Year: 2023
Type: preprint
Abstract: Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G3362436182, G5590920851, Physics Frontier Center for Living Systems, G6671297155, RUI: Strong-Field Control of Polyatomic Molecules, G7452299184, Collaborative Research: DMREF: Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry, CAREER: In-Vivo Analysis of Active Mechanical Mechanisms Driving Animal Morphegenesis, G916760340
OpenAlex ID: W4390154892