Screening of Endophytic Antagonistic Bacteria in Wheat and Evaluation of Biocontrol Potential against Wheat Stripe Rust.

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10-7 cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10-1 cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust.

The Detection of Yr Genes in Xinjiang Wheat Cultivars Using Different Molecular Markers.

Wheat stripe rust is a fungal disease caused by Puccinia striiformis f. sp. Tritici (Pst). It significantly impacts wheat yields in Xinjiang, China. Breeding and promoting disease-resistant cultivars carrying disease-resistance genes remains the most cost-effective strategy with which to control the disease. In this study, 17 molecular markers were used to identify Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr44, and Yr50 in 82 wheat cultivars from Xinjiang. According to the differences in SNP loci, the KASP markers for Yr30, Yr52, Yr78, Yr80, and Yr81 were designed and detected in the same set of 82 wheat cultivars. The results showed that there was a diverse distribution of Yr genes across all wheat cultivars in Xinjiang, and the detection rates of Yr5, Yr15, Yr17, Yr26, Yr41, and Yr50 were the highest, ranging from 74.39% to 98.78%. In addition, Yr5 and Yr15 were prevalent in spring wheat cultivars, with detection rates of 100% and 97.56%, respectively. A substantial 85.37% of wheat cultivars carried at least six or more different combinations of Yr genes. The cultivar Xindong No.15 exhibited the remarkable presence of 11 targeted Yr genes. The pedigree analysis results showed that 33.33% of Xinjiang wheat cultivars shared similar parentage, potentially leading to a loss of resistance against Pst. The results clarified the Yr gene distribution of the Xinjiang wheat cultivars and screened out varieties with a high resistance against Pst.

Epidemic of Wheat Stripe Rust Detected by Hyperspectral Remote Sensing and Its Potential Correlation with Soil Nitrogen during Latent Period.

Climate change affects crops development, pathogens survival rates and pathogenicity, leading to more severe disease epidemics. There are few reports on early, simple, large-scale quantitative detection technology for wheat diseases against climate change. A new technique for detecting wheat stripe rust (WSR) during the latent period based on hyperspectral technology is proposed. Canopy hyperspectral data of WSR was obtained; meanwhile, duplex PCR was used to measure the content of Puccinia striiformis f.sp. tritici (Pst) in the same canopy section. The content of Pst corresponded to its spectrum as the classification label of the model, which is established by discriminant partial least squares (DPLS) and support vector machine (SVM) algorithm. In the spectral region of 325-1075 nm, the model's average recognition accuracy was between 75% and 80%. In the sub-band of 325-1075 nm, the average recognition accuracy of the DPLS was 80% within the 325-474 nm. The average recognition accuracy of the SVM was 83% within the 475-624 nm. Correlation analysis showed that the disease index of WSR was positively correlated with soil nitrogen nutrition, indicating that the soil nitrogen nutrition would affect the severity of WSR during the latent period.