Around the clock: gradient shape and noise impact the evolution of oscillatory segmentation dynamics

Abstract

BackgroundSegmentation, the subdivision of the major body axis into repeated elements, is considered one of the major evolutionary innovations in bilaterian animals. In all three segmented animal clades, the predominant segmentation mechanism is sequential segmentation, where segments are generated one by one in anterior–posterior order from a posterior undifferentiated zone. In vertebrates and arthropods, sequential segmentation is thought to arise from a clock-and-wavefront-type mechanism, where oscillations in the posterior growth zone are transformed into a segmental prepattern in the anterior by a receding wavefront. Previous evo-devo simulation studies have demonstrated that this segmentation type repeatedly arises, supporting the idea of parallel evolutionary origins in these animal clades. Sequential segmentation has been studied most extensively in vertebrates, where travelling waves have been observed that reflect the slowing down of oscillations prior to their cessation and where these oscillations involve a highly complex regulatory network. It is currently unclear under which conditions this oscillator complexity and slowing should be expected to evolve, how they are related and to what extent similar properties should be expected for sequential segmentation in other animal species.ResultsTo investigate these questions, we extend a previously developed computational model for the evolution of segmentation. We vary the slope of the posterior morphogen gradient and the strength of gene expression noise. We find that compared to a shallow gradient, a steep morphogen gradient allows for faster evolution and evolved oscillator networks are simpler. Furthermore, under steep gradients, damped oscillators often evolve, whereas shallow gradients appear to require persistent oscillators which are regularly accompanied by travelling waves, indicative of a frequency gradient. We show that gene expression noise increases the likelihood of evolving persistent oscillators under steep gradients and of evolving frequency gradients under shallow gradients. Surprisingly, we find that the evolutions of oscillator complexity and travelling waves are not correlated, suggesting that these properties may have evolved separately.ConclusionsBased on our findings, we suggest that travelling waves may have evolved in response to shallow morphogen gradients and gene expression noise. These two factors may thus also be responsible for the observed differences between different species within both the arthropod and chordate phyla.Electronic supplementary materialThe online version of this article (10.1186/s13227-018-0113-2) contains supplementary material, which is available to authorized users.

Arthropod segmentation

“…The way in which the oscillation period varies along the SAZ is described phenomenologically by a 'frequency profile' (Morelli et al, 2009), and this can vary over developmental time, as well as between species. Although the shape of the frequency profile is not predicted to affect segmentation rate or segment size, models suggest that a graded profile might make patterning more robust (El-Sherif et al, 2014;Vroomans et al, 2018).…”

How enhancers regulate wavelike gene expression patterns: Novel enhancer prediction and live reporter systems identify an enhancer associated with the arrest of pair-rule waves in the short-germ beetleTribolium

“…the vertebrate neural tube and limb bud, the vertebrate embryo at the early phase of somitogenesis (110), and the blastoderm of short-germ insects). Even for elongating tissues that can be patterned by a classical Clock-and-Wavefront mechanism, wave dynamics have been suggested to foster robustness for the patterning process (30,111,112).…”

Time and space in segmentation

“…Finally, it is significant that the frequency profile plays an explicit role in all three of the polarization mechanisms I have described. Progressively narrowing travelling waves (a consequence of the frequency profile) are one of the most visually striking features of segmenting systems, but in most segmentation models they lack an instructive function [ 15 , 38 , 43 , 225 ].…”