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Ginot, G.

Publications and source records attributed to Ginot, G..

2 recordsLinked to original sources

Multimodal Learning Reveals Plants' Hidden Sensory Integration Logic

Plants integrate complex environmental signals through interconnected molecular networks, but the fundamental rules governing this sensory integration remain unknown. Studying tomato roots interacting with fungal symbionts, we discover how microbial effectors systematically reprogram plant sensory systems by coordinating transcriptional, metabolic, and phenotypic responses. Our multimodal analysis not only confirmed prior experimental findings through purely computational means, but also revealed novel integration hubs where sensory pathways converge. This dual validation approach establishes iron homeostasis rewiring through citrate-mediated redox control. Next, targeted suppression of jasmonate defences. Thus, nuclear splicing isolation from metabolic noise. These findings establish a new paradigm for understanding plant-microbe communication, showing how symbionts exploit latent hubs where sensory pathways converge. The discovered integration logic provides both fundamental insights into plant perception and concrete targets for engineering stress-resilient crops.

plant biology↗

S2-PepAnalyst: A Web Tool for Predicting Plant Small Signalling Peptides

Small signalling peptides mediate cell-to-cell communication playing essential roles in plant growth, development, and stress responses. They specifically bind to the extracellular domain of receptors to trigger biochemical and physiological responses. Despite their significance, accurately identifying novel signalling peptides remains challenging due to their structural diversity, low abundance, and highly specific expression patterns. Here, we present S{superscript 2}-PepAnalyst, a web tool integrating plant-specific datasets and machine learning to predict SSPs with 99.5% accuracy and low false-negative rates. S{superscript 2}-PepAnalyst outperforms existing tools (e.g., SignalP 6.0) by combining protein language models, geometric-topological analysis, and reinforcement learning, enabling robust classification of small signalling peptide families (e.g., CLE, RALF). The tool is freely available at https://www.s2-pepanalyst.uma.es.

plant biology↗