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In silico discovery and functional validation of defense-related proteins across diverse Solanaceae species

The Solanaceae family includes diverse crop species of major agricultural importance. Their defense against pathogens depends on a complex immune network involving pattern-recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat (NLR) proteins. However, the conservation and diversification of these genes across immune-associated pathways have not been systematically examined in a…

The Solanaceae family encompasses key agricultural crops with intricate immune systems that rely on pattern-recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat (NLR) proteins. Yet, the conservation and diversification of these immunity genes across various pathways have yet to be thoroughly investigated in a phylogenetic context.

To address this, researchers have merged phylogenomics, structural modeling, and experimental validation techniques to explore the immune protein landscape within 13 Solanaceae genomes belonging to 11 species.

By conducting orthology analysis on 52 core immunity genes, scientists have unearthed remarkable conservation across all 13 genomes. Although AlphaFold3 accurately predicts receptor-pair interactions like Fls2 flg22, it falls short in capturing other experimentally verified complex structures, highlighting the limitations of structure prediction tools specifically for plant immunity mechanisms.

To bridge this gap, machine-learning pipelines were employed, utilizing known receptor/ligand pairings to spotlight potential orthologs associated with immunogenic elicitors. Focusing on the coldshock receptor CORE, researchers identified distinct LRR-domain polymorphisms between Capsicum and Solanum orthologs. These genetic variations mirror lineage-specific adaptations in immune responses, providing crucial insights into the molecular underpinnings of disease resistance in economically crucial Solanaceae crops.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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