Abstract

Because synonymous mutations do not change the amino acid sequence of a protein, they are generally considered to be selectively neutral. Empiric data suggest, however, that a significant fraction of viral mutational fitness effects may be attributable to synonymous mutation. Bias in synonymous codon usage in viruses may result from selection for translational efficiency, mutational bias, base pairing requirements in RNA structures, or even selection against specific dinucleotides by innate immune effectors. Experimental analyses of codon usage and genome evolution have been facilitated by advances in synthetic biology, which now make it feasible to generate viral genomes that contain large numbers of synonymous mutations. The generally pleiotropic effects of synonymous mutation on viral fitness have, at times, made it difficult to define the mechanistic basis for the observed attenuation of these heavily mutated viruses. We have addressed this problem by developing a bioinformatic tool for the generation and analysis of viral sequences with large-scale synonymous mutation. A variety of permutation strategies are applied to shuffle codons within an open reading frame. After measuring the dinucleotide frequency, codon usage, codon pair bias, and free energy of RNA folding for each permuted genome, we used z-score normalization and a least squares regression model to quantify their overall distance from the starting sequence. Using this approach, the user can easily identify a large number of synonymously mutated sequences with varying similarity to a wild-type genome across a range of nucleic-acid-based determinants of viral fitness. We believe that this tool will be useful in designing genomes for subsequent experimental studies of the fitness impacts of synonymous mutation.

Details

Title
CodonShuffle: a tool for generating and analyzing synonymously mutated sequences
Author
Daniel Macedo de Melo Jorge 1 ; Mills, Ryan E 2 ; Lauring, Adam S 3 

 Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA 
 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA 
 Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA 
Publication year
2015
Publication date
2015
Publisher
Oxford University Press
e-ISSN
20571577
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171763366
Copyright
© The Author 2015. Published by Oxford University Press. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.