scholarly journals Multimodal Analysis Reveals New Genetic Effectors in Parkinson'S Disease-Related Gene LRRK2 Gly2019Ser Mutation

2021 ◽  
Author(s):  
Longping Yao ◽  
Shizhong Zhang
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Interaction Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
In The Wild ◽  
Potential Factors ◽  
Wild Group

Abstract BackgroundMutations in the LRRK2 gene, which encodes leucine-rich repeat kinase 2 (LRRK2), generate one of the most prevalent monogenic forms of Parkinson's disease (PD). Patients with autosomal dominant PD and apparent sporadic PD, who are clinically indistinguishable from those with idiopathic PD, are found to have LRRK2 mutations, particularly the most prevalent variant Gly2019Ser. Nonetheless, potential effectors of Gly2019Ser remain unknown.MethodsWe used the GEO database to undertake and evaluate a multitiered bioinformatic investigation to look into the gene expression implicated in the development of Parkinson's disease. Individual differences in gene expression were then confirmed in whole blood samples collected in the clinic. These genetic factors were also subjected to an interaction analysis and prediction. ResultsIn total, 607 genes in the LRRK2 Gly2019Ser mutation group expressed differently from those in the wild group. The following 10 top hub genes were discovered in protein-protein interaction (PPI) networks: CD44, CTGF, THBS1, VEGFA, SPP1, EGF, VCAM1, MMP3, CXCR4, and LOX. The gene expression of CD44, CTGF, THBS1, SPP1, EGF, and LOX was considerably higher in the LRRK2 Gly2019Ser mutant group than in the LRRK2 wild group. Meanwhile, CXCR4 gene expression in the LRRK2 Gly2019Ser mutant group was significantly lower than in the LRRK2 wild group. We then confirmed the expression of the hub genes in LRRK2 Gly2019Ser mutated iPSC-induced DA cells. As a result, the levels of CD44, CTGF, THBS1, VEGFA, SPP1 were positively correlated to the mutation of LRRK2, displayed promising effectors for discriminating the pathogenesis of PD. ConclusionsWe identified CD44, CTGF, THBS1, VEGFA, and SPP1 as the potential genetic effectors responding to the mutation of LRRK2. They could be a promising mechanism for discriminating the PD and potential factors contributing to the disease's development.

scholarly journals Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

2019 ◽  
Author(s):  
Demis A. Kia ◽  
David Zhang ◽  
Sebastian Guelfi ◽  
Claudia Manzoni ◽  
Leon Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Candidate Genes ◽  
Gwas Data ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Protein Ubiquitination ◽  
Ppi Networks ◽  
Core Proteins

AbstractSubstantial genome-wide association study (GWAS) work in Parkinson’s disease (PD) has led to an increasing number of loci shown reliably and robustly to be associated with the increased risk of the disease. Prioritising causative genes and pathways from these studies has proven problematic. Here, we present a comprehensive analysis of PD GWAS data with expression and methylation quantitative trait loci (eQTL/mQTL) using Colocalisation analysis (Coloc) and transcriptome-wide association analysis (TWAS) to uncover putative gene expression and splicing mechanisms driving PD GWAS signals. Candidate genes were further characterised by determining cell-type specificity, weighted gene co-expression (WGNCA) and protein-protein interaction (PPI) networks.Gene-level analysis of expression revealed 5 genes (WDR6, CD38, GPNMB, RAB29, TMEM163) that replicated using both Coloc and TWAS analyses in both GTEx and Braineac expression datasets. A further 6 genes (ZRANB3, PCGF3, NEK1, NUPL2, GALC, CTSB) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell-types compared to neurons. The WGNCA analysis showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes related with protein ubiquitination (protein ubiquitination (p=7.47e-10) and ubiquitin-dependent protein catabolic process (p = 2.57e-17) in nucleus accumbens, caudate and putamen, while TMEM163 and ZRANB3 were both important in modules indicating regulation of signalling (p=1.33e-65] and cell communication (p=7.55e-35) in the frontal cortex and caudate respectively. PPI analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known Mendelian PD and parkinsonism proteins than would be expected by chance. The proteins core proteins this network were enriched for regulation of the ERBB receptor tyrosine protein kinase signalling pathways.Together, these results point to a number of candidate genes and pathways that are driving the associations observed in PD GWAS studies.

scholarly journals Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Binfeng Liu ◽  
Ang Li ◽  
Hongbo Wang ◽  
Jialin Wang ◽  
Gongwei Zhai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Corneal Scar

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

scholarly journals Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Siwei Su ◽  
Wenjun Jiang ◽  
Xiaoying Wang ◽  
Sen Du ◽  
Lu Zhou ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Males And Females ◽  
Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

2020 ◽  
Vol 23 (5) ◽  
pp. 411-418
Author(s):  
Zhongqiu Li ◽  
Peng Zhang ◽  
Feifei Feng ◽  
Qiao Zhang
Keyword(s):  
Gene Expression ◽  
Interaction Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Gene Expression Profile Data

Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. Aims & Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. Materials & Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

scholarly journals Identification and Characterization of Biomarkers and Their Role in Opioid Addiction by Integrated Bioinformatics Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiuning Zhang ◽  
Hailei Yu ◽  
Rui Bai ◽  
Chunling Ma
Keyword(s):  
Gene Expression ◽  
Sequence Similarity ◽  
Gene Expression Omnibus ◽  
Opioid Addiction ◽  
Local Alignment ◽  
Calculation Methods ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Difficult Challenge

Although numerous studies have confirmed that the mechanisms of opiate addiction include genetic and epigenetic aspects, the results of such studies are inconsistent. Here, we downloaded gene expression profiling information, GSE87823, from the Gene Expression Omnibus database. Samples from males between ages 19 and 35 were selected for analysis of differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were used to analyze the pathways associated with the DEGs. We further constructed protein-protein interaction (PPI) networks using the STRING database and used 10 different calculation methods to validate the hub genes. Finally, we utilized the Basic Local Alignment Search Tool (BLAST) to identify the DEG with the highest sequence similarity in mouse and detected the change in expression of the hub genes in this animal model using RT-qPCR. We identified three key genes, ADCY9, PECAM1, and IL4. ADCY9 expression decreased in the nucleus accumbens of opioid-addicted mice compared with control mice, which was consistent with the change seen in humans. The importance and originality of this study are provided by two aspects. Firstly, we used a variety of calculation methods to obtain hub genes; secondly, we exploited homology analysis to solve the difficult challenge that addiction-related experiments cannot be carried out in patients or healthy individuals. In short, this study not only explores potential biomarkers and therapeutic targets of opioid addiction but also provides new ideas for subsequent research on opioid addiction.

Multimodal analysis of gene expression from postmortem brains and blood identifies synaptic vesicle trafficking genes to be associated with Parkinson’s disease

2020 ◽  
Author(s):  
Xiaoya Gao ◽  
Zifeng Huang ◽  
Cailing Feng ◽  
Chaohao Guan ◽  
Ruidong Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Synaptic Vesicle ◽  
Multinomial Logistic Regression ◽  
Vesicle Trafficking ◽  
Susceptibility Gene ◽  
Blood Levels ◽  
Ppi Networks ◽  
Postmortem Brains

Abstract Objective We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson’s disease (PD) patients and healthy controls (HC). Methods We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein–protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson’s Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. Results We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. Conclusions This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.

scholarly journals Analysis of differential gene expression between right-sided and left-sided colon cancer by bioinformatics analysis

2020 ◽  
Author(s):  
Qiangwei Chi ◽  
Shizuan Chen ◽  
Shaotang Li
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Bioinformatics Analysis ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Abstract Background Colon cancer is a common tumor of the digestive tract worldwide. Recent researches have revealed that colon cancer exhibits distinct differences in clinical and biological characteristics depending on the location of the tumor. However, the underlying genetic and molecular mechanism of the differences between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) are not fully understood. This study aimed to identify molecular potential biomarkers and therapeutic targets for precise treatment of right-sided and left-sided colon cancer using bioinformatics analysis. Methods The gene microarray profile, named GSE44076, from the Gene Expression Omnibus (GEO) public database was downloaded and processed to then select differentially expressed genes (DEGs) on the base of two sample groups of RCC and LCC. Also, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, module analysis, validation of hub genes, and survival analysis. Results Finally, we obtained 2259 DEGs between RCC and LCC, 1300 of which were upregulated in RCC and 945 of which were upregulated in LCC. The results of GO and KEGG analysis of the DEGs indicated that the biological functions of DEGs in RCC and LCC were significantly different. CTLA4, IL10, IL2RB, IFNG, NCAM1, EGFR, MYC, SRC, CUL3, and NCBP2 were identified from the PPI networks as the hub genes of RCC and LCC. Among the hub genes, the log-rank tests for overall survival (OS) and disease free survival (DFS) were applied. Moreover, all hub genes, except CUL3, had differential expression levels of miRNA between tumor group and normal group. Conclusion These hub genes and pathways identified based on bioinformatics analysis might conduce to explain the differences between RCC and LCC, and most of the hub genes were specific to the malignant tissues. Notably, these hub genes, especially the genes associated with immunotherapy such as CTLA4, might be potential specific targets or prognostic markers for precise treatment of colon cancer.

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