Weng, M. | bioRxiv Science

bioRxiv2024

A novel protein Moat prevents ectopic epithelial folding by limiting Bazooka/Par3-dependent adherens junctions

Cortical myosin contraction and cell adhesion work together to promote tissue shape changes, but how they are modulated to achieve diverse morphogenetic outcomes remains unclear. Epithelial folding occurs via apical constriction, mediated by apical accumulation of contractile myosin engaged with adherens junctions, as in Drosophila ventral furrow formation. While levels of contractile myosin correlate with apical constriction, whether levels of adherens junctions modulate apical constriction is unknown. We identified a novel Drosophila gene moat that maintains low levels of Bazooka/Par3-dependent adherens junctions and thereby restricts apical constriction to ventral furrow cells with high-level contractile myosin. In moat mutants, abnormally high levels of Bazooka/Par3-dependent adherens junctions promote ectopic apical constriction in cells with low-level contractile myosin, insufficient for apical constriction in wild type. Such ectopic apical constriction expands infolding behavior from ventral furrow to ectodermal anterior midgut, which normally forms a later circular invagination. In moat mutant ventral furrow, a perturbed apical constriction gradient delays infolding. Our results indicate that levels of adherens junctions can modulate the outcome of apical constriction, providing an additional mechanism to define morphogenetic boundaries. Summary StatementCharacterization of a novel gene moat demonstrates ectopic expansion of apical constriction due to abnormally high levels of Bazooka/Par3-dependent adherens junctions without defects in early patterning gene expression.

developmental biology↗

bioRxiv2022

Digitally Predicting Protein Localization and Manipulating Protein Activity in Fluorescence Images Using Four-dimensional Reslicing GAN

MotivationWhile multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One feasible solution is using deep neural networks to model the localization relationship between two proteins so that the localization of a protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflects the modeled relationship. Accordingly, observing the predictions via repeatedly manipulating input localizations is an explainable and feasible way to analyze the modeled relationships between the input and the predicted proteins. ResultsWe propose a Protein Localization Prediction (PLP) method using a cGAN named Four-dimensional Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of imaged and target proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, with accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein and observe the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on four groups of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins and DA and DI provide guidance to study localization-based protein functions. Availability and ImplementationThe open-source code is at https://github.com/YangJiaoUSA/4DR-GAN.

bioinformatics↗