Massively parallel gene expression variation measurement of a synonymous codon library.

Area of Science:

• Molecular Biology
• Genetics
• Biotechnology

Background:

• Cell-to-cell variation in gene expression impacts population behavior and biological processes.
• The influence of codon usage on single-cell gene expression variation remains unclear.

Purpose of the Study:

• To investigate how codon usage affects gene expression variation between single cells.
• To explore the relationship between codon properties and protein expression noise.

Main Methods:

• Utilized a Sort-seq based massively parallel strategy in Escherichia coli.
• Quantified gene expression variation from a green fluorescent protein (GFP) library with synonymous codons.

Main Results:

• Sequences with higher tRNA Adaptation Index (TAI) and Codon Adaptation Index (CAI) scores exhibited increased GFP variance.
• No correlation was found with Normalized Translation Efficiency Index (nTE) or mRNA secondary structure free energy.
• Protein expression noise (CV²) scaled with mean protein abundance for low-abundance proteins but plateaued at high abundance.

Conclusions:

• The primary source of noise for high-abundance proteins likely does not stem from translation elongation.
• Codon optimization can be pursued for biotechnology applications without necessarily increasing gene expression noise.