Interaction Network Of Hub Genes Research Articles

A glutamine synthetase-DOF transcription factor module regulates N remobilization from source to sink tissues in tea plants.

Nitrogen (N) remobilization from mature leaves to new shoots is closely related to the quality of green tea in the spring season, which subsequently determines its economic value. However, the underlying N remobilization mechanism remains poorly understood. Here, we demonstrated that more than 80% of the recovered-15N was partitioned in the first mature leaves that supply new shoots. N remobilization efficiency (NRE) from mature leaves to new shoots varied significantly among tea cultivars. N fertilization level and NRE showed a significantly positive correlation. Based on weighted gene co-expression network analysis (WGCNA), glutamate metabolism-related genes including glutamine synthetase genes CsGSs were dissected from the interaction network of hub genes regulating N remobilization. Gene expression patterns and the localization of CsGS1.1 in the cytosol and vascular tissue suggest its potential role in N remobilization. Consistent with these findings, source-to-sink N remobilization at the reproductive stage was enhanced in transgenic CsGS1.1-overexpressing plants. Furthermore, we demonstrated that the DOF transcription factor CsDof16 directly binds to the -526 to -426 region of the CsGS1.1 promoter, thereby activating its transcription and regulating N remobilization. Taken together, our findings suggest that the CsDof16-CsGS1.1 module regulates the remobilization of N in the form of glutamate/glutamine from mature leaves to new shoots, constituting an important control point in the regulation of source-to-sink N partitioning in tea plants. Our findings can be employed to reduce fertilizer application and promote the development of sustainable tea production.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Construction of the Interaction Network of Hub Genes in the Progression of Barrett's Esophagus to Esophageal Adenocarcinoma.

Esophageal adenocarcinoma (EAC) is one of the histologic types of esophageal cancer with a poor prognosis. The majority of EAC originate from Barrett's esophagus (BE). There are few studies focusing on the dynamic progression of BE to EAC. R software was used to analyze differentially expressed genes (DEGs) based on RNA-seq data of 94 normal esophageal squamous epithelial (NE) tissues, 113 BE tissues and 147 EAC tissues. The overlapping genes of DEGs between BE and EAC were analyzed by Venn diagram tool. The hub genes were selected by Cytoscape software based on the protein-protein interaction network of the overlapping genes using STRING database. The functional analysis of hub genes was performed by R software and the protein expression was identified by immunohistochemistry. In the present study, we found a large degree of genetic similarity between BE and EAC, and further identified seven hub genes (including COL1A1, TGFBI, MMP1, COL4A1, NID2, MMP12, CXCL1) which were all progressively upregulated in the progression of NE-BE-EAC. We have preliminarily uncovered the probable molecular mechanisms of these hub genes in disease development and constructed the ceRNA regulatory network of hub genes. More importantly, we explored the possibility of hub genes as biomarkers in the disease progression of NE-BE-EAC. For example, TGFBI can be used as biomarkers to predict the prognosis of EAC patients. COL1A1, NID2 and COL4A1 can be used as biomarkers to predict the response to immune checkpoint blockade (ICB) therapy. We also constructed a disease progression risk model for NE-BE-EAC based on CXCL1, MMP1 and TGFBI. Finally, the results of drug sensitivity analysis based on hub genes showed that drugs such as PI3K inhibitor TGX221, bleomycin, PKC inhibitor Midostaurin, Bcr-Abl inhibitor Dasatinib, HSP90 inhibitor 17-AAG, and Docetaxel may be potential candidates to inhibit the progression of BE to EAC. This study is based on a large number of clinical samples with high credibility, which is useful for revealing the probable carcinogenic mechanism of BE to EAC and developing new clinical treatment strategies.

Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear. Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration. Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells. Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.

Maternal transcription profiles at different stages for the development of early embryo in buffalo.

Maternal mRNAs deposited in the egg during oogenesis are critical during the development of early embryo, before the activation of the embryonic genome. However, there is little known about the dynamic expression of maternally expressed genes in mammals. In this study, we made buffalo parthenogenesis as our research model to analyse maternal transcription profiles of pre-implantation embryo in buffalo using RNA sequencing. In total, 3,567 unique genes were detected to be differentially expressed among all constant stages during early embryo development (FPKM>0). Interestingly, a total of 10,442 new genes were discovered in this study, and gene ontology analysis of the new differentially expressed genes indicated that the new genes have a wide cellular localization and are involved in many molecular functions and biological processes. Moreover, we identified eight clusters that were only highly expressed in a particular developmental stage and enriched a number of GO terms and KEGG pathways that were related to specific stage. Furthermore, we identified 1,530 hub genes (or key members) from the maternally expressed gene networks, and these hub genes were involved in 11 stage-specific modules. After visualization using Cytoscape 3.2.1 software, we obtained complex interaction network of hub genes, indicating the highly efficient cooperation between genes during the development in buffalo embryos. Further research of these genes will greatly deepen our understanding of embryo development in buffalo. Collectively, this research lays the foundation for future studies on the maternal genome function, buffalo nuclear transfer and parthenogenetic embryonic stem cells.

Pan-cancer analysis of somatic mutations and transcriptomes reveals common functional gene clusters shared by multiple cancer types

To discover functional gene clusters across cancers, we performed a systematic pan-cancer analysis of 33 cancer types. We identified genes that were associated with somatic mutations and were the cores of a co-expression network. We found that multiple cancer types have relatively exclusive hub genes individually; however, the hub genes cooperate with each other based on their functional relationship. When we built a protein-protein interaction network of hub genes and found nine functional gene clusters across cancer types, the gene clusters divided not only the region of the network map, but also the function of the network by their distinct roles related to the development and progression of cancer. This functional relationship between the clusters and cancers was underpinned by the high expression of module genes and enrichment of programmed cell death, and known candidate cancer genes. In addition to protein-coding hub genes, non-coding hub genes had a possible relationship with cancer. Overall, our approach of investigating cancer genes enabled finding pan-cancer hub genes and common functional gene clusters shared by multiple cancer types based on the expression status of the primary tumour and the functional relationship of genes in the biological network.

Identification of the interaction network of hub genes for melanoma treated with vemurafenib based on microarray data.

The objective of this study was to identify possible biomarkers and to explore the mechanisms of suppression of vemurafenib on melanoma progression. GSE42872 affymetrix microarray data were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) between vehicle-treated samples and vemurafenib-treated samples were identified. Gene ontology and pathway enrichment analysis of DEGs were performed, followed by protein-protein interaction (PPI) network construction. Furthermore, the functional modules of the PPI network were screened using BioNet analysis tool. Finally, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for DEGs in the module. In total, 794 upregulated transcripts corresponding to 214 genes and 977 downregulated transcripts corresponding to 325 genes were screened. The downregulated DEGs were significantly enriched in pathways such as cell cycle, DNA replication, and p53 signaling pathway. Upregulated DEGs were significantly enriched in phosphatidylinositol signaling system and inositol phosphate metabolism. Significantly enriched functions of downregulated DEGs were mitotic cell cycle, nuclear division, DNA metabolic process, cell cycle, and mitosis. Upregulated DEGs were mainly enriched in single multicellular organism process and multicellular organismal process. Moreover, cell division cycle 6, checkpoint kinase 1 (CHEK1), E2F transcription factor 1 (E2F1), epidermal growth factor receptor (EGFR), and phosphoinositide-3-kinase, regulatory subunit 1-α (PIK3R1) of the module were remarkably enriched in pathways such as cell cycle, apoptosis, focal adhesion, and DNA replication. Cell division cycle 6, CHEK1, E2F1, EGFR, and PIK3R1 of the module and their relative pathways, cell cycle, and focal adhesion might play important roles of suppression of vemurafenib on melanoma progression.