Impact of competition between precursor and mature microRNAs on stochastic gene expression

Raunak Adhikary1, Dipjyoti Das1

  • 1Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, Mohanpur, West Bengal 741246, India.

Biophysical Journal
|July 13, 2026
PubMed

Insights

MicroRNA (miRNA) maturation impacts gene expression noise. Competition between precursor and mature miRNAs can create bimodal mRNA distributions, revealing noise-induced variability beyond traditional models.

Area of Science:

  • Molecular Biology
  • Systems Biology
  • Gene Regulation

Background:

  • MicroRNAs (miRNAs) are crucial post-transcriptional regulators.
  • miRNA maturation involves processing precursor miRNAs (pre-miRNAs) into mature miRNAs.
  • Pre- and mature miRNAs can compete for the same target mRNAs, but their impact on gene expression noise is unclear.

Purpose of the Study:

  • Investigate how miRNA maturation influences gene expression noise in feedback loops.
  • Determine the role of pre-miRNA and mature miRNA competition in gene expression variability.
  • Analyze the relationship between noise-induced bimodality and deterministic bifurcations.

Main Methods:

  • Developed a mathematical model for feedback motifs involving miRNA regulation.
  • Analyzed mean-field behavior of positive and negative feedback loops.
  • Performed stochastic simulations to assess gene expression noise and mRNA distributions.

Main Results:

  • miRNA maturation tunes feedback loops into bistable switches or oscillators.
  • Relative degradation rates and co-degradation influence bistability and oscillation regions.
  • Stochastic simulations revealed noise-induced bimodal mRNA distributions outside predicted mean-field regions for both positive and negative feedback.

Conclusions:

  • Noise-induced phenotypic variability is not always tied to deterministic bifurcations.
  • miRNA maturation significantly shapes stochastic gene expression in regulatory networks.
  • Findings are relevant for understanding gene expression in development and disease.

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