Fitness-Driven Scaling Laws between mRNA and Protein Levels

Area of Science:

• Molecular Biology
• Evolutionary Biology
• Systems Biology

Background:

• Gene expression links messenger RNA (mRNA) and protein levels, but the evolutionary drivers of this correlation are unclear.
• The reliability of using mRNA abundance as a proxy for protein levels is questioned due to poorly understood evolutionary forces.
• Understanding the mRNA-protein relationship is vital for accurate gene expression analysis.

Purpose of the Study:

• To uncover the evolutionary basis for the correlation between mRNA and protein levels.
• To establish an optimal scaling law between mRNA and protein that maximizes cellular fitness.
• To investigate the role of transcriptional and translational regulation in determining protein levels from an evolutionary standpoint.

Main Methods:

• Developed a theoretical model balancing the costs and benefits of gene expression.
• Derived an optimal scaling relationship between mRNA (m) and protein (p) levels: m∼p^{γ}.
• Incorporated biologically relevant parameters and simulated genome-wide scaling in Saccharomyces cerevisiae.
• Analyzed genome-wide data across multiple organisms.

Main Results:

• Predicted a lower bound of 0.5 for the scaling exponent (γ), with protein toxicity increasing this value.
• Simulated scaling in Saccharomyces cerevisiae closely matched experimental data.
• Observed genome-wide scaling exponents above the predicted lower bound across various organisms.
• Rationalized the roles of transcriptional and translational regulation in protein level determination.

Conclusions:

• The study provides an evolutionary framework for understanding the mRNA-protein correlation.
• The derived optimal scaling law (m∼p^{γ}) offers insights into gene expression regulation.
• Findings highlight the importance of both transcriptional and translational control in shaping protein abundance.
• The research validates the model using experimental data from yeast and other organisms.