Synonymous codons, ribosome speed, and eukaryotic gene expression regulation

2014 ◽  
Vol 71 (21) ◽  
pp. 4195-4206 ◽  
Author(s):  
Daniel Tarrant ◽  
Tobias von der Haar
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Synonymous Codons ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene

scholarly journals Designing Eukaryotic Gene Expression Regulation Using Machine Learning

2020 ◽  
Vol 38 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Ronald P.H. de Jongh ◽  
Aalt D.J. van Dijk ◽  
Mattijs K. Julsing ◽  
Peter J. Schaap ◽  
Dick de Ridder
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene

Role of small nuclear RNAs in eukaryotic gene expression

2013 ◽  
Vol 54 ◽  
pp. 79-90 ◽  
Author(s):  
Saba Valadkhan ◽  
Lalith S. Gunawardane
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Rna Stability ◽  
Splice Sites ◽  
Branch Site ◽  
Small Nuclear Rnas ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene ◽  
Rna Biogenesis

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.

Implications of DNA replication for eukaryotic gene expression

1991 ◽  
Vol 99 (2) ◽  
pp. 201-206 ◽  
Author(s):  
A.P. Wolffe
Keyword(s):  
Gene Expression ◽  
Dna Replication ◽  
Replication Fork ◽  
Recent Progress ◽  
Gene Activity ◽  
Nucleosome Assembly ◽  
Developmental Processes ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene

DNA replication has a key role in many developmental processes. Recent progress in understanding events at the replication fork suggests mechanisms for both establishing and maintaining programs of eukaryotic gene activity. In this review, I discuss the consequences of replication fork passage for preexisting chromatin structures and describe how the mechanism of nucleosome assembly at the replication fork may facilitate the formation of either transcriptionally active or repressed chromatin.

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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