Quantifying post-transcriptional regulation in the development of Drosophila melanogaster

Area of Science:

• Molecular Biology
• Systems Biology
• Genomics

Background:

• Protein abundance is primarily determined by messenger RNA (mRNA) levels.
• However, the correlation between mRNA and protein levels is often moderate, suggesting complex post-transcriptional regulation.
• Understanding these regulatory mechanisms is crucial for deciphering gene expression control.

Purpose of the Study:

• To investigate the relationship between mRNA dynamics and protein abundances during Drosophila embryogenesis.
• To develop a quantitative framework for identifying post-transcriptional gene regulation.
• To characterize distinct protein regulatory scenarios and identify specific regulatory factors.

Main Methods:

• Generation of a paired transcriptome and proteome time-course dataset across 14 time points during Drosophila embryogenesis.
• Application of mathematical models to describe protein translation and degradation dynamics based on mRNA data.
• In-depth characterization of genes exhibiting potential post-transcriptional regulation.

Main Results:

• A moderate mRNA-protein correlation (ρ = 0.54) was observed.
• Mathematical models successfully explained 84% of protein time-courses using mRNA dynamics alone.
• Proteins were classified into four distinct regulatory scenarios, and the RNA-binding protein Hrb98DE was implicated in post-transcriptional regulation of sugar metabolism.

Conclusions:

• A systems biology framework can effectively identify post-transcriptional gene regulation from time-resolved omics data.
• Protein dynamics can be largely explained by mRNA levels and basic translation/degradation models, with deviations highlighting regulatory events.
• Hrb98DE is a potential key regulator in early embryonic sugar metabolism, warranting further investigation.