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View of Glomerular Constructing Pins Through Four Different Analytical Methods Journal of Clinical Research and Medicine Research Open Volume 2 Issue 5 Research Article Novel Pathology-Related Hub Genes in Focal Segmental Glomerulosclerosis Qianhong Ma1, Dongmei Zhang2 and Zhongying Huang1* 1West China Second Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Chengdu, Sichuan, China 2Southwest Medical University, Luzhou, Sichuan, China *Corresponding author: Zhongying Huang, West China Second Hospital, Sichuan University, Chengdu, Sichuan, Zipcode: 610041, China; Tel: 86-28-85501650; Email: [email protected] Received: October 29, 2019; Accepted: November 11, 2019; Published: November 15, 2019; Abstract Objectives: Focal Segmental Glomerulosclerosis (FSGS) is a progressive glomerular disease. The pathogenesis of this disease, however, remains unclear. Here, we attempted to identify key candidate genes in FSGS through stringent bioinformatic analysis. Methods: We systematically searched the Gene Expression Omnibus database for gene expression microarrays derived from human glomeruli tissues with FSGS. First, we identified differentially expressed genes (DEGs) by using the Limma package in R. Then, we subjected these DEGs to Gene Ontology (GO) analysis for further analysis. Finally, we constructed Protein–Protein Interaction networks (PPI) through four different methods and performed intersection analysis to further refine our results. Results: A total of 627 DEGs were identified between the FSGS and control groups, among which 534 were up-regulated and 93 were down-regulated. GO analysis revealed that the DEGs were enriched in mRNA processing, cell adhesion molecule binding, and cadherin binding. Furthermore, via PPI, 7 DEGs overlapped in the four groups constructed through different analytical approaches. We also validated the overlapped 7 hub genes in in vitro experiments, including RBM5 and HNRNPF, with potentially important roles in the development of FSGS. Conclusion: Our study provides a valuable resource for novel biomarkers and therapeutic targets for FSGS. Keywords: Focal segmental glomerulosclerosis; Bioinformatic analysis; RBM5; HNRNPF Introduction molecular mechanisms and identify novel biomarkers in various diseases [13–16]. Bioinformatic analysis is mainly used to predict Focal Segmental Glomerulosclerosis (FSGS) is a primary novel diagnostic biomarkers and therapeutic targets associated glomerular disease that manifests with heavy proteinuria [1]. It is the with tumors, such as bladder cancer [17], meningioma [18], and leading cause of the development of end-stage renal disease. Typically, hepatocellular carcinoma [19]. The application of bioinformatic FSGS lesions present a segmental manifestation that includes parietal analysis in renal diseases, such as renal cell carcinoma [20], lupus cell migration, hyaline deposition, capillary collapse, and intracapillary nephritis [21], IgA nephropathy [22], and chronic kidney disease thrombi. Recent studies suggest that podocyte injury may play a key [23], has begun to develop gradually. However, up to now, no study role in FSGS lesions [2]. Injury and loss of podocytes result in foot has subjected FSGS to bioinformatic analysis. Thus, exploring the process effacement and protein loss [3]. However, the pathogenesis underlying crucial genes and effective therapeutic targets for FSGS of FSGS remains unclear, and the present diagnostic and therapeutic through bioinformatic analysis is necessary. methods for this disease remain inadequate. Oxidative stress has been In this study, we downloaded the gene expression profile datasets implicated in the development and progression of this FSGS [4,5]. of FSGS from the Gene Expression Omnibus (GEO) database and Nuclear factor E2-related factor 2 (Nrf2) is a transcription factor investigated Differentially Expressed Genes (DEGs) between FSGS that can potently induce the production of numerous antioxidants and control samples by using the Limma package in R. We performed and prevent the generation of oxidative stress in renal fibrosis Gene Ontology (GO) enrichment analysis for all DEGs. In addition, and inflammation [6–8]. Furthermore, apoptosis and the renin– we constructed protein–protein interaction (PPI) networks, identified angiotensin system are strongly involved in FSGS-related injury novel hub genes, and validated them in in vitro experiments. Our [9–12]. Nevertheless, a considerable amount of important FSGS genes study aimed to predict novel diagnostic biomarkers and potential remain unidentified given the lack of global analysis. therapeutic targets for FSGS. With the development of bioinformatic analysis technology, gene expression profiling analysis has been increasingly used to explore J Clin Res Med, Volume 2(5): 1–25, 2019 Huang Z (2019) Novel Pathology-Related Hub Genes in Focal Segmental Glomerulosclerosis Materials and Methods high centrality scores were considered hub genes. We applied the R package Venn diagram (version1.6.17, https://cran.r-project.org/web/ Data collection packages/VennDiagram/) [28] to identify overlapping DEGs among these hub genes. Gene expression profiles were retrieved from NCBI’s GEO database (http://www.ncbi.nlm.nih.gov/geo/) by using the key words Degree (Deg(v) = |N(v)|) is a computing tool in Cytoscape “focal segmental glomerulosclerosis” with the following criteria: 1) the software [29]. The default filter “in and out” was between 7 and 42 in study type is expression profiling by array, 2) the attribute name is the present study. tissue, 3) the organism of interest is Homo sapiens, and 4) the platform MCC is a topological analysis method in CytoHubba [27]. Given a used is the Affymetrix Human Genome U133A Array. Ultimately, on node v, the MCC of v is defined as MCC(v) = ∑C∈S(v)(|C|−1)!. the basis of the above criteria, we selected dataset GSE47185 of FSGS. Original CEL files were used for further bioinformatic analysis. Maximum Neighborhood Component (MNC) is another computing tool in cytoHubba. MNC(v) = |V(MC(v))|, where MC(v) Data preprocessing is a maximum connected component of G[N(v)], and G[N(v)] is the induced subgraph of G by N(v). On the basis of MNC, Lin et al. CEL files were normalized and converted to expression profiles proposed that DMNC(v) = |E(MC(v))|/ |V(MC(v))| ε , where ε = 1.7. by using the Affy package of R[24] . In brief, the original data were read using the Affy Bioconductor package and preprocessed for Cell culture normalization through the robust multiarray analysis method, which includes background correction, normalization, expression calculation Conditionally immortalized human podocytes (LY893) were kindly provided by Dr. Lan Ni and Moin Saleem(Bristol, U.K.). and batch effects removal. After obtaining the gene expression value, Podocytes were cultured in RPMI 1640 medium(Gibco)supplemented genes were annotated with the hgu133A.db and annotate software with 10% Fetal Bovine Serum (Gibco) and 1% Insulin-Transferrin- packages. Selenium(Invitrogen) at 33°C under 5% CO2 for propagation, then DEG analysis were thermo switched to 37°C under 5% CO2 when at 60% confluency for differentiation. The differentiated podocytes were incubated with The Limma package of R[25] was used to analyze DEGs after adriamycin to construct an in vitro model for FSGS [30]. preprocessing. The linear fit method, Bayesian analysis, andt -test algorithm were used to calculate the P and FC values. DEGs were Quantitative Real-Time PCR screened by setting a cut-off value of |log 2 Fold Change (FC)| > 1 Total RNA was extracted using the RNeasy Plus Mini Kit (BioTeke and P < 0.05. The ggplot2 software package was used to visualize RP1202) in accordance with the manufacturer’s instructions. The results. Moreover, to identify and visualize the DEGs between FSGS cDNA was obtained by reverse transcription, amplified and detected and normal samples, we generated a heat map of the top 10% DEGs by using a SYBR Green Supermix kit (Takara). Then, a BIO-RAD CFX- using the heatmap package (Version 1.0.8). 96 Real-Time PCR system (Bio-Rad) was used for PCR analysis under GO enrichment analysis for DEGs the following conditions: 95 °C for 3 min, followed by 40 cycles of 95 °C for 10 s and 51 °C for 30 s. The primer sequences used for PCR are The GO consortium includes three independent branches: listed in Table 1. Statistical differences were determined by Student’s Biological Process (BP), Cellular Component (CC), and Molecular t-test using R “ggpubr” package(version 0.1.8, https://CRAN.R- Function (MF). In this study, we subjected the identified DEGs to GO project.org/package=ggpubr). enrichment analysis by using R and the clusterProfiler package [26]. P < 0.05 was used as the threshold for the identification of significant Results GO terms. DEGs identification PPI Network Construction This dataset GSE47185 contains the mRNA expression profiles of To explore the relationships among the top 30% DEGs, we used 13 FSGS samples and 14 control samples (normal tissue of renal tumor the online tool STRING (Search Tool for the Retrieval of Interacting excision). Under the threshold of |log 2 fold change (FC)| > 1 and Genes/Proteins; http://string.embl.de/) database for the construction adj.P value < 0.05, 627 DEGs were identified between the FSGS and of PPI networks. The minimum required interaction score of 0.4 control groups. These DEGs included 534 up-regulated and 93 down- was used as the significant cut-off threshold. Then, the obtained PPI regulated DEGs. The results of expression-level analysis are presented interaction networks were visualized by using Cytoscape software as a volcano plot in Fig. 1A. As shown in Table 2, RPS4Y1, PLPP3, (version 6.3). DDX3Y, SART3, TCF4, TROVE2, IQGAP1, MBP, CALD1, and RBFOX2 are the 10 most significantly up-regulated genes, whereas Hub-gene screening CYP4A11, FOSB, EGR1, G6PC, ALB, CTSZ, PPP3R1, XIST, PCK1, and HPGD are the 10 most significantly down-regulated genes. The On the basis of the STRING results, we introduced the four more information of all DEGs is listed in supplementary Table 1. methods Degree, EPC, Maximal Clique Centrality (MCC), and DMNC [27] to rank the importance of nodes in the PPI networks and The heatmaps of the top 10% DEGs are shown in Fig.
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