Codon usage/bias
- 1. By Vishnu Prasad Nair.R.U 100716517001 M.Sc Genetics OSMANIA UNIVERSITY
- 2. INTRODUCTION:- One of the main characteristics of the genetic code is that it is degenerate, i.e., multiple synonymous codons specify the same amino acid. Based on the degeneracy of codons, it would be predicted that all synonymous codons for any chosen amino acid would appear randomly distributed along the genes. for example, the three codons that specify isoleucine. Given that they all code for the identical amino acid, it would be predicted that AUU, AUC, and AUA would all appear in DNA strands one-third of all the times that isoleucine is coded for. In the early 1980s, studies have indicated this is not the case. Synonymous codons do not appear to be used equally and randomly to code for an amino acid. Some codons are repeatedly preferred over others; THIS PHENOMENON IS TERMED CODON USAGE BIAS. Codon usage frequencies in fact vary among genomes, among genes, and within genes.
- 3. To study codon bias, numerous measures to quantify codon usage have been developed. These include the widely used measures . 1. CAI ( Codon Adaptation Index ) 2. ENC ( Effective Number of Codons ) The Codon Adaptation Index (CAI) was developed in 1987 and uses a reference set of genes in a given species to determine which codons are preferred. The CAI score for a gene is calculated from the frequency of use of all codons in that gene. The index can be used to compare codon usage in different genes and in different organisms. The CAI for a gene ‘g’ can be calculated as:- where N is the number of codons in a gene g’ without the initiation and stop codons. where f i is the frequency of a codon ( i ) and f max( i ) is the frequency of the codon most often used to code for the considered amino acid in the subset of highly expressed genes . i.e the
- 4. The Effective Number of Codons (ENC) index was developed three years later in 1990. • The measure quantifies how far the codon usage of a gene departs from equal usage of synonymous codons using codon usage data and is independent of gene length and amino acid composition. • ENC does not rely on organism-specific data and is easily applied to the study of new organisms. • While CAI delivers values ranging from0 (no bias) to 1.0 or larger (complete bias), • ENC values range from 61 (no bias; each of the 61 sense codons used equally) to 20 (complete bias; only one codon is used for each amino acid).
- 5. why codon preference has evolved ? Ans :-The term “synonymous codons” is innately misleading; not all codons are created equal. The use of one codon over its synonyms does affect fitness, and selection has primarily driven the evolution of codon bias.
- 6. Factors Affecting Codon Usage Bias
- 7. 1. Selection for optimized translation :- • Translation is very energetically expensive; inefficient and inaccurate translation wastes limited cellular resources. • Throughout the evolution of genomes, mutations that reduce the energy required for translation have been favored. The phenomenon of codon usage bias is thus often explained by selection for translational optimization. • This hypothesis contends that the use of optimal codons can increase both the efficiency and the accuracy of translation. • Codon bias avoids slowly translated codons, which are more prone to incorporate the wrong amino acid. • T4 phages studies reveal codon usages are largely determined by the most abundant tRNAs of their hosts.
- 8. 2. Expression :- • Gene expression is the process by which the information within double-stranded DNA is transcribed into messenger RNA (mRNA) and then, following post transcriptional modification, it is translated by ribosomes to produce a protein polypeptide. • Studies of the genomes of a wide variety of organisms have revealed a correlation between gene expression level and codon usage bias, namely that high gene expression leads to high bias. • In genes that are translated often and at high volumes, codon bias appears to be especially high because the cost of a missense error is elevated. The ability to produce more accurately translated sequences faster through codon bias in highly expressed genes is thus selected for. • Study in humans examined the relationship between gene expression level and gene expression breadth and codon bias and showed that codon usage is more strongly related to breadth of expression than to maximum expression level.
- 9. 3. Location within genes :- • The degree of codon usage bias in a gene can vary based on codon location within a gene sequence. In Saccharomyces cerevisiae, codon bias increases along genes in the direction of translation.This location-based pattern of codon preference has been explained by two different hypothesis. 1. First, the existence of low-usage codon clusters slows translation extensively; when these clusters are located at the 3’ end of a gene, so much ribosomal slowing occurs upstream that it is as if the entire sequence were composed of low-usage codons.Thus, the increased number of optimal codons at the 3’ end of a gene increases the speed of translation and works to prevent ribosomal pile-up 2. The abundance of optimal codons may increase along the length of a gene sequence in order to prevent nonsense errors that would become increasingly expensive. In E. coli this pattern of increasing codon bias is stronger in longer genes than in shorter genes, and codon bias is positively correlated with gene length. This suggests that as a gene becomes longer, and more energy is required for translation, it is increasingly important to prevent nonsense errors at the 3’ end of a gene that would terminate translation prematurely and make the peptide synthesized up to that point useless.
- 10. 4. Rate of evolution :- • Studies on Saccharomyces cerevisiae , Drosophila melanogaster, Escherichia coli, and Salmonella typhimurium have revealed a significant negative correlation between codon usage bias and the rate of nucleotide substitution at silent sites. • Additionally, highly expressed genes have high codon bias and low rates of synonymous substitution. • Codon preferences reflect a balance between mutational biases and natural selection for translational optimization, and as mentioned before, optimal codons help to increase translation efficiency and accuracy. • Since optimal codons are favored by selection, and a synonymous substitution to a non-optimal codon would actually decrease fitness, selection among synonymous codons constrains the rate of silent substitution in some genes.