Flory, C. | bioRxiv Science

bioRxiv2025

Dengue Virus NS1 Binds Ephrin B1 to Trigger Endothelial Dysfunction

Dengue virus (DENV), a member of the Flaviviridae family, is the most prevalent and medically important mosquito-borne viral pathogen. Infected cells secrete the viral non-structural protein 1 (NS1) into the bloodstream, where it can interact with endothelial cells to induce vascular leak. Host factors involved in NS1-mediated endothelial dysfunction are incompletely understood, leading us to investigate the host interactome of DENV NS1 in endothelial cells via a comparative mass spectrometry approach. We identified ephrin B1 (EFNB1) as a critical host factor in NS1-mediated endothelial barrier dysfunction and show that phosphorylation of EFNB1 is necessary for induction of barrier dysfunction. Further, we map the interface of the EFNB1-NS1 complex through biochemical and computational approaches, and we show that EFNB1-Fc-fusion proteins can act as decoys to block NS1-induced barrier dysfunction in vitro and in vivo. This study provides insights into the mechanism of flavivirus NS1-mediated endothelial barrier dysfunction and new avenues to target vascular leak.

microbiology↗

bioRxiv2020

EntireAxon: Deep learning deciphers four distinct patterns of axonal degeneration

BackgroundAxonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and to develop effective therapeutic interventions. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device in combination with a deep learning tool, the EntireAxon, that we developed for the enhanced-throughput analysis of AxD on microscopic images. ResultsThe EntireAxon convolutional neural network sensitively and specifically segmented the features of AxD, including axons, axonal swellings, and axonal fragments, and its performance exceeded that of human expert raters. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected the concentration- and time-dependent degeneration of axons leading to a decrease in axon area, while the axonal swelling and axonal fragment area increased. Time course analysis revealed that axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network, we further identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) in cortical axons subjected to hemin. ConclusionsThese findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The combination of the microfluidic device with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.

neuroscience↗