Enhanced MiRISC expression noise reduction by self-feedback regulation of mRNA degradation

Area of Science:

• Molecular Biology
• Systems Biology
• Bioinformatics

Background:

• The microRNA-induced silencing complex (miRISC) is crucial for gene regulation, acting as a major noise-reducing mechanism.
• Negative self-feedback loops, where miRISC targets its own mRNAs, are known noise-reduction strategies.

Purpose of the Study:

• To investigate the relationship between mRNA degradation rate, self-feedback, and gene expression noise.
• To validate mathematical modeling predictions using single-cell RNA sequencing data.

Main Methods:

• Mathematical modeling of mRNA degradation and expression noise.
• Analysis of a total-RNA single-cell RNA sequencing (scRNA-seq) dataset.
• Calculation of degradation rates (Kdeg) and expression noise for various mRNAs.

Main Results:

• Mathematical models predicted a negative correlation between mRNA expression noise and degradation rate, attenuated by self-feedback.
• miRNA-targeted mRNAs showed higher Kdeg and reduced cell-to-cell expression noise.
• miRISC component mRNAs (AGO1/2/3, TNRC6A/B/C) exhibited further noise reduction, consistent with the model.

Conclusions:

• Negative self-feedback regulation of mRNA degradation reinforces miRISC, the core machinery for miRNA-mediated noise reduction.
• This study demonstrates a trade-off between noise suppression and metabolic costs associated with accelerated mRNA degradation and translational inhibition.
• Provides evidence for direct self-feedback regulation of mRNA degradation in noise reduction.