Identification of therapeutic targets for Alzheimer's disease via differentially expressed gene and weighted gene co-expression network analyses

Identification of miRNA-Target Gene Pairs in the Parietal and Frontal Lobes of the Brain in Patients with Alzheimer's Disease Using Bioinformatic Analyses

Alzheimer's disease (AD) is a growing health concern worldwide. MicroRNAs (miRNAs) have been extensively studied in many diseases, including AD. To identify differentially expressed miRNAs (DEmiRNAs) and genes specific to AD, we used bioinformatic analyses to investigate candidate miRNA-mRNA pairs involved in the pathogenesis of AD. We focused on differentially expressed genes (DEGs) that are targets of DEmiRNAs. The GEO2R tool and the HISAT2-DESeq2 software were used to identify DEmiRNAs and DEGs. Bioinformatic tools available online, such as TAM and the Database for Annotation, Visualization and Integrated Discovery (DAVID), were used to perform functional annotation and enrichment analysis. Targets of miRNAs were predicted using the miRTarBase. The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, which are available online, were utilized to construct protein-protein interaction (PPI) networks and identify hub genes. Furthermore, transcription factors (TFs) encoded by the DEGs were predicted using the TransmiR database and TF-miRNA-mRNA networks were constructed. Finally, the expression profile of a hub gene in peripheral blood mononuclear cells was compared between healthy individuals and AD patients. We identified 26 correlated miRNA-mRNA pairs. In the parietal lobe, miRNA-mRNA pairs involved in protein folding were enriched, and in the frontal lobe, miRNA-mRNA pairs involved in synaptic transmission, abnormal protein degradation, and apoptosis were enriched. In addition, HSP90AB1 in peripheral blood mononuclear cells was found to be significantly downregulated in AD patients, and this was consistent with its expression profile in the parietal lobe of AD patients. Our results provide brain region-specific changes in miRNA-mRNA associations in AD patients, further our understanding of potential underlying molecular mechanisms of AD, and reveal promising diagnostic and therapeutic targets for AD.

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BACKGROUND

The underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).

MATERIAL AND METHODS

We obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the "limma" package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.

RESULTS

The preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.

CONCLUSIONS

We identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.

An 85-Gene Coexpression Module for Progression of Hypertension-Induced Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) represents the most lethal form of stroke. We sought to identify potential genes that might contribute to progression of hypertension-induced spontaneous ICH (HIS-ICH). RNA-sequencing data set of cerebral vessel samples from HIS-ICH mice and normal mice was obtained from the Gene Expression Omnibus. Differential expression genes in HIS-ICH samples were obtained compared with normal samples followed by functional enrichment analysis. What is more, we explored the potential gene coexpression module (GCM) for HIS-ICH progression by using weighted gene coexpression network analysis. We further conducted protein-protein interaction network analysis for genes contained in GCM that was closely correlated with HIS-ICH to disclose their biological interactions. As a result, 554 genes were found to aberrantly express in HIS-ICH mice compared with normal mice, which were mainly associated with cancer-related pathways in addition to some well-known ICH-related pathways. A total of 28 GCMs were obtained, and darkturquoise module that contained 85 genes, which were closely associated with mitochondrion and hydrolase activity, was significantly correlated with HIS-ICH progression. Besides, we identified dense biological interactions among some genes in darkturquoise, such as Psma gene family and Hsp90a gene family. This study should shed new light on HIS-ICH progression and its treatment.

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA-associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease-related networks based on 21756 gene expression correlation coefficients, hub-genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits-related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA-associated genes. Moreover, 310 OA-associated genes were found, and 34 of them were among hub-genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)-receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'-kinase (PI3K)-Akt signaling pathway (PI3K-AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.

Small-worldness of brain networks after brachial plexus injury: A resting-state functional magnetic resonance imaging study

Research on brain function after brachial plexus injury focuses on local cortical functional reorganization, and few studies have focused on brain networks after brachial plexus injury. Changes in brain networks may help understanding of brain plasticity at the global level. We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury. Thus, in this cross-sectional study, we recruited eight male patients with unilateral brachial plexus injury (right handedness, mean age of 27.9 ± 5.4 years old) and eight male healthy controls (right handedness, mean age of 28.6 ± 3.2). After acquiring and preprocessing resting-state magnetic resonance imaging data, the cerebrum was divided into 90 regions and Pearson's correlation coefficient calculated between regions. These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1-0.46. Under sparsity conditions, both groups satisfied this small-world property. The clustering coefficient was markedly lower, while average shortest path remarkably higher in patients compared with healthy controls. These findings confirm that cerebral functional networks in patients still show small-world characteristics, which are highly effective in information transmission in the brain, as well as normal controls. Alternatively, varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.

Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach

The aim of the present study was to predict pathogenic genes for primary myelofibrosis (PMF) using a system‑network approach by combining protein‑protein interaction (PPI) network and gene expression data with known pathogenic genes. PMF gene expression profiles, known pathogenic genes and protein‑protein interactions were obtained. Using these data, differentially expressed genes (DEGs) were identified between PMF and normal conditions using significance analysis of microarrays, and seed genes were determined based on the intersection of known pathogenic genes and the PMF gene expression profile. A new network was constructed using the seed genes and their adjacent DEGs within the PPI network. Subsequently, a pathogenic network was extracted from the new network, and contained genes that interacted with at least two seed genes, and the candidate pathogenic genes were predicted based on the cohesion with seed genes. Cluster analysis was performed to mine the pathogenic modules from the pathogenic network, and functional analysis was performed to identify the putative biological processes of the candidate pathogenic genes. Results from the present study identified 845 DEGs between PMF and normal conditions, and 45 seed genes in PMF were screened. Subsequently, a pathogenic network comprising 103 nodes and 265 interactions was constructed, and 4 pathogenic modules (modules A‑D) were mined from the pathogenic network. There were nine candidate pathogenic genes contained within Module A and four potential pathogenic genes, including E1A‑binding protein p300, RAS‑like proto‑oncogene A, von Willebrand factor and RAF‑1 proto‑oncogene, serine/threonine kinase, were identified that may be involved in the same biological process with the seed genes. This study predicted 10 candidate pathogenic genes and several signaling pathways that may be related to the pathogenesis of PMF using a system‑network approach. These predictions may shed light on the PMF pathogenesis and may provide guidelines for future experimental verification.