Can we always sweep the details of RNA-processing under the carpet?

TL;DR

This study models RNA processing cascades, revealing how enzyme copy-number fluctuations influence RNA molecule variability and demonstrating conditions where such fluctuations can be mitigated or amplified.

Contribution

It provides a mathematical framework for understanding how enzyme fluctuations impact RNA processing dynamics and gene expression variability.

Findings

RNA substrates at steady-state follow a Poisson distribution.

Fluctuations in enzyme copy-numbers increase RNA variability linearly with transcription rate.

Under certain conditions, RNA processing can reduce expression fluctuations.

Abstract

RNA molecules follow a succession of enzyme-mediated processing steps from transcription until maturation. The participating enzymes, for example the spliceosome for mRNAs and Drosha and Dicer for microRNAs, are also produced in the cell and their copy-numbers fluctuate over time. Enzyme copy-number changes affect the processing rate of the substrate molecules; high enzyme numbers increase the processing probability, low enzyme numbers decrease it. We study different RNA processing cascades where enzyme copy-numbers are either fixed or fluctuate. We find that for fixed enzyme-copy numbers the substrates at steady-state are Poisson-distributed, and the whole RNA cascade dynamics can be understood as a single birth-death process of the mature RNA product. In this case, solely fluctuations in the timing of RNA processing lead to variation in the number of RNA molecules. However, we show analytically and numerically that when enzyme copy-numbers fluctuate, the strength of RNA fluctuations increases linearly with the RNA transcription rate. This linear effect becomes stronger as the speed of enzyme dynamics decreases relative to the speed of RNA dynamics. Interestingly, we find that under certain conditions, the RNA cascade can reduce the strength of fluctuations in the expression level of the mature RNA product. Finally, by investigating the effects of processing polymorphisms we show that it is possible for the effects of transcriptional polymorphisms to be enhanced, reduced, or even reversed. Our results provide a framework to understand the dynamics of RNA processing.