Soil and water salinity is one of the major abiotic stresses that reduce growth and productivity in major food crops including rice. The lack of congruence of salt tolerance quantitative trait loci (QTLs) in multiple genetic backgrounds and multiple environments is a major hindrance for undertaking marker-assisted selection (MAS). A genome-wide meta-analysis of QTLs controlling seedling-stage salt tolerance was conducted in rice using QTL information from 12 studies. Using a consensus map, 11 meta-QTLs for three traits with smaller confidence intervals were localized on chromosomes 1 and 2. The phenotypic variance of 3 meta-QTLs was ≥20%. Based on phenotyping of 56 diverse genotypes and breeding lines, six salt-tolerant genotypes (Bharathy, I Kung Ban 4-2 Mutant, Langmanbi, Fatehpur 3, CT-329, and IARI 5823) were identified. The perusal of the meta-QTL regions revealed several candidate genes associated with salt-tolerance attributes. The lack of association between meta-QTL linked markers and the level of salt tolerance could be due to the low resolution of meta-QTL regions and the genetic complexity of salt tolerance. The meta-QTLs identified in this study will be useful not only for MAS and pyramiding, but will also accelerate the fine mapping and cloning of candidate genes associated with salt-tolerance mechanisms in rice.

Rice (Oryza sativa), as a staple crop feeding a significant portion of the global population, particularly in Asian countries, faces constant threats from various diseases jeopardizing global food security. A precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain an accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. The integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with a better understanding of their functional roles in disease resistance. The improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because a better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to a more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can result in a transformative impact on rice breeding for enhancing disease resistance.

Bacterial panicle blight (BPB) and sheath blight (SB) are major diseases of rice and few cultivars have shown a high level of resistance to these diseases. A recombinant inbred line (RIL) population developed from the U.S. cultivars Jupiter (moderately resistant) and Trenasse (susceptible) was investigated to identify loci associated with the partial disease resistance to BPB and SB. Disease phenotypes in BPB and SB, as well as the days-to-heading (DTH) trait, were evaluated in the field. DTH was correlated to BPB and SB diseases, while BPB was positively correlated to SB in the field trials with this RIL population. Genotyping was performed using Kompetitive Allele Specific PCR (KASP) assays and whole-genome sequence (WGS) analyses. Quantitative trait locus (QTL) mapping and bulk segregant analysis using a set of WGS data (QTL-seq) detected a major QTL on the upper arm of chromosome 3 for BPB, SB, and DTH traits within the 1.0–1.9 Mb position. Additional QTLs associated with BPB and SB were also identified from other chromosomes by the QTL-seq analysis. The QTLs identified in this study contain at least nine candidate genes that are predicted to have biological functions in defense or flowering. These findings provide an insight into the complex nature of the quantitative resistance to BPB and SB, which may also be closely linked to the flowering trait.

Bacterial panicle blight caused by Burkholderia glumae is a major disease in rice production worldwide. Currently, only a few whole-genome sequences of B. glumae strains isolated in the U.S. are av...

In July 2023, panicle and leaf blight-like symptoms were observed from the rice (Oryza sativa) variety, PVL03, in research field plots in Louisiana (Rayne, LA 70578, USA; 30.21330⁰ N, 92.37309⁰ W). We observed that chlorosis started on the tips and spread throughout the whole leaf. We also observed panicle blight-like symptoms (grain discoloration of rice hulls) on infected rice plants at the heading stage, which ranged from dark brown to black. Disease incidences were approximately 70% in the observed plots. Five plants were sampled from five plots (one plant per plot). Leaf tissues including both symptomatic and healthy parts (~ 1 cm²) were surfaced sterilized and placed on CCNT medium, a semi-selective medium for the rice bacterial panicle blight pathogen Burkholderia glumae (Kawaradani et al., 2000), and on Luria-Bertani (LB) agar supplemented with cycloheximide (50 µg/ml) for non-selective isolation of bacteria. For the panicle samples, the surfaced sterilized rice kernels were dehulled and ground in sterile distilled H₂O using a sterile mortar and pestle. The homogenate was spread on the CCNT media and LB agar supplemented with nitrofurantoin (50 µg/ml) and cycloheximide (50 µg/ml). After ~72 h at 41⁰C, round, smooth and yellow-colored bacterial colonies were observed on both media. The two isolates, one from an infected rice leaf (LPL-1) and the other from the infected rice panicles (LPP-1), were initially identified as Pantoea ananatis based on PCR-amplified 16S rDNA sequences (Weisburg et al., 1991). The taxonomic identity of these two isolates was further confirmed through whole genome sequencing (NCBI Accession ID: PRJNA1092676), which was performed through a hybrid sequencing approach using Oxford Nanopore sequencer & Illumina NextSeq2000 system (Plasmidsaurus Inc., South San Francisco, CA, USA). To satisfy Koch's postulates, we inoculated six one-month-old rice plants (cv. Kitaake) with LPL-1 and LPP-1, respectively, by pricking the stems with a sterile toothpick touched to the overnight grown bacterial culture on LB agar (~ 5-8 x 10⁵ bacterial cells). This virulence assay was conducted twice, and rice plants inoculated with sterile distilled water were included as the negative control. By 27 days post inoculation (DPI), the inoculated plants developed symptoms on leaves and panicles, which were like the symptoms originally observed in the field, and additional symptoms such as shot holes and stem necrosis were also observed. From leaf and panicle samples of the artificially inoculated plants, we reisolated bacteria and confirmed the identity of the bacteria using the P. ananatis specific PCR primers PANA_1080 61F and PANA_1080 1009R (Asselin et al., 2016). To the best of our knowledge, this is the first report of bacterial panicle and leaf blight of rice caused by P. ananatis in Louisiana. Since P. ananatis is a recently emerging pathogen of rice worldwide, including the United States (Yu et. al., 2022; Lu et al., 2022; Luna et al., 2023), it is crucial to monitor the occurrence of this pathogen in other rice-growing areas as a potential threat to US rice production.

Rice, as a staple crop feeding billions, faces constant threats from various diseases jeopardizing global food security. Precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. Integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with better understanding of their functional roles in disease resistance. Improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can give a transformative impact on rice breeding for enhancing disease resistance.