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Abdennour, D.

Publications and source records attributed to Abdennour, D..

2 recordsLinked to original sources

Live imaging reveals polarized calcium transients during plant pathogen development and host colonization

Ca2+ signaling mediates rapid cellular responses across eukaryotes, but its spatiotemporal dynamics remain largely inaccessible in many genetically less tractable microbial lineages. Oomycete plant pathogens, including Phytophthora species, undergo rapid transitions between motile, encysted, germinating, and invasive stages, yet the organization of Ca2+ dynamics during these transitions is poorly understood. Here, we adapt the genetically encoded ratiometric biosensor MatryoshCaMP8s for in vivo calcium imaging in Phytophthora palmivora. The reporter is stably expressed without major detectable effects on sporulation or virulence and reports rapid ratiometric responses to cold shock and calcimycin/A23187. Using live-cell imaging, we uncover stage-specific Ca2+ dynamics across the pre-infective and early infection cycle. Sporangia approaching zoospore release display spatially heterogeneous Ca2+ transients, newly formed cysts occasionally exhibit Ca2+ transients, and germinating cysts show recurrent Ca2+ transients at the germ tube tip. Similar sharp ratiometric pulses occur during early plant infection, indicating that polarized Ca2+ transients are not restricted to in vitro germination but recur at the host surface. Together, our work establishes live ratiometric calcium imaging in oomycetes, reveals polarized Ca2+ transients as recurrent signatures of developmental transitions and early host colonization, and opens the way to mechanistic dissection of signaling, polarity, and infection in a major group of plant pathogens.

microbiology↗

Deep learning enables quantitative subcellular analysis of plant-microbe interfaces

Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder, a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.

cell biology↗