Gursoy, E. | Search bioRxiv

bioRxiv2025

Self-Organizing Assembloids Reveal Enteric Nervous System Dynamics in Gut Homeostasis and Regeneration

The enteric nervous system (ENS) is essential for intestinal health, exhibiting adaptability to environmental and physiological challenges. However, the mechanisms underlying ENS plasticity and resilience remain poorly understood. Organoid technology has revolutionized in vitro modeling by accurately replicating epithelial structures and enabling significant advancements in understanding gastrointestinal biology. However, traditional organoids are limited in their ability to study the ENS, as they lack the multicellular composition and functional architecture necessary to model complex interactions between neurons, glia, mesenchymal, smooth muscle, and epithelial cells. To address these limitations, we developed murine ENS-Rich Assembloids (ERAs) that self-organize to replicate the cellular diversity, including the epithelial structure, and functional architecture of native colonic tissue. These assembloids recreate neuron-glia interactions, reflect regenerative processes, and provide a novel platform for studying ENS dynamics under controlled conditions. Integrating findings from assembloids and an in vivo murine model, we demonstrate that inflammation induces coordinated reorganization of S100b+ glial cells, TUJ1+ neurons, PDGFRA+ mesenchymal cells, and epithelial cells, revealing conserved mechanisms of ENS plasticity. We identify pleiotrophin (PTN) signaling via Protein Tyrosine Phosphatase Receptor Type Z1 (PTPRZ1) as a key pathway facilitating neural elongation and enhancing neuron-glia interactions. Moreover, we show that activated neurons transfer lipids to glial cells, revealing a novel support mechanism during inflammation. These findings position enteric glia as protective hubs for neurons, fostering ENS adaptability and tissue regeneration. By building on the foundational success of organoid technology and addressing its limitations for studying the ENS, ENS-rich assembloids establish a transformative tool for investigating ENS responses in health, disease, and tissue repair.

developmental biology↗

bioRxiv2024

Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes

Various machine learning-assisted directed evolution (MLDE) strategies have been shown to identify high-fitness protein variants more efficiently than typical wet-lab directed evolution approaches. However, limited understanding of the factors influencing MLDE performance across diverse proteins has hindered optimal strategy selection for wet-lab campaigns. To address this, we systematically analyzed multiple MLDE strategies, including active learning and focused training using six distinct zero-shot predictors, across 16 diverse protein fitness landscapes. By quantifying landscape navigability with six attributes, we found that MLDE offers a greater advantage on landscapes which are more challenging for directed evolution, especially when focused training is combined with active learning. Despite varying levels of advantage across landscapes, focused training with zero-shot predictors leveraging distinct evolutionary, structural, and stability knowledge sources consistently outperforms random sampling for both binding interactions and enzyme activities. Our findings provide practical guidelines for selecting MLDE strategies for protein engineering.

bioengineering↗