Finding of IFNγ gene enhancers and their core sequences

Genome ◽  
2013 ◽  
Vol 56 (3) ◽  
pp. 147-154 ◽  
Author(s):  
Zhanjun Lv ◽  
Jianjun Cheng ◽  
Ying Xie ◽  
Xiangyang Jing ◽  
Yuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reporter Gene ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Enhancer Activity ◽  
Gfp Gene ◽  
Gfp Reporter ◽  
Segmentation Methods ◽  
Gfp Reporter Gene ◽  
Dna Segmentation

DNA segmentation methods were used to study which fragments of the human IFNγ gene possess enhancer activity. The human IFNγ gene was divided into 240-bp fragments, which were inserted between the GFP gene and the Alu tandem sequence to determine whether the inserted sequences eliminate the inhibition induced by the Alu tandem sequence. We found that five different 240-bp fragments (FUIFN3F3R, IFN4F4R, IFN6F6R, IFN21F21R, and IFN22F22R) and two 60-bp core sequences (IFN6-2F2R and IFN21-3-4F3-4R) derived from the IFNγ gene contain enhancers that can activate the GFP reporter gene. These enhancers may be targets of IFNγ gene expression regulation.

Interplay of Promoter Usage and Intragenic CpG Content: Impact on GFP Reporter Gene Expression

2015 ◽  
Vol 26 (12) ◽  
pp. 826-840 ◽  
Author(s):  
Simone Krinner ◽  
Asli Heitzer ◽  
Benedikt Asbach ◽  
Ralf Wagner
Keyword(s):  
Gene Expression ◽  
Reporter Gene ◽  
Reporter Gene Expression ◽  
Gfp Reporter ◽  
Gfp Reporter Gene ◽  
Promoter Usage

scholarly journals The Impact of GFP Reporter Gene Transduction and Expression on Metabolomics of Placental Mesenchymal Stem Cells Determined by UHPLC-Q/TOF-MS

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jinfeng Yang ◽  
Nan Wang ◽  
Deying Chen ◽  
Jiong Yu ◽  
Qiaoling Pan ◽  
...  
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Reporter Gene ◽  
Fluorescent Protein ◽  
Medicine Research ◽  
Gfp Gene ◽  
Placental Mesenchymal Stem Cells ◽  
Gfp Reporter ◽  
Gfp Reporter Gene ◽  
The Impact

Introduction. Green fluorescent protein (GFP) is widely used as a reporter gene in regenerative medicine research to label and track stem cells. Here, we examined whether expressing GFP gene may impact the metabolism of human placental mesenchymal stem cells (hPMSCs). Methods. The GFP gene was transduced into hPMSCs using lentiviral-based infection to establish GFP+hPMSCs. A sensitive 13C/12C-dansyl labeling LC-MS method targeting the amine/phenol submetabolome was used for in-depth cell metabolome profiling. Results. A total of 1151 peak pairs or metabolites were detected from 12 LC-MS runs. Principal component analysis and partial least squares discriminant analysis showed poor separation, and the volcano plots demonstrated that most of the metabolites were not significantly changed when hPMSCs were tagged with GFP. Overall, 739 metabolites were positively or putatively identified. Only 11 metabolites showed significant changes. Metabolic pathway analyses indicated that three of the identified metabolites were involved in nine pathways. However, these metabolites are unlikely to have a large impact on the metabolic pathways due to their nonessential roles and limited hits in pathway analysis. Conclusion. This study indicated that the expression of ectopic GFP reporter gene did not significantly alter the metabolomics pathways covered by the amine/phenol submetabolome.

Gerea: Prediction Of Gene Expression Regulators From Transcriptome Profiling Data To Transition Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Target Gene ◽  
Gene Expression Regulation ◽  
Transcriptome Profiling ◽  
Expression Regulation ◽  
Mouse Gene ◽  
Mouse Gene Expression ◽  
Causality Inference ◽  
Identify Gene Expression

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.

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