iCodon customizes gene expression based on the codon composition.

Area of Science:

• Molecular Biology
• Genetics
• Bioinformatics

Background:

• Messenger RNA (mRNA) stability is a key determinant of cellular gene expression levels.
• Codon composition significantly affects mRNA stability through translation-dependent mechanisms, known as codon optimality.

Purpose of the Study:

• To develop and present iCodon, an algorithm for customizing mRNA expression via synonymous codon substitutions.
• To enable prediction and modification of mRNA stability for various vertebrate transcriptomes.

Main Methods:

• Developed the iCodon algorithm for optimizing mRNA expression by altering codon composition.
• Validated codon optimality predictions against experimental data on mRNA stability and gene expression levels.
• Applied iCodon to mouse, human, frog, and fish transcriptomes.

Main Results:

• Codon optimality predictions correlate with measured mRNA stability using reporter libraries.
• Predictions also correlate with gene expression levels observed in fluorescent reporters and endogenous gene studies.
• iCodon successfully generates both more stable and less stable mRNA variants.

Conclusions:

• iCodon provides a tool to predict and manipulate mRNA stability through codon optimization.
• This algorithm has broad utility in basic biological research, biotechnology, and biomedicine.
• Customizing mRNA expression via codon composition offers a novel approach for controlling gene expression.