Amongst serious biotic factors deteriorating crop yield, the most destructive pathogen of rice is Xanthomonas oryzae pv. oryzae (Xoo), which causes bacterial leaf blight (BLB) disease. This study involved targeted use of biogenic silver nanoparticles (AgNPs) to control BLB in order to cope with the disadvantages of chemical disease control. AgNPs were biologically synthesized from natively isolated Bacillus cereus strain SZT1, which was identified through 16S rRNA gene sequence analysis. Synthesis of AgNPs in bacterial culture supernatant was confirmed through UV-VIS spectroscopy. Fourier transform infrared spectroscopy (FTIR) confirmed that the existence of AgNPs was stabilized with proteins and alcoholic groups. X-ray diffraction (XRD) data revealed the crystalline nature and imaging with scanning electron microscopy (SEM) and transmission electron microscopy (TEM), showing the spherical shape of AgNPs with particle sizes ranging from 18 to 39 nm. The silver presence in AgNPs was further confirmed by energy dispersive spectra. Biogenic AgNPs showed substantial antibacterial activity (24.21 ± 1.01 mm) for Xoo. In a pot experiment, AgNPs were found to be effective weapons for BLB by significantly increasing the plant biomass with a decreased cellular concentration of reactive oxygen species and increased concentration of antioxidant enzyme activity.

Bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most devastating diseases, resulting in significant yield losses in rice. The extensive use of chemical antibacterial agents has led to an increase the environmental toxicity. Nanotechnology products are being developed as a promising alternative to control plant disease with low environmental impact. In the present study, we investigated the antibacterial activity of biosynthesized chitosan nanoparticles (CSNPs) and zinc oxide nanoparticles (ZnONPs) against rice pathogen Xoo. The formation of CSNPs and ZnONPs in the reaction mixture was confirmed by using UV-vis spectroscopy at 300–550 nm. Moreover, CSNPs and ZnONPs with strong antibacterial activity against Xoo were further characterized by scanning and transmission electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction. Compared with the corresponding chitosan and ZnO alone, CSNPs and ZnONPs showed greater inhibition in the growth of Xoo, which may be mainly attributed to the reduction in biofilm formation and swimming, cell membrane damage, reactive oxygen species production, and apoptosis of bacterial cells. Overall, this study revealed that the two biosynthesized nanoparticles, particularly CSNPs, are a promising alternative to control rice bacterial disease.

Bacterial blight (BB) of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious bacterial diseases that hinder the normal growth and production of rice, which greatly reduces the quality and yield of rice. The effect of traditional methods such as chemical control is often not ideal. A series of production practices have shown that among the numerous methods for BB controlling, breeding and using resistant varieties are the most economical, effective, and environmentally friendly, and the important basis for BB resistance breeding is the exploration of resistance genes and their functional research. So far, 44 rice BB resistance genes have been identified and confirmed by international registration or reported in journals, of which 15 have been successfully cloned and characterized. In this paper, research progress in recent years is reviewed mainly on the identification, map-based cloning, molecular resistance mechanism, and application in rice breeding of these BB resistance genes, and the future influence and direction of the remained research for rice BB resistance breeding are also prospected.

Background: Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results: In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions: This study illustrated that informative VIS–NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.

Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R_p² of 0.86 with an RMSE_p of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.

Most endolysins of dsDNA phages are exported by a holin-dependent mechanism, while in some cases endolysins are exported via a holin-independent mechanism. However, it is still unclear whether the same endolysins can be exported by both holin-dependent and holin-independent mechanisms. This study investigated the lysis system of OP2-like phage X2 infecting Xanthomonas oryzae pv. oryzae, causing devastating bacterial leaf blight disease in rice. Based on bioinformatics and protein biochemistry methods, we show that phage X2 employs the classic "holin-endolysin" lysis system. The endolysin acts on the cell envelope and exhibits antibacterial effects in vitro, while the holin facilitates the release of the protein into the periplasm. We also characterized the role of the transmembrane domain (TMD) in the translocation of the endolysin across the inner membrane. We found that the TMD facilitated the translocation of the endolysin via the Sec secretion system. The holin increases the efficiency of protein release, leading to faster and more efficient lysis. Interestingly, in E. coli, the expression of either holin or endolysin with TMDs resulted in the formation of long rod shaped cells. We conclude that the TMD of X2-Lys plays a dual role: One is the transmembrane transport while the other is the inhibition of cell division, resulting in larger cells and thus in a higher number of released viruses per cell.