Gene Expression Regulation: Chromatin Modification in the CNS

2009 ◽  
pp. 611-617
Author(s):  
J. Hsieh ◽  
A. Tsai ◽  
K. Ure
2019 ◽  
Vol 20 (22) ◽  
pp. 5755 ◽  
Author(s):  
Denise Drongitis ◽  
Francesco Aniello ◽  
Laura Fucci ◽  
Aldo Donizetti

The biology of transposable elements (TEs) is a fascinating and complex field of investigation. TEs represent a substantial fraction of many eukaryotic genomes and can influence many aspects of DNA function that range from the evolution of genetic information to duplication, stability, and gene expression. Their ability to move inside the genome has been largely recognized as a double-edged sword, as both useful and deleterious effects can result. A fundamental role has been played by the evolution of the molecular processes needed to properly control the expression of TEs. Today, we are far removed from the original reductive vision of TEs as “junk DNA”, and are more convinced that TEs represent an essential element in the regulation of gene expression. In this review, we summarize some of the more recent findings, mainly in the animal kingdom, concerning the active roles that TEs play at every level of gene expression regulation, including chromatin modification, splicing, and protein translation.


2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  

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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