Delete search term


Quick navigation

Main navigation

Exploring the silent fitness landscape

At a glance


Since Darwin, natural selection has been recognized as one of major biological forces
shaping genetic patterns in molecular data. Detecting selection on proteins has
become an indispensible part of genome studies. Remarkably selection can act not
only on proteins, but also on synonymous codons translating into the same amino
acid. This manifests itself as codon bias, with no influence on the protein sequence,
but with potentially strong impact on the protein product and associated cellular
processes. In addition, mechanisms such as biased gene conversion may result in an
excess of synonymous changes with mild deleterious effect. The role of selection on
synonymous changes is often studied by measuring codon usage on the entire gene.
This approach however lacks power: it ignores evolutionary information and the impact
of site-specific synonymous rate variation, found in >1/3 of proteins. For instance, the
use of rare codons at certain sites may slow down translation producing a ribosomal
pause for ubiquitin modification or for co-translational protein folding. Codon choice at
such sites may affect protein synthesis or product’s properties. Synonymous changes
at sites of miRNA or siRNA binding may have impact on protein abundance in a
process known as RNA interference (RNAi). Recently single synonymous mutations
have been shown to contribute to human diseases such as cancers and diabetes. Such
sites often use rare codons or exhibit high synonymous variability.
Here we focus on site-specific synonymous codon bias due to selection or
biased gene conversion. We develop statistical methods to identify candidate
sites in genome-wide scans of species orthologs. A deeper insight into evolutionary
dynamics at synonymous sites will come from contrasting fixed differences between
species and polymorphisms within populations. To test predictions of the neutral
theory about macro- and microevolutionary forces acting on genomes, we develop a
statistical framework for analyzing mixed population/species data, thereby bridging
the existing methodological gap between molecular evolution and population genetics

Further information