ABSTRACT
Dynamins are GTPases that are required for separation of vesicles from the plasma membrane and thus are key regulators of endocytosis in eukaryotic cells. This role for dynamin proteins is especially crucial for the proper function of neurons, where they ensure that synaptic vesicles and their neurotransmitter cargo are recycled in the presynaptic cell. Here we have characterized the dynamin protein family in the freshwater planarian Schmidtea mediterranea and showed that it possesses six dynamins with tissue specific expression profiles. Of these six planarian homologs, two are necessary for normal tissue homeostasis, and the loss of another, Smed-dynA-1, leads to an abnormal behavioral phenotype, which we have quantified using automated center of mass tracking. Smed-dynA-1 is primarily expressed in the planarian nervous system and is a functional homolog of the mammalian Dynamin I. The distinct expression profiles of the six dynamin genes makes planarians an interesting new system to reveal novel dynamin functions, which may be determined by their differential tissue localization. The observed complexity of neurotransmitter regulation combined with the tools of quantitative behavioral assays as a functional readout for neuronal activity, renders planarians an ideal system for studying how the nervous system controls behavior.
ABSTRACT
Many biological tissues are viscoelastic, behaving as elastic solids on short timescales and fluids on long timescales. This collective mechanical behaviour enables and helps to guide pattern formation and tissue layering. Here, we investigate the mechanical properties of three-dimensional tissue explants from zebrafish embryos by analysing individual cell tracks and macroscopic mechanical response. We find that the cell dynamics inside the tissue exhibit features of supercooled fluids, including subdiffusive trajectories and signatures of caging behaviour. We develop a minimal, three-parameter mechanical model for these dynamics, which we calibrate using only information about cell tracks. This model generates predictions about the macroscopic bulk response of the tissue (with no fit parameters) that are verified experimentally, providing a strong validation of the model. The best-fit model parameters indicate that although the tissue is fluid-like, it is close to a glass transition, suggesting that small changes to single-cell parameters could generate a significant change in the viscoelastic properties of the tissue. These results provide a robust framework for quantifying and modelling mechanically driven pattern formation in tissues.
