Discovery of Potential Drug Targeting Key Genes in Alzheimer's Disease: Insights from Transcriptome Analysis and Molecular Docking.

Assessment of activation of the plasma kallikrein-kinin system in frontal and temporal cortex in Alzheimer's disease and vascular dementia

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Some individuals show intact cognition despite the presence of neuropathological hallmarks of Alzheimer's disease (AD). The plasticity of parvalbumin (PV)-containing interneurons might contribute to resilience. Perineuronal nets (PNNs), that is, extracellular matrix structures around neurons, modulate PV neuron function. We hypothesize that PNNs play a role in resilience to AD. PNN amount and morphology were determined in immunolabelled sections of the frontal cortex of control, AD and resilient subjects. Expression levels of genes related to PNNs and microglia signatures were evaluated by bulk RNA sequencing. The expression of the PNN-component aggrecan around PV neurons is decreased in resilient and AD subjects, whereas PNN-sugar chains are reduced only in resilient subjects. In AD, fewer presynaptic terminals on PV neurons are detected and genes related to PNN degradation are upregulated. These data show distinct PNN changes in individuals resilient to AD, which may contribute to preserved cognition despite the neuropathology. Aggrecan levels are decreased in the frontal cortex of AD and resilient subjects. In resilient subjects, WFA+ PNNs are reduced around neuronal somata. In AD patients, PV neurons show disrupted WFA peridendritic staining and synaptic loss. Expression levels of PNN-degrading enzymes are higher in AD. Excitatory neurons bearing a PNN show low amounts of ptau.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Normalization of gene expression using SYBR green qPCR: A case for paraoxonase 1 and 2 in Alzheimer's disease brains

BackgroundTo analyze the key prognostic genes and potential traditional Chinese medicine targets in glioblastoma (GBM) by bioinformatics and network pharmacology.MethodsGBM datasets were obtained from the Gene Expression Omnibus (GEO) database to clarify the differentially-expressed genes (DEGs) in the carcinoma and paracancerous tissues. The molecular functions (MF) and signaling pathways of enriched DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The STRING database and Cytoscape software were used to construct the protein-protein interaction (PPI) network and screen hub genes to focus on genes with greater clinical significance. The transcription expression and prognosis of hub genes were analyzed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. The important compounds and target molecules were obtained via the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) database. We identified the active ingredients by setting the property values of pharmacokinetic attribute values. We constructed the network of “Chinese medicine ingredients-DEGs target” and screened out the target genes and active ingredients with high correlation scores. Finally, molecular docking verification was carried out using AutoDock Tools and PyMOL.ResultsWe obtained 271 DEGs, including 212 up-regulated genes and 59 down-regulated genes and screened ten hub genes. GO and KEGG analyses suggested that the hub genes were mainly involved in the following biological processes: the cell cycle, cell division, and cell adhesion, as well as extracellular matrix adhesion-related pathways, the p53 signaling pathways, and cadherin binding involved in cell-cell adhesion. We established the interaction network between the components and DEGs to screen out the traditional Chinese medicine active component (luteolin) and target genes (BIRC5 and CCNB1) for the treatment of GBM. The molecular docking results showed that the bindings of protein receptors, BIRC5 and CCNB1, with the compound ligand, luteolin, were stable and formed by hydrogen bonding interaction.ConclusionsIn this study, we determined that luteolin potentially inhibits glioblastoma proliferation and migration through key target genes, BIRC5 and CCNB1, via bioinformatics and network pharmacology analysis, and affects the prognosis of GBM patients, providing new ideas for clinical targeted therapy and new drug development.

HLA is a potent immunoinflammatory target in asymptomatic Alzheimer’s disease

BackgroundObstructive sleep apnea syndrome (OSA) is associated with an increased risk of Alzheimer’s disease (AD). This study aimed to identify the key common genes in AD and OSA and explore molecular mechanism value in AD.MethodsExpression profiles GSE5281 and GSE135917 were acquired from Gene Expression Omnibus (GEO) database, respectively. Weighted gene co-expression network analysis (WGCNA) and R 4.0.2 software were used for identifying differentially expressed genes (DEGs) related to AD and OSA. Function enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein–protein interaction network (PPI) using the STRING database were subsequently performed on the shared DEGs. Finally, the hub genes were screened from the PPI network using the MCC algorithm of CytoHubba plugin.ResultsSeven modules and four modules were the most significant with AD and OSA by WGCNA, respectively. A total of 33 common genes were screened in AD and OSA by VENN. Functional enrichment analysis indicated that DEGs were mainly involved in cellular response to oxidative stress, neuroinflammation. Among these DEGs, the top 10 hub genes (high scores in cytoHubba) were selected in the PPI network, including AREG, SPP1, CXCL2, ITGAX, DUSP1, COL1A1, SCD, ACTA2, CCND2, ATF3.ConclusionThis study presented ten target genes on the basis of common genes to AD and OSA. These candidate genes may provide a novel perspective to explore the underlying mechanism that OSA leads to an increased risk of AD at the transcriptome level.

This study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored. The Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking. A total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding. Our findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

Although available literature indicates that the incidence of dementia in the epilepsy population and the risk of seizures in the Alzheimer's disease (AD) population are high, the specific genetic risk factors and the interaction mechanism are unclear, rendering rational genetic interpretation rather challenging. Our work aims to identify the common core ion channel genes in epilepsy and AD. In this study, we first integrated gene expression omnibus datasets (GSE48350 and GSE6834) on AD and epilepsy to identify differentially expressed genes (DEGs), performing Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. The related protein-protein interaction (PPI) network was constructed for DEGs, and the hub gene was evaluated. A total of 2800 and 35 genes were identified in GSE48350 and GSE6834, and 12 DEGs were significantly differentially expressed between the datasets. KEGG pathway analysis showed that DEGs were primarily enriched in glutamatergic synapse and dopaminergic synapse pathways. SCN2A, GRIA1, and KCNJ9 were the hub genes with high connectivity. The findings suggest that the three genes, SCN2A, GRIA1, and KCNJ9, may serve as potential targets for treating AD comorbid with epilepsy.

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

Matrix metalloproteinases (MMPs) -2, -3 and -9 are up-regulated in several cell types on exposure to amyloid beta peptide (Abeta) and have Abeta-degrading activity in vitro. The aims of this study were to determine (i) the distribution of MMP-2, -3 and -9 in the cerebral cortex in Alzheimer's disease (AD) and control brains; (ii) whether the levels and activity of these proteases are increased in AD; and (iii) whether their activity is related to Abeta load. In addition, we examined whether promoter polymorphisms in the MMP-3 and -9 genes are associated with AD in the study cohort. Paraffin sections of frontal lobe from AD and control cases were immunostained for MMP-2, -3 and -9 and tissue homogenates used for MMP activity assays. DNA from these cases was genotyped for the MMP-3 5A/6A (-1171) and MMP-9 C-1562T promoter polymorphisms. Immunohistochemistry revealed MMP-3 in plaques and both MMP-3 and -9 around scattered neurones. The levels and activity of all three MMPs were similar in AD and control brains and bore no relationship to Abeta load. Analysis of MMP-3 -1171 5A/6A allele frequencies showed that the 6A allele (with reduced promoter activity) was associated with AD; the MMP-9 C-1562T polymorphism was not. The levels and activities of MMP-2, -3 and -9 are not increased in the frontal cortex in AD and are not related to Abeta load. Our findings suggest that altered expression of these proteases does not make a significant contribution to the accumulation of Abeta in AD.

It is well known that dermal papilla cells (DPCs) are crucial for hair follicle growth and regeneration. However, dermal papilla cells in 2D culture could lose their ability of regeneration after several passage intervals. As opposed to DPCs in 2D culture, the DPCs in 3D culture could passage extensively. However, the molecular mechanisms of DPCs’ regeneration in 3D culture remain unclear. Accordingly, gene sequencing is recommended for the investigation of hair regeneration between 2D and 3D culture, the three groups were established including DPCs in passage 2 in 2D culture, DPCs in passage 8 in 2D culture and DPCs in passage 8 in 3D culture. The differentially expressed genes (DEGs) were identified using the Venn diagram of these three groups, which included 1642 known and 359 novel genes, respectively. A total of 1642 known genes were used for Gene Ontology (GO), Kyoto Gene, Genomic Encyclopedia (KEGG) pathway enrichment and protein‐protein interaction (PPI) analyses, respectively. The functions and pathways of DEGs were enriched in biological regulation, signal transduction and immune system, etc. The key module and the top 10 hub genes (IL1B, CXCL12, HGF, EGFR, APP, CCL2, PTGS2, MMP9, NGF and SPP1) were also identified using the Cytoscape application. Furthermore, the qRT‐PCR results of the three groups validated that the hub genes were crucial for hair growth. In conclusion, the ten identified hub genes and related pathways in the current study can be used to understand the molecular mechanism of hair growth, and those provided a possibility for hair regeneration.

Alzheimer's disease (AD) is one of the most prevalent forms of dementia globally and remains an incurable condition that often leads to death. PANoptosis represents an emerging paradigm in programmed cell death, integrating three critical processes: pyroptosis, apoptosis, and necroptosis. Studies have shown that apoptosis, necroptosis, and pyroptosis play important roles in AD development. Therefore, targeting PANoptosis genes might lead to novel therapeutic targets and clinically relevant therapeutic approaches. This study aims to identify different molecular subtypes of AD and potential drugs for treating AD based on PANoptosis. Differentially expressed PANoptosis genes associated with AD were identified via Gene Expression Omnibus (GEO) dataset GSE48350, GSE5281, and GSE122063. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to construct a risk model linked to these PANoptosis genes. Consensus clustering analysis was conducted to define AD subtypes based on these genes. We further performed gene set variation analysis (GSVA), functional enrichment analysis, and immune cell infiltration analysis to investigate differences between the identified AD subtypes. Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and the DGIdb database was consulted to identify potential therapeutic compounds targeting these hub genes. Single-cell RNA sequencing analysis was utilized to assess differences in gene expression at the cellular level across subtypes. A total of 24 differentially expressed PANoptosis genes (APANRGs) were identified in AD, leading to the classification of two distinct AD subgroups. The results indicate that these subgroups exhibit varying disease progression states, with the early subtype primarily linked to dysfunctional synaptic signaling. Furthermore, we identified hub genes from the differentially expressed genes (DEGs) between the two clusters and predicted 38 candidate drugs and compounds for early AD treatment based on these hub genes. Single-cell RNA sequencing analysis revealed that key genes associated with the early subtype are predominantly expressed in neuronal cells, while the differential genes for the metabolic subtype are primarily found in endothelial cells and astrocytes. In summary, we identified two subtypes, including the AD early synaptic abnormality subtype as well as the immune-metabolic subtype. Additionally, ten hub genes, SLC17A7, SNAP25, GAD1, SLC17A6, SLC32A1, PVALB, SYP, GRIN2A, SLC12A5, and SYN2, were identified as marker genes for the early subtype. These findings may provide valuable insights for the early diagnosis of AD and contribute to the development of innovative therapeutic strategies.

This study aimed to construct the molecular biomarkers of autophagy and endoplasmic reticulum stress (ERS), as well as their corresponding protein interaction network in Alzheimer’s disease (AD) patients with different levels of physical activity (PA) by bioinformatics methods. The expression profiles of the genes were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between AD samples with low, moderate and high levels of PA were studied. The autophagy and ERS-related genes (AERSRGs) were extracted from GeneCards and MsigDB databases. The functional enrichment analysis was conducted to determine the function of DEGs. To explore the proteins, miRNAs and transcription factors (TF) interacting with DEGs, the protein–protein network, mRNA-miRNA network and mRNA-TF network were built using Cytoscape software. Then the receiver operating characteristic (ROC) analysis were conducted to verify the diagnostic performance of hub genes. A total of 533 AERSRGs were identified in Group H and 150 AERSRGs were screened in Group M. Functional enrichment analysis suggested genes of AD play vital roles in some biological process (e.g., cell cycle phase transition, mitochondrion organization, proteasomal protein catabolic process). KEGG enrichment analysis suggested that sarcopenia involves the pathways (e.g., GABA, P2Y receptors, serotonin release cycle). A total of 5 hub genes were screened in Group H and 9 were identified in Group M. ROC analysis suggested that several hub genes exhibited a relatively high sensitivity and specificity in both groups of AD. The hub genes screened in this study are closely correlated with autophagy and ERS in AD and can differentiate AD with different levels of PA. SRC, MAPK3 and MAP2K1 exhibit relatively high sensitivity and specificity in diagnosis in Group H; MCM2, CDC42, HNRNPM, ASF1A, NCBP2, SNRNP70 and MCM6 exhibit relatively high sensitivity and specificity in diagnosis in Group M.