Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance

The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly.Genomic selection offers a potential solution to improve efficiency,but accurately predicting plant disease resistance remains a challenge.In this study,we evaluated eight different machine learning(ML)methods,including random forest classification(RFC),support vector classifier(SVC),light gradient boosting machine(lightGBM),random forest classification plus kinship(RFC_K),support vector classification plus kinship(SVC_K),light gradient boosting machine plus kinship(lightGBM_K),deep neural network genomic prediction(DNNGP),and densely connected convolutional networks(DenseNet),for predicting plant disease resistance.Our results demonstrate that the three plus kinship(K)methods developed in this study achieved high prediction accuracy.Specifically,these methods achieved accuracies of up to 95%for rice blast(RB),85%for rice black-streaked dwarf virus(RBSDV),and 85%for rice sheath blight(RSB)when trained and applied to the rice diversity panel I(RDPI).Furthermore,the plus K models performed well in predicting wheat blast(WB)and wheat stripe rust(WSR)diseases,with mean accuracies of up to 90%and 93%,respectively.To assess the generalizability of our models,we applied the trained plus K methods to predict RB disease resistance in an independent population,rice diversity panel II(RDPII).Concurrently,we evaluated the RB resistance of RDPII cultivars using spray inoculation.Comparing the predictions with the spray inoculation results,we found that the accuracy of the plus K methods reached 91%.These findings highlight the effectiveness of the plus K methods(RFC_K,SVC_K,and lightGBM_K)in accurately predicting plant disease resistance for RB,RBSDV,RSB,WB,and WSR.The methods developed in this study not only provide valuable strategies for predicting disease resistance,but also pave the way for using machine learning to streamline genome-based crop breeding.

The ANIP1-OsWRKY62 module regulates both basal defense and Pi9-mediated immunity against Magnaporthe oryzae in rice

During effector-triggered immunity(ETI)against the devastating rice blast pathogen Magnaporthe oryzae,Pi9 functions as an intracellular resistance protein sensing the pathogen-secreted effector AvrPi9 in rice.Importantly,the underlying recognition mechanism(s)between Pi9 and AvrPi9 remains elusive.In this study,We identified a rice ubiquitin-like domain-containing protein(UDP),AVRPI9-INTERACTING PROTEIN 1(ANP1),which is directly targeted by AvrPi9 and also binds to Pi9 in plants.Phenotypic analysis of anip1 mu-tants and plants overexpressing ANIP1 revealed that ANIP1 negatively modulates rice basal defense against M.oryzae.ANiP1 undergoes 26S proteasome-mediated degradation,which can be blocked by both AvrPi9 and Pi9.Moreover,ANIP1 physically associates with the rice WRKY transcription factor OsWRKY62,which also interacts with AvrPi9 and Pi9 in plants.In the absence of Pi9,ANIP1 negatively regulates OsWRKY62 abundance,which can be promoted by AvrPi9.Accordingly,knocking out of OsWRKY62 in a non-Pi9 back-ground decreased immunity against M.oryzae.However,we also observed that OsWRKY62 plays negative roles in defense against a compatible M.oryzae strain in Pi9-harboring rice.Pi9 binds to ANiP1 and OsWRKY62 to form a complex,which may help to keep Pi9 in an inactive state and weaken rice immunity.Furthermore,using competitive binding assays,we showed that AvrPi9 promotes Pi9 dissociation from ANiP1,which could be an important step toward ETI activation.Taken together,our results reveal an immune strategy whereby a UDP-WRKY module,targeted by a fungal effector,modulates rice immunity in distinct ways in the presence or absence of the corresponding resistance protein.