Integrating force-sensing and signaling pathways in a model for the regulation of wing imaginal disc size

Abstract

SUMMARYThe regulation of organ size constitutes a major unsolved question in developmental biology. The wing imaginal disc of Drosophila serves as a widely used model system to study this question. Several mechanisms have been proposed to have an impact on final size, but they are either contradicted by experimental data or they cannot explain a number of key experimental observations and may thus be missing crucial elements. We have modeled a regulatory network that integrates the experimentally confirmed molecular interactions underlying other available models. Furthermore, the network includes hypothetical interactions between mechanical forces and specific growth regulators, leading to a size regulation mechanism that conceptually combines elements of existing models, and can be understood in terms of a compression gradient model. According to this model, compression increases in the center of the disc during growth. Growth stops once compression levels in the disc center reach a certain threshold and the compression gradient drops below a certain level in the rest of the disc. Our model can account for growth termination as well as for the paradoxical observation that growth occurs uniformly in the presence of a growth factor gradient and non-uniformly in the presence of a uniform growth factor distribution. Furthermore, it can account for other experimental observations that argue either in favor or against other models. The model also makes specific predictions about the distribution of cell shape and size in the developing disc, which we were able to confirm experimentally.

Differential growth triggers mechanical feedback that elevates Hippo signaling

“…further growth, along with the observation that cells are more tightly packed at later stages of development, has also been suggested as an explanation for why organs stop growing when they reach their final size (16)(17)(18)(19).…”

“…The pattern that emerged is relevant to a long-standing question in the field: How is it that cells near the center of the disc experience higher levels of the key growth factor Decapentaplegic (Dpp), yet proliferate at similar rates as cells far from the Dpp source (39)? A variety of models have been proposed to explain this observation, including one class of models that essentially invoke the mechanical feedback hypothesis (16)(17)(18)(19). According to these models, uniform growth rates arise because higher mitogenic signaling in the center of the disc is counterbalanced by higher compression as cell numbers increase.…”

A quantitative analysis of growth control in theDrosophilaeye disc

“…According to these models, growth is limited by the mounting pressure in the center of the growing disc domain and stimulated by the increasing strain in the outer parts of the domain, thus enabling uniform growth and growth termination (Aegerter-Wilmsen et al, 2007Hufnagel et al, 2007;Shraiman, 2005). When linked with a signaling model for the disc's key patterning signaling system [Dpp, Wingless (Wg), Notch] as well as for Vestigal, Four-jointed, Dachsous and components of the Hippo pathway, the mechanical feedback model can reproduce many additional experimental observations beyond uniform growth and growth termination, but still fails to yield the experimentally observed growth kinetics and final disc size (Aegerter-Wilmsen et al, 2012). Growth and tension anisotropies have been associated with the regulation of the direction of cell division, thus linking growth to organ shape (Aegerter-Wilmsen et al, 2012;BaenaLópez et al, 2005;Benham-Pyle et al, 2015;González-Gaitán et al, 1994).…”

Impact of implementation choices on quantitative predictions of cell-based computational models

“…Individual results may be relevant to other implementation choices. For example, our finding that the duration of the cell cycle in our model influences simulation outcomes may mean that parameters that control the rate of energy-minimisation may influence results in other vertex model implementations [3,25,62]. In general, further work is required to understand how other choices of implementation schemes may impact computational model predictions.…”