Problem-solving test: The effect of synonymous codons on gene expression

2009 ◽  
Vol 37 (6) ◽  
pp. 371-374
Author(s):  
József Szeberényi
2020 ◽  
Vol 21 (8) ◽  
pp. 2882 ◽  
Author(s):  
Grazyna Leszczynska ◽  
Marek Cypryk ◽  
Bartlomiej Gostynski ◽  
Klaudia Sadowska ◽  
Paulina Herman ◽  
...  

5-Substituted 2-selenouridines (R5Se2U) are post-transcriptional modifications present in the first anticodon position of transfer RNA. Their functional role in the regulation of gene expression is elusive. Here, we present efficient syntheses of 5-methylaminomethyl-2-selenouridine (1, mnm5Se2U), 5-carboxymethylaminomethyl-2-selenouridine (2, cmnm5Se2U), and Se2U (3) alongside the crystal structure of the latter nucleoside. By using pH-dependent potentiometric titration, pKa values for the N3H groups of 1–3 were assessed to be significantly lower compared to their 2-thio- and 2-oxo-congeners. At physiological conditions (pH 7.4), Se2-uridines 1 and 2 preferentially adopted the zwitterionic form (ZI, ca. 90%), with the positive charge located at the amino alkyl side chain and the negative charge at the Se2-N3-O4 edge. As shown by density functional theory (DFT) calculations, this ZI form efficiently bound to guanine, forming the so-called “new wobble base pair”, which was accepted by the ribosome architecture. These data suggest that the tRNA anticodons with wobble R5Se2Us may preferentially read the 5′-NNG-3′ synonymous codons, unlike their 2-thio- and 2-oxo-precursors, which preferentially read the 5′-NNA-3′ codons. Thus, the interplay between the levels of U-, S2U- and Se2U-tRNA may have a dominant role in the epitranscriptomic regulation of gene expression via reading of the synonymous 3′-A- and 3′-G-ending codons.


2019 ◽  
Vol 11 (12) ◽  
pp. 3523-3528 ◽  
Author(s):  
Jérôme Bourret ◽  
Samuel Alizon ◽  
Ignacio G Bravo

Abstract Codon Usage Preferences (CUPrefs) describe the unequal usage of synonymous codons at the gene, chromosome, or genome levels. Numerous indices have been developed to evaluate CUPrefs, either in absolute terms or with respect to a reference. We introduce the normalized index COUSIN (for COdon Usage Similarity INdex), that compares the CUPrefs of a query against those of a reference and normalizes the output over a Null Hypothesis of random codon usage. The added value of COUSIN is to be easily interpreted, both quantitatively and qualitatively. An eponymous software written in Python3 is available for local or online use (http://cousin.ird.fr). This software allows for an easy and complete analysis of CUPrefs via COUSIN, includes seven other indices, and provides additional features such as statistical analyses, clustering, and CUPrefs optimization for gene expression. We illustrate the flexibility of COUSIN and highlight its advantages by analyzing the complete coding sequences of eight divergent genomes. Strikingly, COUSIN captures a bimodal distribution in the CUPrefs of human and chicken genes hitherto unreported with such precision. COUSIN opens new perspectives to uncover CUPrefs specificities in genomes in a practical, informative, and user-friendly way.


2016 ◽  
Vol Volume 6 ◽  
pp. 57-65 ◽  
Author(s):  
Sutanuka Mitra ◽  
Suvendra Kumar Ray ◽  
Rajat Banerjee

10.29007/87r9 ◽  
2020 ◽  
Author(s):  
Zhixiu Lu ◽  
Michael Gilchrist ◽  
Scott Emrich

Codon usage bias has been known to reflect the expression level of a protein-coding gene under the evolutionary theory that selection favors certain synonymous codons. Although measuring the effect of selection in simple organisms such as yeast and E. coli has proven to be effective and accurate, codon-based methods perform less well in plants and humans. In this paper, we extend a prior method that incorporates another evolutionary factor, namely mutation bias and its effect on codon usage. Our results indicate that prediction of gene expression is significantly improved under our framework, and suggests that quantification of mutation bias is essential for fully understanding synonymous codon usage. We also propose an improved method, namely MLE-Φ, with much greater computation efficiency and a wider range of applications. An implementation of this method is provided at https://github.com/luzhixiu1996/MLE- Phi.


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