Glucose transporter gene expression: Regulation of transcription and mRNA stability

1995 ◽  
Vol 66 (3) ◽  
pp. 465-505 ◽  
Author(s):  
Kevin M McGowan ◽  
Sheree D Long ◽  
Phillip H Pekala
Author(s):  
Hsin-Yen Larry Wu ◽  
Polly Yingshan Hsu

ABSTRACTUpstream ORFs (uORFs) are widespread cis-regulatory elements in the 5’ untranslated regions of eukaryotic genes. Translation of uORFs could negatively regulate protein synthesis by repressing main ORF (mORF) translation and by reducing mRNA stability presumably through nonsense-mediated decay (NMD). While the above expectations were supported in animals, they have not been extensively tested in plants. Using ribosome profiling, we systematically identified 2093 Actively Translated uORFs (ATuORFs) in Arabidopsis seedlings and examined their roles in gene expression regulation by integrating multiple genome-wide datasets. Compared with genes without uORFs, we found ATuORFs result in 38%, 14%, and 43% reductions in translation efficiency, mRNA stability, and protein levels, respectively. The effects of predicted but not actively translated uORFs are much weaker than those of ATuORFs. Interestingly, ATuORF-containing genes are also expressed at higher levels and encode longer proteins with conserved domains, features that are common in evolutionarily older genes. Moreover, we provide evidence that uORF translation in plants, unlike in vertebrates, generally does not trigger NMD. We found ATuORF-containing transcripts are degraded through 5’ to 3’ decay, while NMD targets are degraded through both 5’ to 3’ and 3’ to 5’ decay, suggesting uORF-associated mRNA decay and NMD have distinct genetic requirements. Furthermore, we showed ATuORFs and NMD repress translation through separate mechanisms. Our results reveal that the potent inhibition of uORFs on mORF translation and mRNA stability in plants are independent of NMD, highlighting a fundamental difference in gene expression regulation by uORFs in the plant and animal kingdoms.


Diabetologia ◽  
1993 ◽  
Vol 36 (8) ◽  
pp. 696-706 ◽  
Author(s):  
Y. Takao ◽  
S. Akazawa ◽  
K. Matsumoto ◽  
H. Takino ◽  
M. Akazawa ◽  
...  

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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