Bioenergetic costs of gene regulation by microRNAs

What is it about?

MicroRNAs have evolved to play a variety of important regulatory roles in eukaryotic organisms, including controlling the noise levels of protein expression. Since large fluctuations in protein populations can lead to unwanted changes in the state of the cell, keeping noise levels low can be essential for healthy functioning. However this noise control comes at a significant metabolic cost: in order to compensate for the effects of microRNA-mediated interference in translation, the transcription rates of regulated genes have to be increased. In effect, organisms have to sacrifice a fraction of messenger RNAs (via enhanced degradation and translation suppression due to microRNAs) to achieve the same expression levels with lower variability in protein numbers. How would such a costly regulation evolve in the first place, and has microRNA regulation been fine-tuned to minimize the energetic prices? While past research has experimentally validated microRNA noise control, and developed models for the underlying regulatory networks, the metabolic aspects have to date been relatively unexplored. In this work, we study how the metabolic costs of microRNA noise regulation depend on interaction affinities. To achieve this, we developed a stochastic model of microRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs and binding free energies for a wide range of microRNA seeds.

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Photo by DIGITALE on Unsplash

Photo by DIGITALE on Unsplash

Why is it important?

We show for the first time that microRNA-mRNA affinities lie in an evolutionary sweet spot: sequences that are much longer or short would not have the right binding properties to optimally reduce noise. In fact, we argue that the energetic costs of microRNA regulation can become sufficiently high that natural selection could drive them to this precise sweet spot. Moreover, the behaviour of the optimal microRNA network mimics the best possible linear noise filter, a classic concept in engineered communication systems. These results illustrate how selective pressure toward metabolic efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.