Evolution no longer explains Codon Bias

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The mutation-selection-drift (MSD) model has been a prominent NeoDarwinian framework for understanding codon bias, the phenomenon where synonymous codons are used with different frequencies. It tried to explain codon bias within the realms of neo darwinism.  Reason being NeoDarwinisms random mutations should not allow such a bias. The model proposes that codon bias arises from a balance between three evolutionary forces: mutation, selection, and genetic drift. Mutation introduces new codon variants into the population, selection favors codons that lead to more efficient translation, and genetic drift can cause random fluctuations in codon frequencies.

However, recent research has highlighted the importance of GC-biased gene conversion (gBGC) as a major contributor to codon bias. gBGC is a non darwinian non-reciprocal recombination process that preferentially converts AT nucleotides to GC nucleotides. This is opposed to NeoDarwinian random mutations. This process can lead to a genome-wide increase in GC content, which in turn influences codon bias.


Studies have shown that gBGC is a significant force in shaping codon bias, particularly in unicellular organisms. For instance, in the bacterium Escherichia coli, gBGC is estimated to account for about half of the observed codon bias. This suggests that the MSD model may need to be revised or replaced to incorporate the effects of gBGC.

The paper "A General Model of Codon Bias Due to GC Mutational Bias" by Gareth A. Palidwor explores the relationship between GC mutational bias and codon usage bias in various organisms.  Codon usage bias is the unequal usage of synonymous codons, which are different codons that code for the same amino acid.


Note GC dominance

Palidwor proposes a continuous-time Markov chain model to explain the observed patterns of codon usage bias in prokaryotes, plants, and humans. The model assumes that synonymous mutations occur at a constant rate and that the probability of a mutation is determined by the GC content of the codon. The model also takes into account the fact that some amino acids have more GC-rich codons than others.

The model successfully predicts the qualitative usage patterns of all codons, including the nonlinear codon usage observed in isoleucine, arginine, and leucine. The model also accounts for a significant proportion of the observed variability in codon usage, ranging from 52% in humans to 87% in prokaryotes.

Overall, the study provides a comprehensive explanation of codon usage bias due to GC mutational bias and offers a valuable tool for understanding the evolution of codon usage.

The papers conclusion challenges neo-Darwinism in several ways.

  • It suggests that GC mutational bias is a major factor in determining codon usage. This is in contrast to the neo-Darwinian view that codon usage is primarily determined by selection.

  • It shows that GC mutational bias can lead to non-random patterns of codon usage. This is also in contrast to the neo-Darwinian view that codon usage should be random.

  • It suggests that GC mutational bias can have a significant impact on the evolution of proteins. This is in contrast to the neo-Darwinian view that selection is the primary driver of protein evolution.

Overall, the paper suggests that GC mutational bias is a more important factor in evolution than is currently recognized by neo-Darwinists. This could have significant implications for our understanding of how life evolved.

Here are some specific examples of how the paper challenges neo-Darwinism:

  • The paper shows that the usage of some codons is nonlinear as a function of GC bias. This is not expected under the neo-Darwinian view of codon usage.

  • The paper shows that the usage of two G-ending codons, AGG (arginine) and TTG (leucine), decreases with increasing GC bias. This is contrary to the expectation that G/C-ending codon usage should increase with increasing genomic GC bias.

These findings suggest that GC mutational bias can have a complex and counterintuitive effect on codon usage. This is not consistent with the neo-Darwinian view of codon usage as a simple and predictable process.

The paper concludes by suggesting that GC mutational bias is a major factor in determining codon usage and that it can have a significant impact on the evolution of proteins. This is a challenge to the neo-Darwinian view of evolution and suggests that we need to reconsider the role of mutational bias in evolutionary processes as well as putting the NeoDarwinian mutation selection drift model behind us.

Article snippets

In spite of extensive research on the effect of mutation and selection on codon usage, a general model of codon usage bias due to mutational bias has been lacking.

Because most amino acids allow synonymous GC content changing substitutions in the third codon position, the overall GC bias of a genome or genomic region is highly correlated with GC3, a measure of third position GC content.

G/C ending codons usage generally increases with increasing GC bias and decreases with increasing AT bias.

Codon bias, the unequal usage of synonymous codons, varies widely between species and, in some cases, between different regions of a genome in a single species.

Motivated by some unexpected observation on codon usage, we have developed a codon usage model based on GC-biased synonymous point mutations.

The influence of GC bias is a major influence on codon bias both in human and prokaryotic genomes, resulting in a close association between GC% at the third codon position, also called GC3.

This has led to a common belief that the use of synonymous G/C-ending codons should increase in frequency with increasing GC bias, while usage of A/T-ending codons should decrease.

Our model establishes that GC bias is the dominant factor in determining codon bias across a broad variety of life and that the form of the influence admits a particularly simple explanation.

This model provides a natural null model for codon bias subject to GC mutational bias, relative to which further studies of codon usage may be measured.

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