) is mainly characterized by unique morphological features and energy-dependent biochemical processes. The predominant pathway leading to cell death in AD has not been thoroughly analyzed, although there is evidence of neuron loss in AD and numerous pathways of PCD have been associated with this process. A better understanding of the systems biology underlying the relationship between AD and PCD could lead to the development of new therapeutic approaches. To this end, publicly available transcriptome data were examined using bioinformatic methods such as differential gene expression and weighted gene coexpression network analysis (WGCNA) to find PCD-related AD biomarkers. The diagnostic significance of these biomarkers was evaluated using a logistic regression-based predictive model. Using these biomarkers, a multifactorial regulatory network was developed. Last, a drug repositioning study was conducted to propose new drugs for the treatment of AD targeting PCD. The development of 3PM (predictive, preventive, and personalized) drugs for the treatment of AD would be enabled by additional research on the effects of these drugs on this disease.

Aneuploidy, having an aberrant genome, is gaining increasing attention in neurodegenerative diseases. It gives rise to proteotoxic stress as well as a stereotypical oxidative shift which makes these cells sensitive to internal and environmental stresses. A growing body of research from numerous laboratories suggests that many neurodegenerative disorders, especially Alzheimer’s disease and frontotemporal dementia, are characterised by neuronal aneuploidy and the ensuing apoptosis, which may contribute to neuronal loss. Using Drosophila as a model, we investigated the effect of induced aneuploidy in GABAergic neurons. We found an increased proportion of aneuploidy due to Mad2 depletion in the third-instar larval brain and increased cell death. Depletion of Mad2 in GABAergic neurons also gave a defective climbing and seizure phenotype. Feeding animals an antioxidant rescued the climbing and seizure phenotype. These findings suggest that increased aneuploidy leads to higher oxidative stress in GABAergic neurons which causes cell death, climbing defects, and seizure phenotype. Antioxidant feeding represents a potential therapy to reduce the aneuploidy-driven neurological phenotype.

The pathogenesis of Alzheimer’s disease (AD) is complex and involves an imbalance between production and clearance of amyloid-ß peptides (Aß), resulting in accumulation of Aß in senile plaques. Hypercholesterolemia is a major risk factor for developing AD, with cholesterol shown to accumulate in senile plaques and increase production of Aß. ABCG4 is a member of the ATP-binding cassette transporters predominantly expressed in the CNS and has been suggested to play a role in cholesterol and Aß efflux from the brain. In this study, we bred Abcg4 knockout (KO) with the APPSwe,Ind (J9) mouse model of AD to test the hypothesis that loss of Abcg4 would exacerbate the AD phenotype. Unexpectedly, no differences were observed in novel object recognition (NOR) and novel object placement (NOP) behavioral tests, or on histologic examinations of brain tissues for senile plaque numbers. Furthermore, clearance of radiolabeled Aß from the brains did not differ between Abcg4 KO and control mice. Metabolic testing by indirect calorimetry, glucose tolerance test (GTT), and insulin tolerance test (ITT) were also mostly similar between groups with only a few mild metabolic differences noted. Overall, these data suggest that the loss of ABCG4 did not exacerbate the AD phenotype.

ADHD has huge knowledge gaps concerning its etiology. MicroRNAs (miRNAs) provide promising diagnostic biomarkers of human pathophysiology and may be a novel therapeutic option. The aim was to investigate the levels of miR-34c-3p, miR-155, miR-138-1, miR-296-5p, and plasma brain-derived neurotrophic factor (BDNF) in a group of children with ADHD compared to neurotypicals and to explore correlations between these measures and some clinical data. The participants were children with ADHD in Group I (N = 41; age: 8.2 ± 2) and neurotypical ones in Group II (N = 40; age: 8.6 ± 2.5). Group I was subjected to clinical examination, the Stanford Binet intelligence scale-5, the preschool language scale, and Conner’s parent rating scale-R. Measuring the expression levels of the miRNAs was performed by qRT-PCR for all participants. The BDNF level was measured by ELISA. The lowest scores on the IQ subtest were knowledge and working memory. No discrepancies were noticed between the receptive and expressive language ages. The highest scores on the Conner’s scale were those for cognitive problems. Participants with ADHD exhibited higher plasma BDNF levels compared to controls (p = 0.0003). Expression patterns of only miR-34c-3p and miR-138-1 were downregulated with significant statistical differences (p˂0.01). However, expression levels of miR-296-5p showed negative correlation with the total scores of IQ (p = 0.03). MiR-34c-3p, miR-138-1, while BDNF showed good diagnostic potential. The downregulated levels of miR-34c-3p and miR-138-1, together with high BDNF levels, are suggested to be involved in the etiology of ADHD in Egyptian children. Gender differences influenced the expression patterns of miRNAs only in children with ADHD.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .