Reconstruction of Ancestral 16S rRNA Reveals Mutation Bias in the Evolution of Optimal Growth Temperature in the Thermotogae Phylum

Optimal growth temperature is a complex trait involving many cellular components, and its physiology is not yet fully understood. Evolution of continuous characters, such as optimal growth temperature, is often modeled as a one-dimensional random walk, but such a model may be an oversimplification given the complex processes underlying the evolution of continuous characters. Recent articles have used ancestral sequence reconstruction to infer the optimal growth temperature of ancient organisms from the guanine and cytosine content of the stem regions of ribosomal RNA, allowing inferences about the evolution of optimal growth temperature. Here, we investigate the optimal growth temperature of the bacterial phylum Thermotogae. Ancestral sequence reconstruction using a nonhomogeneous model was used to reconstruct the stem guanine and cytosine content of 16S rRNA sequences. We compare this sequence reconstruction method with other ancestral character reconstruction methods, and show that sequence reconstruction generates smaller confidence intervals and different ancestral values than other reconstruction methods. Unbiased random walk simulation indicates that the lower temperature members of the Thermotogales have been under directional selection; however, when a simulation is performed that takes possible mutations into account, it is the high temperature lineages that are, in fact, under directional selection. We find that the evolution of Thermotogales optimal growth temperatures is best fit by a biased random walk model. These findings suggest that it may be easier to evolve from a high optimal growth temperature to a lower one than vice versa.