Hidden patterns of codon usage bias across kingdoms.

Area of Science:

• Genetics
• Computational Biology
• Bioinformatics

Background:

• The genetic code's degeneracy means multiple codons code for one amino acid.
• Synonymous codons are used non-randomly, a bias with an unknown origin.
• Understanding codon usage bias is crucial for molecular biology and evolutionary studies.

Purpose of the Study:

• To derive a first-principles mathematical model for codon usage bias statistics.
• To introduce a novel computational measure, distance $$, for quantifying codon usage bias.
• To analyze codon usage bias across diverse species and identify factors influencing it.

Main Methods:

• Developed a mathematical model based on stochastic thermodynamics.
• Derived a new computational measure, distance $$, assessing codon frequency and mRNA distribution.
• Performed large-scale computational analysis of over 900 species across fungal and bacterial kingdoms.

Main Results:

• The model accurately describes codon usage bias distributions in fungi and bacteria.
• Distance $$ revealed heritable codon usage bias with significant variation within taxonomic classes.
• Common model organisms exhibit median bias, unlike species of medical/agricultural importance with extreme bias.
• Amino acid-specific codon usage patterns vary across life, correlating with tRNA content.

Conclusions:

• Codon usage bias is influenced by heritable traits and varies significantly even within related species.
• The novel measure $$ offers new biological insights into codon bias.
• Model organisms may not represent the full spectrum of codon usage bias found in medically or agriculturally relevant species.