To deploy durable plant resistance, we must understand its underlying molecular mechanisms. Type III effectors (T3Es) and their recognition play a central role in the interaction between bacterial pathogens and crops. We demonstrate that the Ralstonia solanacearum species complex (RSSC) T3E ripAX2 triggers specific resistance in eggplant AG91-25, which carries the major resistance locus EBWR9. The eggplant accession AG91-25 is resistant to the wild-type R. pseudosolanacearum strain GMI1000, whereas a ripAX2 defective mutant of this strain can cause wilt. Notably, the addition of ripAX2 from GMI1000 to PSS4 suppresses wilt development, demonstrating that RipAX2 is an elicitor of AG91-25 resistance. RipAX2 has been shown previously to induce effector-triggered immunity (ETI) in the wild relative eggplant Solanum torvum, and its putative zinc (Zn)-binding motif (HELIH) is critical for ETI. We show that, in our model, the HELIH motif is not necessary for ETI on AG91-25 eggplant. The ripAX2 gene was present in 68.1% of 91 screened RSSC strains, but in only 31.1% of a 74-genome collection comprising R. solanacearum and R. syzygii strains. Overall, it is preferentially associated with R. pseudosolanacearum phylotype I. RipAX2 appears to be the dominant allele, prevalent in both R. pseudosolanacearum and R. solanacearum, suggesting that the deployment of AG91-25 resistance could control efficiently bacterial wilt in the Asian, African and American tropics. This study advances the understanding of the interaction between RipAX2 and the resistance genes at the EBWR9 locus, and paves the way for both functional genetics and evolutionary analyses.
The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental
in vitro
data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with
in vivo
data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.
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