| Summary: | Extracting biologically meaningful information from the continuing flood of genomic
data is a major challenge in the life sciences. Codon usage bias (CUB) is a general
feature of most genomes and is thought to reflect the effects of both natural
selection for efficient translation and mutation bias. Here we present a
mechanistically interpretable, Bayesian model (ribosome overhead costs Stochastic
Evolutionary Model of Protein Production Rate [ROC SEMPPR]) to extract meaningful
information from patterns of CUB within a genome. ROC SEMPPR is grounded in
population genetics and allows us to separate the contributions of mutational biases
and natural selection against translational inefficiency on a gene-by-gene and
codon-by-codon basis. Until now, the primary disadvantage of similar approaches was
the need for genome scale measurements of gene expression. Here, we demonstrate that
it is possible to both extract accurate estimates of codon-specific mutation biases
and translational efficiencies while simultaneously generating accurate estimates of
gene expression, rather than requiring such information. We demonstrate the utility
of ROC SEMPPR using the Saccharomyces cerevisiae S288c genome. When
we compare our model fits with previous approaches we observe an exceptionally high
agreement between estimates of both codon-specific parameters and gene expression
levels (ρ>0.99 in all cases). We also observe strong agreement
between our parameter estimates and those derived from alternative data sets. For
example, our estimates of mutation bias and those from mutational accumulation
experiments are highly correlated (ρ=0.95). Our estimates of codon-specific translational
inefficiencies and tRNA copy number-based estimates of ribosome pausing time
(ρ=0.64), and mRNA and ribosome profiling footprint-based
estimates of gene expression (ρ=0.53−0.74) are also highly correlated, thus supporting the
hypothesis that selection against translational inefficiency is an important force
driving the evolution of CUB. Surprisingly, we find that for particular amino acids,
codon usage in highly expressed genes can still be largely driven by mutation bias
and that failing to take mutation bias into account can lead to the misidentification
of an amino acid’s “optimal” codon. In conclusion, our method
demonstrates that an enormous amount of biologically important information is encoded
within genome scale patterns of codon usage, accessing this information does not
require gene expression measurements, but instead carefully formulated biologically
interpretable models.
|
|---|