Investigating the Effects of Ibuprofen on the Gene Expression Profile in Hippocampus of Mice Model of Alzheimer's Disease through Bioinformatics Analysis

PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm

The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by an adaptive refining process. These low-dimensional features are used to improve the disease-gene heterogenous network, and we apply network propagation to the improved heterogenous network to predict pathogenic genes more effectively. By a series of experiments, we study the effect of PGAGP's parameters and integrated strategies on predictive performance and confirm that PGAGP is better than the state-of-the-art algorithms. Case studies show that many of the predicted candidate genes for specific diseases have been implied to be related to these diseases by literature verification and enrichment analysis, which further verifies the effectiveness of PGAGP. Overall, this work provides a useful solution for mining disease-gene heterogeneous network to predict pathogenic genes more effectively.

Assessment of Post-Radiation Time Effect on Gene Expression Profiles of Saccharomyces cerevisiae Samples After Appling a UV Laser

Introduction: Widespread application of lasers in different fields of medicine implies more investigations into the molecular mechanism of laser effects on the human body. Network analysis of the dysregulated genes of Saccharomyces cerevisiae samples are irradiated by a UV laser and harvested 30 minutes after radiation compared with a 15-minute group is the aim of this research. Methods: The significant dysregulated genes interacted via the STRING database, and the central nodes were determined by "Networkanalyzer" application of Cytoscape software. The critical genes and the related biological terms were identified via action map analysis and gene ontology assessment. Results: The gene expression profiles of the samples with 30-minute post-radiation time were different from the samples with 15 minutes of post-radiation time. 9 potent central genes, 50% of which were similar to the nodes of the 15-minute group, were identified. The terms "positive regulation of telomere maintenance" were targeted in the two sample groups. Conclusion: In spite of large alteration in the gene expression profiles of the samples, the results indicated that the main affected biological term for the 15-minute and 30-minute groups was similar.

Peripheral Blood Biomarkers CXCL12 and TNFRSF13C Associate with Cerebrospinal Fluid Biomarkers and Infiltrating Immune Cells in Alzheimer Disease

Neuroinflammation-induced neurodegeneration and immune cell infiltration are two features of Alzheimer disease (AD). This study aimed to identify potential peripheral biomarkers that interact with cerebrospinal fluid (CSF) and infiltrating immune cells in AD. Blood and CSF data were downloaded from the Alzheimer's disease Neuroimaging Initiative database. We identified differentially expressed genes (DEGs) in AD and assessed infiltrating immune cells using the Immune Cell Abundance Identifier (ImmuCellAI) algorithm. Blood-brain barrier (BBB) and immune-related genes were identified from medical databases, and common genes were used to construct a protein-protein interaction network (PPI). Potential biomarkers reflecting the clinical features of AD were screened using Pearson correlations and logistic regression analysis. We identified 210 DEGs in the AD group. ImmuCellAI indicated that blood samples from patients with AD had a higher abundance of exhausted T (Tex; 0.196 vs. 0.132) and induced regulatory T (iTreg; 0.180 vs. 0.137) cells than controls. Thirty-two genes overlapped between the BBB and immune-related genes, and 27 genes in the PPI network were associated with eight pathways, including the cytokine-cytokine receptor interaction pathway (hsa04060) and the chemokine signaling pathway (hsa04062). Pearson correlations showed that five genes were associated with the CSF biomarkers, Aβ, total, and phosphorylated tau. Logistics analysis showed that the B cell-associated genes, CXCL12 and TNFRSF13C, were independent risk factors for AD diagnosis. Peripheral CXCL12 and TNFRSF13C genes that correlated with immune cell infiltration in AD might serve as easily accessible biomarkers for the early diagnosis of AD.

Role of Flt4 in Skin Protection against UVB Radiation: A System Biology Approach

Background: Although the application of ultraviolet B (UVB) in phototherapy of human skin is a common therapeutic method, it is known as a risk factor for skin cancer. This study aims to assess the role of differentially expressed genes (DEGs) to find the critical one that is mainly responsible for skin protection against UVB radiation. Methods: The gene expression profiles of irradiated mice by UVB that issued skin protection against exposure are extracted from Gene Expression Omnibus (GEO) and analyzed by GEO2R. The significant DEGs are assessed via gene ontology (GO) analysis and the critical individuals are investigated via action mapping. Results: Thirty-eight significant DEGs that provide skin resistance against UVB irradiation were determined. Among the query DEGs, 26 individuals were related to 43 biological terms. Flt4, F3, Tspan6, Cblb, and Itgb6 were highlighted as the critical DEGs to promote skin protection against UVB irradiation. Conclusion: The finding indicates that Flt4 is the key DEG that is mainly responsible for protecting skin from UVB exposure.