Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.7.6 (RNA polymerase)
 34,946 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The present study assessed the feasibility of transferring the insulin gene into liver cells of diabetic individuals using a defective adenoassociated viral (AAV) vehicle. AAV offers several advantages over other viral vectors, since this vehicle can facilitate transfection in vivo without cell division and without any viral coding sequences (thus minimizing inflammation). The rat insulin gene and lacZ were each packed into a defective AAV vehicle (AAV-INS and AAV-lacZ, respectively). Successful AAV-mediated transfection and expression of lacZ into hepatocytes in primary cell culture were demonstrated by chemiluminescent assay of beta-galactosidase. Similarly, AAV-mediated transfection and expression of the insulin gene into hepatocytes was demonstrated by immunocytochemistry and reverse-transcriptase polymerase chain reaction (RT-PCR). After AAV-mediated transfection of the insulin gene into hepatocytes, glucose in the medium was significantly reduced for up to 5 days. After direct injection of AAV-INS into liver parenchyma of diabetic mice, successful transfection was demonstrated by RT-PCR, and blood glucose was significantly decreased for at least 6 days. These studies suggest that the AAV vector may be used to transfer the insulin gene into liver cells in vitro and in vivo.
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PMID:Defective adenoassociated viral-mediated transfection of insulin gene by direct injection into liver parenchyma decreases blood glucose of diabetic mice. 949 94

BACKGROUND The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases. MATERIAL AND METHODS IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein-protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively. RESULTS A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes. CONCLUSIONS The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.
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PMID:Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases. 3265 53