Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Abstract

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.